TMS 2023 152nd Annual Meeting & Exhibition Supplemental Proceedings 3031225236, 9783031225239

Table of contents :
Contents
Part I 2D Materials: Preparation, Properties, Modeling, and Applications
1 An Overview of Graphene-Based Nanomaterials in Electronic Skin Biosensing
2 Highly Exfoliated 2D Nanosheets of MnO₂ Assembled Alternatively with Carbon Layers for High Performance of Thick Electrode (at High Loading Mass)
3 Spectroscopic Studies on Sulfides and Selenides of Mo and W for Photoabsorbers
4 Super-Capacitor Based on Hybrid Architecture with 2D Materials
5 Utilizations of Graphene-Based Nanomaterials for the Detection and Treatment of Mycobacterium Tuberculosis
Part II Accelerated Discovery and Insertion of Next Generation Structural Materials
6 Computational Design of an Ultra-Strong High-Entropy Alloy
Part III Additive Manufacturing and Innovative Powder/Wire Processing of Multifunctional Materials
7 FeSiBCCr Amorphous Fine Powders with High Saturation Magnetization Based on Particle Size Classification and Its Magnetic Powder Cores with Low Core Loss
8 Study on the Optimization of Fe Content of FeSiBC Amorphous Powders
Part IV Additive Manufacturing Fatigue and Fracture: Effects of Surface Roughness, Residual Stress, and Environment
9 High-Cycle Fatigue Property of Ferrite–Pearlite Steel for Engineering Machinery and Effects of Strengthening Mechanisms
10 On the Fatigue Performance of Additively Manufactured Metamaterials: A Combined Experimental and Simulation Study
11 Surface Roughness Measurements of Laser Deposited AlCoCrFeNiTi and AlCoCrFeNiCu High Entropy Alloys for Aerospace Applications
Part V Additive Manufacturing of Metals: Applications of Solidification Fundamentals
12 Assessment of Phase Evolution in Titanium-Niobium-Based Alloys During Rapid Solidification
13 Challenges in Wire-Arc Additive Manufacturing of Fe-Based Shape Memory Alloy
14 Experimental Study on the Influence of Surface Curvature and Cladding Position on Geometric Accuracy for T15 Laser Cladding Layer
15 Impact of Laser Power and Scanning Velocity on Microstructure and Mechanical Properties of Inconel 738LC Alloys Fabricated by Laser Powder Bed Fusion
16 LPBF Fabrication of Thin Cross Sections: Challenges and Printability
17 Modification of H950 Condition for 17-4 PH Stainless Steel Processed by DED
18 Prediction of Solidification Cracking in Rene 80 Superalloy During the Directed Energy Deposition Process
Part VI Additive Manufacturing: Length-Scale Phenomena in Mechanical Response
19 A Multiscale Study of the Interconnection Between Unit Cell Design, Processing Conditions, Microstructure, and Mechanical Properties of Additively Manufactured Titanium Metamaterials
Part VII Additive Manufacturing: Materials Design and Alloy Development V—Design Fundamentals
20 Additive Manufacturing of Inconel 718 by Meltpool and Grain Boundary Engineering
21 Microstructure and Mechanical Properties of Arc-Melted NiSi11Cx Alloys
Part VIII Advanced Biomaterials for Biomedical Implants
22 Application of Magnetic Iron Oxide Nanostructures in Drug Delivery: A Compact Review
23 Candida Albicans Biofilm Formation on an Additive-Manufactured Titanium Alloy
24 Characterization of Spicule Structure
25 Polymeric Biodegradable Biomaterials for Tissue Bioengineering and Bone Rejuvenation
26 The Effects of Thermal Treatment on the Properties and Performance of Hot Extruded Zn-Based Bioresorbable Alloy for Vascular Stenting Applications
27 ZnO-NPs-Coated Implants with Osteogenic Properties for Enhanced Osseointegration
Part IX Advanced Characterization Techniques for Quantifying and Modeling Deformation
28 Characterization and Mechanical Testing of Ordinary Chondrites
29 Influence of Different Temperatures on Mechanical Properties of Flexible Screen
Part X Advanced Functional and Structural Thin Films and Coatings and Honorary Palkowski Session
30 A Review of P(St-MMA-AA) Synthesis via Emulsion Polymerization, 3D P(St-MMA-AA) Photonic Crystal Fabrication, and Photonic Application
31 Effect of Drying on Textured Coat Synthesized from Waste Glass for Building Application
32 In-Situ Alloy Formation During Selective Laser Melting with CuSn10 and Aluminum Powders
33 Nanosized Cadmium Selenide Thin Coatings for Possible Utilization in Optoelectronics
34 Optical Properties of Crystalline Silicon in the Infrared
35 Prediction of Grain Size Evolution During Hot Rolling of HSLA Steels Considering Precipitation
36 Reduction of Friction and Adhesion in Copper and Brass Extrusion by Application of Boron Containing Surface Modifications
37 Thermal Fatigue of Spheroidal Graphite Cast Iron
38 Utilization of Plant Oil-Based Fatliquor in the Processing of Leather
Part XI Advanced Joining Technologies for Automotive Lightweight Structures
39 Joint Strength Optimization of Single-Lap Al 5052-H36 Adhesively Bonded for Off-Road Vehicle Chassis Components
40 Recent Advances in the Transformative Non-fusion Weld-Brazing Process Used to Join Thin-Gauge Alloys Used in the Automotive Industry
41 Study on the Microstructure and Mechanical Properties of Aluminum Alloy 5754 to Advanced High Strength Steel by the Laser Welding-Brazing Technique
Part XII Advanced Materials for Energy Conversion and Storage 2023
42 Aluminum-Anodes for Metal-Air-Batteries
43 Triple-Cation Perovskite Photoabsorbers and Solar Cells
Part XIII Advances in Magnetic Materials
44 Incisive Review on Magnetic Iron Oxide Nanoparticles and Their Use in the Treatment of Bacterial Infections
Part XIV Advances in Multi-principal Element Alloys II
45 Data-driven Search and Selection of Ti-containing Multi-principal Element Alloys for Aeroengine Parts
Part XV Advances in Surface Engineering V
46 Effective Utilization of Metallurgical Characterization for Oxidation Resistance Coatings
Part XVI Advances in Titanium Technology
47 Effect of the Vibratory Peening Parameters on Surface Properties of Ti-6Al-4 V
48 Investigation to Density and Metallurgical Characteristics of Selective Laser Melted Ti-5Al-5 V-5Mo-3Cr Versus Ti-6Al-4 V
49 Systematic Review of the Synthesis of Titanium Oxide Nanoparticles via Plant Mediation Green Approach
Part XVII AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
50 Data Assimilation for Microstructure Evolution in Kinetic Monte Carlo
51 Towards Machine Learning of Crystal Plasticity by Neural Networks
Part XVIII Algorithm Development in Materials Science and Engineering
52 Differential Property Prediction: A Machine Learning Approach to Experimental Design in Advanced Manufacturing
53 Multi-faceted Uncertainty Quantification for Structure-Property Relationship with Crystal Plasticity Finite Element
54 Prediction of Cutting Surface Parameters in Punching Processes Aided by Machine Learning
Part XIX Alloy Development for Energy Technologies: ICME Gap Analysis
55 Molecular Dynamics Study of Gradient Energy Coefficient and Grain-Boundary Migration in Aluminum Foam
56 Phase-Field Modeling of Aluminum Foam Based on Molecular Dynamics Simulations
Part XX Alloys and Compounds for Thermoelectric and Solar Cell Applications XI
57 Stability Study of Cesium-Based Triple Cation Perovskite Solar Cells in Elevated Environmental Ambients
Part XXI Biological Materials Science
58 A Concise Review of the Antibacterial Action of Gold Nanoparticles Against Various Bacteria
59 A Review of Nanovanadium Compounds for Cancer Cell Therapy
60 Biodegradation of Petroleum-Based Plastic Using Bacillus sp.
61 Comparative Characterization and Assay of Cow Horn Waste and Fish Feed as Biomaterials for Reinforcement in Aquaculture Feeds
62 Effect of Some Bio-Stimulants in the Degradation of Petroleum Hydrocarbons in Crude Oil Contaminated Soil
Part XXII Composite Materials for Sustainable and Eco-Friendly Material Development and Application
63 Application Study of Fe-MOF Material for Fluoride Removal from Hydrometallurgy Waste Liquid
64 Detection and Mitigation of Radionuclides in the Environment: Toward a Clean Ecosystem
65 Effect of Waste Glass and Waste Tyre on the Workability and Strength of Concrete
66 Facile Ball-Milling Synthesis of Cellulosic Metal Oxide Composite for Removal of Tetracycline Antibiotic from Aqueous Solution
67 Fiber-Reinforced Polymeric Composites for Low-Carbon Construction Applications
68 Nanocomposite Materials for Accelerating Decarbonization
69 New Eco-Friendly Inorganic Polymeric Materials for the Passive Fire Protection of Structures
70 Optimization of Post-consumer Glass and Sawdust Reinforced Polyester Hybrid Composites by Mixture Design Analysis
71 Solvent-Free Ball-Milling Synthesis of BaO Modified Zeolite for Tetracycline Adsorption
72 Stain Resistant of Building Textured Coatings Developed from Recycled Glass
73 Study on the Removal of Cr(VI) Ions by Fe-MOF from Simulated Hydrometallurgy Wastewater
74 Weather Aged Fique Fabric Reinforced Epoxy Composite: Impact Property Analysis
Part XXIII Computational Thermodynamics and Kinetics
75 Effect of Different Desulfurizers on Hot Metal Pretreatment
76 Modeling of Slag Modification on Inclusions in 54SiCr6 Spring Steel
Part XXIV Deformation-Induced Microstructural Evolution During Solid Phase Processing: Experimental and Computational Studies
77 Analysis of Coarse Crystal Defect During Rolling of 3J1A Alloy
Part XXV Electrical Steels
78 Constitutive Modelling of High-Temperature Flow Behavior of a Non-oriented Electrical Steel with 3.2 wt% Si
79 Effect of Melt Superheat on Microstructure and Texture of Non-oriented Electrical Steel Sheet Produced by the Ultra-Thin Strip Casting
80 Effect of Natural Deposited Films on Interfacial Heat Transfer During Sub-rapid Solidification of Non-oriented Electrical Steel
81 Effects of Normalization Process on Microstructure and Texture of Non-oriented Electrical Steel Produced by Ultra-Thin Strip Casting
82 The Role of Temper Rolling and Annealing on the Magnetic Property Improvement of a Low Si Non-oriented Electrical Steel
Part XXVI Electronic Packaging and Interconnection
83 Dynamic Material Characterization Through In-Situ Electrical Resistivity Measurements of High Temperature Transient Liquid Phase Sinter Alloys
84 Effects of Diameter on Copper Pillar with Solder Cap Interconnections During Reflow Soldering Process
85 The Effect of Grain Boundary Type on Void Formation in a Through Silicon Via (TSV)
Part XXVII Environmental Degradation of Multiple Principal Component Materials
86 High Temperature Oxidation of CoNiFeMnCr High Entropy Alloys Reinforced by MC-Carbides
Part XXVIII Environmentally Assisted Cracking: Theory and Practice
87 Hydrogen Effects on Mechanical and Toughness Properties of Pipeline Steels
88 Improvement of Pitting Corrosion Resistance of 304 Stainless Steel with Lanthanum Addition
89 Influence of High-Temperature Tempering Treatment on Hydrogen Diffusion Behavior in X80 Pipeline Steel Containing Different Vanadium Contents
90 Liquid Metal Embrittlement Behavior of Dual-Phase Steels: The Influence of Microstructure and Strain Rate
Part XXIX Fatigue in Materials: Fundamentals, Multiscale Characterizations, and Computational Modeling
91 Characterization of Low-Cycle Fatigue Deformation Behavior at RT/200 °C of FeMnAlC Lightweight Steel for Low-Pressure Turbine Blade
92 Experimental Study on the Influence of Surface Carbon Content on Crack Initiation for 20Cr2Ni4A Steel
93 Molecular Dynamics Simulations of the Thermal Evolution of Voids in Cu Bulk and Grain Boundaries
Part XXX Frontiers in Solidification: An MPMD Symposium Honoring Jonathan A. Dantzig
94 Design and Technology Research of Copper Ingot Mold for Water-Cooled Mold
95 Design of Light Wind Turbine Parts by Simulation-Based Machine Learning
96 Rationalization of the Modelling of Stress and Strain Evolution in Powder Bed Fusion Additive Manufacturing—A Perspective from a Background in the Simulation of Casting Processes
Part XXXI Functional Nanomaterials 2023
97 Semiconductor Nanomaterials and 3D Systems
Part XXXII High Performance Steels
98 Effect of MgO, Ti₂O₃, and Al₂O₃ Inclusions on the Formation of Manganese-Depleted Zones Through First-Principles Calculation
99 Microstructure and Property Uniformity of 07MnNiMoDR Low Carbon Bainitic Steel Plate
100 Mechanical Properties and Microstructures Development of Quenching and Partitioning (Q&P) Steels During Galvannealing Process
Part XXXIII High Temperature Creep Properties of Advanced Structural Materials
101 Creep Behavior at Elevated Temperatures of Several Polycrystalline Ni-based Superalloys Strengthened by MC-Carbides
102 Strengthening Against Creep at Elevated Temperature of HEA Alloys of the CoNiFeMnCr Type Using MC-Carbides
Part XXXIV High Temperature Electrochemistry V
103 Chloro-Aluminate Species Distribution Correlation with Electrical Conductivity of 1-Ethyl-3-Methyl Imidazolium Chloride (EMIC)-Aluminum Chloride (AlCl₃) System
Part XXXV Light Elements Technology
104 A New Method for Producing Hydrogen, Lithium Metal, and High-Purity Silicon from Spodumene Ore
105 Electrochemical Technology for Lithium-Isotopes Separation
106 High-Grade Li₂SO₄ from a Local Montebrasite Ore as Industrial Raw Material for Managing Bipolar Disorder
107 Process Simulation for Low Emission Hydrogen Production Using DWSIM
108 Recovery of Lithium from Waste LIBs Using Sulfuric Acid Roasting and Water Washing
110 Spark Plasma Sintered Boron Carbide Ceramic Armor
111 Spark Plasma Sintering and Characterization of B₄C-ZrB₂ Composites
112 Thoughts on the Role of Light Elements as Alternative Reductants in Major Ferroalloy Production
113 Utilizing of Tincal Ore Wastes in Ceramic Industry
Part XXXVI Materials and Chemistry for Molten Salt Systems
114 Study of the Assimilation of Inclusions in Molten Mold Flux Under the Action of Low-Frequency Electromagnetic Fields
115 Thermodynamic Analysis of CoCrFeNi High-Entropy Alloys Prepared by Molten Salt Method
Part XXXVII Mechanical Response of Materials Investigated Through Novel In-Situ Experiments and Modeling
116 Modeling of the Bending Behavior to Study Nested-Cylinder Structure in Spicules
Part XXXVIII Nanostructured Materials in Extreme Environments
117 Study on Advanced Cementing Practices Using Inert Graphene Nanoplatelets and Hydraulic Fracturing Fluids for Wellbore Integrity and Sustainability
Part XXXIX Natural Fibers and Its Composites: A Sustainable Solution
118 Corozo Palm Fibers: Mechanical Behavior and Potential Use for Composites
119 Feasibility Study of Incorporation of Dyeing Sludge in Red Ceramics
120 Influence of the Incorporation of Particulates from the Pineapple Crown on the Impact Strength of Epoxy Systems
121 Mitigation of Urban Noise Through the Implementation of Pumice with an Air Chamber on Building Facades
122 Natural Vegetable Fibers Used from Colombia and Their Use as Potential Reinforcement for Composite Materials
Part XL Phase Stability in Extreme Environments
123 Heat Treatment Design of Inconel 740H Superalloy for Microstructure Stability and Creep Properties Enhancement
Part XLI Phase Transformations and Microstructural Evolution
124 A Comprehensive Investigation on the Sintering Behavior of CaO–SiO₂–CaF₂–Al₂O₃ Slags System
125 Experimental Analysis of R-Phase NiTi Tube Actuators Using in Contact Conductive Heating Stage
126 Fracture Analysis for Intermediate Slabs of Wear-Resistant Steel Based on the Evolution of Surface Decarburization Behavior
127 The Formation and Stability of Nanosphere Composites
128 The Impact of Graphene Nanoplatelets (GNPs) on the Hydration Mechanism of Alite (C₃S) in Class-H Wellbore Cement with Focus on Microstructural Properties
129 Use of the Hollomon-Jaffe Tempering Parameter to Optimize the Microhardness in a Medium Carbon Low Alloy Cr–Mo Steel
Part XLII Printed Electronics and Additive Manufacturing: Functional Materials, Processing Techniques, and Emerging Applications
130 Development of a Metamaterial Honeycomb Structure for Radar Absorbing Materials
131 Liquid Metal Inks for Printed Stretchable Electronics: Gallium Alloy Interactions with the Environment
132 Permanent Magnet Integrated Shock Absorber and Electric Generator
Part XLIII Thermodynamics and Kinetics of Alloys
133 Thermodynamics and Kinetics of Reaction of Rare Earth (La, Ce, Y) with MgO Refractories
Author Index
Subject Index

Citation preview

SUPPLEMENTAL PROCEEDINGS

The Minerals, Metals & Materials Series

The Minerals, Metals & Materials Society Editor

TMS 2023 152nd Annual Meeting & Exhibition Supplemental Proceedings

Editor The Minerals, Metals & Materials Society Pittsburgh, PA, USA

ISSN 2367-1181 ISSN 2367-1696 (electronic) The Minerals, Metals & Materials Series ISBN 978-3-031-22523-9 ISBN 978-3-031-22524-6 (eBook) https://doi.org/10.1007/978-3-031-22524-6 © The Minerals, Metals & Materials Society 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

This volume is a collection of papers from the TMS 2023 Annual Meeting & Exhibition, held March 19–23 in San Diego, California, USA. The contributions represent 44 symposia from the meeting. This volume, along with the other proceedings volumes published for the meeting, and TMS archival journals, represent the available written record of the 99 symposia held at TMS2023.

Contents

Part I

2D Materials: Preparation, Properties, Modeling, and Applications

An Overview of Graphene-Based Nanomaterials in Electronic Skin Biosensing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Raphael O. Ekun, Eribe M. Jonathan, Okeke I. Emmanuel, Best Atoe, and Ikhazuagbe H. Ifijen Highly Exfoliated 2D Nanosheets of MnO2 Assembled Alternatively with Carbon Layers for High Performance of Thick Electrode (at High Loading Mass) . . . . . . . . . . . . . . . . . . . . . . . . . Jae-Min Jeong, Ho Jun Kim, and Bong Gill Choi

3

14

Spectroscopic Studies on Sulfides and Selenides of Mo and W for Photoabsorbers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Anupama B. Kaul

19

Super-Capacitor Based on Hybrid Architecture with 2D Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Daniel Choi

26

Utilizations of Graphene-Based Nanomaterials for the Detection and Treatment of Mycobacterium Tuberculosis . . . . . . . . . . . . . . . . . . . . . . Nyaknno U. Udokpoh, Jacob N. Jacob, Ukeme D. Archibong, Gregory E. Onaiwu, and Ikhazuagbe H. Ifijen Part II

30

Accelerated Discovery and Insertion of Next Generation Structural Materials

Computational Design of an Ultra-Strong High-Entropy Alloy . . . . . . . . M. Ponga, O. K. Orhan, and D. Funes Rojas

43

vii

viii

Contents

Part III Additive Manufacturing and Innovative Powder/Wire Processing of Multifunctional Materials FeSiBCCr Amorphous Fine Powders with High Saturation Magnetization Based on Particle Size Classification and Its Magnetic Powder Cores with Low Core Loss . . . . . . . . . . . . . . . . . . . . . . . . Yan-nan Dong, Zheng-qu Zhu, Jia-qi Liu, Huan Zhao, Jing Pang, Pu Wang, and Jia-quan Zhang Study on the Optimization of Fe Content of FeSiBC Amorphous Powders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zheng-qu Zhu, Yan-nan Dong, Jia-qi Liu, Jing Pang, Pu Wang, and Jia-quan Zhang

53

64

Part IV Additive Manufacturing Fatigue and Fracture: Effects of Surface Roughness, Residual Stress, and Environment High-Cycle Fatigue Property of Ferrite–Pearlite Steel for Engineering Machinery and Effects of Strengthening Mechanisms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shuo Gong, Haijuan Wang, Fuming Wang, Ming Li, Yong Feng, and Liang Su On the Fatigue Performance of Additively Manufactured Metamaterials: A Combined Experimental and Simulation Study . . . . . Daniel Barba, Antonio Vazquez-Prudencio, Conrado Garrido, and Sergio Perosanz-Amarillo Surface Roughness Measurements of Laser Deposited AlCoCrFeNiTi and AlCoCrFeNiCu High Entropy Alloys for Aerospace Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dada Modupeola and Popoola Patricia Part V

77

91

102

Additive Manufacturing of Metals: Applications of Solidification Fundamentals

Assessment of Phase Evolution in Titanium-Niobium-Based Alloys During Rapid Solidification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Theo Mossop, David Heard, and Mert Celikin

111

Challenges in Wire-Arc Additive Manufacturing of Fe-Based Shape Memory Alloy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Soumyajit Koley, Kuladeep Rajamudili, and Supriyo Ganguly

120

Experimental Study on the Influence of Surface Curvature and Cladding Position on Geometric Accuracy for T15 Laser Cladding Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yingtao Zhang, Guangming Lv, Lijuan Zhao, Charlie Li, and Gang Wang

131

Contents

ix

Impact of Laser Power and Scanning Velocity on Microstructure and Mechanical Properties of Inconel 738LC Alloys Fabricated by Laser Powder Bed Fusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yixuan Chen, Weihao Wang, Yao Ou, Yingna Wu, Zirong Zhai, and Rui Yang

138

LPBF Fabrication of Thin Cross Sections: Challenges and Printability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . John Daniel Arputharaj, Shahrooz Nafisi, and Reza Ghomashchi

150

Modification of H950 Condition for 17-4 PH Stainless Steel Processed by DED . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . I. Mathoho, N. Arthur, and M. Tlotleng

165

Prediction of Solidification Cracking in Rene 80 Superalloy During the Directed Energy Deposition Process . . . . . . . . . . . . . . . . . . . . . Hamed Hosseinzadeh, Lang Yuan, Luke Mohr, Lee Kerwin, Anindya Bhaduri, Arushi Dhakad, Chen Shen, Shenyan Huang, Changjie Sun, and Alexander L. Kitt Part VI

Additive Manufacturing: Length-Scale Phenomena in Mechanical Response

A Multiscale Study of the Interconnection Between Unit Cell Design, Processing Conditions, Microstructure, and Mechanical Properties of Additively Manufactured Titanium Metamaterials . . . . . . Massimiliano Casata, Conrado Garrido, Enrique Alabort, and Daniel Barba Part VII

177

189

Additive Manufacturing: Materials Design and Alloy Development V—Design Fundamentals

Additive Manufacturing of Inconel 718 by Meltpool and Grain Boundary Engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mallikharjun Marrey, Amir Eftekharian, Vasyl Harik, Abhishek Kumar, Rashid Miraj, and Frank Abdi Microstructure and Mechanical Properties of Arc-Melted NiSi11Cx Alloys . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Foysal Kabir Tareq, Even Wilberg Hovig, Ragnhild E. Aune, and Geir Grasmo

203

216

Part VIII Advanced Biomaterials for Biomedical Implants Application of Magnetic Iron Oxide Nanostructures in Drug Delivery: A Compact Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Inono C. Omoruyi, Jeffery I. Omoruyi, Oscar N. Aghedo, Ukeme D. Archibong, and Ikhazuagbe H. Ifijen

229

x

Contents

Candida Albicans Biofilm Formation on an Additive-Manufactured Titanium Alloy . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mari Koike, Tetsuro Horie, Susan K. Hummel, Richard J. Mitchell, and Toru Okabe Characterization of Spicule Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fariborz Tavangarian, Jennifer L. Gray, Trevor Clark, and Chao Gao Polymeric Biodegradable Biomaterials for Tissue Bioengineering and Bone Rejuvenation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Eribe M. Jonathan, Andrew O. Ohifuemen, Jacob N. Jacob, Aaron Y. Isaac, and Ikhazuagbe H. Ifijen The Effects of Thermal Treatment on the Properties and Performance of Hot Extruded Zn-Based Bioresorbable Alloy for Vascular Stenting Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . Henry D. Summers, Morteza S. Ardakani, and Jaroslaw W. Drelich ZnO-NPs-Coated Implants with Osteogenic Properties for Enhanced Osseointegration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kate E. Mokobia, Ikhazuagbe H. Ifijen, and Esther U. Ikhuoria

243

260

267

278

288

Part IX Advanced Characterization Techniques for Quantifying and Modeling Deformation Characterization and Mechanical Testing of Ordinary Chondrites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mohamed H. Hamza, Charles A. Galluscio, M. F. Rabbi, Laurence A. J. Garvie, Desireé Cotto-Figueroa, Erik Asphaug, and A. Chattopadhyay Influence of Different Temperatures on Mechanical Properties of Flexible Screen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Qiujun Wang, Weiwei Su, Zeyu Zhang, Di Zhang, Bo Wang, and Fang Zhang Part X

303

313

Advanced Functional and Structural Thin Films and Coatings and Honorary Palkowski Session

A Review of P(St-MMA-AA) Synthesis via Emulsion Polymerization, 3D P(St-MMA-AA) Photonic Crystal Fabrication, and Photonic Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ikhazuagbe H. Ifijen, Esther U. Ikhuoria, Stanley. O. Omorogbe, Godfrey O. Otabor, Aireguamen I. Aigbodion, and Salisu D. Ibrahim

327

Effect of Drying on Textured Coat Synthesized from Waste Glass for Building Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Andrew Ojonugwa Adejo and Jeff Kator Jomboh

337

Contents

xi

In-Situ Alloy Formation During Selective Laser Melting with CuSn10 and Aluminum Powders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Farzad Foadian and Robert Kremer

344

Nanosized Cadmium Selenide Thin Coatings for Possible Utilization in Optoelectronics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ikhazuagbe H. Ifijen and Bala Anegbe

353

Optical Properties of Crystalline Silicon in the Infrared . . . . . . . . . . . . . . Allyson Tarifa and Nuggehalli M. Ravindra Prediction of Grain Size Evolution During Hot Rolling of HSLA Steels Considering Precipitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Goran Kugler, Jan Foder, Boštjan Bradaškja, and David Bombaˇc Reduction of Friction and Adhesion in Copper and Brass Extrusion by Application of Boron Containing Surface Modifications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Stefan Lechner, Alexander Thewes, and Sören Müller Thermal Fatigue of Spheroidal Graphite Cast Iron . . . . . . . . . . . . . . . . . . Primož Mrvar, Mitja Petriˇc, and Milan Terˇcelj Utilization of Plant Oil-Based Fatliquor in the Processing of Leather . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . I. H. Ifijen, I. O. Bakare, E. O. Obazee, O. C. Ize-Iyamu, N. U. Udokpoh, A. O. Ohifuemen, F. U. Mohammed, E. A. Fagbemi, and P. O. Ayeke Part XI

364

383

394 406

416

Advanced Joining Technologies for Automotive Lightweight Structures

Joint Strength Optimization of Single-Lap Al 5052-H36 Adhesively Bonded for Off-Road Vehicle Chassis Components . . . . . . . . M. Nodeh, A. Maslouhi, and A. Desrochers Recent Advances in the Transformative Non-fusion Weld-Brazing Process Used to Join Thin-Gauge Alloys Used in the Automotive Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . M. Shehryar Khan, Y.-H. Cho, F. Goodwin, and Y. Norman Zhou Study on the Microstructure and Mechanical Properties of Aluminum Alloy 5754 to Advanced High Strength Steel by the Laser Welding-Brazing Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . Tianhan Hu, Zheng Li, Wufeng Dong, Kai Ding, and Yulai Gao

431

442

455

xii

Part XII

Contents

Advanced Materials for Energy Conversion and Storage 2023

Aluminum-Anodes for Metal-Air-Batteries . . . . . . . . . . . . . . . . . . . . . . . . . Janne Max Heydrich-Bodensieck, Maik Negendank, and Sören Müller

467

Triple-Cation Perovskite Photoabsorbers and Solar Cells . . . . . . . . . . . . . Mahdi Temsal, Sujan Aryal, and Anupama B. Kaul

478

Part XIII Advances in Magnetic Materials Incisive Review on Magnetic Iron Oxide Nanoparticles and Their Use in the Treatment of Bacterial Infections . . . . . . . . . . . . . . . Muniratu Maliki, Stanley O. Omorogbe, Ikhazuagbe H. Ifijen, Oscar N. Aghedo, and Augustine Ighodaro

487

Part XIV Advances in Multi-principal Element Alloys II Data-driven Search and Selection of Ti-containing Multi-principal Element Alloys for Aeroengine Parts . . . . . . . . . . . . . . . . Tanjore V. Jayaraman and Ramachandra Canumalla Part XV

Advances in Surface Engineering V

Effective Utilization of Metallurgical Characterization for Oxidation Resistance Coatings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hariharan Sundaram and Veerakumar Kandaraj Part XVI

501

519

Advances in Titanium Technology

Effect of the Vibratory Peening Parameters on Surface Properties of Ti-6Al-4 V . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Maxime Paques, Benoit Changeux, Anindya Das, Hongyan Miao, Martin Levesque, Sylvain Turenne, and Etienne Martin Investigation to Density and Metallurgical Characteristics of Selective Laser Melted Ti-5Al-5 V-5Mo-3Cr Versus Ti-6Al-4 V . . . . . David Yan and Roman Bolzowski Systematic Review of the Synthesis of Titanium Oxide Nanoparticles via Plant Mediation Green Approach . . . . . . . . . . . . . . . . . Ifeanyi J. Odiachi, Oghomwen C. Ize-Iyamu, Osaro K. Ize-Iyamu, Chikaodili D. Ikechukwu, and Ikhazuagbe H. Ifijen

531

539

548

Contents

xiii

Part XVII AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification Data Assimilation for Microstructure Evolution in Kinetic Monte Carlo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Anh Tran, Yan Wang, and Theron Rodgers

561

Towards Machine Learning of Crystal Plasticity by Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Christoph Hartmann

576

Part XVIII

Algorithm Development in Materials Science and Engineering

Differential Property Prediction: A Machine Learning Approach to Experimental Design in Advanced Manufacturing . . . . . . . . . . . . . . . . Loc Truong, WoongJo Choi, Colby Wight, Elizabeth Coda, Tegan Emerson, Keerti Kappagantula, and Henry Kvinge

587

Multi-faceted Uncertainty Quantification for Structure-Property Relationship with Crystal Plasticity Finite Element . . . . . . . . . . . . . . . . . . Anh Tran, Pieterjan Robbe, and Hojun Lim

596

Prediction of Cutting Surface Parameters in Punching Processes Aided by Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Schenek, M. Görz, M. Liewald, and K. R. Riedmüller

607

Part XIX Alloy Development for Energy Technologies: ICME Gap Analysis Molecular Dynamics Study of Gradient Energy Coefficient and Grain-Boundary Migration in Aluminum Foam . . . . . . . . . . . . . . . . . Chaimae Jouhari, Yucheng Liu, and Doyl Dickel

623

Phase-Field Modeling of Aluminum Foam Based on Molecular Dynamics Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chaimae Jouhari, Yucheng Liu, and Doyl Dickel

632

Part XX

Alloys and Compounds for Thermoelectric and Solar Cell Applications XI

Stability Study of Cesium-Based Triple Cation Perovskite Solar Cells in Elevated Environmental Ambients . . . . . . . . . . . . . . . . . . . . . . . . . . Sujan Aryal, Mahdi Temsal, Ehsan Ghavaminia, and Anupama B. Kaul

645

xiv

Part XXI

Contents

Biological Materials Science

A Concise Review of the Antibacterial Action of Gold Nanoparticles Against Various Bacteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ikhazuagbe H. Ifijen, Muniratu Maliki, Nyaknno U. Udokpoh, Ifeanyi J. Odiachi, and Best Atoe A Review of Nanovanadium Compounds for Cancer Cell Therapy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ikhazuagbe H. Ifijen, Nyaknno U. Udokpoh, Muniratu Maliki, Esther U. Ikhuoria, and Efosa O. Obazee Biodegradation of Petroleum-Based Plastic Using Bacillus sp. . . . . . . . . Rahulkumar Sunil Singh, Eddie Bryan Gilcrease, Ramesh Goel, Michael L. Free, and Prashant K. Sarswat Comparative Characterization and Assay of Cow Horn Waste and Fish Feed as Biomaterials for Reinforcement in Aquaculture Feeds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ita E. Uwidia, Onyeka K. Chisom, and Osalodion E. Uwidia Effect of Some Bio-Stimulants in the Degradation of Petroleum Hydrocarbons in Crude Oil Contaminated Soil . . . . . . . . . . . . . . . . . . . . . . Ita E. Uwidia, Uzuazor O. Eyibara, and Osalodion E. Uwidia Part XXII

655

665

675

686

695

Composite Materials for Sustainable and Eco-Friendly Material Development and Application

Application Study of Fe-MOF Material for Fluoride Removal from Hydrometallurgy Waste Liquid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wenjuan Wang, Yanfang Huang, and Guihong Han

707

Detection and Mitigation of Radionuclides in the Environment: Toward a Clean Ecosystem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Simona E. Hunyadi Murph and Cristian Maldonado-Figueroa

715

Effect of Waste Glass and Waste Tyre on the Workability and Strength of Concrete . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . O. S. Olasehinde, A. D. Garkida, C. M. Gonah, and Y. D. Amartey

725

Facile Ball-Milling Synthesis of Cellulosic Metal Oxide Composite for Removal of Tetracycline Antibiotic from Aqueous Solution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nergiz Kanmaz, Mehmet Bu˘gdaycı, and Pelin Demirçivi Fiber-Reinforced Polymeric Composites for Low-Carbon Construction Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhiye Li and Michael Lepech

733

740

Contents

xv

Nanocomposite Materials for Accelerating Decarbonization . . . . . . . . . . Simona E. Hunyadi Murph and Henry Sessions Jr. New Eco-Friendly Inorganic Polymeric Materials for the Passive Fire Protection of Structures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ponsian M. Robert, Ioanna P. Giannopoulou, Pericles Savva, Konstantinos-Miltiades Sakkas, Michael F. Petrou, and Demetris Nicolaides Optimization of Post-consumer Glass and Sawdust Reinforced Polyester Hybrid Composites by Mixture Design Analysis . . . . . . . . . . . . Kator Jeff Jomboh, Adele Dzikwi Garkida, Emmanuel Majiyebo Alemaka, Mohammed Kabir Yakubu, Vershima Cephas Alkali, and Wilson Uzochukwu Eze Solvent-Free Ball-Milling Synthesis of BaO Modified Zeolite for Tetracycline Adsorption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pelin Demircivi, Nergiz Kanmaz, and Mehmet Bugdayci Stain Resistant of Building Textured Coatings Developed from Recycled Glass . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Andrew Ojonugwa Adejo, Bidemi Omowunmi Elesho, and Adele Dzikwi Garkida Study on the Removal of Cr(VI) Ions by Fe-MOF from Simulated Hydrometallurgy Wastewater . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Junpeng Zuo, Wenjuan Wang, Yanfang Huang, and Guihong Han Weather Aged Fique Fabric Reinforced Epoxy Composite: Impact Property Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Michelle Souza Oliveira, Fernanda Santos da Luz, Artur Camposo Pereira, Noan Tonini Simonassi, Lucio Fabio Cassiano Nascimento, and Sergio Neves Monteiro Part XXIII

758

768

780

786

794

802

Computational Thermodynamics and Kinetics

Effect of Different Desulfurizers on Hot Metal Pretreatment . . . . . . . . . . Liang Tian, Wufeng Jiang, Suju Hao, and Yuzhu Zhang Modeling of Slag Modification on Inclusions in 54SiCr6 Spring Steel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xuefeng Bai, Yanhui Sun, and Huibin Wu Part XXIV

748

815

824

Deformation-Induced Microstructural Evolution During Solid Phase Processing: Experimental and Computational Studies

Analysis of Coarse Crystal Defect During Rolling of 3J1A Alloy . . . . . . Jing Jianfa, Wang Shuai, Chen Feng, Yang Lingzhi, and Fu Baoquan

839

xvi

Part XXV

Contents

Electrical Steels

Constitutive Modelling of High-Temperature Flow Behavior of a Non-oriented Electrical Steel with 3.2 wt% Si . . . . . . . . . . . . . . . . . . . Gyanaranjan Mishra, Kanwal Chadha, Youliang He, and Clodualdo Aranas Effect of Melt Superheat on Microstructure and Texture of Non-oriented Electrical Steel Sheet Produced by the Ultra-Thin Strip Casting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lulu Song, Wanlin Wang, Peisheng Lyu, Huhu Wang, Xueying Lyu, and Yunli Zhang Effect of Natural Deposited Films on Interfacial Heat Transfer During Sub-rapid Solidification of Non-oriented Electrical Steel . . . . . . Yunli Zhang, Wanlin Wang, Peisheng Lyu, Huihui Wang, Xueying Lyu, and Lulu Song

849

859

867

Effects of Normalization Process on Microstructure and Texture of Non-oriented Electrical Steel Produced by Ultra-Thin Strip Casting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Huihui Wang, Wanlin Wang, Hualong Li, Peisheng Lyu, Shengjie Wu, Xueying Lyu, Lulu Song, and Yunli Zhang

875

The Role of Temper Rolling and Annealing on the Magnetic Property Improvement of a Low Si Non-oriented Electrical Steel . . . . . Youliang He, Tihe Zhou, Haden Lee, Chad Cathcart, and Peter Badgley

884

Part XXVI

Electronic Packaging and Interconnection

Dynamic Material Characterization Through In-Situ Electrical Resistivity Measurements of High Temperature Transient Liquid Phase Sinter Alloys . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . G. Nave and P. McCluskey Effects of Diameter on Copper Pillar with Solder Cap Interconnections During Reflow Soldering Process . . . . . . . . . . . . . . . . . . . Jing Rou Lee, Mohd Sharizal Abdul Aziz, Mohd Arif Anuar Mohd Salleh, Chu Yee Khor, and Mohammad Hafifi Hafiz Ishak The Effect of Grain Boundary Type on Void Formation in a Through Silicon Via (TSV) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Armin Shashaani and Panthea Sepehrband

897

909

921

Contents

Part XXVII

xvii

Environmental Degradation of Multiple Principal Component Materials

High Temperature Oxidation of CoNiFeMnCr High Entropy Alloys Reinforced by MC-Carbides . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Patrice Berthod Part XXVIII

933

Environmentally Assisted Cracking: Theory and Practice

Hydrogen Effects on Mechanical and Toughness Properties of Pipeline Steels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xin Pang and Su Xu

945

Improvement of Pitting Corrosion Resistance of 304 Stainless Steel with Lanthanum Addition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Qiang Ren and Lifeng Zhang

956

Influence of High-Temperature Tempering Treatment on Hydrogen Diffusion Behavior in X80 Pipeline Steel Containing Different Vanadium Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . Wensen Cheng, Bo Song, and Jinghong Mao Liquid Metal Embrittlement Behavior of Dual-Phase Steels: The Influence of Microstructure and Strain Rate . . . . . . . . . . . . . . . . . . . . Pallavi Pant, B. Hilpert, H. Schubert, and L. N. Brewer Part XXIX

962

973

Fatigue in Materials: Fundamentals, Multiscale Characterizations, and Computational Modeling

Characterization of Low-Cycle Fatigue Deformation Behavior at RT/200 °C of FeMnAlC Lightweight Steel for Low-Pressure Turbine Blade . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Eui-Seok Ko, Chi-Won Kim, Seong-Jun Park, and Hyun-Uk Hong Experimental Study on the Influence of Surface Carbon Content on Crack Initiation for 20Cr2Ni4A Steel . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yingtao Zhang, Benxiang Gong, Yunpeng Guo, Gang Wang, and Xiulin Ji

987

992

Molecular Dynamics Simulations of the Thermal Evolution of Voids in Cu Bulk and Grain Boundaries . . . . . . . . . . . . . . . . . . . . . . . . . . 1001 Vasileios Fotopoulos, Corey S. O’Hern, and Alexander L. Shluger

xviii

Part XXX

Contents

Frontiers in Solidification: An MPMD Symposium Honoring Jonathan A. Dantzig

Design and Technology Research of Copper Ingot Mold for Water-Cooled Mold . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1013 Zhenglei Tang, Fuming Wang, Ran Guo, Zheng Yaxu, Shaopu Xu, and Hongyang Li Design of Light Wind Turbine Parts by Simulation-Based Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1027 Y. Bami, C. Huang, E. Subasic, F. Weber, J. Zimmermann, V. Züch, and J. Jakumeit Rationalization of the Modelling of Stress and Strain Evolution in Powder Bed Fusion Additive Manufacturing—A Perspective from a Background in the Simulation of Casting Processes . . . . . . . . . . . 1038 Pegah Pourabdollah, Farhad Rahimi, Asmita Chakraborty, Farzaneh Farhang Mehr, Daan Maijer, and Steve Cockcroft Part XXXI

Functional Nanomaterials 2023

Semiconductor Nanomaterials and 3D Systems . . . . . . . . . . . . . . . . . . . . . . 1051 J. A. Rogers Part XXXII

High Performance Steels

Effect of MgO, Ti2 O3 , and Al2 O3 Inclusions on the Formation of Manganese-Depleted Zones Through First-Principles Calculation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1057 Er-kang Liu, Qi Wang, Chen-yu Ma, Zhi-hong Guo, Ya-xu Zheng, and Li-guang Zhu Microstructure and Property Uniformity of 07MnNiMoDR Low Carbon Bainitic Steel Plate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1067 Xiaoqing Zhou, Li Shi, Ruihao Zhang, Sheng Liu, and Hongpo Wang Mechanical Properties and Microstructures Development of Quenching and Partitioning (Q&P) Steels During Galvannealing Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1078 Lei Chen, Kyeong Sik Shin, Han Sol Maeng, and Chun Ku Kang Part XXXIII

High Temperature Creep Properties of Advanced Structural Materials

Creep Behavior at Elevated Temperatures of Several Polycrystalline Ni-based Superalloys Strengthened by MC-Carbides . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1093 Patrice Berthod, Safa Tlili, and Dame Assane Kane

Contents

xix

Strengthening Against Creep at Elevated Temperature of HEA Alloys of the CoNiFeMnCr Type Using MC-Carbides . . . . . . . . . . . . . . . . 1103 Patrice Berthod Part XXXIV

High Temperature Electrochemistry V

Chloro-Aluminate Species Distribution Correlation with Electrical Conductivity of 1-Ethyl-3-Methyl Imidazolium Chloride (EMIC)-Aluminum Chloride (AlCl3 ) System . . . . . . . . . . . . . . . 1115 A. N. Ahmed, M. K. Nahian, and R. G. Reddy Part XXXV

Light Elements Technology

A New Method for Producing Hydrogen, Lithium Metal, and High-Purity Silicon from Spodumene Ore . . . . . . . . . . . . . . . . . . . . . . 1123 Huimin Lu and Neale R. Neelameggham Electrochemical Technology for Lithium-Isotopes Separation . . . . . . . . . 1132 Prashant K. Sarswat and Michael L. Free High-Grade Li2 SO4 from a Local Montebrasite Ore as Industrial Raw Material for Managing Bipolar Disorder . . . . . . . . . . . . . . . . . . . . . . . 1138 Alafara A. Baba, Daud T. Olaoluwa, Ayo F. Balogun, and Oluwagbemiga A. Adebola Process Simulation for Low Emission Hydrogen Production Using DWSIM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1144 Ganesan Subramanian and Neale R. Neelameggham Recovery of Lithium from Waste LIBs Using Sulfuric Acid Roasting and Water Washing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1155 Manis Kumar Jha, Pankaj Kumar Choubey, Rekha Panda, Om Shankar Dinkar, and Nityanand Singh Spark Plasma Sintered Boron Carbide Ceramic Armor . . . . . . . . . . . . . . 1162 B. Gökçe Dara and Gamze Sapanci Spark Plasma Sintering and Characterization of B4 C-ZrB2 Composites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1171 Leyla Yanmaz and Filiz Cinar Sahin Thoughts on the Role of Light Elements as Alternative Reductants in Major Ferroalloy Production . . . . . . . . . . . . . . . . . . . . . . . . . 1174 Joalet Dalene Steenkamp and Xolisa Camagu Goso Utilizing of Tincal Ore Wastes in Ceramic Industry . . . . . . . . . . . . . . . . . . 1177 Levent Özmen, Yıldız Yıldırım, Dilek Ba¸so˘glu, and Onuralp Yücel

xx

Part XXXVI

Contents

Materials and Chemistry for Molten Salt Systems

Study of the Assimilation of Inclusions in Molten Mold Flux Under the Action of Low-Frequency Electromagnetic Fields . . . . . . . . . . 1191 Fushen Li, Mingxing Wang, Yu Wang, Bo Bai, Hongpo Wang, Yijia Wang, and Jian Kang Thermodynamic Analysis of CoCrFeNi High-Entropy Alloys Prepared by Molten Salt Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1202 Hui Li, Sheng Zhang, and Jinglong Liang Part XXXVII

Mechanical Response of Materials Investigated Through Novel In-Situ Experiments and Modeling

Modeling of the Bending Behavior to Study Nested-Cylinder Structure in Spicules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1215 Olivia Lowe, Christian Peco, and Fariborz Tavangarian Part XXXVIII

Nanostructured Materials in Extreme Environments

Study on Advanced Cementing Practices Using Inert Graphene Nanoplatelets and Hydraulic Fracturing Fluids for Wellbore Integrity and Sustainability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1225 Gabriel Awejori, Havila Jupudi, Cody Massion, and Mileva Radonjic Part XXXIX

Natural Fibers and Its Composites: A Sustainable Solution

Corozo Palm Fibers: Mechanical Behavior and Potential Use for Composites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1239 Henry A. Colorado, Jimmy Unfried-Silgado, and Luis Armando Espitia-San Juan Feasibility Study of Incorporation of Dyeing Sludge in Red Ceramics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1245 H. C. Rangel, G. C. G. Delaqua, J. A. T. Linhares Jr, A. R. G. de Azevedo, S. N. Monteiro, M. P. Babisk, and C. M. F. Vieira Influence of the Incorporation of Particulates from the Pineapple Crown on the Impact Strength of Epoxy Systems . . . . . . . . . . . . . . . . . . . . 1252 D. C. R. Velasco, J. A. T. Linhares, N. T. Simonassi, C. M. F. Vieira, A. R. G. Azevedo, M. T. Marvila, and S. N. Monteiro Mitigation of Urban Noise Through the Implementation of Pumice with an Air Chamber on Building Facades . . . . . . . . . . . . . . . . 1258 Jeiser Rendón and Henry A. Colorado

Contents

xxi

Natural Vegetable Fibers Used from Colombia and Their Use as Potential Reinforcement for Composite Materials . . . . . . . . . . . . . . . . . 1263 Henry A. Colorado, Sergio Neves Monteiro, Geovana Carla Girondi Delaqua, and Carlos M. Vieira Part XL

Phase Stability in Extreme Environments

Heat Treatment Design of Inconel 740H Superalloy for Microstructure Stability and Creep Properties Enhancement . . . . . . 1273 Dong-Min Kim, Chiwon Kim, Cheol-Hyeok Yang, Hyun-Uk Hong, and Hi-Won Jeong Part XLI

Phase Transformations and Microstructural Evolution

A Comprehensive Investigation on the Sintering Behavior of CaO–SiO2 –CaF2 –Al2 O3 Slags System . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1283 Liang Yu, Shaopeng Gu, Guanghua Wen, Chunhua Ran, Funian Han, and Zhe Wang Experimental Analysis of R-Phase NiTi Tube Actuators Using in Contact Conductive Heating Stage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1293 Lehar Asip Khan, Hasan Ayub, Corné Muilwijk, Eanna McCarthy, Inam Ul Ahad, and Dermot Brabazon Fracture Analysis for Intermediate Slabs of Wear-Resistant Steel Based on the Evolution of Surface Decarburization Behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1302 Hao Geng, Yun-He Chang, Zhuang Zhang, Jian-Feng Jin, Pu Wang, and Jia-Quan Zhang The Formation and Stability of Nanosphere Composites . . . . . . . . . . . . . 1313 Rahul Basu The Impact of Graphene Nanoplatelets (GNPs) on the Hydration Mechanism of Alite (C3 S) in Class-H Wellbore Cement with Focus on Microstructural Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . 1322 Havila Jupudi, Cody Massion, and Mileva Radonjic Use of the Hollomon-Jaffe Tempering Parameter to Optimize the Microhardness in a Medium Carbon Low Alloy Cr–Mo Steel . . . . . 1331 P. G. Díaz-Villaseñor, E. López-Martínez, O. Vázquez-Gómez, P. Garnica-González, and H. J. Vergara-Hernández

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Part XLII

Contents

Printed Electronics and Additive Manufacturing: Functional Materials, Processing Techniques, and Emerging Applications

Development of a Metamaterial Honeycomb Structure for Radar Absorbing Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1341 Mariam Mansoori, Safieh Almahmoud, and Daniel Choi Liquid Metal Inks for Printed Stretchable Electronics: Gallium Alloy Interactions with the Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1346 Robin Dietrich, Zachary Farrell, and Christopher Tabor Permanent Magnet Integrated Shock Absorber and Electric Generator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1355 Richard Daly, B. S. Mani, and N. M. Ravindra Part XLIII

Thermodynamics and Kinetics of Alloys

Thermodynamics and Kinetics of Reaction of Rare Earth (La, Ce, Y) with MgO Refractories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1367 Jian Kang, Hongpo Wang, Yu Wang, and Xuewei Lv Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1379 Subject Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1385

Part I

2D Materials: Preparation, Properties, Modeling, and Applications

An Overview of Graphene-Based Nanomaterials in Electronic Skin Biosensing Raphael O. Ekun, Eribe M. Jonathan, Okeke I. Emmanuel, Best Atoe, and Ikhazuagbe H. Ifijen

Abstract Skin, the largest organ in the body, is capable of detecting and reacting to a variety of external stimuli. The development of electronic skin (E-skin) for the imitation of the human sensory system has recently gained a lot of attention due to its potential applications in wearable human health monitoring and care systems, advanced robotics, artificial intelligence, and human–machine interfaces. Electronic skin sensing devices have accelerated due to graphene’s capacity to achieve unique functionality using a variety of assembly processable processes. Consequently, the use of graphene and the components that make it up in biomedicine is growing. This review focuses on high-performance electronic skin that has been developed for biosensing applications through a number of research projects. Additionally, a brief discussion of electronic skin’s production processes, research obstacles, and future prospects was included. Keywords Graphene-based nanomaterials · Electronic skin · Biosensing · Nanotechnology

R. O. Ekun Department of Electrical Electronics, Cyprus International University, Mersin 10, Haspolat, Lefkosa, North Cyprus, Turkey E. M. Jonathan Department of Chemistry, Benson Idahosa University Benin City, PMB 1100, Edo State, Nigeria O. I. Emmanuel Department of Chemistry, University of Benin, Benin City, Edo State, Nigeria B. Atoe Department of Daily Need, Worldwide Healthcare, 100, Textile Mill Road, Benin City, Edo State, Nigeria I. H. Ifijen (B) Department of Research Operations, Rubber Research Institute of Nigeria, Iyanomo, Benin City, Nigeria e-mail: [email protected]; [email protected] © The Minerals, Metals & Materials Society 2023 The Minerals, Metals & Materials Society, TMS 2023 152nd Annual Meeting & Exhibition Supplemental Proceedings, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-031-22524-6_1

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Introduction One of the effective and interesting in flexible electronics is the flexible electronic skin, or e-skin [1]. E-skins imitate the entire spectrum of human sensing abilities, and because they are flexible and biocompatible, they have a wide range of potential applications in touch-sensor technologies, artificial intelligence systems, personal healthcare monitoring, and human–machine interfaces [2, 3]. The e-skin resembles a stretchable, flexible skin with intelligent processing abilities [4]. Pressure sensing, which turns pressure into electrical signals and processes them appropriately, is the main purpose of the system. Traditionally, an electrocardiogram (ECG) measures the electrical potential on the body’s surface, which is created when the action potential of the heart muscle cells changes in a person. The e-skin transmits energy and signals in order to remotely get the ECG signal. In contrast, the e-skin can reach the goal of realtime monitoring of human health and disease prevention by meeting the requirements of anytime, anywhere detection and long-term monitoring in the prevention and treatment of cardiovascular diseases [5]. Nanomaterials are a component of a commercial revolution that has given rise to an explosion of hundreds of new products as a result of their varied physico-chemical properties, which allow for their use in a wide variety of creative applications [6–23]. For the purpose of developing flexible and stretchable sensors, nanomaterials such as nanoparticles, metal nanowires, carbon nanotubes, graphene, and porous silicon are commonly selected as sensing materials [24, 25]. Among the materials mentioned above, graphene is a promising 2D material in many applications due to its extraordinary multiple properties, including having a high electrical conductivity, being extremely light, ultrahigh carrier mobility, excellent electrical conductivity, superior thermal conductivity, large theoretical specific surface area, high optical transmittance, high Young’s modulus, and having exceptional mechanical strength and outstanding mechanical flexibility [26, 27]. The powerful bonds and van der Waals interactions between layers in particular give graphene a tendency to aggregate [28], creating a regulated self-assembly structure that distinguishes it from other materials and makes it a great option for e-skin. Because of graphene’s exceptional biosensor capabilities, including its high electron transfer rate, wide potential window, and large specific surface area, receptors including enzymes, antibodies, and deoxyribonucleic acid (DNA) can be effectively immobilized on its surface [29, 30]. These graphene bioelectrodes also had excellent durability up to 5,000 compression cycles, super stretchability with a maximum strain of 150%, and a low sheet resistance of about 1.5 k per square, which indicated the potential for low-cost processing and rage-scale application for future wearable electronic skin [30]. This review focuses on new, high-performance electronic skin that has been developed for biosensing applications through a number of research projects. Additionally, a brief discussion of electronic skin’s production processes, research obstacles, and future prospects was included.

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The Fabrication Methods of Electronic Skin Electronic interface-based bionics, or e-skin, is flexible. Similar to a biological skin, it can detect changes in pressure, touch, humidity, and temperature as well as identify the various shapes and textures of outside materials [31, 32]. Typically, flexible sensing electronic devices have a four-part structure. [5]: (i) Elastic and flexible substrates (ii) electrodes that conduct, (iii) sensing materials, and (iv) encapsulation materials. The conducting electrodes are used to transmit electrical signals, the sensing materials transform environmental stimuli into detectable electrical signals, and encapsulation material is used to protect the sensing material from potential external damage. A flexible substrate supports the e-skin and fits it to the biological skin. Therefore, choosing stretchy materials with an elastic modulus is necessary for the preparation of e-skin. Stretchable materials, hard materials with breaks in them, and bendable and flexible materials are the three options that are typically offered. The wavy flexible device, which has a particular amount of stretchability, is produced by combining the flexible device with the elastic substrate that has already been pre-stretched. While the wavy structure also affects the contact tightness, some electrical materials’ characteristics change as they are bent. Although it is currently very challenging to obtain a high match between the tensile qualities and the device layout density, stretchable devices can be made employing discontinuous rigid components on flexible substrates. This technology can use existing high-performance devices. Also necessary for e-skin are superior mechanical and electrical qualities for stretchable materials. One remedy is to inject electroactive fillers into the insulating elastomer, but doing so too often will compromise the material’s stretchability. Lowering the percolation threshold and constructing two-dimensional networks of one-dimensional materials, such as carbon nanotube networks, can increase the conductivity and stretchability. Altering the chemical and physical characteristics of polymeric electronic materials is another approach. One example is the addition of nonionic plasticizers to PEDOT: PSS, which has strong electrical conductivity but poor stretchability.

High-Performance Electronic Skin Preparation Using Novel Techniques Researchers have been creating new techniques to make it simpler and more practical to prepare electronic skin with excellent performance in order to introduce it into people’s lives as soon as possible. For example, Chen et al. [33] developed a wearable touch sensor that can simultaneously sense external pressure and has object recognition capabilities after being inspired by the superior electrical properties of electron-induced perpendicular graphene (EIPG) materials. The sensor employs a layered structure with elastic dielectric layers as electrodes (the dielectric layers are only 50 nm thick), and it primarily employs an electron cyclotron resonance (ECR)

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sputtering system to prepare vertically aligned graphene sheets, which results in a more efficient electron transfer channel and a quick and sensitive response in a broad pressure sensing range. Based on the drop in capacitance when approaching and leaving, this capacitive sensor can distinguish between neighboring objects. This is an efficient way to create vertical graphene nanosheets in flexible sensor electrodes, which take on a new function in the creation and advancement of e-skin. Electronic skin with long-term stability, rapid response and high sensitivity has great worth in biomedicine, robotics, and in other fields. In terms of sensitivity and response time, electronic skin still faces difficulties. Lü et al. [34] presented flexible electronic skin based on piezoresistive graphene films with high sensitivity and quick reaction to address this issue. The SEM micrograph and image of the manufactured graphene film are shown in Fig. 1. A pressure sensor array made up of a 4 × 4 tactile sensing unit made up the electronic skin. The underlying substrate (polyimide substrate), the middle layer (graphene/polyethylene terephthalate film), and the upper substrate bump were all present in each sensing unit (polydimethylsiloxane). The dimensions of the electronic skins and a photo of an electronic skin made from produced graphene films are shown in Fig. 1. The flexible electronic skin achieved a positive resistance characteristic in the range of 0–600 kPa, a sensitivity of 10.80/kPa in the range of 0–4 kPa, a loading response time of 10 ms, and a spatial resolution of 5 mm, according to the results of the measurement and analysis experiments designed in this paper. Additionally, using a regular-shaped object and a change in the resistance value of each unit, the electronic skin is an accomplished form of detection. The high sensitivity flexible electronic skin developed in this work has significant potential applications in artificial intelligence, medical diagnosis, and other domains (Fig. 2). Researchers are drawn to practical pressure sensing devices, especially tactile sensors, because graphene foam (GF) infiltrated with polymer. However, given a particular kind and level of pressure, the interface between the polymer and the graphene plays a crucial function. Tang et al. examined the effects of static and

Fig. 1 a SEM micrograph; and b photograph of the graphene film [34]

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Fig. 2 The size of the electronic skins (a); Photograph of the electronic skin (b) [34]

dynamic pressure on GF-polymer in this study [35]. During static pressure measurement, a recovery time of ∼0.1 s is noted (Fig. 3). GF-polymer samples, in contrast to static pressure, are extremely sensitive to dynamic load, and a short recovery period of 0.06 s is noted. When compared to the static pressure sensor’s sensitivity of ∼2.1 ± 0.3 Pa−1 , the dynamic pressure sensor’s sensitivity of ∼1.17 ± 0.1 Pa−1 is almost two times higher. In terms of technology, pressure is exerted via tactile sensors in a variety of ways, and GF-polymer is a great material to use for creating pressure sensors. The authors advised that dynamic pressure-based sensors, such as microphones for the measurement of sound pressure levels of GF-polymer, are preferable for applications involving the technological sensitivity of the sensors and are therefore more suitable for GF-based pressure sensors. A prior study [36] presented a revolutionary self-assembly technology based on the Marangoni effect to manufacture large-area ultra-thin graphene films. It was inspired by the fish scale structure of the predecessors, which used the tunneling effect to alter the resistance of graphene sheets. In response to this impact, graphene films are quickly generated on the liquid/gas interface, layered on top of one another via π–π interaction, and the films thus obtained have high transparency (86–94% at 550 nm), a thickness of 2.5–5.0 nm, tunable sheet resistance, and structural homogeneity. Due to the tunneling effect, a suitable dense film exhibits a GF of 1037 at 2% strain, demonstrating extremely high sensitivity. The fabrication of high-sensitivity strain sensors using this versatile and straightforward approach of self-assembling graphene films is ideal for e-skin applications and industrial production. In addition to the technique of electronically inducing the vertical arrangement of graphene sheets and the Marangoni effect, high-sensitivity sensors can also be made by deoxidizing graphene oxide through flame heating in a small area. For instance, Song et al. [37]’s confinement of the graphene oxide film to two quartz plates and swift thermal reduction of the graphene oxide’s gas resulted in the film’s rapid expansion into a 2D porous structure. By adjusting the sp2 and sp3 domains in graphene oxide, it is possible to fabricate a variety of controllable microstructures that can be used

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Fig. 3 Static pressure unit and schematic of metal electrodes on the GF-polymer sample (a). The change in the resistance of GF-Ecoflex sample with a recovery time ∼ 0.1 s (b). The stability test was performed under periodic pressure for a long period of time which shows good stability (c) [35]

to create flexible pressure sensor arrays, voice recognition technology, and pulse detection technology in addition to high-performing conventional pressure sensors. In a recent work, Yin et al. demonstrated a high-performance, skin-like pressure sensor and sensing arrays based on graphene/polyamide composite interlocking fabric [38]. They did this using a straightforward but effective, affordable, and largescale capable approach. By attaching CRG to interlocking fabric, flexible G/IF may be made instantly. The entire fabrication procedure can be scaled up without the use of cumbersome, expensive machinery or traditional wafer-based methods. Due to the special microstructure of the composite fabric, the pressure sensor that is produced has extremely high sensitivity (2.34 kPa−1 ), an extremely low detection limit (99.9%), industrial silicon (purity >99.5%), metallic chromium (purity >99.0%), ferroboron (B = 12.5%), and carbon powder as raw materials. According to the composition of FeSiBCCr spherical amorphous powder, the raw materials are proportioned and smelted in an intermediate frequency induction furnace. The smelting power is 80–200 kW. After the raw materials are completely melted, the molten steel is poured into the tundish; after that, the molten steel enters the atomizing chamber through the liquid guide pipe and is broken into spherical amorphous powder under the action of atomizing media such as high-pressure water and high-pressure airflow in the spray disc; after atomization, the powder is separated by water powder, vacuum dried, powder classified, and batch processed to obtain the final FeSiBCCr amorphous powder. After that, the sample powders with different particle sizes are obtained by sieving through a standard sieve, which is divided into 140 mesh, 200 mesh, 300 mesh, and 400 mesh. The corresponding experiment numbers of powders with different particle sizes are shown in Table 1. After comparing and analyzing the properties of the six powders, the 5# (−400 mesh) powder with the best comprehensive properties was screened out and the corresponding magnetic powder cores were prepared. In the experiment, EP (epoxy

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Fig. 1 Schematic diagram of the production process of the gas–water combined atomization process

Table 1 Mesh range of sample powders with different particle sizes

Sample

Sieve mesh range

Mass ratio/%

1#

+140 mesh

14.8

2#

+200 mesh to −140 mesh

18.1

3#

+300 mesh to −200 mesh

25.2

4#

+400 mesh to −300 mesh

14.3

5#

−400 mesh

28.6

0#

Original powder

100

resin) was used to coat the amorphous powder, and the annular magnetic powder core sample (ϕ14 mm × ϕ8 mm × h 3.1 mm, weighing 2 g) was prepared under 550 MPa and bidirectional pressing. The chemical composition of the atomized powder was detected by direct reading spectrometer; the morphology of products with different particle sizes was observed by scanning electron microscopy (SEM, Phenom Pro), and the sphericity of different particle sizes was counted by Phenom Prosuite software. The particle sizes of powders with different mesh numbers were measured by laser particle size analyzer (BT9300S); the phases of powders with different particle sizes were analyzed by X-ray

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diffraction (XRD, D2 Phaser). The crystallization enthalpy of powders with different particle sizes was determined by high-temperature comprehensive thermal analyzer (DSC). The saturation magnetic induction and coercivity of powders with different particle sizes were measured by vibrating sample magnetometer (VSM, Lake Shore 8604), in which the measurement range of saturation magnetization is ±10,000 Oe and the coercivity is ±200 Oe. The loss value Pcv of the magnetic powder core under the conditions of Bm = 0.02 T, f = 100 kHz–1 MHz was measured by B-H analyzer (IWATSU-SY-8219).

Results and Discussion Particle Size Distribution, Morphology, and Amorphism of FeSiBCCr Amorphous Powder Figure 2 shows the X-ray diffraction patterns of FeSiBCCr amorphous powders with different particle sizes prepared by a novel gas–water combined atomization process. It can be seen from the figure that the 1# and 2# powders have a small diffraction peak corresponding to α-Fe(Si) at the scattering angle of 45°, while the powders of other particle sizes show a steamed bread-like diffuse scattering peak, without clear crystal phase peak. The DSC curves of 1–5# powders were measured and the results are shown in Fig. 3. It can be seen from Fig. 3a that at the heating rate of 10 K min−1 , the FeSiBCCr amorphous powders with five particle sizes all showed three clear exothermic peaks. It corresponds to the eutectic reaction of “Amorphous → Fe3 B + α-Fe(Si)”, the decomposition process of “Fe3 B → Fe2 B + α-Fe”, and the structural transition between Fe-Si phases, respectively [14]. Taking 1# powder as a reference, the starting Fig. 2 X-ray diffraction patterns of FeSiBCCr amorphous powders with different particle sizes

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temperatures Tx1 , Tx2 , and Tx3 of the three crystallization reactions are indicated in the figure. With the decrease of powder particle size, Tx1 increases linearly as a whole, which is beneficial to improving the amorphous forming ability (GFA) of the powder. To evaluate the crystalline volume fraction and amorphism of powders with different particle sizes, the areas of three exothermic peaks were measured, as shown by H in Fig. 3a. By comparison, it is found that the crystallization enthalpy of the 4# sample is the highest, which is 123.46 J g−1 . The amorphous degree of this sample is regarded as 1, and the amorphous degree of other samples is calibrated. The results are shown in Fig. 3b. With decreasing particle size, the amorphous degree of the powder shows a substantially linear increase trend. Among them, the amorphous degrees of 1# and 2# powders are lower, which are 0.53 and 0.86, respectively, and the amorphous degrees of 3–5# powders are the highest, all reaching 0.93, which is basically consistent with the change of powder Tx1 in Fig. 3a. Figure 4 is a graph of the cumulative percentage and interval percentage of FeSiBCCr original powder particle size distribution. It can be seen from the figure that the particle size curve of the powder shows a peak, which conforms to the normal distribution and the overall distribution is uniform. Among them, the D10 , D50 , and D90 of the original powder were 16.40, 48.16, and 113.1 μm, respectively, and the powder particle size distribution range was wide, which was conducive to the screening of amorphous powders with different mesh numbers. Further analysis of the soft magnetic properties of amorphous powders with different particle size ranges can provide theoretical guidance and reference for the selection and proportion of powder particle size in the preparation of back-end products. Figure 5a, c shows the electron microscope pictures of 1# and 5# amorphous powders. It can be seen from Fig. 5a that the 1# powder has a coarse overall particle size, most of which are tadpole-like irregular-shaped powders, and the number of spherical powders is very small; the 5# powder has a smooth surface and good dispersion, and the particle shape is mainly spherical, similar spherical, with a small part of the ellipsoid at the same time, this kind of powder is beneficial to the subsequent

Fig. 3 Thermodynamic behavior of FeSiBCCr amorphous powders with different particle sizes, a DSC curve; b amorphous degree

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Fig. 4 FeSiBCCr original powder particle size distribution

 

D10=16.40 D50=48.16 D90=113.1



   



Diff%

Cum%



 



 

 











particle size(μm)

insulation coating treatment and the preparation of high-density AMPCs. Figure 5b, d shows the corresponding relationship between powder particle size and sphericity. It can be seen from the figure that the 1# powder has poor overall sphericity due to the incomplete atomization process and the coarse particle size, while the 5# powder has fine particle size and spherical shape overall high. And it can be seen from the figure that the larger the powder particle size, the lower the sphericity. This is because when the particle size of the powder is relatively large, the surface of the powder particles will be pre-solidified in the process of atomization and crushing, while the interior is still in a molten state, and the molten metal inside will impact the surface during the descending process, so that the powder shape changes from spherical and spheroid becomes ellipsoid or mallet shaped. In addition, the collision of the completely solidified amorphous powders and the strong impact of high-pressure water during the atomization process also lead to the generation of more irregularly shaped powder particles. Figure 6 shows a graph of the sphericity and D50 of FeSiBCCr amorphous powder under different particle size conditions. It can be seen from the figure that as the particle size of the powder decreases, the D50 linearly decreases from 158.8 to 16.37 μm, the sphericity increases linearly from 0.225 to 0.979, and the sphericity of the powder below −200 mesh (3–5#) is above 0.90, that is, the sphericity of the powder is significantly improved while the particle size of the powder is reduced. Studies have shown [16] that the morphology of the atomized powder mainly depends on the difference between the solidification time (tso ) of the molten metal droplet and the spheroidization time (tsp ). For large particles, due to the low degree of fragmentation in the first stage of the gas atomization and crushing process, most of them retain the long strip shape at high temperature, and their cooling time and spheroidizing time are long, so that tso /tsp < 1. It is easy to cause the metal droplets to solidify before completing the spheroidization, so the powders are mostly irregular shapes such as special shaped, mallet shaped, ellipsoid, etc., and the phenomenon of mutual bonding will also occur, as shown in Fig. 5a; for small particles, the time required for solidification of particles and powders is extremely short, and most of the small

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Fig. 5 Morphology of FeSiBCCr amorphous powders with different particle sizes and the corresponding relationship between particle size and sphericity, 1#: (a) and (b); 5#: (c) and (d)

particles satisfy tso /tsp > 1. In this case, the droplets have spheroidized and contracted before solidification, so the obtained powder is nearly spherical, as shown in Fig. 5c. 1.0

Fig. 6 The relationship between FeSiBCCr powder with different particle sizes and sphericity

150

0.6

100

Circularity D50

0.4

50 0.2

0.0

1#

2#

3#

Sample

4#

5#

0

D50/μm

Circularity

0.8

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Characterization of Soft Magnetic Properties of FeSiBCCr Amorphous Powders Figure 7A shows the relationship between the coercive force and amorphous degree of FeSiBCCr amorphous powder with different particle sizes. It can be seen from the figure that with the decrease of particle size, the coercive force of the magnetic powder first decreases and then increases, which is highly consistent with the change of powder amorphous degree analyzed above (that is, the higher the amorphous degree, the lower the coercivity of the powder), and the coercivity of the 4# and 5# powders is relatively close, 0.08 and 0.128 Oe, respectively. Figure 7b shows the hysteresis curves of FeSiBCCr amorphous powders with different particle sizes. Since the composition of the five powders is the same, and the saturation magnetization of the alloy is positively correlated with the Fe content in the system, the saturation magnetization of the powders with different particle sizes is only located on both sides of the original powder. Among them, the 5# powder is the highest, which is 144.2 emu g−1 . The spherical amorphous powder used in the power electronics industry needs to meet the conditions of good sphericity, amorphism, and excellent soft magnetic properties. In this study, the sphericity, amorphism, and coercivity of 4# and 5# powders are very close, but considering that the saturation magnetization of 5# powder is significantly higher than that of 4# (133 emu g−1 ), the comprehensive performance is optimal. Therefore, in this study, 5# powder was used to prepare the corresponding AMPCs, and their properties such as permeability and loss were measured and compared with AMPCs prepared in other literature. The results are shown in Table 2. The 5# powder prepared in this study has high sphericity and excellent soft magnetic properties, especially the AMPCs coated with epoxy resin have a low loss (224.10 mW cm–3 , Bm = 0.05 T, f = 100 kHz), but its permeability is low. This may be because the particle size of the 5# powder is small, which leads to the reduction of the mass fraction of the ferromagnetic powder per unit volume after coating, which 1.0

5 1 4.21 0.865

0.8 Hc 4.21 1.11 0.56 0.08 0.128

1# 2# 3# 4# 5#

3 0.537

Amorphous degree 0.537 0.865 0.933 1 0.967

Hc

2

0.6

0.4

1.11 1

0.2

0.56

Amorphous degree

Hc(Oe)

4

(b)

0.967

0.933

Mass magnetization/emu·g-1

(a)

150 100 50

0# 1# 2# 3# 4# 5#

0 144 142

-50

140 138 136

-100

134 132

0.08

0.128





130

-150

8000

8500

9000

9500

10000

0.0

0 





Sample

-10000

-5000

0

5000

10000

Magnetic field intensity/Oe

Fig. 7 The magnetic moment of FeSiBCCr amorphous powders with different particle sizes as a function of magnetic field strength, a coercivity and amorphousness; b saturation magnetization

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Table 2 Comparison of this study with magnetic powder cores reported in previous literature Sample

H c (Oe)

μe

Core loss, Pcv (mW cm–3 )

100 kHz

100 kHz/0.05 T

1 MHz/0.02 T

Reference

FeSiBCCr/5#

0.128

19.62

224.10

1441.5

FeSiB

/

66

472

/

This work [7]

FeSiBCCr

15.3

/

310

1442

[17]

FeSiCr

11.74

17.2

/

/

[18]

FeSiCrB

23.22

19.95

/

/

[19]

FeSiB + FeNi

/

31

350

[20]

in turn causes the reduction of the magnetic permeability. Therefore, by further optimizing the powder particle size proportioning scheme and coating process, the soft magnetic performance of the powder still has some room for improvement.

Conclusions In this paper, five kinds of FeSiBCCr amorphous spherical powders with different particle size ranges were prepared by a novel gas–water combined atomization process, and the powder with the best comprehensive performance was prepared into AMPCs, and its loss and magnetic permeability were characterized. The main conclusions are as follows: (1) With the decrease of particle size, the comprehensive soft magnetic properties of the powder are improved. Among them, the 5# powder with particle size ≤38 μm has both high amorphousness (0.967), best sphericity (0.979), extremely low coercivity (Hc = 0.128 Oe), and the highest saturation magnetization (Ms = 144.2 emu g−1 ), the overall performance is the best. (2) The magnetic permeability of FeSiBCCr@EA core–shell AMPCs prepared by coating 5# powder with particle size ≤38 μm with epoxy resin is 19.62, and the core losses are 224.10 mW cm–3 (Bm = 0.05 T, f = 100 kHz) and 1441.5 mW cm–3 (Bm = 0.02 T, f = 1 MHz), respectively. The amorphous fine spherical powder ≤38 μm and its corresponding AMPCs prepared in this study have better comprehensive performance, which are helpful to promote the development of miniaturization, high frequency, and integration of devices, and have certain application potential.

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References 1. Han L, Maccari F, Souza Filho IR et al (2022) A mechanically strong and ductile soft magnet with extremely low coercivity. Nature 608:310–316 2. Silveyra JM, Ferrara E, Huber DL et al (2018) Soft magnetic materials for a sustainable and electrified world. Science 362(6413):eaao0195 3. Jiang C, Lu J, Liu W et al (2020) Corrosion resistance of plasma-sprayed Fe-based coatings by using core-shell structure powders. J Market Res 9(6):12273–12280 4. Li Y, Chen WZ, Dong B et al (2018) Effects of phosphorus and carbon content on the surface tension of FeSiBPC glass-forming alloy melts. J Non-Cryst Solids 496:13–17 5. Meng LB, Yu HC, Lv SY et al (2021) Study on amorphous soft magnetic powder with high sphericity. Powder Metall Ind 31(5):105–110 6. Chi Q, Xie L, Chang L et al (2021) Study on the properties of carbonyl iron powder/FeSiBCCr composite amorphous magnetic powder core. Mater Rep 35(10):10023–10028 7. Li Z, Dong Y, Pauly S et al (2017) Enhanced soft magnetic properties of Fe-based amorphous powder cores by longitude magnetic field annealing. J Alloy Compd 706:1–6 8. Qian K, Sokolov AS, Li Q et al (2020) High performance metallic amorphous magnetic flakebased magnetodielectric inductors. IEEE Magn Lett 11:1–5 9. Lee Y, Jeon J, Nam S et al (2018) Soft magnetic properties of Fe-based amorphous/nanocrystalline hybrid materials. Powder Technol 339:440–445 10. Yoshida K, Bito M, Kageyama J et al (2016) Unusual high B s for Fe-based amorphous powders produced by a gas-atomization technique. AIP Adv 6(5):055933 11. Zhao T, Chen C, Wu XJ et al (2021) FeSiBCrC amorphous magnetic powder fabricated by gas-water combined atomization. J Alloy Compd 857:157991 12. Zhang Y, Dong Y, Liu L et al (2019) High filling alumina/epoxy nanocomposite as coating layer for Fe-based amorphous powder cores with enhanced magnetic performance. J Mater Sci Mater Electron 30(16):14869–14877 13. Wang P, Wei M, Dong YN et al (2022) Crystallization evolution behavior of amorphous Fe85.7 Si7.9 B3.6 Cr2 C0.8 powder produced by a novel atomization process. J Non-Cryst Solids 594:121824 14. Liu JQ, Pang J, Wang P et al (2022) Research progress of liquid metal atomization technology and preparation of its amorphous powders. China Metall 32(2):1–14 15. Dong Y, Liu J, Wang P et al (2022) Study of bulk amorphous and nanocrystalline alloys fabricated by high-sphericity Fe84 Si7 B5 C2 Cr2 amorphous powders at different spark-plasmasintering temperatures. Materials 15(3):1106 16. Miller SA, Giles WB (1981) Effect of process variables on atomization of metals and alloys. Mod Dev Powder Metall 1:113–128 17. Zhou B, Dong Y, Liu L et al (2019) The core-shell structured Fe-based amorphous magnetic powder cores with excellent magnetic properties. Adv Powder Technol 30(8):1504–1512 18. Yu H, Zhou S, Zhang G et al (2022) The phosphating effect on the properties of FeSiCr alloy powder. J Magn Magn Mater 552:168741 19. Woo HJ, Ahn JH, Kim CP et al (2022) Effect of the particle size classification of FeSiCrB amorphous soft magnetic composites to improve magnetic properties of power inductors. J Non-Cryst Solids 577:121309 20. Li B, Zheng ZG, Yu HY et al (2017) Improved permeability of Fe based amorphous magnetic powder cores by adding Permalloy. J Magn Magn Mater 438:138–143

Study on the Optimization of Fe Content of FeSiBC Amorphous Powders Zheng-qu Zhu, Yan-nan Dong, Jia-qi Liu, Jing Pang, Pu Wang, and Jia-quan Zhang

Abstract In this paper, four types of amorphous spherical powders with different Fe contents were produced by a novel gas–water combined atomization process, and the corresponding magnetic powders cores (MPCs) were fabricated. It was found that as Fe content was increased from 92.66 to 94.55 wt%, both the D50 and enthalpy of crystallization of the powders decreased and then increased, while the coercivity increased and then decreased together with a linearly enhanced saturation magnetization. Among them, Fe94.55 Si1.05 B4.3 C0.1 amorphous powders have smaller particle size (D50 = 31.67 μm), excellent circularity (0.95), good thermal stability (T = 46 K), and highest saturation magnetization (172 emu g−1 ), thus showing the most excellent overall properties. The core loss and the permeability of the corresponding MPCs for the Fe94.55 Si1.05 B4.3 C0.1 amorphous powders are 79.76 kW m−3 (0.02 T, 100 kHz) and 25.95, respectively, which shows the possibility to develop amorphous powders and MPCs with high Fe content through the present gas–water combined atomization. Keywords Amorphous powders · Fe content · Magnetic powders core · Saturation magnetization · Core loss

Z. Zhu · Y.-n. Dong · J. Liu · P. Wang (B) · J. Zhang (B) School of Metallurgical and Ecological Engineering, University of Science and Technology Beijing, Beijing 100083, People’s Republic of China e-mail: [email protected] J. Zhang e-mail: [email protected] J. Pang Qingdao Yunlu Advanced Materials Technology Co., Ltd., Qingdao 266232, People’s Republic of China © The Minerals, Metals & Materials Society 2023 The Minerals, Metals & Materials Society, TMS 2023 152nd Annual Meeting & Exhibition Supplemental Proceedings, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-031-22524-6_8

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Introduction Fe-based amorphous magnetic powders cores (AMPCs) are a new type of soft magnetic composite material pressed by amorphous magnetic powders covered with insulating medium, which is widely used in transformers, inductors, filters, and switching power supplies due to its high resistivity, high saturation magnetization, high permeability, and low coercivity [1]. With the development of electronic industry, various electronic devices tend to be high-frequency and miniaturization [2], which puts forward higher requirements for the performance of AMPCs. The performance of the magnetic powders has a direct connection with AMPCs when irrespective of the type of insulating agent and the coating process. The system of amorphous alloys generally consists of two types of elements: ferromagnetic elements (Fe, Co, Ni) and amorphous forming elements (B, Si, Cr, C, P, etc.) [3], the design of the composition of amorphous powders is usually based on these two types of elements. Fe-based amorphous alloys have the characteristics of high saturation magnetization and low coercivity due to the high content of Fe element with the highest magnetic moment in composition and the long-range disorder of atomic arrangement in microstructure, respectively. B is a constituent element of almost all amorphous or nanocrystalline alloys and is characterised by its small atomic radius and few outer electrons, which is very beneficial for the formation of amorphous alloys. Partial replacement of B by Si will improve the crystallization temperature and thermal stability of amorphous, which is beneficial to enhance the glass-forming ability (GFA) of the alloy, and other metalloid elements such as P, C, and Cr also have similar effects [4]. In order to comply with the development trend of miniaturization and high efficiency of electronic devices, the design of alloy composition to obtain magnetic powders with high saturation magnetization, low coercivity, high thermal stability, and strong GFA has always been a research hotspot of researchers. Li et al. [5] prepared Fe78 Si9 B13 amorphous ribbons based on the ball milling method, and prepared the corresponding AMPCs after mechanical crushing, who found the AMPCs have excellent soft magnetic properties with a μe of 66, core loss of 86 mW g−1 (0.05 T, 100 kHz), and a DC bias capability of 72%. Woo et al. [6] prepared AMPCs of composition Fe88 Si6 Cr3 B3 by adding Cr to Fe–Si–B, which shows quality factor of 41.48 and the core loss of 155.6 kW m−3 (0.01 T, 1 MHz). In addition, it was found that the addition of a small amount of Cr could form an oxide layer on the powders’ surface, which enhanced the resistivity and corrosion resistance of the powders. Meng et al. [7] prepared Fe91 Si5.5 B3 C0.5 amorphous magnetic powders based on a gas–water combined atomization process and found that element C had a good effect on improving the sphericity and thermal stability of the powders. Chi et al. [8] prepared Fe79 Si3 B4 P10 C4 magnetic powders by adding P and C to Fe-SiB, the AMPCs with high permeability and low loss were obtained after an optimal coating treatment by hydrothermal oxidation, with a saturation magnetization of 140 emu g−1 and low core loss of 187 kW m−3 (0.05 T, 100 kHz).

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The saturation magnetization of the Fe-based amorphous powders currently studied is generally between 120 and 150 emu g−1 , making the DC bias of the corresponding AMPCs poor, which is not beneficial to further reduction of the volume of electronic components and also limits their application in AC–DC superimposed circuits. Some scholars have pointed out that the saturation magnetization of amorphous alloys is positively correlated with the Fe content of the powders, and that powders prepared by atomization are ideal for the preparation of AMPCs because of their high sphericity without the absence of sharp edges [9]. Therefore, based on the previous development of the FeSiBCCr system [10–12], the author’s team further explored the relationship between alloy composition and powders properties, in order to determine the effects of different Fe and B contents on the saturation magnetic magnetization (M s ) and amorphous degree of amorphous powders. In this paper, four amorphous powders such as Fe92.66 Si3.86 B3.15 C0.35 , Fe93.22 Si2.08 B4.36 C0.34 , Fe94.11 Si2.86 B2.56 C0.45 , and Fe94.55 Si1.05 B4.3 C0.1 with high Fe content were prepared by adjusting the Fe and B content of FeSiBC alloy using a new gas–water combined atomization process. The corresponding AMPCs of the powders with the best overall soft magnetic properties are then insulated with PA (polyamide), then the properties of the amorphous powders and AMPCs were characterized by various means in order to provide some reference for the preparation of high Fe content amorphous powders and high-quality magnetic powders cores.

Experimental Procedure In this study, a new gas–water combined atomization process was used to produce high iron spherical amorphous powders. The specific process flow is shown in Fig. 1, the experiment selects industrial purity raw materials, industrial pure iron (purity greater than 99.9%), ferrosilicon (purity greater than 99.6%), ferroboron (purity greater than 99.0%), and carbon flakes (purity greater than 99.0%). After comparing and analyzing the properties of the four powders, the Fe94.55 Si1.05 B4.3 C0.1 powders with the best comprehensive properties were screened out and prepared the corresponding magnetic powders cores. In the experiment, polyamide was used to coat the amorphous powders, and the toroidal powder cores with ϕ14 mm × ϕ8 mm × h3.1 mm were fabricated by bidirectional pressing under 550 MPa at room temperature. Then the AMPCs were annealed at 150 °C for 2 h in nitrogen atmosphere. The chemical composition of the atomized powders was detected by direct reading spectrometer (results can be seen in Table 1). The morphology of powder particles was observed by Phenom Pro desktop scanning electron microscope (SEM) and Phenom Prosuite software was used to calculate the sphericity of different particle sizes. The particle size and distribution of the powders were detected by a BT-9300S laser particle size analyzer. The phase identification and analysis of the powders were analyzed by a German Bruker D2 Phaser X-ray diffraction (XRD) and the target material was copper. The glass transition temperature, crystallization temperature,

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Fig. 1 Schematic diagram of the production process of the gas–water combined atomization process

and crystallization enthalpy of amorphous powders were measured by Setaram Setsys Evo power-compensated differential scanning calorimeter (DSC). The saturation magnetization and coercivity of the powders were measured with a Lake Shore 8604 vibrating sample magnetometer (VSM). The B-H analyzer (IWATSU-SY-8219) was used to measure the loss value Pcv of the magnetic powders core under the conditions of Bm = 0.02 T, f = 100 kHz–1 MHz. Table 1 Composition of FeSiBC sample powders Sample

1#

2#

3#

4#

Fe

92.66

93.22

94.11

94.55

Si

3.86

2.08

2.86

1.05

B

3.15

4.36

2.56

4.3

C

0.35

0.34

0.45

0.1

Element (wt%)

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Results and Discussion Morphology, Structure, Particle Size Distribution, and Thermodynamic Properties of FeSiBC Amorphous Powders Table 2 and Fig. 2 are the particle size parameters and powders morphology SEM photos of the 1–4# sample powders prepared in this study, respectively. It can be seen from the table that the D50 of 1–4# powders first decreased and then increased, and the particle size of 4# powders was obviously larger. It can be seen from the figure that 1–3# powders have some spheroids and ellipsoids, mainly mallet shaped, droplet shaped, and cohesive powder particles, and the finer the powder particle size, the more obvious the cohesion phenomenon. Compared with the first three powders, the sphericity of the 4# sample powders is better and the sticking is very little. Studies have shown that the morphology of powder particles during atomization is mainly affected by the solidification time t solidification of molten metal droplets and the spheroidization time t spheroidization [13], that is, when the t spheroidization is shorter, the t solidification is longer (that is, the larger the particle size), the higher the sphericity of the powders, and vice versa, which is basically consistent with the observed changes in the particle size and morphology of the powders. Figure 3 shows the X-ray diffraction patterns of FeSiBC amorphous powders with four different compositions. It can be seen from the figure that the four powders have different degrees of crystallization, and the degree of crystallization is closely related to the crystal precipitation behavior and Fe content. Among them, powders 1# and 2# have no obvious sharp diffraction peaks in the entire scanning range, and only a small α-Fe(Si) crystal phase corresponding to the (110) lattice plane appears at the scattering angle of 45° peak, and the diffraction peak intensity (degree of crystallinity) of 2# powders is higher than 1#. When the Fe content increased to 94.11wt%, the 3# powders exhibited body-centered cubic α-Fe(Si) diffraction peaks of the crystalline phase corresponding to the (110), (200), and (210) lattice planes at 45°, 64°, and 83°, respectively. And there are also peaks around 45° corresponding to the Fe3 B and Fe2 B phases, indicating that the powder’s amorphous degree is further reduced. Compared with the 3# powders, when the Fe content continued to increase Table 2 Size distribution and sphericity of FeSiBC sample powders Sample

1#

2#

3#

4#

Classification range D10 (μm)

9.93

9.39

8.07

13.93

D50 (μm)

26.58

25.07

21.94

31.67

D90 (μm)

51.31

49.67

45.22

53.18

Sphericity

0.95

0.909

0.869

0.969

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Fig. 2 SEM morphology of FeSiBC sample powders. a 1#; b 2#; c 3#; d 4#

to 94.55 wt%, the diffraction peaks at 64° and 83° of the XRD pattern of the 4# powders disappeared, and the crystallization peak intensity around 45° decreased and Fe5 Si3 phase was precipitated, which may be due to the increase of B element content in the system to enhance the GFA of the alloy, which in turn increases the amorphous degree of the powders. The above analysis shows that the amorphous magnetic powders of these four components are non-completely amorphous soft magnetic alloys with mixed amorphous phase and crystalline phase, the main reason is that the cooling capacity of the atomization equipment and the GFA of the alloy system are insufficient. Fig. 3 XRD patterns of FeSiBC sample powders

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(b)

100

8

Passing(%)

Volume(%)

80 6

4

1# 2# 3# 4#

2

60

40

1# 2# 3# 4#

20

0

0 0

30

60

particle size(μm)

90

120

0

30

60

90

120

particle size(μm)

Fig. 4 Particle size distribution of FeSiBC sample powders. a Volume % and b Passing %

Figure 4 shows a plot of volume fraction and pass percentage of powders particle size distribution for four FeSiBC samples. It can be seen from the figure that the particle size curves of the four types of amorphous powders conform to the normal distribution, and the distribution is uniform. Among them, the particle size distribution of 1–3# powders is relatively close, while the volume fraction curve of 4# powders is significantly higher and narrower. Combined with the morphology in Fig. 2, it can be seen that the powders has a smooth surface without sharp edges, and has a uniform and concentrated particle size distribution, which satisfies the conditions for subsequent insulation treatment, cold pressing, and high-density AMPCs preparation. Figure 5 shows the DSC curves of the four FeSiBC sample powders at a heating rate of 10 K/min, and the inset is the thermodynamic characteristic temperature of powders. It can be seen from the figure that the crystallization enthalpy of 1–4# powders first decreased and then increased, which is consistent with the analysis results of the aforementioned powders XRD amorphous degree. Among them, the DSC curves of 1#, 2#, and 4# powders all show one crystallization peak, but 3# has two crystallization peaks. In addition, 4# powders has only one crystallization peak, but the crystallization initiation temperature of this peak is lower than that of 1#, 2#, and 3# powders. Combined with the phase detected by the XRD pattern in Fig. 3, it can be judged that α-Fe(Si) phase is precipitated in powders 1# and 2#, and the two crystallization peaks of 3# powders corresponding to the simultaneous formation of Amorphous → α-Fe(Si) + Fe3 B eutectic reaction and Fe3 B → Fe2 B + α-Fe(Si) decomposition reaction, and 4# powders due to the lowest mass fraction of amorphous forming elements, the thermal stability of the alloy system and the decrease of GFA, making the temperature window between the two crystallization reactions is greatly reduced and the reaction temperature is close, which is why the DSC curve only shows one crystallization peak. It can be seen from the inset that as the Fe content increases from 92.66 to 94.55%, the width T = T x − T g of the supercooled liquid phase of the 1–4# amorphous powders decreases linearly,

Study on the Optimization of Fe Content of FeSiBC Amorphous Powders Fig. 5 DSC curve of FeSiBC sample powders

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Exothermic up

Tp Code 1#

Tg (K) 701

Tx (K) 811

Tp (K) 820

△T (K) 110

2# 3#

716 678

801 757

813 769

85 79

4#

708

754

770

46

1# 2# 3# 4#

Tp

10 K/min Tp

Tg

Tx Tx

Tp

Tg Tg

Tx

Tg

700

750

Tx

800

850

900

Temperature(K)

reflecting the deterioration of the GFA and thermal stability of the alloy, but 4# powders still has good thermal stability [14] (T = 46 K) and GFA.

Effect of Channel Height on Flow Field of Tundish Figure 6 shows the changes of the magnetic moments of four FeSiBC sample powders in the magnetic field. Figure 6a is the hysteresis loop, and Fig. 6b is the coercivity and saturation magnetization of different powders. It can be seen that the saturation magnetization of the powders increases linearly with the increase of Fe content, while the coercivity first increases and then decreases. As shown in Table 1, the Fe mass fractions of 1–4# powders are 92.66, 93.22, 94.11, and 94.55%, respectively, and the content of amorphous forming elements of 1–3# powders is close, while 4# powders greatly improved B content, resulting in an increase in the GFA of the alloy. That is to say, increasing the B content has no effect on the saturation magnetization of the amorphous powders, but it can increase the amorphous degree, thereby reducing the coercivity of the powders. At the same time, it is verified that M s is only related to the Fe content, but not directly related to the amorphous degree, which provides some inspiration and ideas for the further optimization of the subsequent amorphous powders’ composition. Compared with several other FeSiBC sample powders, the 4# powders with the best comprehensive performance was prepared as the corresponding AMPCs, and the change of its core loss with frequency was measured as Fig. 7. The core loss of AMPCs increases with the increase of frequency, which means that under the alternating magnetic field, the higher the frequency, the higher the energy loss of AMPCs will be caused by the self-resistance, especially at high frequency. Among them, the core loss of AMPCs has a low rise in the range of 100–500 kHz (79.76 kW m−3 ,

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150 100



(b)

25

Coerivity(Oe)

Moment/Mass(emu/g)

(a)

1# 2# 3# 4#

50 0 -50



20  15  10

-100 

5

Magnetic Saturation(emu/g)

30

200

-150 0

-200 -10000

-5000

0

5000

1#

10000

Magnetic Field(Oe)

2#

Sample

3#

4#



Fig. 6 a Magnetic hysteresis loop of FeSiBC sample powders; b coercive force and saturation magnetization

0.02 T, 100 kHz), and increases sharply in 500–1000 kHz (1258.6 kW m−3 , 0.02 T, 1000 kHz). The soft magnetic properties of FeSiBC AMPCs prepared from 4# powders in this study and Fe-based AMPCs in literatures are shown in Table 3. It can be seen from the table that the FeSiBC AMPCs in this study show extremely high saturation magnetization and low core loss, and the above analysis shows that the 4# amorphous powders is partially crystalline with the precipitation of Fe-B and other hard magnetic phases lead to higher coercivity of powders [15], making the corresponding AMPCs inevitably have some hysteresis losses that cannot be eliminated. Therefore, the amorphous powders of this system can further improve its GFA and thermal stability through the subsequent composition adjustment and the improvement of the cooling capacity of the atomization equipment, resulting in a completely amorphous magnetic powders and low core loss AMPCs suitable for high frequency. 1400

Fig. 7 Total loss of FeSiBC sample powders

4# Bm=20 mT 1200

Pcv(kw/m3)

1000 800 600

Pcv 79.76 kW·m–3

400 200 0 100

200

300

400

500

600

700

Frequence (kHz)

800

900

1000

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Table 3 Comparison of soft magnetic properties of AMPCs prepared in this study and other literature Sample

M s (emu/g) H c (Oe) μe @100 kHz Core Loss, Pcv (kW m–3 )

Reference

100 kHz/0.02 T 1 MHz/0.02 T FeSiBC/4#

172

9

25.95

79.76

1258.6

This work

FeSiCr

132.5

11.74

17.2

104.2

1442

[16]

FeSiCrB

136.07

23.22

19.95

/

/

[6]

FeSiBCr

144.4

6.5

20.4

/

/

[17]

FeSiBCCr

146.4

15.3

/

/

/

[18]

/

36.49

398.5

/

[19]

FeSiPS/Al2 O3 /

Conclusions In this paper, four types of FeSiBC amorphous spherical powders with high iron content were prepared by a novel gas–water combined atomization process, and the 4# powders with the best comprehensive performance was prepared into AMPCs, and then the loss and permeability of AMPCs were characterized. The main conclusions are as follows: (1) The four types of FeSiBC amorphous powders all with varying degrees of crystallization. Among them, 1–3# powders have some spherical and ellipsoidal shapes with many adhesions, while the 4# sample powders have good sphericity and high thermal conductivity. With high thermal stability (T = 46 K), extremely high saturation magnetization (M s = 172 emu g−1 ), and low coercivity (H c = 9 Oe), the overall performance is the best. (2) The permeability of FeSiBC@PA core–shell structure 4#AMPCs coated with polyamide is 25.95, and the core loss of 79.76 kW m−3 at 100 kHz for Bm = 0.02 T. The FeSiBC amorphous spherical powders with high iron content prepared in this study has excellent comprehensive properties, which will contribute to realize the miniaturization, high efficiency and modularization of devices, and has a good application prospect. However, the preparation of amorphous powders and magnetic powder cores with better comprehensive soft magnetic properties requires further optimization of the atomization process and continuous exploration by researchers.

References 1. Zhou B, Dong Y, Chi Q et al (2020) Fe-based amorphous soft magnetic composites with SiO2 insulation coatings: a study on coatings thickness, microstructure and magnetic properties. Ceram Int 46(9):13449–13459 2. Zhang G, Shi G, Yuan W et al (2021) Magnetic properties of iron-based soft magnetic composites prepared via phytic acid surface treatment. Ceram Int 47(7):8795–8802

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3. Ding J, Xu H, Shi Z et al (2021) Effect of primary α-Fe on soft magnetic properties of FeCuNbSiB amorphous/nanocrystalline alloy. J Non-Cryst Solids 571:121079 4. Li Y, Chen WZ, Dong B et al (2018) Effects of phosphorus and carbon content on the surface tension of FeSiBPC glass-forming alloy melts. J Non-Cryst Solids 496:13–17 5. Li Z, Dong Y, Pauly S et al (2017) Enhanced soft magnetic properties of Fe-based amorphous powders cores by longitude magnetic field annealing. J Alloy Compd 706:1–6 6. Woo HJ, Ahn JH, Kim CP et al (2022) Effect of the particle size classification of FeSiCrB amorphous soft magnetic composites to improve magnetic properties of power inductors. J Non-Cryst Solids 577:121309 7. Meng LB, Yu HC, Lv SY et al (2021) Study on amorphous soft magnetic powders with high sphericity. Powders Metall Ind 31(5):105–110 8. Chi Q, Chang L, Dong Y et al (2021) Enhanced high frequency properties of FeSiBPC amorphous soft magnetic powders cores with novel insulating layer. Adv Powders Technol 32(5):1602–1610 9. Luo S, Wang H, Gao Z et al (2021) Interaction between high-velocity gas and liquid in gas atomization revealed by a new coupled simulation model. Mater Des 212:110264 10. Wang P, Wei M, Dong YN et al (2022) Crystallization evolution behavior of amorphous Fe85.7 Si7.9 B3.6 Cr2 C0.8 powders produced by a novel atomization process. J Non-Cryst Solids 594:121824 11. Liu JQ, Pang J, Wang P et al (2022) Research progress of liquid metal atomization technology and preparation of its amorphous powders. China Metall 32(2):1–14 12. Dong Y, Liu J, Wang P et al (2022) Study of bulk amorphous and nanocrystalline alloys fabricated by high-sphericity Fe84 Si7 B5 C2 Cr2 amorphous powders at different spark-plasmasintering temperatures. Materials 15(3):1106 13. Miller SA, Giles WB (1981) Effect of process variables on atomization of metals and alloys. Mod Dev Powders Metall 1:113–128 14. Xia C, Peng Y, Yi Y et al (2019) The magnetic properties and microstructure of phosphated amorphous FeSiCr/silane soft magnetic composite. J Magn Magn Mater 474:424–433 15. Herzer G (2013) Modern soft magnets: Amorphous and nanocrystalline materials. Acta Mater 61(3):718 16. Yu H, Zhou S, Zhang G et al (2022) The phosphating effect on the properties of FeSiCr alloy powders. J Magn Magn Mater 552:168741 17. Yu H, Li J, Li J et al (2022) Enhancing the properties of FeSiBCr amorphous soft magnetic composites by annealing treatments. Metals 12(5):828 18. Zhou B, Dong Y, Liu L et al (2019) The core-shell structured Fe-based amorphous magnetic powders cores with excellent magnetic properties. Adv Powders Technol 30(8):1504–1512 19. Lei J, Zheng J, Zheng H et al (2019) Effects of heat treatment and lubricant on magnetic properties of iron-based soft magnetic composites with Al2O3 insulating layer by one-pot synthesis method. J Magn Magn Mater 472:7–13

Part IV

Additive Manufacturing Fatigue and Fracture: Effects of Surface Roughness, Residual Stress, and Environment

High-Cycle Fatigue Property of Ferrite–Pearlite Steel for Engineering Machinery and Effects of Strengthening Mechanisms Shuo Gong, Haijuan Wang, Fuming Wang, Ming Li, Yong Feng, and Liang Su Abstract The aim of this work is to evaluate the high-cycle fatigue property of the experimental steel for engineering machinery, investigate the initiation of fatigue fracture, and establish a stress–life curve (S–N curve) with high confidence. With smooth specimens, the rotating bending fatigue tests were carried out on the steels used for movable arms of excavator, and the different fatigue fractures were observed. The S–N curve and expression in the form of a three-parameter power function were obtained by using MATLAB. The results showed that fatigue cracks mainly originated from the surface of the samples in the studied strength range. The microstructure characteristics of the steels are the main reasons for the difference in fatigue strength. The addition of microalloying element Nb (0.01%) can refine ferrite grains and improve fatigue strength. The increase of Mn content (0.35, 1.02, 1.44%) is beneficial to the fatigue strength by increasing the solid solution strengthening. Apart from this, the fatigue strength ratio (fatigue strength/tensile strength) of the steel with the continuous band structure formed by the segregation of Mn is higher than that of the steel with the discontinuous segregation bands. S. Gong · H. Wang (B) · F. Wang · L. Su School of Metallurgical and Ecological Engineering, University of Science and Technology Beijing, Beijing 100083, China e-mail: [email protected] S. Gong e-mail: [email protected] F. Wang e-mail: [email protected] L. Su e-mail: [email protected] M. Li · Y. Feng Nanjing Iron and Steel Co., Ltd., Nanjing 210035, Jiangsu, China e-mail: [email protected] Y. Feng e-mail: [email protected] © The Minerals, Metals & Materials Society 2023 The Minerals, Metals & Materials Society, TMS 2023 152nd Annual Meeting & Exhibition Supplemental Proceedings, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-031-22524-6_9

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Keywords Steel for engineering machinery · High-cycle fatigue · S–N curve · Crack initiation · Strengthening mechanisms

Introduction The engineering machinery industry is an important part of the machinery industry. Investigation [1] shows that structural damage caused by fatigue fracture of engineering machinery steels accounts for about 90% of engineering machinery structural failures. In China, previous studies on the steels for engineering machinery were focused on conventional mechanical properties. However, the actual working conditions of engineering machinery equipment represented by excavators [2] and loaders [3] are complex, and key parts are prone to fatigue failure. In the fatigue resistance design of the movable arms or overall of engineering machinery [4], the high-cycle fatigue stress–life curves applied in the nominal stress method [5] were mainly obtained from industrial software or the reference book, which always did not match the actual conditions. Majority of fatigue fractures occurred from the small defects, inclusions, or the inhomogeneities. However, if the size of small defects or inclusions did not reach an order of magnitude, the crack source would not be affected by either of them, but mainly would depend on statistical factors of the microstructure [6]. With regard to ferrite–pearlite steels, several studies [7–9] were devoted to the field about contributions of various strengthening mechanisms to fatigue strength. The results showed that the contributions of solid solution strengthening and precipitation strengthening for improving the fatigue strength ratio were higher than those of other strengthening mechanisms [10, 11]. Narasaiah and Ray [12] suggested that fatigue cracks originated from ferrite–pearlite interface, ferrite–ferrite grain boundary, and ferrite body in pipe steels; Korda et al. [13] believed that the banded structure could produce a large number of branches of fatigue cracks which reduced the driving force of the local crack tip. The studies of strengthening mechanism of fatigue strength and the causes of fatigue fracture were based upon bridge steel [14], connecting rod steel [11], etc. Less research has been conducted in the fatigue property of steel for engineering machinery. In this paper, three low-alloy steels most widely used in the field of engineering machinery were selected to study the rotating bending fatigue property of smooth specimens. The three-parameter power function was used to obtain a high confidence level S–N curve expression. Thereafter, the fatigue sources of the samples were analyzed, and the causes of fatigue fracture were determined. After that, the difference in fatigue strength of the three steels was explained from the perspective of strengthening mechanism.

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Materials and Methods The materials investigated were three 18-mm-thick hot-rolled plates for engineering machinery applied by NJ STEEL. The chemical composition is shown in Table 1. Both steels 1 and 2 contain 0.01% Nb and the three steels have different Mn contents. The O content of steel 1 is lower than that of either steel 2 or steel 3. Steel 1 was refined by RH refining process. The slab thickness after continuous casting was 220 mm and the rolling cycle was terminated at ~800 °C. From recommendations made in GB/T4337-2015 [15], standard, smooth, and round bar was adopted for fatigue tests and the dimensions of the specimens are shown in Fig. 1. To avoid the effect of surface roughness on the results, the surface of all the fatigue specimens was polished. QBWP-6000J rotating bending fatigue machine was employed in test with a strain ratio of R = −1 and the rate of the stress cycling was 4,200 rpm/min. The test would be interrupted if it reached 107 cycles. Optical microscope (MX6R) and field emission scanning electron (FE-SEM, FEI MLA-250) were used to observe the microstructure which was revealed by 4% nital-alcohol solution. The parameters of the microstructure were determined from micrographs by applying Image J software. The hardness tester (VED512) was carried out to measure the Vickers hardness values of different phases. After fatigue tests, fracture surfaces were protected and observed on SEM (JSM-6480LV). The thermodynamic calculation of equilibrium precipitation phases was carried out by Thermo-Calc software. DICTRA was applied to simulate the segregation process. Table 1 The chemical compositions of the test steels (wt%) Steel

C

Mn

Si

P

S

Alt

Nb

N

O

1

0.15

1.44

0.12

0.010

0.0032

0.025

0.013

0.0042

0.0012

2

0.17

1.02

0.20

0.013

0.0060

0.028

0.010

0.0037

0.0026

3

0.17

0.35

0.24

0.016

0.0070

0.024

0.002

0.0038

0.0026

Fig. 1 Dimensions of the specimens for fatigue test

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Theoretical Calculation Fatigue Strength The staircase data was interpreted by the method recommended by GB/T24176-2009 [16] with high-confidence statistical processing methods [17]. Fatigue strength was calculated by means of the following equations:  μ = S0 + d

i  i=1

i fi ; B =

(1)

σ = 1.62d(D ± 0.029), D ≥ 0.3

(2)

σ = 0.53d, D < 0.3

(3)

Cv = define: A =



A ± 0.5 C

i 

i 2 fi ; C =

i=1

i 

σ μ fi ; D =

i=1

(4)

BC−A2 , C2

where i is an integer denoting the stress level, f i is the number of specimens which survived for each stress level. μ is fatigue strength mean, σ is standard deviation, S0 is an initial stress amplitude, and d is the stress step. The minus sign in the equation for μ is used in this paper. Cv is the coefficient of variation, which is used to assess the confidence level.

S–N Curve Based on Weibull The three-parameter power function proposed by Weibull and Rockey [18] was used in this paper for stress–life relationship description of the three steels. 

S − Sf

α

N = C,

(5)

where S f , C, and α are constants; S is stress, MPa; N is fatigue life, cycles. S–N curve can be expressed in the following form: x = b − ay   in which x = lg N , y = lg S − S f , b = lg C, a = α. Then, a and b can be obtained by least squares method:

(6)

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L

a = − L xyyy

(7)

b = ay + x n 



define: L x x = −  n  n i=1  1  xi yi . n i=1

xi2

1 n

n 

2 xi

; L yy =

i=1

n  i=1

 yi2

−

1 n

n 

2 ; Lxy =

yi

i=1

n 

xi yi −

i=1

i=1

An optimization method for determining a three-parameter power function proposed by HuiMing et al. [19] was used to define S f . The square of the linear correlation coefficient R is R2 =

d Lxy dSf d L yy dSf

L 2x y

(8)

L x x L yy

   L 2x y d R2 1 2 d Lxy = − dSf L x x L yy L x y d S f L yy  n

n  n −1 xi 1 = − xi ln 10 i=1 Si − S f n i=1 S i=1 i  n

n  n yi −2 1 = − yi ln 10 i=1 Si − S f n i=1 S i=1 i

d L yy dSf 1 − Sf 1 − Sf

 =0 

(9)

=

−1 L x0 ln 10

=

−2 L y0 ln 10



(10)

Equation (10) was substituted into Eq. (9). Equation (11) can be obtained as follows:   L y0 L x0 − =0 H Sf = L yy Lxy

(11)

    It is assumed that S f be the estimation value of S f , H S f and d R 2 /d S f have     the same sign. If S f < S f , H S f > 0; if S f > S f , H S f < 0. S f can be obtained according to the following method. The first step was to calculate H (0). in (0, Simin ), where S fis located   Simin is the minimum stress amplitude in the test. If H S f ≤ 0, S f = 0. If H S f > 0, divided the range (0, Simin ) into two sectors, and calculated H (Smid ). Then, if H (Smid ) < 0,S f belongs to the range (0, Smid ); if not, S f belongs to the other range. By analogy, the range is continuously reduced by half until S f is obtained with the required accuracy. MATLAB was used to achieve the above process.







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Results and Discussion Microstructures and Mechanical Properties Figure 2 shows the metallographic structure of the three test steels. Tables 2 and 3 summarize the microstructure parameters and mechanical properties. Compared to steel 3, steels 1 and 2 were selected to investigate the effects of Nb and various Mn additions on fatigue strength. It can be seen in Fig. 2 that the microstructures of all the three steels are ferrite–pearlite, but the morphologies of the microstructures are different. The addition of Nb results in significant change in ferrite grain size among the three steels. The ferrite grain sizes in steels 1 and 2 are 7.38 and 7.89 um, respectively, while the ferrite grain size (13.55 um) in steel 3 is about two times that in steel 1 or steel 2. The increase in Mn content results in different distribution characteristics of the microstructure. The pearlite band in steel 2 or steel 3 is less continuous than that in steel 1, which is much uniformly distributed in ferrite. There is higher volume fraction of pearlite in steel 1 than that in steel 2 or steel 3; at the same time, the pearlite lamella spacing of the tested three steels is almost the same. In terms of mechanical properties, the hardness values of ferrite and pearlite of steels 1 and 2 are similar, which is higher than that of steel 3. It can be seen from Table 3 that the strength of steel 1 or steel 2 is higher than that of steel 3. The tensile strength and yield strength of steel 1 are slightly lower than those of steel 2. The fatigue strength and fatigue strength ratio of steels 1, 2, and 3 are successively decreased.

Fig. 2 The microstructures of the three steels a steel 1 b steel 2 c steel 3

Table 2 The microstructural parameters of the three steels Steel

Microhardness of pearlite H P (HV0.05)

Microhardness of ferrite H F (HV0.05)

H P /H F

Volume fraction of ferrite (%)

Lamellar spacing of pearlite (µm)

Grain size of ferrite (µm)

1

233

181

1.29

70.2

0.28

2

236

190

1.24

80.6

0.24

7.89

3

197

148

1.33

83.1

0.24

13.55

7.38

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Table 3 The mechanical properties of the experimental steel Steel

Tensile strength Rm /MPa

Yield strength Rp0.2 /MPa

Yield strength ratio Rp0.2 /Rm

Fatigue strength σ −1 /MPa

Fatigue strength ratio σ −1 /R m

1

488

382

0.78

273

0.56

2

517

392

0.76

257

0.50

3

416

298

0.72

195

0.47

Fatigue Test Results and Analysis The fatigue test was carried out in two parts, namely, the staircase method for an unlimited fatigue life criterion and the group method for the limited fatigue endurance range.

Results and Fitting Results The fatigue strength of the three test steels was measured by the staircase method, and the statistical data is shown in Table 4. With regard to the confidence level, the coefficient of variation in Table 4 is within the range in the literature [20] for a 95% confidence level (Cv = 0.0403–0.0476, when minimum number of observations = 6). Therefore, it can be derived that the fatigue strengths of steel 1, 2, and 3 are 273, 257, and 195 MPa, respectively, with probability of 0.5 for a 95% confidence interval. The limited fatigue endurance range in S–N curve was measured by the group method. The logarithmic fatigue life mean value μi and the logarithmic fatigue life standard deviation σi at each stress level were obtained by the following equation: 1 × lg Ni j mi j=1 j

μi =    σi = 

1 (lg Ni j − μi )2 , m i − 1 j=1

(12)

j

(13)

where m i is the number of fatigue life data under the i stress level and Ni j is the j sample under the i stress level. In the same way, the coefficient of variation at each stress level was compared with that in the literature [20]. The results under each stress level in Table 5 show that the requirements for the minimum number of observation samples are met when the confidence level is 95%. It can also be observed that steels 1 and 3 can meet the confidence requirement by testing 3 samples under each stress level, while 5 and 4 samples of Steel 2 were tested at 280 and 290 MPa to meet the same confidence requirement. That is, the fatigue life of steel 2 is more scattered than that of steel 1 or steel 3 at high stress levels.

S/MPa

260, 280, 300

240, 250, 260, 270

180, 190, 200, 210

Steel

1

2

3

0, 1, 2, 3

0, 1, 2, 3

0, 1, 2

i

0, 1, 4, 1

0, 1, 3, 2

0, 5, 1

fi

Table 4 Parameter values of the staircase method

0, 1, 8, 3

0, 1, 6, 6

0, 5, 2

ifi

0, 1, 16, 9

0, 1, 12, 18

0, 5, 4

i2 f i

12

13

7

A

26

31

9

B

6

6

6

C

0.33

0.47

0.14

D

195

257

273

μ/MPa

5.87

8.12

10.6

σ /MPa

0.0301

0.0316

0.0388

Cv

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Table 5 Parameter values of the group method Steel

S/MPa

μi

σi

Cv

1

320, 330, 340

5.33, 5.17, 4.93

0.05, 0.05, 0.05

0.0097, 0.0099, 0.0033

2

280, 290, 300

5.58, 5.15, 4.79

0.21, 0.10, 0.08

0.0371, 0.0186, 0.0159

3

220, 235, 250

5.72, 4.99, 4.72

0.02, 0.07, 0.01

0.0390, 0.0143, 0.0031

Fig. 3 S–N curves of the three steels

The S–N curve with probability of 0.5 for a 95% confidence interval of the threeparameter power function is illustrated in Fig. 3, and the curve fits well with the fatigue test data. The S–N curves in Fig. 3 show that the fatigue strength values of steels 1 and 2 have increased significantly as compared to steel 3. While the strength of steel 2 is increased, the fatigue life stability at high stress levels (280 and 290 MPa) decreases, and steel 1 does not have the same phenomenon. The fatigue life of steel 1 is also higher than that of steel 2 under the same stress level. It is understood that the fatigue properties of steels 1, 2, and 3 decrease in order.

Fracture Morphology The typical fatigue fracture morphology of the three steels is shown in Fig. 4. The fatigue crack nucleated in the specimen surface and propagated to the other side of the specimen in a divergent shape. In addition, there are fatigue bands, secondary cracks, tire indentations (Fig. 4g and h), etc. in the crack propagation zone. Due to the influence of the band structure on the crack propagation, the directions of the secondary cracks are almost parallel to each other. The fatigue final rupture region (Fig. 4i) is mainly mixed fracture which has morphological characteristics with transition from ductile fracture (dimples) to brittle fracture (river pattern). Statistics of the fatigue crack initiation positions of all specimens of the three steels in this

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Fig. 4 SEM observation of fatigue failure specimen: Macroscopic morphology and crack source zone: a, d steel 1 (320 Mpa, 186891 cycle); b, e steel 2 (270 MPa, 1,156,882 cycle); c, f steel 3 (190 MPa, 4,102,948 cycle); g fatigue striations and secondary cracks in fatigue crack growth zone; h tire indentation in fatigue crack growth zone; i transition from crack propagation zone to transient zone

study found that the fatigue source of all specimens in steel 1 was on the surface of the specimen. There was only one case caused by non-metallic inclusions in steels 2 and 3. The other specimens were all broken from surface weak phase. Therefore, it can be inferred that inclusion is not the main reason for the fatigue fractures of the three steel base materials although steel 1 has undergone RH refining treatment.

Analysis of Strengthening Mechanism of Fatigue Strength FE-SEM was used to investigate the small secondary phase in the three steels, as shown in Fig. 5. The EDS analysis result in Fig. 5 shows that there are Nb, C, N, Fe, and Mn elements in the secondary phase. Therefore, it can be proved that the addition of Nb in steels 1 and 2 can make Nb precipitate as the secondary phase Nb(C, N) in the steel. Since the fine Nb(C,N) precipitates have pining effect on grain boundaries in steels 1 and 2, which plays a role in refining grains, the ferrite grains are refined obviously as shown in Fig. 2. Normally, fine grains hinder the initiation and propagation of cracks effectively, which is beneficial for improving fatigue strength.

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Fig. 5 SEM observation of the Nb precipitates a morphologies of Nb(C, N); b EDS analysis of Nb(C, N)

So the fine ferrite grains of steels 1 and 2 are one of the main reasons for improving fatigue strength. Figure 6 shows the calculation results of Mn segregation by dictra in the three steels. There are obvious differences in the degree of Mn segregation in the three steels. Among them, the Mn contents in steels 1, 2, and 3 vary from 1.28 to 1.70%, 0.90 to 1.24%, and 0.30 to 0.43%, respectively. As the Mn content decreases, the degree of segregation will gradually reduce, which is consistent with the distribution of the pearlite colony mentioned above. Due to the most serious degree of segregation in steel 1, the pearlite bands in steel 1 are significantly more continuous, while the pearlite bands in steels 2 and 3 are intermittent and are basically evenly distributed in ferrite matrix. The continuous band structure can arrest the cracks propagation in the fatigue tests by making fatigue cracks more tortuous and reducing the driving force of cracks [21]. Not only the morphology of pearlite in steel 3 is different from that of steel 2, but the volume fraction of pearlite is also conspicuously higher than that of steel 2. High volume fraction of pearlite hinders the extension of fatigue crack, which can be attributed to the fact that the hard phase in steel will cause more cracks to deflect. There are the main following strengthening mechanisms in the ferrite–pearlite steels: solid solution strengthening (σss ), precipitation strengthening (σ ppt ), fine grain strengthening (σgr ), pearlite strengthening (σ prlt ), and dislocation strengthening (σdis ). Literature [14] summarizes the contributions of various strengthening mechanisms to the fatigue strength of steel: σw = 8.4 + 0.92σss + 0.70σ ppt + 0.53σ prlt + 0.43σgr + 0.23σdis .

(14)

Each strengthening amount in Eq. (14) is given by the contribution of each strengthening mechanism to strength in the equation of Pickering. Here, σss = 1 8.5Si +3.3Mn + 36.1N f , σ ppt = 153 × N b, σgr = 1.77d − 2 , σdis = αGbρ 1/2 , and   −1/2 σ prlt = 18.2 + 0.39S0 × 1 − f αa . The relation between the calculated fatigue strength and the measured fatigue strength of the three steels is shown in Fig. 7a.

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Fig. 6 Segregation of Mn in the three steels (3,000 s)

It can be seen that the predicted value is in good agreement with the experimental value. Figure 7b also shows the contributions of different strengthening mechanisms to the fatigue strength (1 kgf/mm2 = 9.8 MPa). Compared with steel 3, the ratios of solid solution strengthening and fine grain strengthening to the increase in fatigue strength of steel 1 are 37, 32%, and that of steel 2 are 42% and 38%. The fatigue strength deterioration of steel 3 is caused by coarse ferrite grain size and low Mn content. The coarsening of the grains in Steel 3 is due to the lack of Nb addition in

Fig. 7 a The relationship between predicted fatigue strength and experimental fatigue strength; b The contribution of different strengthening mechanisms to fatigue strength

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the steel, so there is no fine Nb (C, N) pinning the grain boundaries. Although the Mn content of steel 1 is higher than that of steel 2, the Si content is lower. For the reason that both have similar solid solution strengthening values, and this can also be proved by the similar hardness value of the microstructure. With the exception of the solid solution strengthening, steel 1 has the largest increase in pearlite strengthening (44%) compared to steel 2, which is due to the higher volume fraction of pearlite steel 1 and the obvious band structure mentioned above. Besides, the difference in the content of Mn in the two phases caused by the segregation of Mn has little effect on the fatigue strength. The segregation in steel 1 is the most serious. Assuming that the Mn content in steel 1 is the lowest segregation value of 1.28%, the fatigue strength estimated by Eq. (14) is 268 MPa, which is not much different from 273 MPa measured in the fatigue test.

Conclusions (1) Test fatigue strengths of the three steels are 273, 257 and 195 MPa, respectively, with probability of 0.5 for a 95% confidence interval. Based on Weibull’s equation, S–N curves of the steels for engineering machinery have been established. The compound addition of Nb and Mn in steels 1 and 2 increases fatigue strength due to pining effect on grain boundaries by Nb(C, N) particles and solid solution effect from Mn atoms. (2) Within the strength range of the steels studied in this work, the fatigue failure of the three steels mainly originated from the specimen surface and inclusion is not the reason of fatigue fracture. (3) The improvement of fatigue ratio in steel 1 is attributed to the band structure formed by the segregation of Mn. The perlite band structure can hinder crack growth but the reduction of Mn content in ferrite will not obviously reduce the fatigue strength. Funding This work was supported by the National Natural Science Foundation of China under Grant [No. 51974017]. Conflict of Interest The authors declare no financial interest or benefit that has arisen from the direct applications of the research.

References 1. Qi S (2014) Strength analysis and fatigue life prediction of hydraulic excavator boom. MA. Eng thesis, Central South University

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2. Yu Tian H, Peng Min L, Xuan Ping N (2016) Finite element analysis of working device of large-sized hydraulic excavator. Road Mach Constr Mech 33(01):102–105 3. Yipin W et al (2016) Structural strength analysis and experimental research for working device of loader. J Mech Strength 38(04):772–776 4. Yingshuang Z (2014) Research on the load spectra acquisition and application of loader powertrain. PhD thesis, Jilin University 5. Li XB (2009) Anti-fatigue design concept and product development of construction machinery. Constr Mech 30(09):35–38 6. Murakami Y, Endo M (1994) Effects of defects, inclusions and inhomogeneities on fatigue strength. Int J Fatigue 16(3):163–182 7. Kitano T et al (1999) Effects of solution strengthening and precipitation strengthening on fatigue limit of smooth and notched specimens in low-carbon steel. Weld Int 13(6):440–447 8. Kurita M et al (1997) Improvement and formulation of fatigue limit of ferrite-pearlite hot-rolled sheet steel. J Soc Mat Sci Jpn 46(10):1143–1148 9. Kurit M et al (1996) Effects of strengthening mechanisms on ferrite-pearlite hot-rolled mechanisms sheet steel. ISIJ Int 36(4) 10. Abe T et al (1984) Quantitative correlation of static strengthening mechanisms to fatigue property in low and medium carbon steels. Tetsu-to-Hagane 11. Hui W et al (2015) Effect of vanadium on the high-cycle fatigue fracture properties of mediumcarbon microalloyed steel for fracture splitting connecting rod. Mater Des 66:227–234 12. Narasaiah N, Ray KK (2005) Small crack formation in a low carbon steel with banded ferrite– pearlite structure. Mater Sci Eng A 392(1–2):269–277 13. Korda AA et al (2006) In situ observation of fatigue crack retardation in banded ferrite–pearlite microstructure due to crack branching. Scripta Mater 54(11):1835–1840 14. Zong L, Shi G, Wang Y (2015) Experimental investigation on fatigue crack behavior of bridge steel Q345qD base metal and butt weld. Mater Des 1980–2015(66):196–208 15. Metallic materials-fatigue testing-rotating bar bending method. In: GB/T 4334-2015 (2015) 16. Metallic materials-fatigue testing-statistical planning and analysis of data. In: GB/T 241762009 17. Xin B et al (2015) Fatigue test methods and their data processing methods of metallic materials. Phys Test Chem Anal (Part A: Physical Testing) 51(6):375–380 18. Weibull W, Rockey KC, Fatigue testing and analysis of results. J Appl Mech 29(3) 19. HuiMing F, ZhenTong G, Meixun L (1988) P-S-N curve fitting method. Acta Aeronautica ET Astronautica Sinica 7:338–341 20. ZhenTong G (1986) Fatigue applied statistics 21. Furuya Y et al (2006) Effects of strengthening mechanisms on fatigue properties of ultrafine ferrite-cementite steels. Tetsu-to-Hagane

On the Fatigue Performance of Additively Manufactured Metamaterials: A Combined Experimental and Simulation Study Daniel Barba, Antonio Vazquez-Prudencio, Conrado Garrido, and Sergio Perosanz-Amarillo Abstract Architected metallic metamaterials fabricated by additive manufacturing are called to expand infinitely the variety of available properties observed in bulk alloys. However, the high surface-to-volume ratio of the architected metamaterials due to their intricate geometries and the surface inherited of the AM process is translated to a complex fatigue behaviour when compared with bulk conventional alloys. This is a serious concern in the use of this new class of architected AM materials in technological applications. In this work, this problem is tackled by a systematic multiscale study of the metamaterial design—processing and defects—fatigue properties’ interconnection. Commercial aluminium alloy, AlSi10Mg, processed by selective laser melting is used as the base material. By means of combined fatigue experimentation, computational modelling and defect identification, the effect of processing conditions and design geometry on microstructural defects and surface quality is rationalised and connected with the fatigue life of metamaterials. Keywords Aluminium · Additive manufacturing · Fatigue · Mechanical testing

D. Barba (B) · A. Vazquez-Prudencio · C. Garrido · S. Perosanz-Amarillo Escuela Técnica Superior de Ingeniería Aeronáutica y del Espacio, Universidad Politécnica de Madrid, 28040 Madrid, Spain e-mail: [email protected] A. Vazquez-Prudencio e-mail: [email protected] C. Garrido e-mail: [email protected] S. Perosanz-Amarillo e-mail: [email protected] © The Minerals, Metals & Materials Society 2023 The Minerals, Metals & Materials Society, TMS 2023 152nd Annual Meeting & Exhibition Supplemental Proceedings, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-031-22524-6_10

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Introduction Metamaterials can be defined as an arrangement of artificial structural elements designed to achieve advantageous or tailored properties compared to the original ones [1]. Among them, lattice structured materials are a class of metamaterials with unique mechanical properties, extending the design range of current bulk materials [2, 3]. However, building complex structures increases the likelihood of forming defects and other suboptimal microstructural features. Moreover, these parts often have internal surfaces that are difficult to post-process to reduce the roughness of the net-shape surfaces. The modelling of these lattice structures remains a major challenge due to the influence of manufacturing irregularities, which affect the mechanical properties. Finite element (FE) simulations can have major discrepancies compared to experimental data due to manufacturing irregularities that affect the mechanical properties [4]. The first attempt for modelling the mechanical properties was the Gibson–Ashby model, which states the elastic modulus, yield stress and fatigue life is a function of relative density for highly porous materials [5]. Afterwards, more complex FEM models were developed to capture the nonlinear phenomena and anisotropic behaviour of lattice structures [6]. The prediction of fatigue properties for lattice structures remains a major challenge. Some of the applications of these lattice structures undergo millions of cycles along the component life [7] (e.g., metallic implant). That is why the fatigue behaviour and prediction of fatigue properties are essential. In addition, computational models for the prediction of fatigue life are not fully developed yet [8]. Several attempts have been made to estimate fatigue life based on beam models and considering only local tensile stresses [9] or normalizing S–N curves with yield strength or plateau stress [9, 10]. However, the majority of models do not consider the peculiarities of the AM material e.g., their dependency on building orientation or the real geometry of the lattice structure or especially the manufacturing defects. The main goal of this work is to propose solutions to accurately predict the mechanical and fatigue properties of lattice structures and study the effect of processing defects, beam orientation and geometrical deviation on the fatigue performance. FE modelling is the necessary tool to achieve this goal.

Methods In this section, the material along with a description of the additive manufacturing process are given. Then, the experimental methodology employed to measure the mechanical properties and defect analysis is detailed.

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Fig. 1 a AM lattice with an octet unit cell. b Printing strategy of the lattice structures. c Example of octet unit cell highlighting in red the struts printed at 0° and in blue the ones at 45°

Table 1 Manufacturing parameters for the SLM process

Build plate preheating

260 °C

Laser power

300 W

Scanning speed

5 °C/min

Layer thickness

30 μm

Hatch distance

75 μm

Material and the Selective Laser Melting Process The lattice sample geometry presented in Fig. 1 formed by an octet unit cell with 6.67 unit cell and 1 mm diameter was chosen for this study. The samples were manufactured using the titanium alloy AlSi10Mg—a common aluminium alloy used in additive manufacturing [11]. A Renishaw AM400 machine was used to manufacture the samples. A summary of the build parameters is shown in Table 1. After manufacturing, the specimens were removed from the build plate using electro discharge machining (EDM). The samples have an area approximately of 21 × 21. Plates were added to the sample to provide a correct contact between the lattice and the testing machine. A total of 5 samples were manufactured (1 for static testing and 4 for fatigue). Each lattice had one of the lateral faces supported with the building plate. It is worth mentioning that the Octet unit cell has only two different building orientations: Horizontal (0°) and Inclined (45°). The building orientation is the angle formed between the strut and the building platform. This is essential for the modelling of lattice structure as is necessary to consider the anisotropy inherent to the building orientation.

Tensile Testing and Digital Image Correlation Compression static mechanical tests were performed at a strain rate of 10–3 /s that was used in the tests. A servo-hydraulic MTS (model 810) universal testing machine

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equipped with a 100 kN load cell was used. The strain was monitored using digital image correlation (DIC) as a virtual extensometer, although the strain rate was controlled by a mechanical Linear Variable Differential Transducer (LVDT). For fatigue testing, the same servo-hydraulic TMS testing machine was used. The fatigue testing is force-controlled at a frequency of 1 Hz and a R = 0.1. The tests were continued until failure. The fatigue tests were designed at two different stress states: at 0.75x σy and 0.6x σy . The values for σy are obtained from quasi-static compression. A Matlab script was developed to analyse the raw data, including the variation of the elastic modulus and the mean strain of each cycle.

Analysis of Strut Diameter and Defects The process to obtain the distribution of strut’s diameter for each type of lattice and building orientation is described as following. First, photographs with an optical microscope were taken of all different lattice’s faces for the 4 samples. Second, all the pictures went over a thickness analyser implemented in house for this specific purpose which output the average strut thickness of each strut. Finally, the analysis provides with a distribution of each of the strut’s diameter as a function of the building orientation. Furthermore, the lattices are analysed to detect defects such as: (1) broken struts, (2) struts with section reduced or (3) strut’s waviness, see Fig. 2. All the micrographs for this analysis were taken with an optical microscope and SEM. Images of all the faces in the 4 samples were taken. The images were then analysed manually to obtain the population of defect for each type of defect and each type of beam orientation.

Fig. 2 Types of defects analysed in this study

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Results and Discussion In this section, the results arising for the defect analysis are presented first. Then the statics and fatigue properties of the lattice are presented and finally the effect of defects on the fatigue life of the AM lattice is discussed.

Defect Analysis and Geometrical Deviations Statistical distribution of the strut diameters measured in the AM lattice is presented in Fig. 3 for both 0° and 45° oriented beams. Statistical measurements such as mean diameter and standard deviation of from the mean diameter are presented in Table 2. It can be deduced that in all orientations the mean diameter of the struts is coarser compared to the ideal one. This error might partially caused by the slicing AM process and could be corrected by reducing the ideal strut diameter for the AM printing machine. This effect is more acute on 45° oriented struts, due to the powder attached and the “staircase effect”. The powder attached to the struts can explain the higher standard deviation present on 45° oriented struts. In terms of defects, the percentage of defective beams identified for each strut orientation is presented in Fig. 4. Due to the nature of SLM process, 0° orientated struts has a larger proportion of defects. Larger population of struts with the section reduced or strut’s waviness has been found when compared with broken struts. On Fig. 3 Statistical distribution of strut diameters along the 5 different samples printed

Table 2 Statistical properties of strut diameters

Orientation

0°

45°

Mean diameter [μm]

1030

1064

30

58

Standard deviation [μm]

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Fig. 4 Percentage of defective struts for each of the type of defects and beam orientations

45° oriented struts it is more common the waviness than broken or reduce section type of defects. These geometrical defects or deviations might lead to a worse mechanical performance, especially influencing fatigue life. This will further discussed at the end of this work.

Static and Fatigue Properties of AlSi10Mg Lattices The compressive static mechanical response of the octet lattice is presented in Fig. 5. The behaviour is typical of a stretch-dominated lattice structure [12]. Stretchdominated unit cells are characterized by a fluctuation on the stress–strain response due to the collapse of the struts one after another. This is associated with a more brittle fracture. This is coherent with the Maxwell number of the octet unit cell (36 struts and 12 nodes, the Maxwell’s number is 6). The elastic modulus E, yield stress σ y and ultimate tensile stress σ UTS extracted from the mechanical response of the lattice are presented in Table 3 (Fig. 5). Once the yield strength of the lattice has been stablished, the fatigue tests at 0.6 and 0.75 σy has been conducted. The cycle mean strain evolution along the number of cycles is presented on Fig. 6 for both stress levels. It can be observed and initial stage of the elastic loading, followed by a plateau corresponding typically to crack nucleation followed by an abrupt increase in the strain produce by crack nucleation and struts failure. Both repeats present consistent results. The S–N graph for all four fatigue tests is presented in Fig. 7. The fatigue life at 0.6 σ y is an order of magnitude higher than at 0.7 σ y . The effect of defects on this fatigue life is presented next.

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Fig. 5 Static mechanical behaviour of the AlSi10Mg lattice

Table 3 Mechanical properties extracted from the stress–strain response

E [GPa]

σ y [MPa]

σ UTS [MPa]

2.7

21

31

Fig. 6 Mean cycle strain as a function of the fatigue cycle for both 0.6 σy and 0.7 σy

Effect of Defects on Fatigue Properties In order to study the effect of defects on the fatigue properties of SLM AlSi10Mg lattices, a finite element framework was used. The lattice was modelled as a beam structure with a statistical distribution of beam thicknesses extracted from Fig. 3. The material properties of those beams were assumed to follow the ones observed experimentally by Nazir et al. [13]. The FEA model of the lattice was cyclic stressed to 0.6 σ y and 0.7 σ y following the experimental loading path. To reduce the computational cost, stresses were calculated between 100 and 1000 cycles depending on the

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Fig. 7 S–N curves extracted for the octet AlSi10Mg lattice

stage in the fatigue life. The maximum stress of each beam was identify to calculate the damage accumulation of each beam j as Dj =

 n i i

Ni, j

where n i is the accumulated number of cycles for a simulation step i and N i,j is the fatigue life at the stress experience by the beam. Beams were assumed to be broken when the damage reaches 0.99. Lattice failure was stablished when the stiffness of the structure reached 95% of the initial one. The fatigue life of AlSi10Mg was modelled in three different parts as indicated in Fig. 8. First, a low cycle-fatigue stage assuming a logarithmic linear relationship between the number of cycles and the stress level, then a high-cycle stage modelled as a power law [14] and finally a horizontal endurance limit. The values to fit these 3 stages were extracted from experimental data on AlSi10Mg [15]. The effect of defects on the fatigue life was included by reducing the ideal life by a SLM material damage parameter DMAT . Fatigue simulations with different material damage parameters were computed. The SN curves for each value of the damage parameter is presented in Fig. 9. By positioning the experimentally obtained fatigue life on this simulated SN chart, the effect of processing defects can be estimated. It can be observed that for both level of stresses the material damage parameters are close to 0.75. This is a definitive proof the important effect of processing effects on the fatigue life of SLM lattice structures.

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Fig. 8 Fatigue life of the SLM bulk AlSi10Mg as a function of the material damage

Fig. 9 Static mechanical behaviour of the AlSi10Mg lattice

Conclusions In the study reported in this paper, the influence of additive manufacturing defects on the fatigue mechanical performance of mechanical octet lattices fabricated in AlSi10Mg is investigated. The following specific conclusions can be drawn: 1. Additively manufactured AM lattices made of AlSi10Mg presented deviations in the measured strut thicknesses. For all the cases, the observed mean diameter is higher than the designed one, being more acute for beams oriented at 45°. The presence of defects has been studied presenting a higher rate of reduced sections in 0° beams and waviness in 45° beams. 2. The mechanical behaviour of the lattices has been measured. The lattice present a typical stretch dominated behaviour with numerous stress fluctuation peaks.

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The fatigue life has been analysed at 0.6 σy and 0.7 σy , presenting a significant lower fatigue life than bulk SLM AlSi10Mg. 3. A fatigue FE framework has been used to define the damage level of the printed material associated with manufacturing defects. To obtain the experimentally observed fatigue life, the SLM beam material is estimated to present a damage producing almost a 75% reduction of the fatigue life of the bulk SLM material. Acknowledgements Proyecto PID2020-116440RA-I00 financiado por MCIN/AEI/10.13039/ 501100011033.

References 1. Sihvola A (2007) Metamaterials in electromagnetics. Metamaterials 1(1):2–11 2. Barba D, Alabort C, Tang YT, Viscasillas MJ, Reed RC, Alabort E (2020) On the size and orientation effect in additive manufactured Ti-6Al-4V. Mater Des 186:108235 3. Barba D, Alabort E, Reed RC (2019) Synthetic bone: design by additive manufacturing. Acta Biomater 97:637–656 4. Geng X, Ma L, Liu C, Zhao C, Yue Z (2018) A FEM study on mechanical behavior of cellular lattice materials based on combined elements. Mater Sci Eng A 712:188–198 5. Gent AN, Thomas AG (1959) The deformation of foamed elastic materials. J Appl Polym Sci 1(1):107–113 6. Deshpande VS, Fleck NA (2000) Isotropic constitutive models for metallic foams. J Mech Phys Solids 48(6–7):1253–1283 7. Silva M, Shepherd EF, Jackson WO, Dorey FJ, Schmalzried TP (2002) Average patient walking activity approaches 2 million cycles per year: pedometers under-record walking activity. J Arthroplasty 17(6):693–697 8. Benedetti M, Du Plessis A, Ritchie RO, Dallago M, Razavi SMJ, Berto F (2021) Architected cellular materials: a review on their mechanical properties towards fatigue-tolerant design and fabrication. Mater Sci Eng R Rep 144:100606 9. Van Hooreweder B, Apers Y, Lietaert K, Kruth JP (2017) Improving the fatigue performance of porous metallic biomaterials produced by selective laser melting. Acta Biomater 47:193–202 10. Yavari SA, Ahmadi SM, Wauthle R, Pouran B, Schrooten J, Weinans H, Zadpoor AA (2015) Relationship between unit cell type and porosity and the fatigue behavior of selective laser melted meta-biomaterials. J Mech Behav Biomed Mater 43:91–100 11. Frazier WE (2014) Metal additive manufacturing: a review. J Mater Eng Perform 23(6):1917– 1928 12. Gibson LJ, Ashby M, Celullar solids, structure and properties, 2nd edn. Cambridge University Press 13. Nazir A, Abate KM, Kumar A, Jeng JY (2019) A state-of-the-art review on types, design, optimization, and additive manufacturing of cellular structures. Int J Adv Manuf Technol 104(9):3489–3510

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14. Hedayati R, Hosseini-Toudeshky H, Sadighi M, Mohammadi-Aghdam M, Zadpoor AA (2016) Computational prediction of the fatigue behavior of additively manufactured porous metallic biomaterials. Int J Fatigue 84:67–79 15. Beretta S, Patriarca L, Gargourimotlagh M, Hardaker A, Brackett D, Salimian M, ... Ghidini T (2022) A benchmark activity on the fatigue life assessment of AlSi10Mg components manufactured by L-PBF. Mater Des 218:110713

Surface Roughness Measurements of Laser Deposited AlCoCrFeNiTi and AlCoCrFeNiCu High Entropy Alloys for Aerospace Applications Dada Modupeola and Popoola Patricia

Abstract Investigating the surface roughness of metals in the field of precision engineering is vital because surface roughness explains if there are any irregularities on the surface of the as-built aerospace components, which can be nucleation sites for corrosion. In this study, AlCoCrFeNiTi and AlCoCrFeNiCu high entropy alloys were produced via laser metal deposition and the comparative study of two surface roughness (Ra) measuring instruments were used; Gwydion software and a stylus Profilometer. The results showed that the AlCoCrFeNiTi HEA had a higher degree of surface roughness variation; hence, a rougher surface than the AlCoCrFeNiCu HEA, however, the 3D plots and data analysis showed the AlCoCrFeNiCu HEA had more texture. This study also showed that the surface measurements taken from the stylus Profilometer were comparable and in good correlation with the statistical analysis. Keywords Surface roughness · Profilometry · Gwyddion software · High entropy alloys · Additive manufacturing

Introduction Aerospace technologies are of current research interest with their constant need for improvement in the quality and efficiency of parts [1–3]. Materials used for aerospace applications must be lightweight, durable, corrosion, creep, erosion, and fatigue resistant [4–7]. While conventional alloys can achieve these characteristics, they show certain limitations that restrict their structural applications, such as high density, low specific weight, and poor mechanical properties. High entropy alloys (HEAs) are materials that are potential replacements for conventional materials with D. Modupeola (B) · P. Patricia Department of Chemical, Metallurgical and Materials Engineering, Tshwane University of Technology, Staatsartillerie Rd, Pretoria West, Pretoria 0183, South Africa e-mail: [email protected] P. Patricia e-mail: [email protected] © The Minerals, Metals & Materials Society 2023 The Minerals, Metals & Materials Society, TMS 2023 152nd Annual Meeting & Exhibition Supplemental Proceedings, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-031-22524-6_11

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impressive properties that were limited by operational loading conditions and extreme temperatures [8–10]. AlCoCrFeNiCu and AlCoCrFeNiTi HEAs have high strength, good compressive strength, corrosion, and wear resistance compared with traditional alloys for aerospace applications [11–13]. However, high entropy alloy properties are greatly influenced by surface topography, which is difficult to define but contributes to the material’s performance. For instance, materials with flat surfaces can be used for different engineering applications. Nonetheless, most material surfaces have steep gradients, undulations, and pores [14–16]. Hence, the fabrication technique also influences the surface finishing of these alloys. Additive manufacturing (AM) uses a three-dimensional computer-aided design to fabricate as-built components layer-bylayer, resulting in high-quality surface characteristics [17–19]. The measurement of the roughness and thickness of laser-deposited materials is conducted using several instruments, such as atomic force microscopy (AFM), scatterometry, scanning electron microscope (SEM), and a confocal laser scanning microscope (CLSM) [14]. Surface roughness can also be measured using commercial and non-commercial software like Gwyddion software [20], by using an optical Profilometer [21], or a combination of both methods [22]. Yadhuraj et al. [23] reported that the Gwyddion software is capable of detecting roughness with a resolution of a few nanometers, and it can be used to assess thickness and roughness with a high degree of accuracy. Pavlovic et al. [22] stated that indirect image-based profilometry is a useful and effective method for characterizing the topographies of various surfaces. Hence, this comparative study shows the surface roughness results using stylus profilometer with results from the Gwyddion software to examine the performance of both methods and the usefulness for the characterization of the surface topography of laser-deposited AlCoCrFeNiTi and AlCoCrFeNiCu HEAs for aerospace applications.

Methodology The AlCoCrFeNiTi and AlCoCrFeNiCu HEAs were fabricated on a steel baseplate using a 3 kW IPG Nd: YAG fiber laser with a wavelength of 1073 nm. The KUKA robot deposition system used a carrier and shielding gas in an inert environment. The HEAs powders were carried by a powder feeder and deposited through a threeway nozzle head. Both HEAs powders were supplied by F. J Brodmann & Co. LLC and deposited as-received with a particle size distribution of 45–90 µm. The laser processing parameters used in fabricating the as-built samples are shown in Table 1. The parameters consisted of the laser power at 1400 W, scanning speed 12 mm/s, spot size diameter 2 mm, and shielding gas rate of 12 L/min. After deposition, metallographic procedures were carried out on the samples, such as mounting, grinding, and polishing. Then the samples were analyzed using a scanning electron microscope. The sample morphology was extracted through signals gotten from interactions of electrons in a low vacuum at room temperature. The SEM micrographs were studied and loaded into the Gwyddion software which was used to analyze the SEM data and obtain the surface roughness results. The

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Table 1 Laser parameters for the comparative study of the As-built AlCoCrFeNiTi and AlCoCrFeNiCu entropy alloys High entropy alloys

Sample

Laser power (W) (J/s)

Scan speed (V) (mm/s)

Beam diameter (mm)

Energy density (E = P/V*d) (J/mm2 )

AlCoCrFeNiCu

C D

1400 1600

12 10

2 2

58.3 80

AlCoCrFeNiTi

G I

1400 1600

12 10

2 2

58.3 80

Fig. 1 Taylor Hobson Surtronic S-128 series optical profilometer

surface roughness of the as-built samples was also measured using a diamond stylus arm attached to Taylor Hobson Surtronic S-100 series—S-128 optical profilometer shown in Fig. 1.

Results and Discussion The influence of the compositional differences on the surface roughness was examined to determine the functionality, machinability, and properties of the AlCoCrFeNiTi and AlCoCrFeNiCu high entropy alloys. Investigating the surface roughness of these alloys also explains if there are any irregularities on the surface of the as-built HEA samples which can be nucleation sites for corrosion. The roughness of the AlCoCrFeNiTi and AlCoCrFeNiCu high entropy alloys was measured using a surface profilometer when a diamond stylus arm was placed in contact with the sample surfaces. The profilometer provided four parameters for examining the surface characteristics of the as-built samples; the mean square roughness (Ra ), the maximum value depth which is within a single sampling length and below the mean

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line (Rv ), the maximum peak height which is within a single sampling length and above the mean line (Rp ), and the maximum value to a peak height of the profile which is also within a single sampling length (Rz ), all measured in micrometer. Therefore, the surface roughness parameters Ra , Rv , Rp , and Rz extracted from the profilometer are presented in Table 2. Using the Gwyddion software to analyze the surface roughness parameters of samples C and G from the SEM micrographs in Fig. 2, several representative parameters were added and shown in Table 3 such as the surface skewness (Rsk ) which shows the integrity of the sample’s surface roughness, and it represents the symmetry between the distributions of the surface height. The corresponding surface roughness parameters from the optical profilometer and the Gwyddion software were compared. If a negative value is observed, it indicates that the distribution of the surface height is biased to the left side having the surface height well above the mean value, a positive value indicates that the distribution of the surface height is biased to the right side while zero value means the distribution is normal as shown in Fig. 3. The mean square roughness (Rq ) shows the sample’s degree of change in surface roughness. The calculation equations for Ra , Rq, and Rsk are shown in Eqs. 1–4 [23]; Table 2 Surface roughness statistics of AlCoCrFeNiTi and AlCoCrFeNiCu HEAs using profilometer Sample

Ra (µm)

Rv (µm)

Rp (µm)

Rz (µm)

C

0.13

1.51

0.54

2.05

D

0.61

1.8

2.33

4.13

G

0.38

1.12

1.61

2.73

I

0.78

3.64

2.58

6.22

Fig. 2 SEM micrograph using the threshold method in the Gwyddion software of laser-deposited a AlCoCrFeNiTi and b AlCoCrFeNiCu HEA

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Table 3 Surface roughness statistics of AlCoCrFeNiTi and AlCoCrFeNiCu HEAS using Gwydion software Sample

RMS roughness (Rq )

RMS (grain-wise)

Mean roughness (Ra )

Skew (Rsk )

(Rp )

(Rv )

(Rz )

C

0.05239

0.05239

0.04231

−0.6469

0.1485

0.2280

0.3765

G

0.05446

0.05446

0.05029

−1.063

0.2681

0.2260

0.4941

Fig. 3 Surface skewness (Rsk )

Ra =

Ny Nx  1  |z(i, j) − z mean | N x N y i=1 j=1

(1)

Ny Nx  1  = z(i, j) N x N y i=1 j=1

(2)

Z mean  Rq =

Rsk =

Ny Nx  1  (z(i, j) − z mean )2 N x N y i=1 j=1

1 Nx N y

 Nx  N y i=1

j=1 (z(i,

Rq3

j) − z mean )3

(3)

(4)

where N y and N x are the scanning points on the y-axis and x-axis of the SEM image, respectively. The mean height of the measuring points from the SEM image is z mean while the measuring points (i, j) are represented by the height z(i, j). The two and three-dimensional representations of the surface morphologies are represented in Fig. 4, which shows the sample’s height, surface undulation, and chromaticity of samples C and G representing the AlCoCrFeNiTi and AlCoCrFeNiCu high entropy alloys, respectively. The AlCoCrFeNiTi HEA has a higher degree of surface roughness variation, hence a rougher surface than the AlCoCrFeNiCu HEA which is in good correlation with the experimental profilometry results. However, the AlCoCrFeNiCu HEA has more texture than the AlCoCrFeNiTi HEA. While the negative skew values show that the surface height is well above the mean value.

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Fig. 4 2D-3D surface morphologies of a AlCoCrFeNiTi and b AlCoCrFeNiCu HEAs

Conclusion AlCoCrFeNiTi and AlCoCrFeNiCu High entropy alloys were fabricated via laser metal deposition and two approaches in examining the surface roughness properties of the as-built samples were investigated and the results compared using an optical stylus profilometer and through the analysis of an SEM micrograph through Gwyddion software. There were little differences in the values of the parameters from the software and the profilometry results. The results show that the optical profilometry was a flexible, valuable tool in characterizing the surface topography of laser-deposited high entropy alloys. The AlCoCrFeNiTi HEA has a higher degree of surface roughness variation, hence a rougher surface than the AlCoCrFeNiCu HEA which is in good correlation with the experimental profilometry results.

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References 1. Brandt M et al (2013) High-value SLM aerospace components: from design to manufacture. In: Advanced materials research. Trans Tech Publ 2. Williams JC, Boyer RR (2020) Opportunities and issues in the application of titanium alloys for aerospace components. Metals 10(6):705 3. Saadat M, Cretin L (2002) Measurement systems for large aerospace components. Sens Rev 4. Mangalgiri P (1999) Composite materials for aerospace applications. Bull Mater Sci 22(3):657– 664 5. Jayakrishna K et al (2018) Materials selection for aerospace components. In: Sustainable composites for aerospace applications. Elsevier, pp 1–18 6. Nayak NV (2014) Composite materials in aerospace applications. Int J Sci Res Publ 4(9):1–10 7. Zhu L, Li N, Childs P (2018) Light-weighting in aerospace component and system design. Propuls Power Res 7(2):103–119 8. George EP, Raabe D, Ritchie RO (2019) High-entropy alloys. Nat Rev Mater 4(8):515–534 9. Li W et al (2021) Mechanical behavior of high-entropy alloys. Prog Mater Sci 118:100777 10. George EP, Curtin W, Tasan CC (2020) High entropy alloys: a focused review of mechanical properties and deformation mechanisms. Acta Mater 188:435–474 11. Ma E, Wu X (2019) Tailoring heterogeneities in high-entropy alloys to promote strength– ductility synergy. Nat Commun 10(1):1–10 12. Li Q et al (2020) Effects of AlCoCrFeNiTi high-entropy alloy on microstructure and mechanical properties of pure aluminum. J Mater Sci Technol 52:1–11 13. Zhou Y et al (2022) Effect of vacuum heat treatment on microstructure and mechanical properties of HVOF sprayed AlCoCrFeNiCu high-entropy alloy coating. Mater Lett 132551 14. Gong Y, Xu J, Buchanan RC (2018) Surface roughness: a review of its measurement at micro/nano-scale. Phys Sci Rev 3(1) 15. Assender H, Bliznyuk V, Porfyrakis K (2002) How surface topography relates to materials’ properties. Science 297(5583):973–976 16. Hull D (1999) Fractography: observing, measuring and interpreting fracture surface topography. Cambridge University Press 17. Moghaddam AO et al (2021) Additive manufacturing of high entropy alloys: a practical review. J Mater Sci Technol 77:131–162 18. Chen S, Tong Y, Liaw PK (2018) Additive manufacturing of high-entropy alloys: a review. Entropy 20(12):937 19. Torralba JM, Campos M (2020) High entropy alloys manufactured by additive manufacturing. Metals 10(5):639 20. Neˇcas D, Klapetek P (2012) Gwyddion: an open-source software for SPM data analysis. Open Phys 10(1):181–188 21. Ekici Ö et al (2021) Evaluation of surface roughness after root resection: an optical profilometer study. Microsc Res Tech 84(4):828–836 22. Pavlovi´c Ž, Risovi´c D, Novakovi´c D (2012) Comparative study of direct and indirect imagebased profilometry in characterization of surface roughness. Surf Interface Anal 44(7):825–830 23. Yadhuraj S, Kumari U (2016) Measurement of thickness and roughness using Gwyddion. In: 2016 3rd International conference on advanced computing and communication systems (ICACCS). IEEE

Part V

Additive Manufacturing of Metals: Applications of Solidification Fundamentals

Assessment of Phase Evolution in Titanium-Niobium-Based Alloys During Rapid Solidification Theo Mossop, David Heard, and Mert Celikin

Abstract In this work, microstructural evolution in β-Ti alloys during solidification is studied as the cooling rate increases, approaching the cooling rates found in additive manufacturing processes. Using suction casting of thin rods, high cooling rates can be studied and compared, to find a trend in how these phases evolve under a broad range of solidification conditions. The effect of varying cooling rates is studied on the microstructural evolution of Titanium-Niobium (Ti-Nb)-based alloys with Tantalum (Ta) additions. A combined simulation and experimental approach is used to investigate the predictability of differences in microstructural evolution during rapid-solidification casting. Rods of binary Ti–25Nb and ternary Ti–20Nb– 10Ta (wt% and hereafter) alloys were synthesized in diameters of 3, 5, and 10 mm using suction casting into copper moulds. Finite element (FE) and thermodynamic modelling was used to calculate the cooling rates and temperature gradients of the alloys. The microstructural and mechanical differences were determined via XRD, SEM/EDS, and mechanical testing. Keywords Titanium alloys · Rapid solidification · Phase transformation · Beta-Ti alloys · Niobium · X-ray diffraction

T. Mossop (B) · M. Celikin School of Mechanical and Materials Engineering, University College Dublin, Dublin, Ireland e-mail: [email protected] Materials Design and Processing Laboratory, University College Dublin, Dublin, Ireland D. Heard Stryker Advanced Technology & Research, Kalamazoo, MI, USA © The Minerals, Metals & Materials Society 2023 The Minerals, Metals & Materials Society, TMS 2023 152nd Annual Meeting & Exhibition Supplemental Proceedings, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-031-22524-6_12

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Introduction Titanium (Ti) alloys are used in biomedical applications due to their high specific strength, good biocompatibility, and excellent fatigue resistance [1]. The most common biomedical Ti alloy, Ti–6Al–4V (Ti64), has a relatively high Young’s Modulus of 115 GPa in comparison to that of cortical bone (up to 30 GPa) [2]. This stiffness mismatch can result in a phenomenon known as stress-shielding, where the bone surrounding an implant loses bone-density, eventually resulting in implant failure. In addition to this, Ti64 also contains significant amounts of aluminium (Al) and vanadium (V) which have been linked to cell toxicity and neurological disorders [3]. Therefore, there is significant impetus to find superior biomedical alloys, both from a mechanical and biocompatibility perspective; β-Ti alloys with their composition space look promising. β-phase with a body-centered cubic (BCC) crystal structure has the lowest Young’s modulus of all the allotropes, as low as 50 GPa, and many alloying elements used in β-alloys are biocompatible—especially niobium (Nb), tantalum (Ta), and zirconium (Zr) [4]. In addition to this, binary Ti–Nb alloys can exhibit very high UTS, up to 1100 MPa, with elongations as high as 150% [5–7]. This is important for biomedical implants where these properties are critical. Ternary and higher order compositions have been shown to increase mechanical performance even further [5, 8, 9]. As there are many metastable phases that can coexist throughout the range of β-stabilised compositions, understanding the microstructural evolution with varying processing and post-processing conditions is critical for controlling mechanical properties [10, 11]. Additive Manufacturing (AM) with its design flexibility in comparison to traditional manufacturing exhibits high potential for the development of novel biomedical implants [12–14]. However, the design of novel Ti-based alloys suitable for additive manufacturing technology is very complex. This study is a preliminary work which will form a basis to shed light into the development of novel β-Ti alloys for AM. Our objective is to understand how microstructure of Ti–Nb-based β alloys evolves as the cooling rate increases, approaching the cooling rates found in additive manufacturing processes. Using suction casting of thin rods, high cooling rates can be studied and compared, to find a trend in how these phases evolve under a broad range of solidification conditions.

Experimental The binary Ti–25Nb alloys were synthesized using commercially pure (c. p.) titanium (grade 1–) and 99.9% pure niobium (99.85% Nb, 0.1% Ta, 0.01% C, 0.01% N, 0.01% O, 0.05), the numeral data were analyzed using one-way ANOVA followed by Tukey’s post-hoc test (α = 0.05, IBM SPSS Statistics ver. 23, NY, USA).

Results Alloy Powder Used SEM micrographs of typical alloy particles of Ti-6Al-4V alloys and Ti-6Al-4V ELI alloys used for the electron beam melting (EBM) and the laser beam (LBM) additive layer manufacturing are shown in Fig. 3, respectively. The diameters of particles range from 45 to 100 μm. As seen in the image, the particles used for the LBM are smaller than that for the EBM. The frequency of the number and volume of particles as the function of particle diameter are shown in Fig. 4, respectively. This distribution of the particles for EBM and LBM fairly conforms with the SEM images.

Candida Albicans Biofilm Formation on an Additive-Manufactured … A

Fig. 3 The alloy powders used for EBM (A) and LBM (B) additive manufacturing

251

B

Ti-6Al-4V alloy for LBM

Ti-6Al-4V ELI alloy for EBM

A

B

Count

Fig. 4 Frequency of the number (A, B) and the volume percent (C, D) of alloy particles as a function of alloy diameter

Ti-6Al-4V Ti-6Al-4V Diameter of the powder particles used D

Volume (%)

C

Ti-6Al-4V Ti-6Al-4V Diameter of the powder particles used

Surface Morphology Since the distribution of particle sizes might have affected surface and internal porosity, tweaking sintering variables might have needed to adjust power levels for eliminating unwanted porosity. Figure 5A–C show typical SEM images of the surface of the Ti-6Al-4V alloy specimens made using three different methods: EBMAM, LBM-AM, and lost-wax casting. Figure 5D also shows a surface image of the Teflon® negative control. The differences in surface roughness are most vividly seen in the CSI topographic images (which emphasize the z-axis) of the three types of specimens and the control (Fig. 5E–H). Among these four surfaces, the EBM specimen is exceptionally rough, displaying a rippled look compared with the other three surfaces. Alloy particles, some half melted, are observed on this surface; some particles appear welded to the surface below, but others appear to be physically locked to the surface by other particles. Close inspection of the surfaces of the LBM specimen, cast specimen, and Teflon® control reveal that all of these are clearly smoother than

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the EBM surfaces. The “rippled look” seen in the SEM images of the EBM specimen is also evident in the images produced by CSI (Fig. 5E). The quantitative roughness value (Sa: μm) of the EBM specimen was 36.80 μm, whereas the Sa values of the LBM specimen, cast specimen, and Teflon® negative control were 6.48, 2.30, and 0.03 μm, respectively. The Sa values were ranked as follows: EBM > LBM > cast > Teflon® . Figure 6 shows the SEM image of C. albicans biofilm-covered alloys (A–C, E– G) and Teflon® control (D and H), respectively. C. albicans biofilm formation was initiated with the adhered round yeast cells to all the solid surfaces. In all the SEM images, typically the view of the lower magnification indicated that the C. albicans on the Teflon® control developed a more maturated stage consisting of cell proliferation and early-stage filamentation of the adhered cells, compared to that on EBM, LBM, and cast substrates. The enlarged view of SEM images, it can be observed that C. Fig. 5 SEM and Coherence scanning interferometry images of biofilm-free alloys and Teflon® disks used (Sa: roughness, mm). A–C show typical SEM images of the surface of the Ti-6Al-4V alloy specimens made using three different methods: EBM-AM, LBM-AM, and lost-wax casting, respectively. D shows a surface image of the Teflon® negative control. Also, the CSI topographic images (which emphasize the z-axis) of the three types of specimens and the control (E–H)

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Fig. 6 SEM images of C. albicans biofilm-covered alloys (A–C) and Teflon® (D), and enlarged view of each substrate (E–H)

albicans biofilm formation on the EBM substrate showed more yeast cells than on the other substrates.

Biological Activity for the Alloy A quantitative expression of the biofilm accumulation, mean ATP luminescence values, expressed as the percentage of the Teflon® negative control is shown as a bar graph for each type of specimen in Fig. 7. The ATP value is proportional to the number of biologically active biofilm cells in the accumulated biofilm. There were no significant differences in the amount of accumulated biofilm among different types of specimens (p > 0.05). Also, Fig. 7 includes the surface roughness values, Sa, for

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each type of specimen plotted in a line graph. There appears to be no correlation between surface roughness and the amount of biofilm accumulation.

Discussion Dental implants, to be considered successful, must be non-mobile, non-painful, and have healthy tissue surrounding them. Once implants are healed, and considered osseointegrated, they can be restored. Two goals of dental implant materials would be to become osseointegrated to the bone as rapidly as possible and resist the formation and growth of oral biofilms that lead to peri-implant disease. The major two conditions to let these succeed at the point of contact between bacteria and the bulk of the materials are the oral environment and biomaterials. In the oral environment, a biofilm is always a multi-species affair [26]. The definition of biofilm is a thin resistant layer of microorganisms (such as bacteria) that form on and coat various surfaces. Also, biofilms have displaced bacteria as the mediators of oral disease on the implant surface. Within the film, several species of bacteria work together to optimize their survival in niches within the oral environment. In the microbial cells in humans, C. albicans is the most prevalent fungal species, asymptomatically colonizing many areas of the body, including the gastrointestinal and genitourinary tracts, oral cavity, and skin of healthy individuals. Once host immunity, stress, resident microbiota, and other factors are varied, C. albicans can be led to overgrowth, causing a wide range of infections in the body. Biofilms are notorious for forming on medical and dental devices implanted, including catheters, pacemakers, prosthetic joints, dentures, and dental implants, which provide surfaces for biofilm growth. Since C. albicans biofilms are basically resistant to conventional antibiotics, the host immune system, and other environmental changes, prevention is important against

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biofilm-based infections in clinical practice. Therefore, this study focused on the biofilms formed by C. albicans that is closely related to fungal species [27]. Another important factor is that the biomaterials used for dental implants are required to have adequate characteristics to succeed with osseointegration within the oral spaces where adhered bacteria on the surface, not floating bacteria, are present. Depending on the evolution of additive manufacturing techniques, dental implants are considered to be a candidate for additive manufacturing techniques due to possible cost and time reduction. Since the alloy powders were used for fabricating the medical and dental devices, the surface morphologies of these fabrications should be considered. In fact, the surface of these devices fabricated using AM is rougher than that of devices fabricated using other methods, for example, grinding, cutting, and casting [28]. For two decades, scientists have been aware that biofilms seldom form on clean surfaces. Within seconds, an “acquired pellicle” of proteins from the saliva covers any surface that is exposed to the oral environment [29, 30]. As awareness increased that metal implants were being colonized by diseasecausing biofilms, the relationship between the bacteria and the surface condition of the implants has began to be investigated. For example, which surfaces resisted bioflim attachment or promoted osteogenesis [31–35]. Implant designers were faced with the conundrum that a surface that was good for promoting osteogenesis will simultaneously produce surfaces that are prone to attachment by biofilms. There have been few studies of the interaction between oral bacteria and implant materials. Almaguer-Florer et al. is the most recent [36]. Using a method similar to the one described here, they placed cultures of nine species of oral bacteria, each separately as a monoculture, on Ti thin film substrates or on an amorphous carbon thin film substrate. They incubated the bacteria in human saliva and mycoplasma. They found that depending on the bacteria species used, adhesion depended on the chemistry of the implant material, its roughness, and the media it was incubated in. Reviewing literature, this study, and Almaguer-Florer et al. [36] are the only investigators to directly evaluate the effect of Ti alloy on surface roughness and culture media. The previous study [14] showed the specific results for the adhesion of S. mutans biofilm on Ti-6Al-4V. Instead of oral biofilms consisting of a mixture of many species of bacteria, the tested biofilm consisted of a single bacterial species. S. mutans was selected because this bacterium is an important etiological agent within the dental biofilm. In vivo, a mixed-species biofilm may behave very differently. The interactions within a biofilm produced by multiple bacterial species may be complicated. This study on the effect of alloy constituents using single-specie biofilm accumulation [14] reported that the amount of the biofilm formed was reduced when the substrate alloy contained a cytotoxic element like Pd. This suggests that biofilm accumulation could be controlled or minimized by rational alloy selection. As a continuation of the previous study, the interaction between various medical and dental materials and biofilms produced by C. albicans of bacteria was investigated. Also note that the present results are for biofilm accumulation on the Ti-6Al-4V substrates over a rather short period, 48 h.

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In biofilm formation, microorganisms such as C. albicans are initially floating in a planktonic mode in saliva. Once a device is seated intraorally, it quickly becomes coated with mucins and/or other proteins. This coated surface facilitates the adhesion of microorganisms by cell–surface interaction. These C. albicans adhesions contribute to biofilm growth through initial attachment. The next step is the development of the biofilm by cell proliferation and early-stage filamentation of adhered cells. This stage is often referred to as the “seeding” step and is essential for normal biofilm development. The next stage in biofilm development consists of cell proliferation and early-stage filamentation of the adhered cells, followed by biofilm maturation, resulting in a complex network of several layers of polymorphic cells, including hyphal cells (chains of cylindrical cells), pseudohyphal cells (ellipsoidal cells joined end to end), and round yeast cells, encased in an extracellular matrix, giving the biofilm a thick and structured appearance as well as providing protection from chemical and physical injury. A mature biofilm typically forms by 24 h, and can be visualized by the eye as a cloudy surface structure on top of the solid surface, and under a microscope, as an organized collection of different cell types [37]. In this study, the amount of biofilm accumulation of C. albicans on specimens of Ti-6Al-4V alloy using the recently developed additive manufacturing methods EBM or LBM had no significant differences compared with the conventional method, lostwax casting technique. All specimens had different surface roughness. The Sa value of the Ti-6Al-4V specimen by EBM was more than 36 μm, whereas the values for other alloys and Teflon® control were less than 3 μm. Although the surfaces of the EBM specimens were rougher than those of the LBM and cast alloy, this roughness, did not increase the biofilm accumulation. This is supposed to be the surface of the EBM specimens with rougher but curvaceous and smooth configuration compared with that of LBM and cast specimens with less rough but coarse configuration, resulting in the different stages of development of the C. albicans on each surface. There was, no significant difference found in the amount of biofilm formed on these specimens after 48 h of accumulation. The results may be different if the time for the experiment gets longer due to the development stages of the C. albicans biofilm formation. Future tests should include longer periods of biofilm growth to account for the fact that bacteria in a biofilm have different properties than the same bacteria in isolation. In the presence of a biofilm overlay, the effect of a substrate material on biofilm accumulation may be significantly changed. In addition, one concern arises after examining biofilm-covered EBM specimens with the roughest surface: since the biofilm appeared to attach itself mechanically, especially by penetrating microscopic crevices, biofilm removal was exceedingly difficult. The mature biofilm formed on the EBM specimen will be a great concern for the biomaterials. Removing the biofilm could be difficult from the EBM surfaces. Therefore, because the surface properties were affected by the type of machine and the parameter materials used, the surface treatments should be considered depending on the dental applications. Typically, research on EBM is needed to reduce the surface roughness of devices produced by electron beam melting.

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Conclusion After relatively brief exposure in this study, surface roughness appears to have no effect on C. albicans biofilm accumulation. Metals used in additive manufacturing techniques, if they are to be used in as-manufactured condition without surface improvement, may require methods of removing biofilms to minimize adverse outcomes associated with excess biofilm formation. The clinical biological risks to cell viability are probably higher because intraoral conditions are more dynamic and harsher. Additional research is needed to evaluate whether the Ti-6Al-4V alloys continue the same rates of biofilm formation when exposed for longer periods. Conflict of Interest The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding This study was partially supported by a Grant-in Aid for Scientific Research (C) from the Japan Society for the Promotion of Science (16K11619, 19K10235).

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Characterization of Spicule Structure Fariborz Tavangarian, Jennifer L. Gray, Trevor Clark, and Chao Gao

Abstract Nature has been a great source of inspiration for engineers and scientists for centuries. It provides unique ideas to overcome the unmet needs of human beings. Spicules are structural elements of Euplectella Aspergillum sponges that reside in the deep ocean. They have an exceptional microstructure that provides excellent mechanical properties. Although spicules are composed of a brittle material, silica (SiO2 ), they behave differently under load compared to other ceramics. This behavior is due to their concentric cylindrical structure. To produce a similar structure with potential engineering and biomedical applications, one needs to investigate its microstructure in depth. In this study, we examined the microstructure of spicules to understand their architecture as a foundation to better design biomedical implants for tissue engineering applications. Keywords Spicules · Mechanical properties · Tissue engineering · Bio-inspiration · Implants

Introduction Many of the improvements and innovations in technology that help to improve our way of life come from observing and mimicking nature. Processes, functions, and designs in nature have stood the test of time and continue to perform optimally in trying scenarios, making them ideal for implementing into our everyday lives. F. Tavangarian (B) Mechanical Engineering Program, School of Science, Engineering and Technology, Pennsylvania State University, Harrisburg, Middletown, PA 17057, USA e-mail: [email protected]; [email protected] J. L. Gray · T. Clark Materials Research Institute, The Pennsylvania State University, University Park, PA 16802, USA C. Gao Department of Mechanical and Industrial Engineering, Norwegian University of Science and Technology, 7034, Trondheim, Norway © The Minerals, Metals & Materials Society 2023 The Minerals, Metals & Materials Society, TMS 2023 152nd Annual Meeting & Exhibition Supplemental Proceedings, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-031-22524-6_24

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One particularly intriguing design is found in marine sponges. Studies have shown siliceous sponges are incredibly strong and durable under load, and have credited this to their unique structure on the micron and nanoscale. The building blocks of the sponge skeleton are comprised of small silica-based scaffolding called spicules, and collagen [1]. The siliceous sponges are not the only sponge family which contains spicules, rather there is also a classification of sponges that contain calcareous spicules. However, these spicules are more glasslike in nature and do not have the unique structural attributes of the former. The location of spicule formation in the sponge skeleton is largely attributed to genetic influence, giving different sponge classifications their own unique shape [2]. Two different types of sponges produce spicules derived from silica, the Demospongiae and the Hexactinellida. The main differences between the two are the orientation and pattern of the spicules as well as the cellular makeup [3]. The traits of both types of spicules produce a strong foundation that enables the sponge to survive the oceanic pressure and maintain an upward direction when growing. This is critical as it lets the sponge successfully move more water through the body, a process essential for survival. Spicules are tested very favorably using the Brillouin Scattering method, and the same test is used to evasively test the strength of spider webs [4]. The unique capacity for stress these spicules possess is credited to their structure. The anatomy of a spicule is organic at the origin but the majority of the structure is an inorganic material. The siliceous spicule is protected by a collagen sheath, forming a sort of net around the outermost part of the silica-based layers. The purpose of this collagen is to facilitate the formation of the lamellae layers and keep the overall shape of the spicule cylindrical [5]. The following segment of the spicule is a hard, rigid section comprised of many circular layers stacked on top of each other with the axial canal at the center [1]. These layers are strong and brittle, and make up the majority of the spicule’s diameter. The axial cylinder is the next component and it is made of a dense mass of silica [1]. This is the part of the spicule that the significant elasticity originates from. The silica here is somewhat flexible, allowing the spicule to absorb impact if necessary. This softer cylinder is complemented by the rigid outer section resulting in a structural element that is both strong and durable. The axial filament is the final region of the siliceous spicule, located at the center of the spicule in the axial canal, and it is made of proteins called silicateins. These silica-based proteins are the brains of the spicule, responsible for using the silicate in the water around them. This organic core then converts it into silica to deposit into a spicule layer [5]. The combination of a flexible core and a rigid strong outer layer makes these spicules structurally intriguing. A better understanding of the microstructure of the spicules will help us to improve our knowledge and apply it toward new structures from brittle materials with improved performance under load-bearing applications. In this paper, we study the structure of spicules using scanning electron microscopy (SEM) and transmission electron microscopy (TEM) to investigate the cross section of spicules and inspect how organic/inorganic layers come in contact with each other. Epoxy was used to fix the sponges in place and to generate cross sections. The samples were sanded and polished to generate a clean surface for microscopic

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studies. To prepare very thin layers for TEM, focused ion beam (FIB) was used. The results can be used to produce similar structures from brittle materials with unexpected mechanical properties similar to spicules.

Materials and Methods Sample Preparation Euplectella Aspergillum sponges have been cut into small sections and then put into circular silicone molds with a height and diameter of 25 mm. Two-part epoxy (Allied High Tech Products 145-20,005 as resin and 145-20,010 as hardener) was poured into the molds and put aside until solid samples were obtained. Then the samples with a height of 10–15 mm were prepared using a slicing saw (TechCut 5, Allied High Tech Products, USA). The surface of each sample was sanded using SiC grits from 120 down to 1200 and then polished with 0.05 µm colloidal silica (Allied High Tech Products, Lot: 017,543/CR, USA) to make sure a smooth sample is prepared for microscopic evaluation. Some specimens were etched using ammonium hydroxide and hydrogen peroxide and deionized (DI) water (1:1:5) followed by a quick hydrofluoric acid (HF): DI water (1:50) for 1 min. To prepare a sample for TEM evaluation, first, a scaffold of the sponge was cut and then place fixed on top of a stub using a hot glue. Then one strand of the sponge was selected and milled using focused ion beam (FIB).

Sample Characterization Scanning electron microscopy (SEM) was utilized to investigate the microstructure as well as the morphology of the samples. For this purpose, FEI Helios Nanolab 660 (Hillsboro, OR, USA) and Apreo S (ThermoFisher Scientific) were used with an acceleration voltage of up to 20 kV. To prepare a thin slice of the cross section of spicules, focused ion beam (FIB, ThermoFisher Scientific, Scios 2, USA) was used. A strand of spicule was selected and then milled by FIB to generate the required specimen for TEM analysis. Transmission electron microscopy (TEM, ThermoFisher Scientific, Talos F200c, USA) was utilized to investigate the nanostructure and elemental analysis of the samples.

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Results and Discussion Figure 1 shows a Euplectella Aspergillum sponge with the microstructure of spicules in the base and body of the sponge. As seen, the strands (fiber) on the body of the sponge are composed of concentric cylindrical structure (Fig. 1b). However, in the base, they are composed of several spiculic structures covered with a shield (Fig. 1c). The high strength of spicules reported in the literature can be ascribed to their layered structure [6, 7]. When a crack is initiated on the surface of the outer layer of spicule, it can only propagate to its interface with the next layer. When the crack reaches the interface, it stops as the next layer is discrete and separated from the outer layer. The interface between the two layers arrests the crack and prevents it from further propagation. To initiate a new crack, higher stress is required. Consequently, the layered structure provides higher strength compared to a solid rod. Also, crack deviation is another mechanism that may play a role in preventing crack development and hence increase the strength of the spicules. Similar mechanisms have been observed in other naturally occurring structures in nature such as what has been reported in abalone nacre [8, 9]. To study the layered structure, the cross sections of spicules were investigated by SEM before and after etching. Figure 2 shows the cross sections of the samples before and after the etching process. As can be seen, the samples are composed of concentric cylindrical structures. This layered structure can prevent the progress of the cracks from one layer to the next and prevent catastrophic failure in spicule structure. The concentric cylindrical structure is not very clear in those samples before etching (Fig. 2a). However, after the etching process, the concentric cylinders can be distinguished from each other due to the removal of the organic materials in between the layers and consequently the higher contrast between the layers and the interfaces. To investigate the morphology and the existing elements in the samples, a thin layer of the cross section was milled by FIB and then EDS analysis was performed. Figure 3 shows the TEM images as well as the elemental analysis of the sample. The yellow arrows show the organic material in between the silica layers. As seen, the organic material is mostly composed of Na and K but the inorganic portion of the structures which are the cylindrical layers are composed of silica. Exposure of the samples to an electron beam for a longer period of time caused damage to the structure and Fig. 1 a Image of Euplectella Aspergillum sponge, b the microstructure of a spicule, as seen, consisted of concentric cylindrical structure, and c several spicules are covered in a shield at the base

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Fig. 2 The cross-sectional area of a typical spicule a before and b after etching

changed the morphology at the interface between the inorganic and organic materials (the pic is not shown here). A cryo microscope TEM with a direct electron detector should be used for imaging at much lower doses than in a conventional microscope. It is equipped with a K2 direct electron detector for very low-dose TEM imaging of beam-sensitive samples. In addition, it can also be equipped with a Gatan Quantum GIF with EELS/EFTEM (Electron Energy Loss Spectroscopy/Energy Filtered TEM) capabilities. Using this GIF allows the scientists to collect elemental maps on the K2 direct electron detector in EFTEM mode or collect EELS spectra on the K2 using the GIF which will allow elemental mapping in STEM mode. The ability to use the K2 for both imaging as well as elemental mapping will allow characterization of the sample structure as well as elemental distributions, at lower electron doses than would otherwise be possible, therefore minimizing beam damage. In future studies, cryo microscope should be used to better study and analyze the spicule samples.

Conclusions Recently, spicules have received much interest due to their unique architecture. They show excellent mechanical properties although they are mainly composed of silica (SiO2 ) which is a brittle material. Studies have proved that spicule-inspired structures with a concentric cylindrical architecture do not break catastrophically. This behavior is due to the unique structure of spicules. As shown in this study, spicules are composed of concentric cylinders with organic materials in between the layers. The layers are mainly composed of SiO2 and the organic layers in between them consist of Na and K. Exposure of the samples to an electron beam for a long time damaged the samples and changed the morphology of the structure. To better analyze the specimens, one should use cryo TEM to decrease the exposure dose and prevent structural changes. Further studies are required to better understand the interface of organic/inorganic materials and the morphology of the interface between silica and organic materials. Inspiring by this structure can open up a new venue for designing novel structures with potential applications in biomedical implants, automotive, building, and aerospace industries.

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Fig. 3 High-angle annular dark field (HAADF) image of spicule along with the elemental analysis of the sample

Acknowledgements This project was partially supported by the NSF-CAREER under the NSF Cooperative Agreement CMMI-2146480.

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Polymeric Biodegradable Biomaterials for Tissue Bioengineering and Bone Rejuvenation Eribe M. Jonathan, Andrew O. Ohifuemen, Jacob N. Jacob, Aaron Y. Isaac, and Ikhazuagbe H. Ifijen

Abstract The necessity for multiple surgeries is decreased by tissue engineering techniques, which also lessen donor site morbidity in graft procedures. Biodegradable scaffolds are created to contain cells; as new tissue develops; it gradually replaces the biodegradable scaffold to restore full bodily function. Due to their resemblance to extracellular matrices, high biocompatibility and biodegradability, natural and synthetic polymeric materials have been used extensively in bone tissue engineering. To adapt polymeric materials to the unique needs of bone regeneration, a range of approaches have been used to modify their characteristics. This review focused on current research on collagen and synthetic polymer-based scaffolds for tissue bioengineering and bone regeneration, such as polycaprolactone, poly(glycolic acid), poly(lactic-co-glycolic acid), and poly(lactic-acid-glycolic acid) (PCL). If we can better manage the interface between the material and the surrounding bone tissue, the next generation of biodegradable materials may benefit from our understanding of how cells interact with materials. Keywords Biomaterials · Tissue bioengineering · Bone rejuvenation

E. M. Jonathan Department of Chemistry, Benson Idahosa University, PMB 1100, Benin City, Edo State, Nigeria A. O. Ohifuemen · I. H. Ifijen (B) Department of Research Operations, Rubber Research Institute of Nigeria (RRIN), Km. 19, Benin-Sapele Road, Iyanomo, Edo State, Nigeria e-mail: [email protected] J. N. Jacob Department of Chemistry, University of Benin, PMB 1154, Benin City, Edo State, Nigeria A. Y. Isaac Department of Chemistry, University of Ilorin, P.M.B. 1515, Ilorin 240003, Nigeria © The Minerals, Metals & Materials Society 2023 The Minerals, Metals & Materials Society, TMS 2023 152nd Annual Meeting & Exhibition Supplemental Proceedings, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-031-22524-6_25

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Introduction Artificial scaffolds that imitate natural extracellular matrix (ECM), which offers an ideal environment for cell recruitment, proliferation, differentiation, and ultimately bone regeneration, are necessary for bone tissue engineering [1, 2]. Ideal scaffolds should not trigger immunological reactions and disintegrate in a controlled manner with harmless chemicals that can be eliminated by metabolism when confronted with complicated and sensitive biological systems [3, 4]. To encourage the growth of new bone tissues, biological substances must also be included. In order to establish an ideal milieu for cell functions and to sustain the flow of nutrients and metabolites, the macro- and micro-structures (such as porosity) of the scaffolds should also be carefully engineered [5]. There are numerous requirements for scaffold design in tissue engineering. Many of these are dynamic and are still not fully understood [6]. These scaffolds should additionally have adequate mechanical qualities to give the neo-tissues the required stress environment, having both mass and biocompatible deteriorated [7]. The scaffolds should also have the necessary surface chemistry and porosity for cell adhesion [7], as well as be porous and permeable to allow the passage of cells and nutrients. As scaffolds for bone regeneration, a variety of materials, including metals, bioactive ceramics and glasses, natural and synthetic polymers, and their composites, have been studied and used thus far [8, 9]. Numerous applications have previously employed polymeric materials and their composites [10–17]. Due to their favorable biocompatibility and biodegradability, biodegradable polymers have garnered the most attention among these applications for tissue bioengineering and bone rejuvenation [8, 9]. More notably, polymers have an extremely flexible design capacity, allowing their numerous features to be easily adjusted to match particular requirements by modifying their chemical structures and compositions [18]. For bone tissue regeneration, a wide variety of natural polymers, such as collagen, gelatin, and chitosan, as well as synthetic polymers, such as poly(lactic acid), poly(glycolic acid), and polycaprolactone (PCL), have been used. To improve their osteogenic performance, these materials are typically composited with one another or other inorganic materials, such as Hydroxyapatite (HA) [19, 20]. This review primarily looked at recent studies on collagen and synthetic polymer-based scaffolds for tissue bioengineering and bone regeneration, including polylactic acid (PLA), poly(glycolic acid), poly-lactic-co-glycolic acid (PLGA), and polycaprolactone (PCL).

Collagen-Based Scaffolds in Bone Tissue Engineering Collagen is an essential component of the natural bone matrix and is used for bone regeneration and biomimetic applications [21]. Compared to the relatively low bioactivity of biomimetic materials, collagen has sufficient flexibility, high biodegradability, and biocompatibility. Collagen can therefore be used in a variety of ways [21].

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Collagen can be obtained from a variety of sources and from different animals (such as mammals, marine organisms and invertebrates). Natural collagen has a low immunogenicity already, and chemical processing can further reduce it. Collagen regulates the activity of osteoblasts and osteoclasts through a number of signaling channels and assists in the healing of bone defects [22]. Collagen can now be used widely because to improvements in collagen extraction technology. Current research suggests that a variety of materials can be used to alter collagen-based biomimetic materials in order to enhance their biological qualities [22]. Flexible hydrogel and rigid scaffold are the two most often used kinds of collagen application. Alginate, chitosan, and hyaluronic acid are all biocompatible, hydrophilic, and biodegradable substances. By mixing chitosan, hyaluronic acid, and alginate with collagen in various ratios, collagen-based biomimetic materials can be created [23]. For instance, Becerra et al. (2022) used the solvent casting approach to create composite membranes made of chitosan, collagen, and hydroxyapatite [24]. Containing good hydroxyapatite dispersion in the organic matrix, membranes with micro and nanopores were produced. The thermal stability and thermal breakdown of the composites are improved by the addition of collagen and hydroxyapatite to chitosan. The highest cell adhesion was demonstrated by the membranes with the highest hydroxyapatite and collagen contents, and none of the manufactured membranes displayed any cytotoxicity, indicating that these materials have a significant potential for usage in tissue engineering applications. Additionally, hydroxyapatite (HA) and bioactive glass are anticipated to enhance the materials’ mechanical characteristics and structural stability. Rigid scaffolds are created by cyclic freeze-drying and bio-inspired mineralization, while hydrogels are often made by combining aqueous solutions and various cross-linking agents. Collagen-based hydrogel, which is suitable for osseointegration viscosity and rheology, was created by adjusting the types and proportions of various materials. The porous structure of collagen-based hydrogel allows them to exchange substances with blood, allowing cells to receive continuous nutrient supply. By combining native collagen from the jellyfish Rhizostoma pulmo with marine gelatin that has been functionalized with hydroxy-phenyl-propionic acid (HPA), Rigogliuso et al. (2020) created an injectable marine collagen-based hydrogel [25]. Due to the ability to enzymatically reticulate utilizing horseradish peroxidase (HPR) and H2 O2 , this biocompatible hydrogel formulation has the potential to trap cells inside, without harmful consequences, throughout the cross-linking process. Additionally, it permits modifying the hydrogel stiffness by changing the H2 O2 concentration without altering the concentration of polymer precursors. Following that, morphological analyses of cell phenotypic, GAG production, and cytoskeleton organization were used to assess the maintenance of differentiated chondrocytes in culture. Additionally, the enhancement of the chondrogenic gene expression program was supported by gene expression profiling of differentiation/dedifferentiation markers (Fig. 1). In order to use autologous chondrocytes in regenerative medicine procedures, this gives a viable technique for retaining the cellular phenotype in vitro in combination with the biochemical characteristics of marine collagen. Collagen-based scaffolds also have better compressive strength, stiffness, and pore structure when mixed with other materials, which can considerably enhance

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Fig. 1 Culturing in MCh does not alter cytoskeleton organization and NCs express markers of the differentiated state. Staining of NCs after 8 days of culture, respectively, within RTCh (a), MCh (b), and 2D (c) [25]

the efficacy of bone healing. Through a number of signaling pathways, bioactive substances, such as chemicals, cells, and growth factors, can encourage the osteogenesis and angiogenesis of scaffolds [26]. For instance, by activating the SMAD and MAPK pathways, the bone morphogenetic protein 2 (BMP-2) can encourage the differentiation of bone marrow stromal cells (BMSCs) into osteoblasts [27]. Hydrogel made of collagen is frequently utilized as a delivery system. The continual release of bioactive chemicals to the local part is made possible by the breakdown and diffusion of the gel. The release rate and breakdown rates of collagen-based hydrogel can both be adjusted by adjusting the proportions of various components [28]. Bioactive ingredients were loaded into collagen-based scaffolds using physical mixing and electrostatic adsorption to promote regional bone repair [29]. Numerous collagen complexes have so far been examined in vitro for bone repair [30]. To confirm the viability of these scaffolds, comprehensive in vivo tests are still lacking. In order to accomplish flawless bone regeneration, it is still difficult to develop composites that can meet all the necessary parameters, such as porosity, pore size, biocompatibility, mechanical integrity, structural stability, bone conductivity, and osteoinductivity [30]. The internal and external multi-layered complex structure of real bone, as well as the natural condition of bone regeneration, cannot yet be precisely replicated by any technology. The clinical success of composite collagen-based materials in bone regeneration is just around the corner thanks to the advancements in bioprinting technology, tissue engineering, and biomimetic mineralization.

Bone-Tissue Scaffolding Using Synthetic Polymers In order to allow for regenerated bone to replace the support lost from the scaffold, the delicate interplay between mechanical support and degradation time must be regulated because of the specific mechanical requirements of bone-tissue scaffolds [31]. For bone regeneration or osteoinductivity, a porosity of between 80 and 90% and a pore size greater than 300 m are desirable. By including osteoinductive, or growth,

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substances that can be released during deterioration, this may be improved [32]. Collagen, a polymer, and the inorganic ceramic apatite are the two main components of natural bone [33]. By simulating this natural environment with composite scaffolds made of both polymeric and inorganic phases, regeneration may be facilitated [34]. Several polymers and polymer composites have been used to create clinical-grade scaffolds that have been successful in bone regeneration and have led to the development of commercial products [35]. These scaffolds have the optimal characteristics for bone-tissue engineering applications. Aliphatic polyesters such polylactic acid (PLA), poly(glycolic acid) (PGA), poly-lactic-co-glycolic acid (PLGA), and polycaprolactone (PCL) have been used to the greatest extent due to receiving US FDA approval. Following a summary of specific research publications that accelerated commercial development, samples of various goods that are currently on the market are provided [36]. With biopolymers serving as viable carrier options in addition to their application as scaffolds, suture threads, screws, pins, and plates for orthopedic procedures, there is growing interest in creating long-lasting medicine formulations for horses. Focusing on the prolonged biocompatibility and biodegradation of PLA produced by hot pressing at 180 °C, Carvalho et al. Six samples were implanted subcutaneously on the lateral surface of the neck of one horse [37]. For 24–57 weeks, the polymers stayed inside the body. The mechanical nociceptive threshold (MNT), plasma fibrinogen, and physical examination were carried out. The materials were taken out for histochemical analysis using hematoxylin–eosin and scanning electron microscopy after 24, 28, 34, 38, and 57 weeks (SEM). No significant clinical changes occurred. MNT reduced following the implantation operation and then resumed normal levels after 48 h. Histopathologic analysis up to 38 weeks revealed a foreign body response. No polymer or fibrotic capsules were seen at 57 weeks (Fig. 2). With an increase in the median pore diameter, SEM surface roughness indicated a biodegradation process. The polymer could not be found 57 weeks after implantation, just like in the histological evaluation. PLA degraded in a biocompatible manner, and these results may help guide future biomedical research. A major advancement in bone tissue engineering has been the development of three-dimensional (3D) printing technology, which is renowned for its exceptional customizability. Growth agents, like bone morphometric protein 2 (BMP-2),

Fig. 2 SEM micrographs of skin fragments with PLA implanted in one horse a 34 weeks following implantation; b 38 weeks following implantation; and c 57 weeks following implantation. Dotted red lines delimits the area of the implants [37]

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whose effects on bone regeneration have been extensively researched, were typically included to the 3D printed scaffolds. Cha et a. In a rat model for calvarial defects and an ectopic ossification (EO) model, (2021) examined the impact of a different shape of PLA cage/Biogel scaffold as a carrier of BMP-2 [38]. With the use of BMP-2, gelatin- and alginate-based Biogel, and a simple commercial 3D printer, the PLA scaffold was created and used to stimulate bone repair. A PLA scaffold, a PLA scaffold with Biogel, a PLA scaffold filled with BMP-2, and a PLA scaffold with both Biogel and BMP-2 were examined in vitro and in vivo, respectively, in the experimental groups. If a statistically significant difference exists between groups, it was found using one-way ANOVA with Bonferroni post-hoc analysis. The in vitro results demonstrated that the cage/Biogel scaffold released BMP-2 in a sustained slow-release pattern after an initial burst release (Fig. 3). At least 14 days passed before the released BMP-2 lost its osteoinductivity. According to the in vivo findings, in both the rat calvarial defect model and the EO model, the cage/Biogel/BMP-2 group had the highest bone regeneration. Particularly, the EO model’s implanted sites exhibited more frequent bone regeneration, indicating that the cage and Biogel had a remarkable capacity to regulate the morphology of regenerated bone. In summary, the 3D printed PLA cage/Biogel scaffold system was demonstrated to be an effective BMP-2 carrier that caused considerable bone regeneration and generated bone growth in accordance with the planned shape.

Fig. 3 In vivo result of rat calvaria. a PLA cage/BMP-2 group and PLA cage/Biogel/BMP-2 group both showed significant bone regeneration. Scale bar: 2 mm. b Cross-sectional images of rat calvaria. Both the groups with BMP-2 showed bone regeneration that bridged both edges of defect. Scale bar: 2 mm. c Histology sections of rat calvaria. The histology sections confirmed the results of micro-CT. Groups: 1, PLA cage group, N = 9; 2, PLA cage/Biogel group, N = 11; 3, PLA cage/BMP-2 group, N = 12; 4, PLA cage/Biogel/BMP-2 group, N = 9 [38]

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Inorganic material has been added to scaffolds in several research areas to promote biomimicry and bone tissue regeneration. A PGA/hydroxyapatite composite has successfully improved bone regeneration capacity in vivo. A key component of bone grafts for the regeneration of hard tissues is hydroxyapatite (HAp). Sintered HAp, however, has poor mechanical and formability characteristics. To study physicochemical qualities and bone regeneration ability, Yeo et al. (2020) 3D-printed porous PGA/HAp composite scaffolds of various mixing ratios utilizing computer-aided modeling with poly(glycolic acid) (PGA) and Hap and printing settings [39]. A 400 m pore size was used to generate PGA scaffolds that included HAp nanoparticles. The compressive strength, osteogenesis, mineralization, and biodegradation of PGA/HAp scaffolds containing 12.5 wt% HAp were all quite high. 8 weeks following surgery, the PGA/HAp group in in vivo animal tests showed higher bone mineral density and 47% bone regeneration (Fig. 4). The PGA/HAp composite scaffolds were encircled by thick osseous tissue formations, as seen in the enhanced bone development. A workable solution to encourage patient-specific bone regeneration might be 3D-printed PGA/HAp scaffolds. By comparing PLLA/PCL (poly-L-lactic acid/polycaprolactone) with PLLA scaffolds used in bone regeneration, Weng et al. [40] looked at the viability of PLLA/PCL. To test the implants’ capacity to remodel bone, 30 mature and healthy New Zealand rabbits with a 15 mm distal ulna defect model were chosen and then randomly divided into three groups: group A (repaired with PLLA scaffold), group B (repaired with PLLA/PCL scaffold), and group C (no scaffold). Micro-CT analysis showed that group B in three groups had the best potential to regenerate bone. In group B, the surgical site’s bone mineral density was higher than in group A but lower than in group C. While this was going on, both groups A and B’s bone regeneration showed symptoms of inflammation due to the scaffolds’ initial rapid breakdown. Overall, PLLA/PCL scaffolds in vivo initially disintegrate quickly and were more effective at repairing bone defects in New Zealand rabbits than PLLA. Further studies were required to optimize the composite for bone regeneration due to the poor mineral density of new bone and the quick breakdown of the scaffolds. Poly lactic acid (PLA) and poly glycolic acid are copolymers that are used to make polyester (PGA). It is one of the best-defined biomaterials for enhancing bone regeneration that is currently available. The biodegradability of poly(lacticco-glycolic acid) (PLGA) makes it one of the most popular biopolymers for tissue regeneration. However, there are significant clinical issues since the byproducts of PLGA make the implant site’s environment acidic. Osteogenesis, angiogenesis, and the control of excessive osteoclastogenesis are key elements in bone repair. To enhance anti-inflammatory capacity and osteoconductivity, Kim et al. (2021) mixed the porous PLGA (P) scaffold with magnesium hydroxide (MH, M) and bone-extracellular matrix (bECM, E) [41]. Also included in the preexisting PME scaffold was the bioactive polydeoxyribonucleotide (PDRN, P). Due to the interaction of the PDRN with the adenosine A2A receptor agonist, which up-regulates the expression of vascular endothelial growth factor (VEGF) and down-regulates inflammatory cytokines, the prepared PMEP scaffold has pro-osteogenic and proangiogenic effects as well as inhibits osteoclast activity. For human bone marrow

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Fig. 4 Micro-CT images of the top surfaces (A1–C1) and perpendicular and horizontal sections (A2–C2) of control (a), PGA (b), and PGA/HAp 12.5 wt% composite (c) scaffold groups 4 and 8 weeks after surgery [39]

mesenchymal stem cells (hBMSCs) adhesion, proliferation, and osteogenic differentiation in vitro, the PMEP scaffold has better biological capabilities. Additionally, hBMSCs’ gene expressions associated to angiogenesis and osteogenesis increased on the PMEP scaffold, while inflammatory factors reduced. In summary, it offers a promising method and clinically viable candidate for regenerating bone tissue and fixing bone abnormalities. Using a poly-lactic-co-glycolic acid (PLGA) electrospun scaffold with added silica nanoparticles, Yang et al. (2018) demonstrated that this particular scaffold enhances osteogenic differentiation in vitro by increasing bone nodule formation and collagen secretion. In a rat model, a different PLGA composite functionalized with a peptide similar to the osteoinductive bone morphogenetic protein 2 (BMP2) was used to successfully repair a critical-sized cranial lesion [42]. The PLGA composite employed in this study is an appealing scaffold for use in human bonetissue engineering due to its mechanical similarities and the demonstration that it may

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induce osteogenic differentiation as well as bone formation in vivo. In hip replacement surgery, PLA with a metal core has been employed as a biodegradable bone graft, demonstrating that it is mechanically stable and biocompatible for effective bone regeneration [43].

Conclusion This study mainly focused on current research on collagen and synthetic polymerbased scaffolds for bone regeneration and tissue bioengineering, such as polycaprolactone, poly(glycolic acid), poly(lactic acid), and poly(glycolic acid) (PCL). From an engineering and biological point of view, the creation of biomaterials for bone regeneration devices and prostheses is a problem. Since their biodegradable nature permits avoiding the second operation and reduction in the pain and cost for patients, degradable materials for bone repair and regeneration are actively sought after and generate a great deal of interest in the field of biomaterials research. Biodegradable materials made of natural and manmade polymers are already used in healthcare settings. Diverse biomaterials have different mechanical characteristics, biological behaviors, and biodegradation mechanisms. This field of study has particular difficulties because of the special biocompatible and biodegradable needs of tissue scaffolds and the complexity of their interactions within the human body. A scaffold must not only perform and decay correctly, but it must also do so for the proper tissue type, as each has specific mechanical and morphological needs. The materials used to make scaffolds must meet a number of requirements, including having inherent biofunctionality and the right chemistry to encourage molecular biorecognition by cells and promote proliferation, adhesion, and activation. It is suggested that no single material possesses all the ideal qualities for a tissue replacement, notwithstanding the benefits and drawbacks of any unique material. Instead, tissue substitutes that meet all clinical requirements, such as the precise size and type of wound, the age of the patient, and the available preparation method, can be made using a scaffold made from a composite containing more than one natural or synthetic biopolymer, or both, depending on the situation.

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The Effects of Thermal Treatment on the Properties and Performance of Hot Extruded Zn-Based Bioresorbable Alloy for Vascular Stenting Applications Henry D. Summers, Morteza S. Ardakani, and Jaroslaw W. Drelich

Abstract A new series of zinc alloys is in development for bioresorbable stent implantation to alleviate the current materials’ long-term complications. Characterization and optimization of the microstructure and corresponding mechanical properties during manufacturing stages will help researchers meet the required values. In this study, the effect of hot extrusion on the Zn-Ag-Mn-Cu-Zr-Ti alloy is characterized. Additionally, thermal treatments at 390 °C for 15, 25, 40, 60, and 120 min were performed to evaluate the effect of intermetallic phase fractions on the corrosion resistance and mechanical strength. Quantitative analysis of X-ray diffraction data demonstrates that the fractions of the MnZn13 , ZrZn22 , and Zn0.75 Ag0.15 Mn0.10 intermetallic phases decrease as the thermal treatment time increases. Corrosion tests reveal a reduction in the corrosion rate of the extruded alloy after thermal treatment. The results of uniaxial compression tests and tensile tests show lower strength and higher ductility in all heat-treated conditions compared with the as-extruded condition. Keywords Zinc alloys · Biodegradable stent · Corrosion behavior · Mechanical properties

Introduction Bioresorbable metals have received significant attention over the last several decades as candidates for temporary stents to alleviate long-term complications associated with traditionally used materials. Stent implantation is most commonly performed with the use of permanent metallic stents, composed of austenitic stainless steel, Ti-Ni alloys, and Co-Cr alloys [1]. These materials are suitable candidates for stenting due to their high strength and resistance to corrosion, however, they present challenges H. D. Summers · M. S. Ardakani · J. W. Drelich (B) Department of Materials Science and Engineering, Michigan Technological University, Houghton, MI 49931, USA e-mail: [email protected] © The Minerals, Metals & Materials Society 2023 The Minerals, Metals & Materials Society, TMS 2023 152nd Annual Meeting & Exhibition Supplemental Proceedings, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-031-22524-6_26

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such as inflammatory reactions, late stent thrombosis, and in-stent restenosis [1, 2]. Additionally, permanent stents remain in the body for their lifetime in spite of the fact that they are needed only for several months to complete their task as vascular scaffolding. Bioresorbable metallic stents will be broken down, metabolized, and harmlessly excreted by the body after providing mechanical support to widened blood vessels for 4–6 months. The conditions a stent undergoes during implantation and service require not only high strength and ductility, but its corrosion rate must also be optimized to ensure premature degradation does not occur, without hindering its ability to degrade into the body after completion of its role as arterial scaffolding. Over the last several decades, exploratory and pre-clinical research in this area has primarily focused on bioresorbable stents made of either polymers or magnesium (Mg) alloys [1, 3]. Polymeric stents, despite their predictable degradation behavior and acceptable biocompatibility, require a greater strut thickness than metal stents due to their poor mechanical properties and low radial strength [4, 5]. This reduces the flexibility of the stents and restricts access to narrower vessels. Although biocompatible Mg-based alloys have better mechanical properties, due to the rapid corrosion behavior of magnesium, they have been found to be inadequate alternatives for many applications [6, 7]. Zinc (Zn)-based alloys have been introduced as desirable candidates for stenting due to their excellent biocompatibility and corrosion uniformity [8, 9]. Although the ultimate tensile strength of pure Zn is far lower than the benchmark required for vascular stenting, nontoxic low-content alloying additions such as silver (Ag), copper (Cu), and titanium (Ti) have been proven to improve its strength, particularly when paired with thermomechanical processing that promotes refinement of microstructure such as extrusion [9]. Additionally, manganese (Mn) has been shown to drastically improve the elongation of pure Zn [10]. The primary purpose of this study is to investigate the microstructural evolution that results from hot extrusion and thermal treatment of a novel senary Zn-based alloy containing Ag, Cu, Mn, Zr, and Ti. Samples of this alloy in the form of ingots and hot extruded rods are cross-sectioned and analyzed with electron microscopy for typical grain sizes and phase identification, including fine precipitates, produced in the material at manufacturing conditions. The rods are also subjected to thermal treatments in an effort to refine their microstructure and optimize their mechanical and corrosion properties. Mechanical testing is used to characterize the effects of different thermal treatments on the strength and ductility of the alloy and electrochemical techniques are performed to provide insight into the corrosion behavior before and after treatment. The sections that follow describe the experimental procedures, results, and discussion of the results, and the conclusions that can be drawn from this study.

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Experimental Procedures Materials and Processing Here, the response to the thermal treatment of a novel Zn-2.77Ag-0.76Mn-0.50Cu0.11Zr-0.03Ti alloy is investigated. A cast ingot, 500 mm in diameter, and an extruded rod 12.6 mm in diameter were cast and extruded by Fort Wayne Metals Research Products, LLC. Heat treatment of the extruded rod was conducted at 390 °C for the following durations: 10, 15, 25, 40, 60, and 120 min.

Microstructural Characterization Samples of the as-cast ingot and extruded rod, in both the as-extruded and heattreated conditions, were cross-sectioned (perpendicular to the direction of extrusion in the case of the rod) and mounted in epoxy. Samples were ground and polished following standard mechanical polishing procedure and etched in a 3% nital solution. Backscattered electron (BSE) scanning electron microscopy (SEM) was used to observe the microstructural evolution after the various thermal treatments. Grain size distributions were obtained from the SEM images using the Olympus Stream image analysis software (version 2.4.2). X-ray diffraction (XRD) was used to identify intermetallic phases present within the microstructure of the material and to evaluate the quantitative changes in the content of these phases resulting from the various heat treatment durations. Samples were prepared for XRD with the same process of grinding and polishing previously described, however, the final stage of polishing and subsequent etching was repeated a total of three times before breaking the samples out of the epoxy. The data were collected with an XDS-2000 θ-θ diffractometer with CuKα radiation (Kα = 1.540562 Å) operating at −45 kV and 35 mA, from 2θ = 20° to 90° with a 0.02° step size. MDI JADE software (version 8.5) was used for both peak identification and quantitative phase analysis.

Mechanical Testing Tensile bars and cylindrical compression test samples were machined from the extruded rod with the long axis parallel to the direction of extrusion. The tensile bars were machined to a gauge length of 16 mm and a diameter of 4 mm, and the compression samples to a height of 10 mm and diameter of 4 mm. Tensile bars were heat-treated at 390 °C for durations of 10, 15, and 25 min and the compression samples were heat-treated at the same temperature for 15, 25, 40, 60, and 120 min. As-extruded samples were tested in both experiments as well. Both the tensile and

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compression tests were performed using an MTS Instron 4206 load frame at room temperature with an initial strain rate of 1 × 10–3 s−1 in accordance with ASTM E8-04 [11] and ASTM E9-19 [12], respectively. Due to the limited supply of the extruded rod available, only one tensile test was performed for each of the heat treatment conditions. Standard deviation was calculated for the compression tests from a population of two experiments, however, only one trial was performed for the 60-min and 120-min heat treatment conditions due to material supply constraints.

Corrosion Testing The corrosion resistance of the extruded rod was characterized before and after the 15-min heat treatment by potentiodynamic corrosion testing in a three-electrode cell using a Princeton Applied Research PARSTAT 4000 potentiostat/galvanostat measurement system in accordance with ASTM G59-97 [13]. The tests were carried out in a modified Hank’s salt solution with a scanning rate of 0.166 mV/s and an applied potential ranging from −1.5 to −0.2 mV, based on previous studies [14]. The test solution was prepared through the addition of 9.8 g Hank’s balanced salts (H1387) and 0.35 g NaHCO3 to 1 L of distilled water. This solution was brought to a pH of 7.4 through the addition of 1 M HCl or 1 M NaOH as necessary and held at a temperature of 37 ± 2 °C during testing. The samples were mounted in epoxy and polished to 0.25 μm. After the final polishing step, the samples were washed in an ultrasonic bath of ethanol for five minutes and then tested immediately after drying to mitigate surface oxidation. The corrosion rate was calculated from the corrosion current density (icorr ) obtained with Tafel extrapolation using CR = 3.27 ∗ 10−3 ∗

i corr ∗ EW ρ

(1)

where CR is the corrosion rate (mm/year), icorr is the corrosion current density (μA/cm2 ), EW is the equivalent weight, which is 32.6 for pure zinc, and ρ is density (g/cm3 ). Corrosion rates are reported as the mean values with standard deviation calculated from 3 experiments for each sample condition.

Results and Discussion Microstructural Characterization The microstructures of the Zn-Ag-Mn-Cu-Zr-Ti alloy in the as-extruded and heattreated conditions are displayed in Fig. 1, along with the grain size distributions and average grain sizes. The average grain size for the as-cast ingot was found to be 70

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± 40 μm. Substantial refinement of grain size is observed from the as-cast ingot to the as-extruded rod (a reduction in average grain size of approximately 87% from hot extrusion). Heat treatment of the extruded rod results in increasing average grain size as the duration is extended. After 120 min at 390 °C, the average grain diameter increased 16% from the as-extruded state. Evaluation of the microstructural evolution from extrusion and heat treatment also revealed the presence of three different intermetallic phases using energy dispersive spectroscopy (EDS). EDS shows that there are distinct phases rich in manganese,

¯ for Fig. 1 SEM-BSE images of microstructure, grain size distributions, and average grain size ( D) the extruded rod in the a as-extruded state and after heat treatment durations of b 15 min, c 25 min, d 40 min, e 60 min, and f 120 min

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Fig. 2 a SEM-BSE image of as-extruded rod microstructure with green, blue, and purple arrows corresponding to intermetallic precipitates rich in Mn, Zr, and Ag, respectively, and b EDS spectra of these phases

zirconium, and a combination of manganese and silver. Silver appears to be present to some extent in the EDS spectra of all identified phases, likely due to its high weight fraction relative to other alloying elements, and its solubility in the zinc matrix. Intermetallic phases rich in Mn, Zr, and Ag are displayed in Fig. 2 with green, blue, and purple arrows, respectively. Analysis of X-ray diffraction data (Fig. 3a) reveals two intermetallic phases in the as-cast ingot, and three in the extruded rod (in both as-extruded and heat-treated states). The intermetallic phases ZrZn22 and Zn0.75 Ag0.15 Mn0.10 were identified in both the ingot and all the rod samples. Additionally, the MnZn13 intermetallic phase was identified in the as-extruded and heat-treated rod samples. Figure 3b displays the weight fractions of each of the three intermetallic phases plotted as a function of heat treatment duration. The MnZn13 phase is the most prevalent intermetallic prior to heat treatment but is substantially reduced after heat treatment. This phase decreases from 12.4 wt.% to 0.8 wt.% after 120 min. The content of the ZrZn22 intermetallic phase decreases consistently throughout the different treatment durations until the 120-min heat treatment, at which point its concentration is nearly doubled. This indicates that the weight fraction of this phase begins to increase sometime after 60 min. The Zn0.75 Ag0.15 Mn0.10 phase initially decreases but begins to increase after 40 min and its final concentration is just below its concentration prior to any heat treatment.

Mechanical Properties The mechanical properties obtained from tensile and compression testing, including elongation to fracture (EL), ultimate tensile stress (UTS), tensile and compressive yield stress (YS), and elastic modulus (E), are summarized in Table 1, and the experimental curves are presented in Fig. 4. The results of the tensile testing demonstrate that the 390 °C heat treatment substantially increases the elongation to fracture of

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Fig. 3 a XRD spectra and b phase weight fraction data for rod in as-extruded and heat-treated conditions

the alloy at the cost of reduced strength. The alloy in the as-extruded state exhibits a UTS that meets the benchmark of 300 MPa that is required for materials in stent applications established by [8], but it does not meet the required 18–20% elongation to fracture. The three heat treatments do not display a significant difference in their mechanical properties, and while all their elongations at fracture meet the benchmark range, all UTS values fall short of the required value because of the increased grain size induced by the thermal treatment. Similarly, the compression tests show a decrease in strength and increased ductility in all heat-treated conditions when compared with the as-extruded state. The results of mechanical testing suggest that the heat treatment temperature of 390 °C is too high for this series of zinc alloys. The decrease in strength is also correlated with decreased concentration of the ZrZn22 and MnZn13 phases, indicating the strengthening effects of these two intermetallics. Table 1 Elongation to fracture (EL), ultimate tensile stress (UTS), and yield stress (YS) obtained from tensile testing, and YS and elastic modulus (E) with standard deviation obtained from compression testing Sample condition EL (%) UTS (MPa) Tensile YS (MPa) Compressive YS E (MPa) (MPa) 7.2

341

296

327 ± 1

6,200 ± 500

10 min, 390 °C

23.7

253

183

–

–

15 min, 390 °C

23.8

251

185

252 ± 2

5,960 ± 90

25 min, 390 °C

25.7

249

170

219 ± 4

6,000 ± 200

40 min, 390 °C

–

–

–

218 ± 3

6,000 ± 200

As-extruded

60 min, 390 °C

–

–

–

229

5,040

120 min, 390 °C

–

–

–

200

4,270

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Fig. 4 Engineering stress versus strain a tensile and b compressive curves for as-extruded and heat-treated rod samples

Corrosion Properties The corrosion resistance of the as-extruded and heat-treated alloy was evaluated with potentiodynamic corrosion measurements. A reduction in the corrosion rate of roughly 71% was observed from the as-extruded state (average corrosion rate with a standard deviation of 0.7 ± 0.1 mm/year) to the 15-min heat treatment (average corrosion rate with a standard deviation of 0.20 ± 0.02 mm/year). The ideal corrosion rate of a degradable stent must be less than 0.02 mm/year in order to maintain mechanical integrity for the required 4–6 months of service [9, 14, 15]. The corrosion rates of the extruded rod both before and after heat treatment surpass this rate by an order of magnitude. The corrosion rate of this alloy appears to be substantially improved by thermal treatment, potentially related to the decreased content of the three intermetallic phases. The presence of intermetallics has been demonstrated to have a negative impact on the corrosion rates in magnesium alloys due to the enhancement of micro-galvanic corrosion [16]. Similar mechanisms may be responsible for the improved corrosion rate observed in this study, as the content of all intermetallics in the heat-treated specimen was lower when compared to the as-extruded.

Conclusions In this study, the effectiveness of various heat treatment durations on a novel Zn-AgMn-Cu-Zr-Ti alloy was investigated. This characterization included analysis of the microstructural evolution occurring from both hot extrusion and heat treatment, as well as an evaluation of mechanical and corrosive properties before and after heat treatment. Based on the results that were obtained, the following conclusions can be made: • Hot extrusion results in significant refinement of microstructure. The average grain size of 70 ± 40 μm for the as-cast ingot was reduced by 87%.

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• Increasing heat treatment duration at 390 °C beyond 10 min leads to a 10–20% increase in average grain sizes in the extruded rod. • The alloy experiences ~50 MPa decrease in UTS as a result of thermal treatment at 390 °C but the ductility is improved from ~7 to 24–26%. • Corrosion rate was found to be reduced from approximately 0.7 to 0.2 mm/year after 15-min heat treatment. • The results of this study indicate that the temperature for heat treatment of the ZnAg-Mn-Cu-Zr-Ti alloy could be reduced below 390 °C. Further examination of the response to heat treatment at lower temperatures is required for the optimization of alloy processing for stent applications. Acknowledgements U.S. National Institute of Health—National Heart, Lung, and Blood Institute grant 1R01HL144739-01A1 is acknowledged for funding this work.

References 1. Hanawa T (2009) Materials for metallic stents. J Artif Organs 12(2):73–79. https://doi.org/10. 1007/s10047-008-0456-x 2. Heublein B (2003) Biocorrosion of magnesium alloys: a new principle in cardiovascular implant technology? Heart 89(6):651–656. https://doi.org/10.1136/heart.89.6.651 3. Grube E et al (2004) Six- and twelve-month results from first human experience using everolimus-eluting stents with bioabsorbable polymer. Circulation 109(18):2168–2171. https:// doi.org/10.1161/01.CIR.0000128850.84227.FD 4. Bünger CM et al (2007) Sirolimus-eluting biodegradable poly-l-lactide stent for peripheral vascular application: a preliminary study in porcine carotid arteries. J Surg Res 139(1):77–82. https://doi.org/10.1016/j.jss.2006.07.035 5. Im SH, Jung Y, Kim SH (2017) Current status and future direction of biodegradable metallic and polymeric vascular scaffolds for next-generation stents. Acta Biomater 60:3–22. https:// doi.org/10.1016/j.actbio.2017.07.019 6. Bünger CM et al (2007) A biodegradable stent based on poly(L-Lactide) and poly(4hydroxybutyrate) for peripheral vascular application: preliminary experience in the pig. J Endovasc Ther 14(5):725–733. https://doi.org/10.1177/152660280701400518 7. Grabow N et al (2007) A biodegradable slotted tube stent based on poly(l-lactide) and poly(4hydroxybutyrate) for rapid balloon-expansion. Ann Biomed Eng 35(12):2031–2038. https:// doi.org/10.1007/s10439-007-9376-9 8. Bowen PK et al (2016) Biodegradable metals for cardiovascular stents: from clinical concerns to recent Zn-alloys. Adv Healthc Mater 5(10):1121–1140. https://doi.org/10.1002/adhm.201 501019 9. Mostaed E, Sikora- M, Drelich JW, Vedani M (2018) Zinc-based alloys for degradable vascular stent applications. Acta Biomater 71:1–23. https://doi.org/10.1016/j.actbio.2018.03.005 10. Sun S, Ren Y, Wang L, Yang B, Li H, Qin G (2017) Abnormal effect of Mn addition on the mechanical properties of as-extruded Zn alloys. Mater Sci Eng A 701:129–133. https://doi.org/ 10.1016/j.msea.2017.06.037 11. ASTM (2004) Standard test methods for tension testing of metallic materials 12. ASTM (2019) Standard test methods of compression testing of metallic materials at room temperature 13. ASTM (2003) Standard test method for conducting potentiodynamic polarization resistance measurements

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14. Mostaed E et al (2016) Novel Zn-based alloys for biodegradable stent applications: design, development and in vitro degradation. J Mech Behav Biomed Mater 60:581–602. https://doi. org/10.1016/j.jmbbm.2016.03.018 15. Bowen PK, Drelich J, Goldman J (2013) Zinc exhibits ideal physiological corrosion behavior for bioabsorbable stents. Adv Mater 25(18):2577–2582. https://doi.org/10.1002/adma.201300226 16. Zhang Y et al (2021) Influence of the amount of intermetallics on the degradation of Mg-Nd alloys under physiological conditions. Acta Biomater 121:695–712. https://doi.org/10.1016/j. actbio.2020.11.050

ZnO-NPs-Coated Implants with Osteogenic Properties for Enhanced Osseointegration Kate E. Mokobia, Ikhazuagbe H. Ifijen, and Esther U. Ikhuoria

Abstract The failure of orthopedic implants due to prosthesis-associated infections and aseptic loosening emphasizes the pressing need to enhance their antibacterial capacity and osseointegration. The biomedical field has extensively studied zinc oxide nanoparticles (ZnO-NPs). ZnO-NP-coated implants have drawn a lot of interest for their increased osseointegration due to their low toxicity, biocompatibility, high selectivity, good biological functions, and antibacterial and osteogenic properties. The use of ZnO-NPs in covering implants for better osseointegration has undergone significant advances, which were examined in this review. According to studies, ZnO-NPs coating on metal surfaces enhanced osteogenesis and soft tissue integration, which improved implant fixation. Additionally, osteoconductive nanoparticles create a chemical interaction with bone in order to achieve a strong biological attachment for implants. Implants with ZnO-NPs applied to their surfaces have a lower risk of infection, which unquestionably leads to better clinical results. Keywords Zinc oxide · Nanotechnology · Implant · Osseointegration

Introduction Nanostructures are a component of the industrial revolution era that has produced an explosion of hundreds of new products. These products have a variety of unique properties, such as increased strength, chemical reactivity, or conductivity due to K. E. Mokobia Department of Science Laboratory Technology, Delta State Polytechnic, Otefe-Oghara, Delta State, Nigeria I. H. Ifijen (B) Department of Research Operations, Rubber Research Institute of Nigeria, Iyanomo, Benin City, Nigeria e-mail: [email protected]; [email protected] E. U. Ikhuoria Department of Chemistry, University of Benin, Benin City, Edo State, Nigeria © The Minerals, Metals & Materials Society 2023 The Minerals, Metals & Materials Society, TMS 2023 152nd Annual Meeting & Exhibition Supplemental Proceedings, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-031-22524-6_27

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high surface to volume ratios, altered electronic bandgap energy (optical properties), and tailored functionality [1–19]. The discipline of nanotechnology has focused a lot of interest on biomedical science topics including orthopedic and dental implants also known as osseointegration. Implants go through a process known as osseointegration that causes them to become organically attached after being surgically inserted into intended joints. Such a fixation is believed to be crucial for implant supported prostheses to be successful over the long term [20]. The osseointegration of the implant is significantly influenced by the properties of the surrounding bone, which in turn impacts the shape and contour of the soft tissues above and, ultimately, the look. To achieve satisfactory functional and aesthetically pleasing treatment outcomes, it is important to carefully assess the biologic principles of the peri-implant soft and hard tissues. Minutes after implantation, microbes can begin to colonize the area with bacteria [21]. The fixture-interface is susceptible to microbial growth, which creates a bacterial reservoir, inflames the soft tissue adjacent to the fixture-abutment junction, and causes the implants to fail [20, 21]. This occurs despite considerable attempts to reduce such infections. Although antibiotics are occasionally employed in medical devices to help prevent infections, microbial resistance is continually increasing because of a number of limitations, such as a decreased release in the later phases after implantation [22]. This difficulty has prompted numerous studies on how to decrease bacterial penetration by improving the accuracy and stability of the jointed pieces by the manufacturing of extremely high-precision antibacterial mechanical parts. In this regard, antimicrobial zinc oxide nanoparticles (NPs) have attracted a lot of interest as prospective coating materials for future implants [23]. The molecular weight of zinc oxide nanoparticle (ZnO-NP), a white, odorless powder, is 81.38 g/mol. According to the FDA, it is a chemical that is generally acknowledged as safe (GRAS) [24]. Its wide range of uses in orthopedic and dental implants for better osseointegration can be attributed to their special optical, magnetic, morphological, electrical, catalytic, mechanical, photochemical, and antimicrobial properties, all of which can be easily modified to meet specific needs by changing the size, doping with additional compounds, or modifying the conditions of synthesis. The desired properties are better as particle size decreases [25]. These biological characteristics provide ZnO-NPs a lot of potential for use in orthopedic applications. Studies have shown that implants made of different materials, such as metals and polymers adorned with ZnO-NPs by doping or coating, exhibit improved osteogenic and antibacterial properties [25]. The “Trojan Horse effect”, a recent idea that explains this, contends that the acidic lysosomal environment encourages nanoparticle disintegration, which leads to the conversion of core metals to ions and the release of harmful chemicals, which in turn prevents cell division [24]. Other methods of their antimicrobial action include creating reactive oxygen species (ROS), altering the local microenvironments close to the microorganisms, or improving the solubility of these nanoparticles [26]. This can result in interactions with the microorganisms’ –SH group of enzymes and

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malfunctioning organelles that denaturate proteins and damage DNA [27, 28]. Therefore, implant materials coated with ZnO-NPs may have advantageous osteogenic and antimicrobial properties. In order to better understand the toxicity, osteogenic, and antibacterial properties of metallic implants coated with ZnO-NPs for potential medical device applications, this review examined the most recent research findings.

Osteogenic Mechanism of ZnO-NPs Zn2+ release is the cause of the osteogenic characteristics of ZnO-NPs. Zn2+ plays a role in many enzymes’ catalytic activation processes as a necessary trace element [29]. The growth, mineralization, and production of bones can all be encouraged by Zn2+ [30]. When in contact with water molecules, ZnO-NPs create Zn-OH groups, which serve as apatite nuclei and quicken mineralization [31]. The osteocalcin gene area is also promoted for expression by Zn2+ through activation of the mitogenactivated protein kinase pathway. Furthermore, cells overexposed to Zn2+ may upregulate the expression of Zn2+ transporters such ZIP1, and its overexpression might boost Runx2 expression, hence increasing bone formation [32]. Zn2+ was shown to impede osteoclast differentiation by Yamaguchi et al. in the range of 10–250 mol/L [33]. They drew attention to the fact that Zn2+ inhibits nuclear factor-B signaling by decreasing tumor necrosis factor levels in vivo. This inhibiting impact reduces the production of osteoclasts and increases osteoblast proliferation, resulting in significantly increased osteogenesis as a result. In their study of the RUNX2 upstream signaling pathway, Park et al. discovered that Zn2+ can activate protein kinase A signaling by increasing the level of cyclic adenosine monophosphate (cAMP), which in turn enhances the nuclear translocation of phosphorylated cAMP response element-binding protein and increases RUNX2 expression [34]. The mesenchymal stem cells (MSCs) and osteoblasts on the implant surface can be more active and differentiate. ZnO-NPs can help. These characteristics are attributed to the increased modified implant surface area, which might have more active sites and a propensity to absorb more proteins [35]. Additionally, research has demonstrated that the physical characteristics of the scaffold are crucial and can influence bone regeneration [36]. ZnO-NPs changes can also improve these physical characteristics. Different ratios of ZnO-NPs and HA were combined by Sahmani et al. who then sintered the resultant mixture and covered the scaffolds with gelatin-ibuprofen [37]. They discovered that the rate of deterioration increased with increasing ZnO-NP weight percentage. According to Li et al. hypothesis’s doping ZnO-NPs into beta-phase poly (vinylidene fluoride) can result in a scaffold with Young’s modulus that is akin to that of bone, which can facilitate bone healing in vivo [38]. But according to a number of studies, doping ZnO-NPs would weaken the material’s hydrophilicity and increase the water contact angle, which would diminish osteoblast adhesion [36].

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Osteogenic Properties of ZnO-NP-Modified Implants Dental restorative and prosthodontic applications, implantable biomedical nanosensors, and other biomedical uses of ZnO nanostructures have all been made use of [38]. ZnO nanostructures’ biodegradability, biocompatibility, and biosafety have all been studied at the cellular level [39]. Additionally, substantial research was done on the topographic effect and cytotoxicity of ZnO nanorods to regulate cell adhesion and macrophage responses for tissue engineering applications [40]. For bone tissue engineering applications, Park et al. (2010) examined the topographic influence of ZnO nanoflowers on MC3T3-E1 osteoblast development and osseointegration [39]. With the aid of a photoresist layer and a solution-based hydrothermal growth technique, single crystalline ZnO nanoflowers with controlled inter-distance between nanoflower structures were created on silicone (Si) substrates. Analysis of the DNA content, alkaline phosphatase (ALP) activity, and lamellipodia and filopodia production of osteoblasts grown on ZnO nanoflowers revealed their physicochemical properties. For tissue engineering applications, a novel method of fabricating nanoflower structures on biomaterial surfaces was employed. The human body, as is well known, is made up of many different types of cells, including endothelial cells that line the surfaces of internal and external organs, fibroblasts that support other body tissues, muscle cells that are specifically designed for contraction, nerve cells that produce electrical signals and secrete neurotransmitters, osteoblast cells for skeletal structures, and so on. Their cellular sizes range from several to 100 m, and they have a variety of morphologies. For a variety of tissue engineering applications using various cell types, the control of inter-distance between nanoflower structures would be advantageous. SEM was used to examine the cell morphology that had spread and adhered to ZnO film and ZnO nanoflowers. On ZnO nanoflowers rather than ZnO film, osteoblast growth was more vigorous. Osteoblasts nearly completely covered the ZnO nanoflowers in 4 days. Figure 1 depicts lamellipodia in osteoblasts in both the ZnO film and ZnO nanoflower situations. On the movable borders of the cells, two-dimensional actin mesh structures make up lamellipodia. They are where actin microfilaments are mostly formed. The development of filopodia, which are cylindrical projections with actin filament bundles, is seen at the borders of active lamellipodia. For cellular motility, filopodia transmit signals. Cell motility is mostly regulated by the two varieties of lamellipodia and filopodia. Contrary to expectations, ZnO nanoflowers produced more active filopodia than ZnO film. The DNA content and alkaline phosphatase (ALP) activity analyses measuring the reduction of p-nitrophenol phosphate in MC3T3-E1 osteoblasts cultured on the control of TCPS, ZnO film, and ZnO nanoflowers, respectively, were used to further investigate the topographic effect of ZnO nanostructures on osteoblast cell growth. As an early measure of bone cell differentiation, ALP activity is connected to the bioactivity of cells and DNA content represents the total number of cells. The amount of DNA rose in cases of the control and ZnO nanoflower groups up to 4 days, whereas the DNA content grew up to 8 days in cases of ZnO film. The DNA concentration then slightly dropped. In

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comparison to ZnO film, the DNA content rose by 117–369% for the control and 14.3–202% for ZnO nanoflower. Contrarily, ALP activity increased in all groups up to 16 days, with the exception of the control group, which reached a peak in 8 days. ZnO nanoflower group had considerably increased DNA content and ALP activity than ZnO film group (P < 0.05). It was believed that the increased DNA content and ALP activity in osteoblasts cultured on ZnO nanoflowers reflected the topographic effect of the nanoflowers on the adhesion, proliferation, differentiation, and development of MC3T3- E1 osteoblasts for enhanced bone formation. The bio-interphase of metallic materials for medical implants according to Nebe et al. has a significant influence on the physicochemical properties of osteoblasts [41]. They noted a relationship between biological parameters and the topography of the material, indicating that a rough topography might increase osteoblast cell activity. Our findings in this investigation were well matched with the topographic effect of biomaterials [41]. Additionally, under a centrifugal force at 4 °C, the topographic impact of ZnO nanoflowers on osteoblast cell adhesion was examined. The surviving cell adhesion fraction on ZnO nanoflowers was statistically larger than on ZnO film, even if the centrifugation force or speed had an impact on the cell adhesion fraction after centrifugation. In other words, osteoblasts adhered more tightly to ZnO nanoflowers than ZnO film. The osteoblasts’ ability to adhere to ZnO nanoflowers was 55.8% stronger than it was to ZnO film (P < 0.05). Based on the outcomes of all these in vitro tests, it was determined that ZnO nanoflowers had a favorable topographic influence on osteoblast cell adhesion, differentiation, and growth. Synchrotron X-ray imaging was used to examine the osseointegration of ZnO nanoflowers on the substrate in the calvarial bone defects of Sprague Dawley (SD) rats. In the calvarial bone deficiencies, ZnO nanoflowers on the substrates were implanted and coated with grafted hyaluronic acid (HA-PLGA) barrier films. Different medical applications have made use of HA derivatives. ZnO nanoflowers were tightly osseointegrated with no gap between them, in contrast to the regenerated bone’s patchy adhesion to the ZnO film. ZnO nanoflowers’ successful osseointegration was in line with the findings of in vitro tests. ZnO nanoflowers were believed to support osseointegration, cell adhesion, proliferation, and growth when used collectively. Dental implants, orthopedic implants including femoral stems, and other medical devices could be successfully treated using this ground-breaking method. One of the most crucial requirements for endosseous implant success has been deemed to be osseointegration. Zinc oxide nanoparticles (ZnO NPs) are used as a coating material in a novel system described by Memarzadeh et al. (2014) to prevent bacterial adherence and encourage osteoblast formation [42]. In order to deposit combinations of ZnO nanoparticles and nanohydroxyapatite (nHA) onto the surface of glass substrates, electrohydrodynamic atomization (EHDA) was used. To ascertain the antibacterial activity, Staphylococcus aureus suspended in buffered saline or bovine serum was subjected to nano-coated substrates. According to our findings, coated substrates with 100% ZnO-NPs and 75% ZnO NPs/25% nHA composite show strong antibacterial action. By exposing cells to ZnO NPs, osteoblast function was also investigated. ZnO-NPs supernatants treated to UMR-106 cells didn’t cause much harm (Fig. 2). Similar to this, TNF-a and IL-6 cytokine production was not observed in MG63 cells

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Fig. 1 Scanning electron microscopic images of MC3T3-E1 osteoblasts grown on a ZnO film and b ZnO nanoflowers after cultivation for a day. Lamellipodia and filopodia were indicated with pink and red arrowheads, respectively [39]

cultivated on nZnO substrates. Both proliferation and differentiation investigations confirmed these findings, showing that a substrate coated with only ZnO-NPs is more effective than composite surface coatings. Finally, immunofluorescence staining and electron and light microscopy showed that all examined cell types, including human mesenchymal cell (hMSC), were able to maintain normal cell shape when adhering onto the surface of the nano-coated substrates. These results suggest that ZnO-NPs alone can offer an ideal coating for future bone implants that is both antibacterial and biocompatible. ZnO-potential NPs to promote bone growth is concentration-dependent, according to Wang et al. [43]. They claimed that MG63 cells cultivated with 10 g/mL ZnONPs as opposed to 5 g/mL greatly boosted the production of alkaline phosphatase and collagen. They suggested that, within the range of non-toxic concentrations, more ZnO-NPs might have a better osteogenic effect. A hydrothermal technique was used by Shen et al. to apply a ZnO coating to a Ti substrate that was patterned at a millimeter scale [44]. The samples that included ZnO-NP encouraged osteoblast development and proliferation. Additionally, RAW264.7 cells’ quantitative tartrateresistant acid phosphatase (TRAP) activity measurement revealed that the ZnO-NPs modified samples had the lowest TRAP activity, which suggested that osteoclast

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Fig. 2 Osteoblast growth on nZnO-coated surfaces. Seeded UMR-106 (osteoblast-like cells) on the surface of nZnO-coated glass samples. The samples were coated with increasing concentrations of nZnO to observe any morphological changes. Phase images show increased numbers of cells at 2, 24, and 48 h. Red arrows indicate initial attachment of osteoblasts to the substrates after 2 h of incubation, and black arrows show the similarity of osteoblast confluency between a coated and uncoated substrate [42]

development was being slowed down. After being implanted for 4 or 12 weeks, the ZnO-NP alteration was found to effectively promote the development of new bone tissue, according to the results of following animal trials. Numerous strategies can be used to induce osteogenesis when HA and ZnO-NPs are combined, as is frequently the case in orthopedic research. The PCL scaffold’s osteogenic impact was significantly enhanced when Shitole et al. added nano-HA and ZnO-NPs to it [45]. In contrast to the pure HA coating, Maimaiti et al. hypothesized that the HA/Zn coating improved osteogenic performance for bone formation [46]. In order to generate ZnO/HA particles, Gnaneshwar et al. first combined ZnO-NPs with HA particles to create poly (L-lactic acid)-co-PCL and silk fibroin nanofibrous scaffolds [47]. In comparison to scaffolds doped with the same amounts of ZnO-NPs and nHA, they discovered that ZnO/HA particles had stronger impacts on osteogenesis. This outcome might serve as a blueprint for future co-modification efforts involving different NPs. Kachoei et al. (2016) developed a coating that is antibacterial and reduces friction by incorporating zinc oxide (ZnO) nanoparticles on nickel-titanium (NiTi) wire [48]. NiTi braces wires were covered in ZnO nanoparticles using chemical deposition. Physical, mechanical, antibacterial, and coating properties of the wires were also examined. ZnO was applied as a stable and well-adhering coating to the NiTi wires. Both the elastic modulus and hardness of the ZnO nanocoating are 2.3 ± 0.2 and 61.0 ± 3.6 GPa, respectively. Streptococcus mutans was resistant to the coated wires’ antibacterial activity, and frictional forces were reduced by up to 21%. After being incubated for 24 and 48 h at 37 °C, none of the plates with ZnO-coated

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wires showed any color changes, indicating that no bacterial growth had taken place (Fig. 3). The streak culture test, which revealed no bacterial growth on nutrient agar plates, further supported the findings. However, plates with noncoated wires became pink, indicating that resazurin and bacterial growth had decreased (Fig. 3). NiTi wires with ZnO nanocoating have much better surfaces. After coating, there were no appreciable differences in the austenite finish temperature, unloading forces, or modulus of elasticity. Conclusion: Both the patient and the doctor would benefit from using this special coating in practice because it would make therapy safer and quicker. ZnO particles have been found to have an unique antibacterial characteristic, which makes them an intriguing material to include into or deposit on biomaterials, such as coatings over metallic implants [49]. In this case, the performance of ZnO will be greatly influenced by its physicochemical characteristics, particularly the size and shape of the particles. So, in this work, two distinct sonochemical synthesis methods were used to produce ZnO with various morphologies and sizes (Fig. 4). After that, the MC3T3-E1 mouse pre-osteoblasts were exposed to the resulting particles to assess any potential harmful effects (bone cells). The sonochemical method appears to be an effective method for producing crystalline ZnO micro and nanoparticles with various morphologies based on the reported results and analyses. Additionally, the resulting ZnO shape is affected by the precipitation period following the sonochemical treatment of zinc-salt aqueous solutions with the addition of a hydroxyl

Fig. 3 Antibacterial activity assay of the orthodontic wires using resazurin [48]. Plates containing noncoated wires (A & I) showed bacterial growth and color change. Bacterial growth was not observed in plates containing ZnO-coated wires (B–D & J–L). Plates E and F were positive controls and contained only S. mutans. Plates G and H were negative controls containing ampicillin and penicillin

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precursor. In terms of the cytotoxicity of the samples under investigation (Fig. 5), the interaction of pre-osteoblast with zinc ionic species would not result in cytotoxic effects; however, direct contact between these cells and ZnO-NPs might result in undesirable consequences and cell death. The concentration of 5 g/mL for ZnO rodlike structures with an average size of 360 40 nm represents a good compromise between concentration, size, and shape, and therefore, ion releasing rate related pre-osteoblasts. Implant surface shape is crucial for improving cell responsiveness at the interface. Due to their ability to promote osteoblast cell growth and differentiation, zinc ions (Zn2+) offer a great potential for osseointegration. By using a two-step nanosecond laser process, Zhao et al. (2021) successfully created regular micro grooves coated with zinc oxide (ZnO) on Ti-6Al-4V surface to study the synergistic

Fig. 4 SEM images for a NP1 sample, b a single rod of NP1 sample in detail, c NP2 sample, d NP2 sample in detail, e NP3 sample, and f NP3 sample in detail [49]

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Fig. 5 Cell viability assay (MTT) regarding the ionic cytotoxicity of ZnO-NPs (*for p < 0.05, **for p < 0.001, and ***for p < 0.0001) [49]

effects of micro/nanostructures and Zn2+ on cell adhesion, proliferation, and differentiation [50]. Characterized surface properties included surface morphology, roughness, wettability, and phase composition. Through an in vitro cell experiment, the biocompatibility of several substances was examined. These are the main conclusions: (1) A composite structure made of micro grooves and a ZnO coating was created after laser processing on a Ti-6Al-4V surface. The findings of the surface characterisation revealed that the laser treatment’s impact greatly enhanced the surface’s hydrophilicity and roughness. (2) Due to the “contact guiding” effect, cell growth and adherence on the surface with micro grooves coated with ZnO were dramatically increased as compared to the surface treated by acid etching. (3) According to the findings of the immunofluorescence labeling of ALP and OCN, the surfacereleased Zn2+ on the basis of the micro-grooves significantly aided the differentiation of MC3T3 cells. An innovative and practical method for surface modification of Ti-6Al-4V implants is presented in this study.

Conclusions Major therapeutic challenges include promoting osseointegration and avoiding infections in post-implant prostheses. Utilizing ZnO-NP might be a solution to these problems. In order to better understand the toxicity, osteogenic, and antibacterial features of metallic implants coated with ZnO-NPs for potential medical device applications, the current work provides significant evidence in favor of recent research advancements. The study demonstrates that covering metal surfaces with ZnO-NPs enhanced osteogenesis and soft tissue integration, which enhanced implant fixation. Furthermore, in order to provide a strong biological attachment for implants, osteoconductive nanoparticles create a chemical interaction with bone. Implants’ surfaces are coated with ZnO NPs, which unquestionably improves therapeutic outcomes and reduces the risk of infection. Nonetheless, there are still a number of issues. First off, it is

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now impossible to calculate an effective non-toxic dose range that takes into account a variety of contributing factors because the precise processes of antibacterial and toxicity are still poorly understood. Second, it is important to identify the precise mechanisms and ideal composition details for ZnO-NPs since they have synergistic antibacterial and osteogenic effects when combined with other materials. Third, although more research is needed, ZnO-NPs might mote cartilage development. The focus of future research should be on elucidating the precise processes underlying osteogenic and antibacterial properties as well as ways to maximize positive outcomes.

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Part IX

Advanced Characterization Techniques for Quantifying and Modeling Deformation

Characterization and Mechanical Testing of Ordinary Chondrites Mohamed H. Hamza, Charles A. Galluscio, M. F. Rabbi, Laurence A. J. Garvie, Desireé Cotto-Figueroa, Erik Asphaug, and A. Chattopadhyay

Abstract Understanding the deformation mechanisms and mechanical properties of asteroids that are Near-Earth Objects is crucial in developing hazard mitigation strategies, as well as unraveling their potential engineering applications. A comprehensive study of the microstructure and mechanical behavior of Viñales (L6) ordinary chondrite is conducted. First, elastic wave velocity measurements are conducted to determine the mechanical properties and the material symmetry of Viñales. Next, optical microscopy is applied for microstructure characterization to identify the primary mineral phases and corresponding texture. Additionally, the composition of each mineral is determined using a scanning electron microscope equipped with wavelength-dispersive spectrometers, where an X-ray intensity map is plotted for ordinary chondrites elements of interest. The Brunauer–Emmett–Teller (BET) method is used to measure the average pore surface area and adsorption pore volume. Finally, quasi-static compression tests, accompanied with in-situ digital image correlation are utilized to investigate the failure type as well as localizing the regions of excessive deformation and failure. M. H. Hamza (B) · M. F. Rabbi · A. Chattopadhyay School for Engineering of Matter, Transport, and Energy, Arizona State University, Tempe, AZ 85281, USA e-mail: [email protected] C. A. Galluscio Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL 32611, USA L. A. J. Garvie Buseck Center for Meteorite Studies, Arizona State University, PO Box 876004, Tempe, AZ 85287-6004, USA D. Cotto-Figueroa Department of Physics and Electronics, University of Puerto Rico at Humacao, Call Box 860, Humacao, PR 00792, USA E. Asphaug Lunar and Planetary Laboratory, University of Arizona, PO Box 210092, Tucson, AZ 85721, USA © The Minerals, Metals & Materials Society 2023 The Minerals, Metals & Materials Society, TMS 2023 152nd Annual Meeting & Exhibition Supplemental Proceedings, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-031-22524-6_28

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Keywords Near-Earth objects · Material characterization · Digital Image Correlation · Axial splitting

Introduction Most meteorites are fragments of asteroids that endured extraterrestrial impact events and survived the atmospheric entry, ablation, and breakup before residing on Earth’s surface [1]. They provide unique opportunities to understand the deformation mechanism and mechanical properties of asteroids through controlled laboratory testing. Because of their inhomogeneous microstructure composed of different minerals with a range of textures, it is challenging to study meteorites due to their mechanical behavior varying with mineral composition [2], preexisting flaws such as pore-size distribution [3, 4], and different load conditions [5, 6]. The microstructure and mineral compositions of meteorites reflect their diverse history from the formation of the parent asteroid and subsequent ejection from the parent body. The meteorite mineralogy gives insights into conditions on the parent body including oxygen fugacity, temperature and pressure, and shock events. These cause changes in the physical and mechanical properties of meteorites. Some asteroids experienced the effects of aqueous alteration forming a complex mixture of clay [7] that alter the physical properties of meteorite from their parent bodies. Structural and chemical properties of chondrite can change with the addition of heat as it equilibrates silicate compositions and crystalizes fine-grained matrix and chondrules. Those changes are evident in the microstructural imprint of chondrules and minerals [8]. Planar micro-cracks, irregular fractures, numerous straight and fine fractures, and intersecting cracks are observed in the microstructure of ordinary chondrites. These preexisting flaws may be caused by the shock-induced metamorphism [8, 9] and the severe thermal gradient between different phases due to different thermal conductivity and elastic modulus [10]. Microstructure, mineral compositions, and preexisting material flaws influence the stress state and deformation mechanism of meteorites [11]. Compression tests have been used to determine the strength of ordinary chondrites as it is analogous to the breakup of the asteroid during the atmospheric entry of meteorites [11–16]. Different-sized samples exhibit a range of strength properties at different length scales that arise from the complexity of understanding the mechanical behavior and deformation mechanisms of meteorites [3, 17, 18]. Controlled laboratory testing of meteorites under quasi-static compressive loading accompanied with the in-situ full-field Digital Image Correlation (DIC) provides information on their mechanical behavior, deformation mechanism, and failure. This paper presents the information on the deformation mechanism of the Viñales (L6) ordinary chondrite, which fell across the province of Pinar Del Rio, Cuba in 2019. This meteorite belongs to the most common types of ordinary chondrites. Hence, studying the Viñales meteorite allows a better understanding of the deformation mechanism of ordinary chondrites. This work begins with computing the elastic

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mechanical properties and determining the material symmetry through elastic wave velocity measurements. Thereafter, understanding the microstructure and mineral compositions is conducted using the confocal microscope and scanning electron microscope (SEM) equipped with wavelength dispersive spectroscopy (WDS). The pore surface area and adsorption pore volume are determined using the Brunauer– Emmett–Teller (BET) method. Quasi-static compression tests with in-situ DIC were performed on cube meteorite samples to analyze the deformation mechanism. Results derived from microstructure and mineralogy study, pore-size analysis, and mechanical tests are essential towards comprehensive understanding of ordinary chondrite deformation mechanisms, as well as developing a fracture mechanics-based model to understand tensile “wing” microcracks propagation and final brittle failure.

Mechanical Properties Computing the mechanical properties of meteorites is crucial to explore its mechanics of materials behavior and determine potential engineering applications for such NearEarth materials. Elastic wave velocity measurements are used to calculate the elastic properties of Viñales meteorite based on the electric pulse propagation characteristics through the samples. First, Olympus 5077 PR electric pulse generator/receiver is used to generate and amplify the electric pulses sent to the transducers as well as filter the background noise for the received pulse. Normal and shear waves are applied to the sample through Olympus V-110RM and V156-RM full contact transducers respectively, which are being used as actuator-sensor pairs. Olympus shear wave coupling fluid is applied to the transducer’s surface to decrease the contact pressure on the meteorite surface, hence reduces the signal attenuation and enhances the propagating electric pulse analysis accuracy. The transducers and meteorite setup are shown in Fig. 1. For this study, eight Viñales cubes were used with an average length of 1cm. The PI-1042 Digital Acquisition system is used for signal data processing and waves characteristic extraction. The normal and shear wave velocities are computed based on actuator and sensor readings which are being used for mechanical properties estimation. Phenomenological equations are used to calculate the elastic properties; where the elastic longitudinal modulus along each direction (E) is represented as follows [12]: E=

VN2 ρ(1 + v)(1 − 2v) 1−v

where Poisson’s ratio (v) is given as:

(1)

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Fig. 1 Olympus V-110RM full contact transducer applied to a Viñales (L6) ordinary chondrite cube for normal waves propagation

v=

1−2 2−2

 

Vs VN Vs VN

2 2

(2)

VN and Vs are the normal and shear wave velocities, respectively. The material symmetry for Viñales is isotropic since the wave velocity components along the principal directions of all the cubes are approximately the same. Hence the shear modulus (G) is computed based on the isotropic linear elastic relations as shown in Table 1. The average Young’s modulus (E avg ) is 38Gpa with G ∼ 16.1Gpa and v ∼ 0.19. The variation of the linear elastic properties among the tested samples is attributed to the size of the cubic samples which cannot fully capture the variability and inhomogeneities of the microstructure. The results agree with Aba Panu (L3) ordinary chondrite published in the literature [14], where v in both cases is similar to concrete, hence indicating the brittle nature of such L-type meteorites. Table 1 Mechanical properties of the Viñales ordinary chondrite using elastic wave velocity measurements Cube no

VN (m/s)

VS (m/s)

E (GPa)

G (GPa)

ν

1

3515

2210

37.8

16.2

0.17

2

3674

2330

41.0

17.6

0.16

3

3646

2239

39.7

16.6

0.20

4

3510

2117

35.5

14.7

0.21

5

3649

2243

39.5

16.7

0.19

6

3452

2073

34.8

14.4

0.21

7

3751

2320

43.0

18.1

0.19

8

3348

2085

33.2

14.1

0.18

Mean

3568

2202

38.0

16.1

0.19

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Microstructure Characterization A field-emission electron microprobe analyzer equipped with a wavelength dispersive spectrometer (WDS) is used to determine the main phases in the Viñales microstructure. WDS analysis is performed at an acceleration voltage of 20KV, and the beam current equals 58.9nA. As shown in Fig. 2, there are three main microstructure phases captured through a backscattered electron (BSE) image. The first is the silicate matrix which is a brittle phase mainly composed of olivine, pyroxene, and Feldspar [19]. Second is the shock melt vein which forms due to the temperature gradients experienced by the extraterrestrial impact events. It is composed of fragments of matrix, troilite, and chromite minerals. Finally, the metal grains which are randomly distributed across the matrix phase and at the shock melt vein interface. X-ray intensity maps are generated using WDS where the main elements of interest are Fe, Mg, Ni, S, and Si. First, a Fe X-ray intensity map is shown in Fig. 3a, where Fe is concentrated in the metal grains and matrix mineral fragments identified in the BSE image in Fig. 2. Additionally, within the metal grain there is a gradient of Fe concentrations, indicating the existence of different metal phases within the grain. Figure 3b shows the Ni X-ray intensity map where it is mainly distributed in the metal grains similar to Fe. Hence, the metal grains mainly consist of kamacite which are Fe-rich phase and smaller sub-grains of taenite which are Ni-rich phase. Kamacite and taenite phases are accounting for the plastic deformation of meteorites during mechanical and thermal loading. Taenite is face-centered cubic (fcc) crystal structure with an abundance of dislocation slip at relatively lower critical resolved shear stress compared to the kamacite phase, which is mainly body-centered cubic (bcc) structure with complex dislocation cores. Figure 3c shows an S X-ray intensity map, where S is randomly distributed as fragments in the matrix. S and average Fe levels (yellow color in Fig. 3a) are following the same distribution, which indicates the presence of troilite fragments in the matrix. Finally, the bulk of the meteorite is Si-rich (Fig. 3d) reflecting the dominant silicate nature of the meteorite. These silicates are brittle and thus dictate the bulk mechanical properties of the meteorite. Fig. 2 Backscattered electron (BSE) image from one face of a Viñales (L6) cube, showing the silicate matrix phase, randomly distributed metal grains, and the shock melt veins

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Fig. 3 X-ray intensity maps for Viñales (L6) ordinary chondrite using wave dispersive spectroscopy (WDS) for a Fe, b Ni, c S, and d Si

In addition to the characterization of different microstructure phases, identification of the shape and size of microstructure flaws are crucial to understand the deformation mechanism and brittle failure of Viñales samples. Due to the brittle nature of such stony meteorites, nucleation, growth, and interactions of micro-cracks are control their inelastic deformation and failure. Upon mechanical loading, pores coalesce forming micro-cracks which propagate through the brittle phases, leading to abrupt fracture. High-fidelity representative volume element including the flaws distribution is needed as the basis for brittle fracture modelling, which ultimately leads to comprehensive understanding of meteorite mechanical behavior. Hence, the Brunauer–Emmett–Teller (BET) method is used for adsorptive characterization to extract porosity features. The test is conducted using nitrogen as the adsorbate at 77K, where nitrogen molecules diffuse to pores, consequently allowing the specific surface area and volume of the pores to be determined. Five samples were prepared for the BET test (Table 2). The results show an average specific surface area of pores equal to 0.927m2 /g, and an average single point adsorption pore volume equals 2.53 × 10−3 cm3 /g.

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Table 2 Adsorptive pore characterization for Viñales ordinary chondrite using the BET method Samples no

Mass (g)

BET surface area (m2 /g)

Adsorption pore volume (cm3 /g)

1

0.3845

0.8801

0.002661

2

0.3036

0.8323

0.002402

3

0.2491

0.9047

0.002406

4

0.2597

0.9608

0.002705

5

0.2137

1.0587

0.002455

Mean

0.28212

0.92732

0.0025258

Failure Mechanism A quasi-static compression test coupled with DIC is conducted to investigate the failure mechanism of Viñales ordinary chondrite. Dirichlet boundary conditions are applied at the top surface with a displacement rate equal to 0.25mm/min using the Instron 5985 universal testing machine. The ASTM D7012 test standard is modified to account for the ∼ 1cm cubic samples, additionally the sample surfaces in contact with the compression platens are lubricated with silicon oil to avoid initial surface slipping and maintain the uniaxial loading state. DIC is conducted using ARAMIS 5 M system with CQ15 × 12 calibration cubes to capture the local displacement and strain field during loading. The cube face where the DIC cameras are directed at is firstly sprayed with white paint, and then dots with a uniform pattern are created using black paint. This allows the DIC system to track the local displacements of these black dots, hence calculating the deformation gradient tensor (F), the displacement gradient tensor (GradU ) and linearized Cauchy strain tensor (ε) as summarized in Eqs. 3–6:

E=

F = Grad x

(3)

GradU = F − I

(4)

  1 1 T F F − I = GradU GradU T + GradU + GradU T 2 2

(5)

where x is the position vector of each black dot, I is the identity second-order tensor and E is the Green–Lagrange strain tensor. By neglecting the second-order terms, the Cauchy strain tensor can be given as: ε=

 1 GradU + GradU T 2

(6)

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Fig. 4 Optical micrograph of Viñales (L6) ordinary chondrite overlaid with maximum principal strain (εmajor ) contour at sample failure, indicating axial splitting

The maximum principal strain (εmajor ) acquired from DIC is plotted over the sample optical micrograph to correlate the localized strain pattern and crack propagation with microstructure characterization at sample failure (Fig. 4). The results show localized strain along the shock melt vein (discussed in Sect. 3) as compressive loading increases. Subsequently, microcracks exhibit frictional sliding at their surfaces [20, 21], resulting in tensile “wing” microcracks formation at an orientation parallel to the uniaxial compression loading. Near fracture strain, tensile microcracks propagate along the shock melt vein due to its brittle nature, followed by axial splitting fracture.

Conclusion The Viñales (L6) ordinary chondrite is investigated to understand its mechanical properties and failure mechanisms. Elastic wave velocity measurements showed isotropic material symmetry with elastic constants similar to brittle materials and stony meteorites studied in the literature. Backscattered electron (BSE) imaging and X-ray intensity maps using SEM equipped with WDS showed three main phases of the Vinales (L6) microstructure. The matrix and shock melt vein are dominated by olivine, pyroxene, troilite, and chromite minerals. While the metal grains show concentration gradients of Fe and Ni elements, indicating the existence of metal sub-grains with different inelastic responses. Quasi-static compression test coupled with digital image correlation (DIC) showed tensile “wing” microcracks propagation along the brittle shock melt vein and subsequent axial splitting. Finally, micro-CT analysis is needed to study the 3D structure of the shock melt vein to fully understand its interactions with tensile microcracks and pre-existing flaws.

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Acknowledgements This work is supported by funds from the National Aeronautics and Space Administration under Agreement Program Announcement No. NNH20ZDA001N-YORPD. The support is gratefully acknowledged.

References 1. Melosh H (1984) Impact ejection, spallation, and the origin of meteorites. Icarus 59:234–260 2. Petrovic J (2001) Review mechanical properties of meteorites and their constituents. J Mater Sci 36:1579–1583 3. Ostrowski D, Bryson K (2019) The physical properties of meteorites. Planet Space Sci 165:148– 178. https://doi.org/10.1016/j.pss.2018.11.003 4. Rabbi MF, Datta S, Chattopadhyay A, Garvie LA, Asphaug E, Cotto-Figueroa D (2021) Mechanical characterization and brittle failure of stony meteorite (Aba Panu) using digital image correlation. In: AIAA Scitech 2021 Forum. American Institute of Aeronautics and Astronautics, VIRTUAL EVENT 5. Pohl L, Britt DT (2020) Strengths of meteorites—an overview and analysis of available data. Meteorit Planet Sci 55:962–987. https://doi.org/10.1111/maps.13449 6. Ramesh KT, Hogan JD, Kimberley J, Stickle A (2015) A review of mechanisms and models for dynamic failure, strength, and fragmentation. Planet Space Sci 107:10–23. https://doi.org/ 10.1016/j.pss.2014.11.010 7. Endress M, Zinner E, Bischoff A (1996) Early aqueous activity on primitive meteorite parent bodies. Nature 379:701–703 8. Leroux H (2001) Microstructural shock signatures of major minerals in meteorites. Eur J Mineral 13:253–272. https://doi.org/10.1127/0935-1221/01/0013-0253 9. Molaro JL, Byrne S, Langer SA (2015) Grain-scale thermoelastic stresses and spatiotemporal temperature gradients on airless bodies, implications for rock breakdown: thermoelastic stresses on airless bodies. J Geophys Res Planets 120:255–277. https://doi.org/10.1002/2014JE004729 10. Liang B, Cuadra J, Hazeli K, Soghrati S (2020) Stress field analysis in a stony meteorite under thermal fatigue and mechanical loadings. Icarus 335:113381. https://doi.org/10.1016/j.icarus. 2019.07.015 11. Hogan JD, Kimberley J, Hazeli K, Plescia J, Ramesh KT (2015) Dynamic behavior of an ordinary chondrite: the effects of microstructure on strength, failure and fragmentation. Icarus 260:308–319. https://doi.org/10.1016/j.icarus.2015.07.027 12. Cotto-Figueroa D, Asphaug E, Garvie LAJ, Rai A, Johnston J, Borkowski L, Datta S, Chattopadhyay A, Morris MA (2016) Scale-dependent measurements of meteorite strength: implications for asteroid fragmentation. Icarus 277:73–77. https://doi.org/10.1016/j.icarus.2016. 05.003 13. Medvedev R, Gorbatsevich F, Zotkin I (1985) Determination of the physical properties of stony meteorites applied to the study of their destruction processes. Meteoritika 44:105–110 14. Rabbi MF, Garvie LAJ, Cotto-Figueroa D, Asphaug E, Khafagy KH, Datta S, Chattopadhyay A (2021) Understanding asteroidal failure through quasi-static compression testing and 3-D digital image correlation of the Aba Panu (L3) chondrite. Meteorit Planet Sci maps.13761. https://doi.org/10.1111/maps.13761 15. Slyuta EN (2010) Physical-mechanical anisotropy of ordinary chondrites and the shape of small rocky bodies. 41:1103 16. Voropaev SA, Kocherov AV, Lorenz CA, Korochantsev AV, Dushenko NV, Kuzina DM, Nugmanov II, Jianguo Y (2017) Features in constructing a certificate of strength of extraterrestrial material by the example of the Chelyabinsk meteorite. Dokl Phys 62:486–489. https:// doi.org/10.1134/S1028335817100111

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17. Popova O, Boroviˇcka J, Hartmann WK, Spurný P, Gnos E, Nemtchinov I, Trigo-Rodríguez JM (2011) Very low strengths of interplanetary meteoroids and small asteroids: very low strengths of interplanetary meteoroids and small asteroids. Meteorit Planet Sci 46:1525–1550. https:// doi.org/10.1111/j.1945-5100.2011.01247.x 18. Slyuta EN (2017) Physical and mechanical properties of stony meteorites. Sol Syst Res 51:64– 85. https://doi.org/10.1134/S0038094617010051 19. Yin F, Dai D (2021) Petrology and mineralogy of the Viñales meteorite, the latest fall in Cuba. Sci Prog 104(2):00368504211019859 20. Paliwal B, Ramesh KT (2008) An interacting micro-crack damage model for failure of brittle materials under compression. J Mech Phys Solids 56(3):896–923 21. Nemat-Nasser S, Horii H (1982) Compression-induced nonplanar crack extension with application to splitting, exfoliation, and rockburst. J Geophys Res Solid Earth 87(B8):6805–6821

Influence of Different Temperatures on Mechanical Properties of Flexible Screen Qiujun Wang, Weiwei Su, Zeyu Zhang, Di Zhang, Bo Wang, and Fang Zhang

Abstract At different temperatures in daily life, the flexible AMOLED screen is prone to device damage and peeling of the adhesive layer during the bending process. The primary way to solve this problem is to explore the stress of the display layer and the strain of the optically clear adhesive (OCA) adhesive layer at the optimal temperature for flexible screen bending. In this paper, a bending simulation model of an AMOLED screen was established to analyze each film layer at different temperatures. Then, the thickness of different layers (the OCA adhesive layer, the back plate, and the protective cover) was investigated. Furthermore, it shows the bending radius at the optimal temperature. The results present that the Mises stress of the flexible screen increases significantly at a high temperature of 100 °C. At a low temperature of −20 °C, there is a significant stress reduction, and the probability of mesh deformation is reduced by 10% compared to −10 °C. The stiffness of the protective cover and the thickness of the OCA adhesive layer do not affect the position of the display layer’s stress-neutral layer. The increase in the thickness of the back plate makes the position of the stress-neutral layer of the display layer move downward, and the increase in the bending radius reduces the structural stress. The decrease in board stiffness, the increase of OCA adhesive layer thickness, and the decrease of back plate thickness all benefit the reduction of OCA adhesive layer strain. Keywords AMOLED · Simulation analysis · Thermal expansion · Flexible displays

Q. Wang · W. Su · Z. Zhang · D. Zhang · B. Wang (B) Hebei Key Laboratory of Flexible Functional Materials, School of Materials Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050021, China e-mail: [email protected] F. Zhang (B) Yungu (Gu’an) Technology Co., Ltd, Lang Fang 065500, China e-mail: [email protected] © The Minerals, Metals & Materials Society 2023 The Minerals, Metals & Materials Society, TMS 2023 152nd Annual Meeting & Exhibition Supplemental Proceedings, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-031-22524-6_29

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Introduction With the rapid development of the information age, information display has become an indispensable part of the information industry. It has gained more widespread attention as a carrier of information presentation [1]. The time people spend on mobile phones and computers every day is enough to prove the importance of display technology. At the same time, it also makes people put forward higher requirements for the power consumption, volume, and softness of display devices. AMOLEDs have excellent features such as ultra-thin, self-luminous, organic materials, planar structures, low-temperature manufacturing processes, and compatibility with plastic substrates, making it possible to manufacture flexible AMOLEDs [2]. At present, the flexible AMOLED screen is developing in the direction of commercialization [3], and the foldable screen is one of its development directions. When the flexible AMOLED screen is folded along a certain midline in a relatively high-temperature environment [4], the optically transparent conductive adhesive (Optically Clear Adhesive, OCA) adhesive layer is prone to peeling [5], and each film layer has a stress-neutral layer. The appearance of, that is, the position where the stress–strain is 0, the optical device of the display layer is most likely to be damaged [6]. Therefore, improving the stress–strain distribution in the entire structure has become the main problem faced by the multi-layer stack structure screen [7]. In response to these situations, some scholars have improved the performance of the flexible screen from the structural design perspective but only compared the protective cover [8]. The influence of the thickness of the plate on the neutral layer is less studied, and there is no discussion on the peeling of the adhesive layer. This paper focuses on exploring the stress factors at the display layer that affect the bending of the flexible screen by ambient temperature to adjust to avoid excessive damage to the display layer caused by the bending process and reduce the strain of the adhesive layer to ease the peeling problem of the adhesive layer [9]. The bending process of the flexible AMOLED screen is simulated and analyzed, and the stiffness of the protective cover, the thickness of the OCA adhesive layer, the thickness of the back plate, and the bending radius of the display layer are compared when it is bent at 90° after static heating. The influence of the defects mentioned above can be improved and eliminated by adjusting material parameters and structure.

Structure and Fabrication Geometry and Boundary Conditions Due to the small bending radius of the flexible AMOLED module during the bending process, it is easy to cause damage to the film and display area due to excessive force, severe deformation of the adhesive layer, and peeling of the film [10]. This phenomenon causes the screen to be damaged, and the flexible AMOLED screen

Influence of Different Temperatures on Mechanical Properties … Table 1 Panel structure diagram

315

Material

Thickness (um)

Cover Film

60

OCA

25

Polarizer

47

OCA

25

TP

50

OCA

20

AMOLED

25

OCA

25

Back Plate Film

75

fails to meet production standards, thereby affecting screen performance, especially in severe working cases. To protect the flexible AMOLED film group from peeling off the adhesive film, it is necessary to keep the film in a lower stress state and inhibit the deformation of the OCA adhesive layer. As a result, processing a detailed understanding of the bending force and deformation process is greatly helpful in facilitating the optimization and do out adjustments. As shown in Table 1, the stacking sequence of the flexible screen module and the thickness of each film layer are shown. The top is the glass cover for protection and decoration; the bottom is the optical adhesive layer, and the touch screen conductive film layer is bonded to it. These three form the commonly known touch screen module. Below the conductive film is followed by an optical adhesive layer, the circular polarizer, the water–oxygen barrier film, and the encapsulation film layer. The screen is supported by a thermally conductive film, which is connected with a flexible screen body and a foam with double-sided tape. Whether static or dynamic bending, flexible modules need to consider aspects such as a neutral layer, high ductility, fracture, and fatigue. The motion of the panel with bending radius r is shown in Fig. 1a. The midframe, which is modeled as a rigid body, is attached to the panel to drive screen motion. During the first second of this period, the rigid body rotates counterclockwise around the reference point at a speed of 1.57 rad/s, while moving to the left at (π/2–1) * r mm/s, and then stays for 300 s, as shown in Fig. 1b. The forward folding means that the fronts of the displays are connected and the observable side is the back of the displays. On the other side, the outward folding means that the back of the display is glued to each other, and the image of the display is still visible from the outside.

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Fig. 1 Geometry model size parameters

Thermal Expansion of Film Material The essence of the thermal expansion of flexible screen materials is that the average distance between crystal atoms increases with temperature. The higher the temperature, the greater the vibration of the particles, which causes the corresponding increase in the distance between the particles and the macroscopic crystal expansion occurs. In the simulation, the mesh is inflated. The flexible screen now uses the more typical double-atom model as the mathematical model. As shown in Fig. 2, let r0 be the position of the diatomic equilibrium, the abscissa is the original distance, and the ordinate is the potential energy between atoms [11]. When the temperature increases, the distance between the atoms becomes r = r0 + x due to the intensified vibration, and the atomic potential energy becomes U (r0 + x). Expand the function U (r0 + x) into a Taiwanese series at r = r0 :     1 d2 u x + x 2+ U (r ) = U (r0 ) + du 2! dr 2 r 0  3 dr r0 1 d u x3 + · · · 3! dr 3 r0

Fig. 2 Sketch map of diatomic model [12]

(1)

Influence of Different Temperatures on Mechanical Properties …

Figure 2 is becomes

 du  dr r0

317

= 0. Ignoring the high-order terms of and after, the formula (1)

U (r ) = U (r0 ) +

  1 d 2u x2 2! dr 2 r0

(2)

At this time, U (r ) represents a parabola, as shown by the dotted line in Fig. 2. As the temperature increases, the atoms increase in amplitude at the equilibrium position r0 , but do not expand [13]. This is the opposite of inflation. Therefore, considering the input term, it can be obtained from formula (1): U (r ) = U (r0 ) +

    1 d3 u 1 d2 u 2 x + x3 2! dr 2 r0 3! dr 3 r0

(3)

The graph of formula (2) is shown by the solid line in Fig. 2. It can be seen that the equilibrium position of its atomic vibration will expand as the temperature increases (parallel lines 1, 2, 3… parallel to the abscissa represent the elevated temperature t, t, t…), as indicated by the AB dotted line in Fig. 2 causing the crystal to expand. For a temperature change of 1 °C, the corresponding linear thermal expansion α is expressed as [14, 15] αt =

L i 1 lim = (dL/dt)L i , t1 < ti < t2 L i t→0 t

(4)

In the formula: αt is the thermal expansion rate, generally expressed in units of 10–6 °C−1 . The thermal expansion coefficient of each film layer is obtained from the mathematical model of the diatomic model. The analysis model is divided into tetrahedral meshes, the size in the length direction is 0.025 mm, and all the structures in the thickness direction are divided into three layers [16]. The mesh element type selects plane strain, hybrid, and reduced integration element.

Results and Discussion The protective cover, OCA, and supporting plate are film layers, which are easy to adjust in the flexible screen structure. Based on this, the material parameters of the three films mentioned above are adjusted to explore their effects on stress and strain at different temperatures. The protective cover, OCA, and support plate with a temperature range of –40 °C–120 °C are analyzed, and the influence of different temperatures on their stress and strain is discussed.

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Comparison of Protective Cover at Different Temperatures As seen in Fig. 3, 0–5 s is a process of heating the module directly, and 5–10 s is a process in which the temperature remains unchanged, and the module is bent dramatically. At different temperatures, the stress of the cover plate stress curve increases significantly in each period of the temperature rising, and the strain of the cover plate layer also changes significantly when the temperature changes. As the temperature increases, the strain of the cover plate layer also shows the same trend to varying degrees. The change of the stress and strain of the protective cover plate has a downward trend at the fifth second, the curve rises after that. Owing to the effect of thermal expansion, a severe deformation of the cover layer occurs, which is related to the expansion of grids in the first five seconds. When the module bends at the fifth second, the cover layer is squeezed inward again, resulting in a brief drop in the stress–strain curve. As the temperature increases, the stress of this layer increases significantly. Hence, the strain of the adjacent OCA adhesive layer also changes, thereby indirectly affecting the strain of other adhesive layers. Appropriately lowering the ambient temperature is conducive to the loss caused by the screen bending process. The protective covers with elastic modulus of 5, 5.5, 6, 6.5, and 7 GPa were selected for comparative analysis, and the influence of protective covers with different elastic modulus on the strain of the display layer AMOLED during the movement was discussed. The results are shown in Fig. 4. As can be seen from Fig. 4a, except for 6GPa under different stiffnesses, the strain curves of the AMOLED layer overlap, and the variation range is about 0.4–1.1%, resulting in a small change in the position of the stress-neutral layer of the AMOLED layer. When the stress of the protective layer is larger than 6 GPa, the strain curve does not change much, but the smaller the elastic modulus, the lower the risk of mesh distortion, which boosted the risk of adhesive layer peeling. Therefore, considering the overall strain of the functional layers and

Fig. 3 a The effect of temperature on the stress of the cover plate. b The effect of temperature on the strain of the cover plate

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319

Fig. 4 a The effect of cover stiffness on the strain of AMOLED layer, b the effect of cover stiffness on the strain of OCA layer

the OCA layer, when the protective layer reaches 5GPa, the overall maximum strain of the AMOLED layer strain is only 0.4%, and leads the strain change slope to become more stable. As shown in Fig. 4b, when the strain of protective layer is 5 GPa, the maximum strain of OCA is 0.23%, which is also relatively stable. When the strain of the protective layer is 6 GPa, the strain of AMOLED reaches 3.53%, and the strain of OCA reaches 0.43%, which is relatively unstable compared to other conditions. It can be seen from Fig. 5a that the strain of the OCA layer under each film layer varies from 2.5 to 2.9%, and the value improves with the increase of the stiffness of the protective film. In particular, the maximum strain of TP layer changes from 2.51 to 2.75%, and the strain growth of other functional layers does not exceed 2.4%. Appropriately reducing the rigidity of the protective cover is beneficial to the optimization of the screen bending process. It can be seen from Fig. 5b that when the thickness of the cover plate increases, the strains of other functional film layers decrease to varying degrees. The strain variation of the OCA layer ranges from 3.11 to 3.68%. The strain of the OCA layer under the Cover Film layer is reduced from 3.56 to 3.11%, which is the OCA layer with the largest strain change. As the strain reduction of this layer increases significantly, the strain of the adjacent OCA buffer layer changes and indirectly affects other layers.

Comparison of Support Plates at Different Temperatures The support plate is often located at the bottom of the flexible screen, which protects and supports the optical device. Therefore, the effect of temperature on the support plate needs to be discussed.

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Fig. 5 a The effect of the stiffness of the cover plate on the strain of each OCA layer, b the effect of the thickness of the OCA under the cover plate on the strain of each OCA layer

It can be seen from Fig. 6a that when the temperature of the support plate changes, the stress of the support plate increases from 0.168 to 0.225 GPa, which is less than an order of magnitude and the change is negligible, and the strain change of the support plate is 0.398–0.548%. Although the change in the support plate’s mechanical properties is small, the temperature span will quickly lead to the deformation of the mesh, so it is necessary to find a suitable bending temperature to reduce the damage to the flexible screen during the bending process. It can be seen from Fig. 6a, b that, when the ambient temperature is around 20 °C, the maximum stress is 0.186GPa and the maximum strain is 0.485%. Owing to the subtle changes in the slope of the stress– strain curve at 20 °C, the model is relatively stable when it is bent. As the ambient temperature increases, the maximum expansion and tensile stress of the support film present similar trends, which promotes the risk of adhesive layer peeling. The support plates with thicknesses of 55, 65, 75, 85, and 95 μm were selected for comparative analysis, and the results are shown in Fig. 7a, b. It can be seen that the stress on the AMOLED layer increases from 0.011 to 0.151 GPa when the support thickness increases. The improvement in the thickness of the support plate enhanced the bending curvature of the AMOLED layer, resulting in increased stress. When the support plate becomes thicker, the functional film layers all increase to different degrees except for the POL layer, but the strain of the POL layer decreases by 0.0012%. Since the change is minor, it will not affect the neutral layer of the overall module. Therefore, reducing the thickness of the support plate is beneficial to relieve the tensile stress of the AMOLED device and protect the display layer, but the increase of the thickness of the support plate will lead to changes in the thickness of the entire module, which requires comprehensive adjustment.

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Fig. 6 a The effect of temperature on the stress of the support plate. b The effect of temperature on the strain of the support plate

Fig. 7 a The influence of the thickness of the support plate on the stress of the display layer. b The influence of the thickness of the support plate on the strain of each OCA adhesive layer

Conclusions In this paper, the stress and strain of the flexible AMOLED screen at different temperatures were simulated. Four factors, including the temperature of the cover plate, the stiffness of the protective cover, the thickness of the OCA adhesive layer, and the thickness of the supporting film, have been discussed. After comparing and analyzing the influence of the thickness of each OCA adhesive layer, a great influence on the stress and strain of the protective cover at different temperatures, which was an important factor causing stress increase and grid distortion, had been revealed. When the stiffness of the protective cover was increased from 5 to 7 GPa, the position of the stress-neutral layer was unchanged, but the strain of each OCA adhesive layer increased. When the thickness of the OCA adhesive layer under the protective cover

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increased from 20 to 60 μm, the position of the stress-neutral layer of the AMOLED layer showed no differences, but the strain of the layer decreased slightly. When the thickness of the backplane increases from 55 to 95 μm, the position of the stressneutral layer of the display layer moves down, and the strain of the adjacent OCA adhesive layer increases. Based on the above, it can be seen that appropriately reducing the temperature, the stiffness of the protective cover, the thickness of the backplane, and increasing the thickness of the OCA under the protective cover are all beneficial to improving the peeling phenomenon of the adhesive layer. In addition, the increase of the folding radius is beneficial to the optimization of the overall stress of the structure and can protect the display layer. Acknowledgements This work was supported by the National Natural Science Foundation of China (22008053, 52002111), the Key Research and Development Program of Hebei Province (20310601D, 205A4401D).

References 1. Cheng A, Chen Y, Jin J et al (2019) 74–3: Study on mechanical behavior and effect of adhesive layers in foldable AMOLED display by finite element analysis. SID Symp Dig Tech Papers 50(1):1060–1063 2. An S, Lee J, Kim Y et al (2010) 47.2: 2.8-inch wqvga flexible AMOLED using highperformance low-temperature polysilicon TFT on plastic substrates. SID Symp Dig Tech Papers 41(1):706–709 3. Yagi I, Hirai N, Miyamoto Y et al (2008) A flexible full-color AMOLED display driven by outfits. J Soc Inf Display 16(1):15–20 4. Slanik ML, Nemes JA, Potvin MJ et al (2000) Time domain finite element simulations of damped multilayered beams using a proxy series representation. Mech Time-Depend Mat 4(3):211–230 5. Lin L, Li Y, Hu K et al (2018) 68–1: invited paper: reliability and failure mode analysis of foldable AMOLED display module. SID Symp Dig Tech Papers 49(1):899–901 6. Kaneko Y, Yamaguchi M, Matsuya H et al (1996) Foldable-display systems as a standard platform for multimedia use. IEEE Trans Consum Electron 42(1):17–21 7. Chiou JY, Liu YW, Niu YF et al (2017) Optimization of TFE structure by FTIR analysis and mechanical simulation to achieve excellent encapsulation and high flexibility AMOLED. Dig Tech Papers 48:437–440 8. Park SG, Lee JH, Lee WK et al (2007) Investigation of the hysteresis phenomenon of an a-Si: H TFT at an elevated temperature for AMOLED displays. J Soc Inf Display 15(12):1145–1149 9. Lin YC, Shieh HPD (2004) A novel current memory circuit for AMOLED. IEEE Trans Electron Dev 51(6):1037–1040 10. Fan CL, Lin YS, Liu YW (2010) Low-temperature polycrystalline silicon thin film transistor pixel circuits for active matrix organic emitting light-emitting diodes. IEICE Trans Electron 93(5):712–714 11. Choi BD, Byun CW (2010) Data driving methods and circuits for compact and high-imagequality AMOLED mobile displays. IEEE Trans Consum Electron 56(2):1102–1107 12. Wang X, Zhang J, Zhang Y et al (2010) Synthesis and thermal expansion of 4J36/ZrW2O8 composites. Rare Met 29(4):35–39

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13. Gaillard A, Rogel R, Crand S et al (2012) An averaging pixel structure using microcrystallinesilicon films prepared at high temperature for AMOLED displays. J Soc Inf Disp 15(12):1137– 1143 14. Lee SJ, Noh S, Shin HS et al (2012) A novel four-mask low-temperature polycrystalline silicon PMOs thin-film transistor with advanced terrace structure for AMOLED application. IEEE Electron Device Lett 33(10):1417–1419 15. Ding Y, Tian L, Huang Z et al (2015) A novel current-biased voltage-programmed pixel circuit with temperature low-temperature polycrystalline silicon thin film transistors for AMOLED. IEICE Electron Expr 12(24):20150899–20150899 16. Lee SM, Kwon JH, Kwon S et al (2017) A review of flexible OLEDs toward highly durable unusual displays. IEEE Trans Electron Dev 64(5):1922–1931

Part X

Advanced Functional and Structural Thin Films and Coatings and Honorary Palkowski Session

A Review of P(St-MMA-AA) Synthesis via Emulsion Polymerization, 3D P(St-MMA-AA) Photonic Crystal Fabrication, and Photonic Application Ikhazuagbe H. Ifijen, Esther U. Ikhuoria, Stanley. O. Omorogbe, Godfrey O. Otabor, Aireguamen I. Aigbodion, and Salisu D. Ibrahim Abstract The possible applications of photonic crystals (PhCs) in photonics and optics have increased their relevance in recent times. The propensity of PhCs to interact with light in their structure has led to a variety of thrilling and extraordinary features, which have shown possible usage in the generation of full-colour displaying films, coatings, switches, filters, photonic papers, responsive optical devices, etc. Polymeric materials have played an important part in the fabrication of PhCs owing to exceptional properties such as high strength, resistance to corrosion, resilience, colour, transparency, processing, and low cost. Among the utilized polymers, poly(styrene-methylmethacrylate-acrylic acid) (P(St-MMA-AA) has been utilized by several studies to generate photonic crystals with unique structural colours for photonic application due to the exceptional features introduced by its functional groups. This paper provided a brief explanation of the synthesis P(St-MMA-AA) of colloidal particles via emulsion polymerization, 3D photonic crystal fabrication, and photonic application. Keywords Photonic crystals · P(St-MMA-AA) · Structural colours · Nanotechnology

I. H. Ifijen (B) · Stanley. O. Omorogbe · A. I. Aigbodion Department of Research Operations, Rubber Research Institute of Nigeria, Benin City, Nigeria e-mail: [email protected]; [email protected] E. U. Ikhuoria · G. O. Otabor Department of Industrial Chemistry, Edo University Iyamho, Okpella, Edo State, Nigeria I. H. Ifijen · E. U. Ikhuoria · Stanley. O. Omorogbe · G. O. Otabor · A. I. Aigbodion · S. D. Ibrahim Department of Chemistry, University of Benin, P.M.B. 1154, Benin City, Nigeria S. D. Ibrahim Plant Protection Division, Department of Agronomy, Rubber Research Institute of Nigeria, Benin City, Nigeria © The Minerals, Metals & Materials Society 2023 The Minerals, Metals & Materials Society, TMS 2023 152nd Annual Meeting & Exhibition Supplemental Proceedings, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-031-22524-6_30

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Introduction Over the last few years, an extensive collection of fascinating utilization in areas such as coatings, catalysis, medicine, photonic crystals, corrosion protection, ionexchange beads, chromatography packing materials, drug delivery and medical diagnostics, calibration standards, etc. has been achieved using colloidal particles with narrow size distributions [1–10]. One of the key problems encountered by investigators in time past has been to attain the most favourable reaction conditions for the generation of replicable monodispersed colloidal nanoparticles (NPs), particularly polymer-based NPs for possible desired applications [11–13]. As a consequence, numerous examinations have employed the use of diverse techniques such as emulsion-solvent evaporation, solvent displacement, double emulsion and evaporation, dialysis, emulsion-diffusion, salting out, interfacial polymerization, and emulsion polymerization to fabricate polymer colloidal particles [19]. Our interest in this study is in the application of polymeric-based photonic crystals for diverse photonic applications. Photonic crystals (PhCs) are dielectric structures with a periodic feature that are created to produce the energy band structure for photons, which either permits or blocks the propagation of electromagnetic waves of specific frequency ranges, making them ideal for light-harvesting utilizations [13–15]. Photonic crystals (PhCs) have attracted enormous significance owing to their extraordinary light tuning features [15–18] and have displayed prospective utilization in several areas such as optic devices, chemical and biological sensors, catalytic supports, and coating materials [18, 19]. In particular, the interaction of materials possessing a periodic modulation with light in their structure has led to a range of exciting and exceptional effects, which have shown potential utilizations in the development of full-colour displays, Bragg mirrors, coatings, switches, photonic papers, filters, responsive optical devices, super-prisms, waveguides, UV protection, and optical resonators [16–18]. Polymers have played a significant role in the generation of photonic crystals due to the following reasons: they have demonstrated compatibility with various patterning approaches; they are relatively inexpensive and can be functionalized to realize required electronic, optical, or mechanical properties. Over the years, polymers such as polystyrene, polyethylene glycol, poly(methyl acrylate), polyurethane acrylate, and poly(styrene-methylmethacrylate-acrylic acid) (P(St-MMA-AA)) have been used to fabricate photonic crystals. However, the superhydrophilic property introduced by the incorporation of carboxylic functional groups and polar acrylate into the matrix of non-polar PS has made it stand out when compared to the others [20]. Emulsion polymerization is one of the most widely used strategies for synthesizing P(St-MMA-AA) due to the following benefits; rapid polymerization to a high molecular weight with narrow molecular weight distribution, lower viscosity, when compared to other techniques and heat, which is comparatively easy to remove from the reactor with water as the continuous phase.

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The synthesis of P(St-MMA-AA) colloidal particles using emulsion polymerization, 3D photonic crystal fabrication, and photonic application was briefly explained in this research.

Emulsion Polymerization Emulsion polymerization is an exceptional route that involves the emulsification of hydrophobic monomers by an oil-in-water emulsifier, after that the initiation of the reaction with either an oil-soluble initiator (e.g., ammonium persulfate) or a watersoluble initiator (e.g., potassium persulfate) in the presence of stabilizer which may be nonionic or ionic to disperse hydrophobic monomer via aqueous solution [21]. Emulsion polymerization is a somewhat complicated procedure because polymeric growth, nucleation, and stabilization of particles are regulated with the aid of free radical polymerization mechanistic pathways in combination with multifarious colloidal phenomena [21]. In comparison to other polymerization strategies, emulsion polymerization presents an increasing molecular weight of the constituted latexes via lowering the rate of polymerization induced by a decrease in reaction temperature or initiator concentration [22]. Approaches to emulsion polymerization involve (1) inverse emulsion polymerization [23], where the polymerization media employed in the emulsifying of the hydrophilic monomers are organic solvents of extremely low polarities such as xylene or paraffin [23], then a hydrophobic initiator is used to start the initiation process [22]. (2) Traditional emulsion polymerization, whereby the emulsification of a hydrophobic monomer, is achieved in water and a water-soluble initiator is used to initiate the polymerization process [22]. These two polymerization classes are said to be water-in-oil (w/o) or oil-in-water (o/w) emulsions [22]. (3) Microemulsion polymerization is known to possess very small amounts of monomer droplets (approximately 10–100 nm) and is distinguished by the following properties: polymer particles = 10–50 nm, surfactant concentration > CMC, generally utilised waterbased soluble initiators [9, 10]. The kinetics, nucleation, and growth mechanisms are quite different from miniemulsion, microemulsion, and traditional emulsion polymerizations [1]. (4) Miniemulsion polymerization is about monomer droplets in water systems with considerably more smallish droplets when compared to emulsion polymerization. It is differentiated by surfactant concentration < critical micelle concentration (CMC), monomer droplet = 50–1000 nm, polymer particle size equal to monomer droplet size = 50–1000 nm, water-insoluble co-stabilizer as hexadecane to prevent Ostwald ripening, and both the use of oil-soluble and water-soluble initiators [23].

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Synthesis, Fabrication, and Photonic Applications of Emulsion Polymerization-based-P(St-MMA-AA) Colloidal Particles Chen et al. (2019) synthesized monodispersed P(St-MMA-AA) microspheres with an average particle size of approximately 240 nm via an emulsion polymerization approach [24]. The refractive index of P(St-MMA-AA) latex having an amino group on the microsphere surfaces was grafted with fullerene acetic acid via an amidation reaction pathway to generate fullerene-modified P(St-MMA-AA) colloids. The generated microspheres were subsequently utilized in the fabrication of photonic crystals and analyzed. The SEM/TEM images of the synthesized P(St-MMA-AA), aminated P(St-MMA-AA) colloids, and the colour reaction of ninhydrin on aminated microspheres show that the generated microspheres have excellent dispersibility level and a uniform particle size of 240 nm (Fig. 1). The distinct blue colour formed by the drop-wise introduction of 0.2% ninhydrin ethanol solution in P(St-MMA-AA)-NH2 microspheres signified is an indication that amino modification was carried out on the surface of the polymer microspheres. The corresponding SEM images and reflection spectra of the generated fullerene-modified P(St-MMA-AA) colloidal crystals show a three-dimensional ordered periodic structure [Fig. 2]. The Bragg diffraction of fullerene is modified and a centre wavelength redshift from 567 to 756 nm. The modification of the P(St-MMA-AA) latex by the introduction of an amino group and fullerene acetic acid drastically improved the refractive index of the terpolymer microspheres. Polydispersed polymers are generally simpler to prepare than their monodispersed polymers since their synthetic techniques do not involve any precise reaction conditions unlike that of their monodispersed counterparts [11]. In an attempt to attain monodispersed polymers with specific desired properties, several researchers have altered reaction parameters such as initiator amount, reaction temperature monomer amount, ionic strength of the system, stirring speed, initiator types, etc. Ikhuoria et al. (2018) generated P(St-MMA-AA) microspheres via a soap-seeded

Fig. 1 a SEM images of the synthesized 240 nm P(St-MMA-AA) colloids, b TEM images of the synthesized 240 nm aminated P(St-MMA-AA) colloids, c color reaction of ninhydrin on aminated microspheres [24]

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Fig. 2 Fullerene-modified P(St-MMA-AA) colloidal crystals with 0.4 wt% a SEM images, b reflection spectra [24]

emulsion polymerization using anionic, cationic, and Gum Arabic (GA) emulsifiers in a one-pot synthesis [20]. Thereafter, the photonic crystals were fabricated using the different surfactant types from the as-synthesized P(St-MMA-AA) terpolymers. The resultant colloidal latex samples were applied in the formation of coloured tunable photonic crystals via the vertical deposition technique. The effects of using different surfactants, anionic, cationic, and natural stabilizers (Gum Arabic) during the synthetic process of the P(St-MMA-AA) terpolymers with particular emphasis on the morphology, particle size, and optical features of the photonic applications were evaluated. The SEM and TEM images of the CTAB emulsified P(St-MMA-AA) photonic crystals revealed a raspberry-like structure making it a promising material in the formation of superhydrophilic and superhydrophobic coating films (Fig. 3e, f). This structure was absent in the SDS and GA emulsified photonic crystals. However, the structure of all the photonic crystals generated from the CTAB, SDS, and GA emulsified P(St-MMA-AA) possessed a cubic close-packed arrangement (Fig. 3). The outcome revealed that the utilized emulsifiers efficiently decreased the zeta potential and the polydispersity index (PDI) of the terpolymer samples. The authors also observed changes in the particles sizes as well as the resulting morphologies with variation in the emulsifier type. Examination of the colour reflectance and irradiance emissivity revealed that the generated photonic crystals were seen to reflect varying wavelengths (iridescent colours) with alteration in the viewing angles. This observation was ascribed to the changes in bandgap caused by the varied morphologies linked to the difference in emulsifier types. The authors concluded that the disparity in the colour reflectance observed as the viewing angle changed affirms the photonic prospect of the generated P(St-MMA-AA) crystals. In another study, Ifijen et al. (2018) produced a monodispersed poly(styrenemethyl-methacrylate acrylic acid colloidal spheres (P(St-MMA-AA)) via soapseeded emulsion polymerization and examined the effects of reaction parameters such as stirring speed, temperature, initiation concentration, etc. [10]. Thereafter, photonic crystals were generated from the synthesized P(St-MMA-AA) microspheres. The average particle diameter of the microspheres was seen to increase with an increase in the concentration of monomer and reduce with an increase in

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Fig. 3 SEM (a, c, and e) and TEM (b, d, and f) images of P (St-MMA-AA) photonic crystal films fabricated using SDS, GA, and CTA B emulsifiers, respectively [20]

the reaction temperature, initiator concentration, and stirring speed. The generated particles demonstrate steady mechanical features within the heating and transition temperatures of 388 and 111.9 °C correspondingly. The produced latexes all exhibited a stable dispersion of colloidal particles according to the obtained range of zeta-potential values from 31.8 to 36.5 mV. Microscopic analyses show that the colloidal latex assembled into an ordered structure with principally hexagonal threedimensional structures with multi-facet arrangements. The terpolymer particles were also confirmed to be spherically shaped monodispersed core–shell particles. A number of theories regarding the assembly of P(St-MMA-AA) colloidal particles have been postulated. Specifically, the effect of the wettability on latex assembly is significantly understood with regard to the creation of precise functional PCs due to its influence on the spreading, wetting, and evaporation time of the colloidal suspension. Hydrophilic or hydrophobic substrates have been shown to produce patterned

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PCs with improved features [18]. Wang et al. (2016) demonstrated an effortless production of eye-patterned PCs that is comparable to peacock tail feathers with superhydrophilic flat substrate sandwiching of P(St-MMA-AA) latex suspension by the hydrophobic one via self-assembly of colloidal particles in a sandwich mode [18]. The possibility of coating the surfaces of wood with an emulsion containing P(St-MMA-AA) microspheres to induce structural colours has been reported [25]. Liu et al. (2021) synthesized P(St-MMA-AA) microspheres for the fabrication of structural coloured photonic crystal coatings on wood surfaces via a thermal-assisted gravity deposition method [26]. Morphologies and self-assembly of the microparticles along with the photonic crystal coatings on the surface of the wood were investigated. The high-quality monodispersed microspheres guarantee a periodic arrangement of the photonic crystal particles on the wood surface. The generated crystal particles were also observed to possess core–shell morphology. The colloidal microspheres aggregated at the emulsion surface under the thermal influence and were arranged in an ordered manner creating two-dimensional structural coloured photonic crystals during the self-assembly course. Owing to the non-stop evaporation of water, the photonic crystals assembled, solidify and eventually generated the structural coloured coating with iridescent influence on the surface of the wood (Fig. 4). The outcome of their examination produced an eco-friendly and novel strategy to advance the colour of wood surfaces. The structural colours of P(St-MMA-AA) photonic crystals can be enhanced by incorporating other materials into their matrix. For instance, the incorporation of dispersed dyes into photonic crystals takes advantage of both structural colour iridescence and traditional colouration. Yavuz et al. (2018) examined the collective influence of disperse dye with the P(St-MMA-AA) PhCs on the photonic crystals’ distribution, shape, iridescence, organization, thermal stability, chemical structure, and Fig. 4 Self-assembly of microspheres of varying diameters via thermal-assisted gravity deposition on the wood surface [26]. The diameters of the microspheres in each row are I 234 nm, II 206 nm, III 178 nm, IV 169 nm, and V 152 nm, respectively [26]

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Fig. 5 Dyed PCs recovered after filtering process. Images were captured at specific observation angles

reflectance [27]. Photonic crystals were effectively formed in the form of extremely monodisperse, spherical, colloidal structures. Fourier-transformed infrared spectroscopy was used to affirm the existence of dye in the photonic crystal’s inner core–shell structure. The photonic crystal’s iridescent effect and brightness were improved by the infiltration of the dye into their matrix, thereby promoting successful self-assembly of the colloidal spheres in an array form. A successful textile fabric coating which assembled into well-ordered face-centred cubic, closed-packed arrays and beautiful structural colours and an enhancement of its reflectance feature were achieved by coating the dyed polyamide fabrics with the dyed photonic crystals (Fig. 5). The authors concluded that the incorporation of dispersed dyes with PhCs is a somewhat complex idea that could unlock novel ways to understand the effect of the PhCs photonic-band structure and the photoluminescence features of the dyes located in the inner space of the photonic crystals.

Conclusion This study has successfully reported a brief description of P(St-MMA-AA) synthesized via the emulsion polymerization approach, 3D P(St-MMA-AA) photonic crystals and its photonic application. The reviewed literature revealed that photonic crystal films generated using this terpolymer have outstanding microstructures, which produce structural brilliant colours that change with the lattice and micro-nanostructure variations. This study showed that the synthesis of P(St-MMA-AA) microspheres via emulsion polymerization can be used to fabricate photonic crystals with unique properties for various applications.

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References 1. Omorogbe SO, Ikhuoria EU, Igiehon LI, Agbonlahor GO, Ifijen IH, Aigbodion AI (2017) Characterization of sulphated cellulose nanocrystals as stabilizer for magnetite nanoparticles synthesis with improved magnetic properties. Nig J Mater Sci and Eng 7(2):23–31 2. Ifijen IH, Ikhuoria EU, Aigbodion AI, Omorogbe SO (2018) Impact of varying the concentration of tetraethyl-orthosilicate on the average particle diameter of monodisperse colloidal silica spheres. Chem Sci J 9(1):183–185 3. Ikhuoria EU, Ifijen IH, Georgina OP, Ehigie AC, Omorogbe SO, Aigbodion AI (2020) The adsorption of heavy metals from aqueous solutions using silica microparticles synthesized from sodium silicate. In: Ni-Co 2021: the 5th International symposium on Ni and Co, pp 195–205 4. Omorogbe SO, Aigbodion AI, Ifijen HI, Ogbeide-Ihama N, Simo A, Ikhuoria EU (2020) Low temperature synthesis of super paramagnetic Fe3 O4 morphologies tuned using oleic acid as crystal growth modifier. In: TMS 149th annual meeting & exhibition supplemental proceedings, pp 619–631 5. Ifijen IH, Itua AB, Maliki M, Ize-Iyamu CO, Omorogbe SO, Aigbodion AI, Ikhuoria EU (2020) The removal of nickel and lead ions from aqueous solutions using green synthesized silica microparticles. Heliyon 6(9):e04907 6. Ifijen IH, Ikhuoria EU, Maliki M, Otabor GO, Aigbodion AI (2022) Nanostructured materials: a review on its application in water treatment. In: The Minerals, Metals & Materials Society (eds) TMS 2022 151st annual meeting & exhibition supplemental proceedings. The minerals, metals & materials series. Springer, Cham, pp 1172–1180 7. Ifijen IH, Aghedo ON, Odiachi IJ, Omorogbe SO, Olu EL, Onuguh IC (2022) Nanostructured graphene thin films: a brief review of their fabrication techniques and corrosion protective performance. In: The Minerals, Metals & Materials Society (eds) TMS 2022 151st annual meeting & exhibition supplemental proceedings. The minerals, metals & materials series. Springer, Cham, pp 366–377 8. Ifijen IH, Maliki M, Omorogbe SO, Ibrahim SD (2022) Incorporation of metallic nanoparticles into alkyd resin: a review of their coating performance. In: The Minerals, Metals & Materials Society (eds) TMS 2022 151st annual meeting & exhibition supplemental proceedings. The minerals, metals & materials series. Springer, Cham, pp 338–349 9. Ifijen IH, Maliki M, Odiachi IJ, Aghedo ON, Ohiocheoya EB (2022) Review on solvents-based alkyd resins and water borne alkyd resins: impacts of modification on their coating properties. Chem Afri. https://doi.org/10.1007/s42250-022-00318-3 10. Ifijen IH, Ikhuoria EU, Omorogbe SO (2018) Correlative studies on the fabrication of poly (styrene-methyl-methacrylate-acrylic acid) colloidal crystal films. J Dispersion Sci Tech 40(7):1–8 11. Ifijen IH, Jonathan EM, Jacob, Udokpoh NU, Archibong UD (2022) Synthesis of polydispersed P(St-MMA-AA) microspheres and fabrication of colloidal crystals with non-compact morphology. Tanzania J Sci 48(1):140–147. https://doi.org/10.4314/tjs.v48i1.13 12. Omorogbe SO, Ikhuoria SO, Ifijen IH, Simo A, Aigbodion AI, Maaza M (2019). Fabrication of monodispersed needle-sized hollow core polystyrene microspheres. In: The Minerals, Metals & Mater Soc (eds) TMS 2019 148th annual meeting & exhibition supplemental proceedings, pp 155–164 13. Ifijen IH, Ikhuoria EU (2019) Generation of highly ordered 3d vivid monochromatic coloured photonic crystal films using evaporative induced technique. Tanzania J Sci 45(3):439449 14. Ifijen IH, Ikhuoria EU (2020) Monodisperse polystyrene microspheres: studies on the effects of reaction parameters on particle diameter. Tanzania J Sci 46(1):19–30 15. Ifijen IH, Ikhuoria EU, Omorogbe SO, Aigbodion AI (2019) Ordered colloidal crystals fabrication and studies on the properties of poly (styrene-butyl acrylate-acrylic acid) and polystyrene latexes. In: Srivatsan T, Gupta M (eds) Nanocomposites VI: nanoscience and nanotechnology in advanced composites. The minerals, metals & materials series. Springer, Cham, pp 155–164

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16. Ifijen IH, Maliki M, Ovonramwen OB, Aigbodion AI, Ikhuoria EU (2019) Brilliant coloured monochromatic photonic crystals films generation from poly (styrene-butyl acrylate-acrylic acid) latex. J Appl Sci Environ Manage 23(9):1661–1664 17. Ifijen IH, Omorogbe SO, Maliki M, Odiachi IJ, Aigbodion AI, Ikhuoria EU (2020) Stabilizing capability of gum Arabic on the synthesis of poly (styrene-methylmethacrylate-acrylic acid) latex for the generation of colloidal crystal films. Tanzania J Sci 46(2):345–435 18. Wang M, Meng F, Wu H, Wang J (2016) Photonic crystals with an eye pattern similar to peacock tail feathers. Curr Comput-Aided Drug Des 6(8):99 19. Pal SL, Jana U, Manna PK, Mohanta GP, Manavalan R (2011) Nanoparticle: An overview of preparation and characterization. J Appld Pharmaceutical Sci 01(06):228–234 20. Ikhuoria EU, Omorogbe SO, Sone BT, Nuru ZY, Khamlich S, Maaza M (2018) Raspberry-like and other hexagonal closepacked morphologies of P(St-MMA-AA) particles obtained from different emulsifiers for photonic applications. J Mod Opt 65(15):1817–1826 21. Chern CS (2006) Emulsion polymerization mechanisms and kinetics. Progress in Polymer Scien 31(5):443–486 22. Odian G (2004) Emulsion polymerization, principles of polymerization. Fourth edition John Wiley & Sons, Inc, pp 350–371 23. El-hoshoudy ANMB (2018). Emulsion polymerization mechanism. In: Cankaya N (ed) Recent research in polymerization. IntechOpen, London 24. Chen Q, Ding X, Yu B, Shen Y, Cong H (2019) Synthesis of fullerene-modified P(St-MMA-AA) colloids and optical performance in colloidal crystals. Integr Ferroelectr 197(1):43–48 25. Liu Y, Hu J, Wu Z (2020) Fabrication of coatings with structural color on a wood surface. Coatings 10:32 26. Liu Y (2021) Self-assembly of poly(styrene-methyl methacrylate-acrylic acid) (P(St-MMAAA)) colloidal microspheres on wood surface by thermal-assisted gravity deposition. Wood Sci Technnol 55(2):403–417 27. Yavuz G, Felgueiras HP, Ribeiro AI, Seventekin N, Zille A, Souto AP (2018) Dyed poly (styrene-methyl methacrylate-acrylic acid) photonic nanocrystals for enhanced structural color. ACS Appl Mater Interfaces 10(27):23285–23294

Effect of Drying on Textured Coat Synthesized from Waste Glass for Building Application Andrew Ojonugwa Adejo and Jeff Kator Jomboh

Abstract Interior and exterior coat samples were collected and subjected to viscosity (ASTM D-4741), adhesion (ASTM D-3359), dry-time (ASTM D-1640 M), and abrasion (ASTM D-4060) analysis, respectively to determine its drying effect on application. The result shows that both interior and exterior coatings had 35poise viscosity, 4A adhesion, dry-time of 5 min set-to-touch time, 10 min dust-free time, 20 min tack-free and 530 min hard-dry time, and 0.2 g (interior coating) and 0.35 g (exterior coating) abrasion rate which correspond with the American Society for Testing Materials (ASTM) and Standard Organization of Nigeria (SON) standard values of 40 ± 0.5poise for viscosity, 1A–5A adhesion rate, 30 ± 5 min set-to-touch time, 30 ± 5 min dust-free time, 60 min tack-free, 1440 ± 5 min hard-dry time, and 4 ± 5 g abrasion rate. These indicate that the drying effect of synthesized building textured coating has a workable viscosity with zero orange peeling upon drying, a less than 5% flick rate on adhesion, and excellent abrasion resistance attributable to sufficient drying on application. Keywords Drying · Waste glass · Textured coating

Introduction Drying, also known as dehydration or dewatering is one of the oldest curing and preservation processes available to mankind which is traceable to prehistoric times. Early men explored the use of this process in the preservation and slowing down the spoilage time of agricultural products such as fruits, vegetables, grains, fish, meat, A. O. Adejo (B) Department of Glass and Silicate Technology, Federal University of Lafia, Lafia, Nasarawa, Nigeria e-mail: [email protected]; [email protected]; [email protected] J. K. Jomboh Department of Industrial Design, University of Maiduguri, Maiduguri, Borno, Nigeria e-mail: [email protected] © The Minerals, Metals & Materials Society 2023 The Minerals, Metals & Materials Society, TMS 2023 152nd Annual Meeting & Exhibition Supplemental Proceedings, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-031-22524-6_31

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wood, and other agricultural products using sun rays [1, 2]. Drying is an operation that thermally removes water content to yield a solid product. In drying, heat energy is transferred from the surrounding environment to the wet solid surface of a material such that the moisture content of the material is evaporated. This is possible through the various processes of heat transfer, i.e., convection, conduction, or radiation, and in some cases, a combination of any of these transfer methods [3, 4]. Drying is a fundamental procedure in the building-construction and finish industries as most materials used in this field are largely dependent on this procedure to attain their optimum application and usage [5, 6]. Coating or paint, as it is generally referred to, is a liquid, liquefiable or mastic composition which, upon application to a substrate in a thin layer, is converted to a solid film, [7, 8] This conversion of a mastic composition to a solid film is possible due to the drying and evapouration of moisture content in the coat, leaving behind dried pigment or extender which is adhered to the surface of application to give it protective and aesthetic properties. Coatings are majorly composed of binder, solvent, and pigment (extenders and fillers) and they are of various types and classifications based on their composition. Due to their low toxicity and environmental friendliness, water-based coatings are the most widely employed in the world, mainly for building applications [8]. As such, this has given rise to high demand for materials utilization, which in turn is seen in high exploration and demand for natural and synthetic raw materials (pigment, extender or filler) such as clays, calcium carbonate, mica, silica, talc engineered molecules, calcined clays, precipitated calcium carbonate, and synthetic pyrogenic silica [9–11]. Though these raw materials present coatings with various properties like toughness, texture, and cost reduction, to mention but a few, they are posed with the disadvantage of varying drying effects due to their water absorption properties, which leads to their degradation [10]. Glass as a user-friendly material has gained wide acceptance in our world today. This is evident in its usage that cuts across various manufacturing industries. As such, when used and disposed of, they end up in the environment as waste, which can be processed and recycled into cullet for further usage and application. Though glass is known for its excellent water resistance ability, this property makes it suitable for application as an extender in coating production owing to the fact that they do not absorb moisture from the environment and also from their surfaces of application as compared with other extending materials. It is therefore important to determine the effect of drying on a textured coat synthesized from waste glass for building application.

Methodology Viscosity analysis was conducted on the collected samples of coatings using the ASTM D4741 [12] procedures. 40 g of interior textured coating synthesized from waste glass was dispensed into a viscometer cup (Model-35 viscometer) with the spindle inserted into the filled cup and it was allowed to spin at a speed of 300 rpm

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until it reached a stoppage point and the readings were taken; this procedure was repeated on the exterior coating. Adhesion test as described by ASTM D3359 [13] was employed to determine the binding ability of building textured coating synthesized from waste glass using the standard tape test method-A. Samples of produced interior coating were applied to a flat metal sheet such that it is free from blemishes and all forms of surface imperfection. Placed on a firm base and using a straightedge clean sharp cutting knife at steady motion, the coat was cut through in two cuts of 40 mm long such that it intersects at the middle with a small angle range of 30° and 45° to make an X inscription thereby making the metal sheet visible. Cutting off two revolutions of 2.5 mm wide Abro-masking tape from its roll, 75 mm length of the tape was cut and placed on the center of the intersection of the cuts with the tape running in the same direction as the smaller angles which was smoothened using a finger in the area of the incisions such that entrapped air is avoided as the tape is rubbed firmly with some pressure until it appears uniformly thereby indicating good, uniform contact between the tape’s adhesive and the coating surface. Seizing a free end of the tape, it was pulled off rapidly backward at an angle 180° after every 60 s of application; this was carried out three respective times. This procedure was repeated on the exterior coatings. Drying time analysis investigates the behaviour of the synthesized coating upon application to building walls viz-a-viz its set-to-touch time, dust-free time, tack-free time, and hard-dry time. The standard dry time procedure as proposed by ASTM D1640M [14] was employed. Samples of interior and exterior coatings were separately applied on a plate at a temperature of 25 °C and a humidity of 50%. After some seconds of exposure, set-to-touch time was determined by touching the coating gently with the fingertip and transferring it immediately to a clean glass sheet to observe its registration on the glass; this procedure was carried out at an interval of 30 s until no registration was observed and the time was recorded accordingly. The dust-free time of the coating was determined by dropping a cotton fibre on the coated plates and blowing gently at 5 psi. This procedure was repeated at an interval of 1 min until the cotton fibre was unable to stick to the coated plates and the time was recorded accordingly. To determine the tack-free time, samples of both the interior and exterior coatings were applied separately to a plate under a control condition, and a tack paper of 50 mm by 75 mm was placed on the coated plates. A pressure of 13.8 kPa (2psi) was exerted on the plates for 5 s and the plates with the tack paper were inverted for 10 s to see if the paper would drop. This was repeated until the tack paper dropped, and the time was recorded accordingly. Also, in the case of the hard-dry test, the coated plates of both the interior and exterior coatings were separately exposed to maximum downward pressure using the thumb on the films without twisting them for an interval of 30 min. The surface of the coated plates was lightly cleaned with a soft napkin to remove any registered print by the thumb. This process was repeated continuously until it reached a point where no mark of the thumb was left after cleaning, and the time was recorded accordingly. Abrasion analysis of interior and exterior textured coats synthesized from waste glass was conducted in line with ASTM D4060 [15]. Samples of both coatings were

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analysed using a Taber abraser to investigate the effect of drying as it affects the synthesized coating. The samples of the produced coatings were applied separately on a plate and it was allowed to dry hard. Their weights were determined upon drying (P1) and, with the aid of a Taber abraser having a known weight of 3.5 kg, the coated plate was kept at a rigid position and the abraser was used to make a 60 cycle of rotation on the sample, after which the weight of the coated plate was measured again at the end of the experiment (P2) and it was recorded both for the interior and exterior coatings. Abrasion Rate = Weight of Coated plate before analysis (P1) – Weight of coated plate after analysis (P2)

Results and Discussion The result of this study shows the level of fluidity of the coat viz-a-viz its effects on workability and drying, as it can be seen on the orange peel upon drying. The study reveals that a uniform viscosity value is attained regardless of the area of application, i.e., interior or exterior (see Table 1), implying that the coating has a controlled fluidity level of standard 40 ± 0.5 poise ASTM D-4741 [12] and Ajayi et al., [16]. Also, considering the fact that the recycled glass, which acts as an extender, is not moisture absorbing, this helped in controlling the solvent content of the coating during formulation, which helped in controlling the fluidity level, increasing binding ability, and controlling the rate of solvent [17–19]. The dry time analysis of the coatings (interior or exterior) reveals that the synthesized coating shows a good dry time ability owing to the fact that its results fall within the approved dry time standard for each of the parameters as stipulated by the Standard Organization of Nigeria [16] and ASTM 1640 [14] (see Table 2). At five minutes of dryness, both interior and exterior coatings exhibit uniform set-to-touch time that is in line with the standard value 30 ± 5 min., a dust free 10 min which is consonant with the standard value of 30 ± 5 min, a tack free and hard dry time that falls within the standard of 60 ± 5 min, and 1440 ± 5 min. Ordinarily, it would be expected that coatings used for exterior applications would have some variation in drying rate at different levels, but because this study was conducted in a controlled environment, as well as due to the non-solvent/moisture absorbing nature and nonhydroscopic properties of the extender used, the result shows that waste glass has an inherent characteristic that supports the uniform drying of synthesized building coat from waste. Table 1 Viscosity rate of produced coat

S/N

Sample of coatings

Viscosity (Centi poise)

1

Interior

36

2

Exterior

36

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Table 2 Dry Time analysis on produced coat S/N

Sample

Set to touch (Min)

Dust free (Min)

Tack free (Min)

Hard dry (Min)

1

Interior

05

10

20

530

2

Exterior

05

10

20

530

Adhesion analysis of textured coat synthesized from waste glass as seen in Table 3 is an indication that both the interior and exterior coatings exhibit an excellent binding ability to their substrate of application ASTM D3359 [13]. Also the effect of waste glass recycled as extender in the coating is such that it does not encourage sagging or falling of the material upon application as no extra weight is created either by absorbing solvent during production or at post production when they are applied to surface. This implies that in relations to adhesion classification scale and existing researches [20, 21]. This study posses a less than 5% flick rate owing to a good viscosity rate that reflecting in an excellent and uniform drying which give rise to an adhesion rate that is non-supportive of orange pilling, cracking, crazing to mention but a few and also or any form as such trace peeling of particles along incisions lines was noticed 13]. The result in Table 4 shows the abrasion rate of the developed coats, both the interior and exterior coatings are posed to abrasion since they are composed of coarse aggregates. However the coatings under a load of 3.5 kg in 60 cycles exhibit an abrasion rate of 0.2 g for the interior coat and a 0.35 g for the exterior coat which implies that the recycled glass used as extender in the synthesis of the coat exhibit a negligible physical degradation as they are with the standard abrasion rate of 4 g ± 0.5 g as approved by ASTM D4060 [15] and is in line with the study of Wu, Guo, & Zhang, [22] and Møller, [23]. Table 3 Adhesion test of produced PCG coat Interior coat

Exterior coat

S/N Coat colour Classification % of removed Coat colour Classification % of removed area area 1

Interior

4A

Less than 5

Yellow

4A

Less than 5

2

Exterior

4A

Less than 5

Red

4A

Less than 5

Table 4 Abrasion test of the produced PCG coat

S/N

Coating type

Abrasion rate (g)

1

Interior Coat

0.20

2

Exterior Coat

0.35

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Conclusion Considering the results of the various analyses conducted on textured coats synthesised from waste glass for building applications, it is evident that a viscosity of 36 poises serves as a catalyst which influences the drying ability of the coatings. The extender developed from waste glass which serves as the source of texture in the coating is non-moisture absorbing and it encourages uniform dryness upon application. This is evident in the set-to-touch time of 5 min, dust-free time of 10 min, tack-free time of 20 min, and hard-dry time of 530 min, which gives rise to an adhesion of 4A that supports less than 5% flick rate as confirmed by a negligible abrasion rate of 0.20 g and 0.35 g respectively.

References 1. Mali SB, Butale MC (2019) A review paper on different drying methods. Int J Eng Res Technol 8(5):211–216 2. Phalak, M., & Banerhee, D. (2022). A Review on the Effects of Drying and Humidity on Food Products. 3. Ojike O, Nwoke OO, Okonkwo WI (2011) The influence of different solar drying systems on the vitamin content of pawpaw (Carica ppaya). Aust J Agric Eng 2(1):8–11 4. Mustapa NAH, Ahmad SR (2019). Effects of various drying methods on the vitamin C level of papaya locally grown in brunei darussalam. Pertanika J Sci & Technol, 27(1). 5. Mujumdar, A. S. (2014). Principles, classication, and selection of dryers. In: Handbook of industrial drying. CRC Press (pp 33–60) 6. Zaccaron A, de Souza Nandi V, Bernardin AM (2021) Fast drying for the manufacturing of clay ceramics using natural clays. J Build Eng 33:101877 7. Sanyaolu NO, Awosanya A, Sonde OI, Kareem FA, Yussuf ST, Akinwunmi F, Ibikunle AA (2019). Comparative evaluation of the alkyd resins of the composite oils of soybean (Ricinus communis) and castor (Glycine max) Seed oils with castor seed oil for alkyd paint formulation. J Chem Soc Niger, 44(5). 8. Rani, A., & Kumar, R. (2019) Forensic application of energy dispersive X-Ray fluorescence to analyse a vehicle paint sample, J Forensic Sci & Criminal Inves 11(4): JFSCI.MS.ID.555820 (2019) 9. Horvath L (2008) Coatings go beyond appgarance to provide quality control. Foundry Manag & Technol 136(1):26–27 10. Stanley OC (2013) The Effect of gypsum plaster on the dry rate of emulsion. Chem Mater Res 3(11):79–85 11. Vidyasagar M, Kumar GA, & Balamurugan S (2015) Productivity improvement in CED paint plant by jig modification. In: Applied mechanics and materials. Trans Tech Publications Ltd (Vol 766, pp 1159–1167) 12. ASTM D4741–13, (2013) Standard test method for measuring viscosity at high temperature and high shear rate by tapered-plug viscometer, ASTM International, West Conshohocken, PA, www.astm.org 13. ASTM D3359–17 (2017) Standard test methods for rating adhesion by tape test, ASTM International, West Conshohocken, PA, www.astm.org 14. ASTM D1640 / D1640M-14 (2018) Standard test methods for drying, curing, or film formation of organic coatings, ASTM International, West Conshohocken, PA, 2018, www.astm.org 15. ASTM D4060–19 (2019) Standard test method for abrasion resistance of organic coatings by the taber abraser, ASTM International, West Conshohocken, PA, www.astm.org

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16. Ajayi O, Ajekwene KK, Keren C (2015) DN Mayer raw material specification according to standard organization of Nigeria (SON) Oregun Lagos 17. Akinterinwa A, Osemeahon SA, Nkafamiya II, Dass PM (2015) Formulation of emulsion paint from a copolymer composite of dimethylol urea/polystyrene. Chem Mater Res 7(7):20–26 18. Schoff CK (2017) Craters and other coatings defects: mechanisms and analysis. In: Protective coatings. Springer, Cham. (pp 403–425) 19. Eley RR (2019) Applied rheology and architectural coating performance. J Coat Technol Res 16(2):263–305 20. Boentoro TW, Szyszka B (2013) Protective coatings for optical surfaces. In: optical thin films and coatings. Woodhead Publishing. (pp 540–563) 21. Wypych, G. (2018). Mechanisms of adhesion. WYPYCH, G. Handbook of adhesion promoters. [S. l.]: Chem Tec Pub, 5–44 22. Wu L, Guo X, Zhang J (2014) Abrasive resistant coatings—a review. Lubricants 2(2):66–89 23. Møller VB, Dam-Johansen K, Frankær SM, Kiil S (2017) Acid-resistant organic coatings for the chemical industry: a review. J Coat Technol Res 14(2):279–306

In-Situ Alloy Formation During Selective Laser Melting with CuSn10 and Aluminum Powders Farzad Foadian and Robert Kremer

Abstract Thanks to metal additive manufacturing (AM), the way metal parts are made has changed in recent decades. Almost unlimited designs are possible, and local material properties such as microstructural properties can be realized through regional process variations. Although many scientists and engineers have worked and are working on AM and their efforts have led to the commercialization of AM metal technologies, the effort required to create new and customized alloys is still high. This is due to the fact that a completely created alloy has to be brought into the powdered initial form before it can be manufactured, which involves quite a lot of effort. In-situ alloys can remedy this situation by mixing powder particles of different materials with each other before production and the actual target material is only created during the production process when it is melted by the laser beam. This paper gives a brief overview of the in-situ alloying of a CuAl12Sn9 alloy by selective laser melting of CuSn10 and pure aluminum powder. Keywords Additive manufacturing · Selective laser melting · In-situ alloying · Alloy design · Rapid alloying

Introduction Selective laser melting is an additive manufacturing process with which a 3D part is produced layer by layer from a powdery starting material. The starting material usually consists of pre-alloyed powder or, more rarely, elemental powder mixtures. A 3D model with the intended properties and geometries is first designed using CAD software. As a rule, the STL (Standard Tessellation Language) format is required for further processing of the file. Slicing" takes place subsequently. In this step, the 3D model is divided into individual layers, and support structures can be positioned on the F. Foadian (B) · R. Kremer Faculty of Mechanical Engineering, Sciences and Arts, Dortmund University of Applied, Sonnenstr. 96, 44139 Dortmund, Germany e-mail: [email protected] © The Minerals, Metals & Materials Society 2023 The Minerals, Metals & Materials Society, TMS 2023 152nd Annual Meeting & Exhibition Supplemental Proceedings, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-031-22524-6_32

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component as required. Once slicing is complete and the manufacturing parameters for the build process have been defined, the model can be transferred to the SLM system. For the manufacturing process, the energy of a laser beam is used to create a molten pool and thus locally weld the individual powder particles together. The high-power laser selectively scans the powder bed, resulting in complete melting of the powder and rapid solidification. After exposure, the build plate lowers by one layer thickness, and the powder chamber is raised by one layer thickness. A coater applies the new powder layer to the build plate and scrapes excess powder into a collection container. The component can be removed from the build chamber after completion of the manufacturing process [1–4]. For selective laser melting, metal powder is used as the starting material, which can have different chemical compositions. On the one hand, it can be a pure element, or it is possible to produce mixtures of chemical elements as powder and use them in selective laser melting. The particle size used in laser-based powder bed processes is usually 20 to 100 µm. Thinner material layers can be achieved with finer powder and correspondingly finer laser. Manufacturing methods are numerous and have a strong influence on morphology and particle size. In the atomization of metals, water, gas or plasma can be used as the medium [5]. The industry’s most commonly used powder production method is the water atomization of a melt. In this method, molten metal is discharged from a ceramic nozzle located below a casting vessel. The molten metal flows in free fall and is mechanically disintegrated just below the outlet of annular nozzles from which water exits at very high pressure (100–200 bar). This method produces irregular or even “splashy” particle shapes because the rapid solidification does not allow time for the particles to assume a spherical shape due to surface tension. Figure 1a shows an image from the scanning electron microscope for illustration purposes, showing water-atomized pure iron powder. The irregular particle shape and the high occurrence of agglomerates can be clearly seen [5, 6]. Figure 1b demonstrates high-alloy steel powder from a gas atomization process with nitrogen. The powder shown has a very uniform shape and, due to this fact,

(a)

(b)

Fig. 1 (a) Water atomized pure iron powder [7] and (b) Nitrogen atomized steel powder [5]

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achieves a very high bulk density and good flowability, which is slightly limited due to the isolated saddle formation on the larger particles [5]. Today, laser melting with metals in a powder bed is mainly carried out with prealloyed powders, which are obtained by atomization processes. This process is more complex but offers sufficient quality for am fabrication. The range of available alloys from powder manufacturers is severely limited, and custom-made powder alloys in small quantities are only available at an enormous additional cost. Rapid alloying is a process that is becoming increasingly important. The flexible and cost-effective production of alloys, which are created by selective laser melting during the construction process, is a decisive advantage. In contrast to prealloyed powders, not all powder particles in elemental powder mixtures have the same chemical composition. This characteristic offers many opportunities and possibilities in additive manufacturing to cost-effectively form alloys during the manufacturing process of a component. However, in-situ alloying presents many challenges. For example, two powder components that have a high difference in density tend to segregate strongly [8]. The following publications address this issue, among others. In the field of rapid alloying with a powder mixture of two base elements, more research was carried out on alloys with titanium or aluminum. The alloys with elemental aluminum as a base were often mixed with copper [1, 9, 10]. Martinez et al. (2019) used an Al-Cu12 rapid alloy. For their purpose, the powder bed was preheated to 400 °C, and the resulting components were compared to those fabricated at room temperature. In addition, parameters such as laser power and scanning speed are varied to investigate the effects on density or porosity, chemical composition, and microstructure. The main results were comparable values in terms of tensile strength compared to castings. A preheated powder bed, with the correct settings of laser power and scan energy, supports the complete fusion of the powder particles, resulting in higher ductility compared to parts manufactured at room temperature. However, attention must be paid to the shape and size of the powder particles in this process to maximize the packing density of the powder and thus ensure consistent distribution of elements in the powder bed [10]. In another work by Skelton et al. (2022), the mixing of different-sized elemental particles of an Al/Cu alloy was investigated. For the Al-33wt%Cu alloy, different grain sizes of both the copper powder and the aluminum powder were mixed together to form a total of four mixtures, and the homogeneity of the mixed powder was investigated first, followed by the homogeneity of the microstructure of the fabricated component. It was found that the distribution of particle sizes has a direct influence on the homogeneity of the composition of the samples. This was found to be the case for a number of parameter sets. Particle sizes typically used in laser-based powder bed fusion produced larger variations in composition than samples with smaller particle sizes in the powder mixture [9]. The main advantages of rapid alloys, as described above, are greater flexibility in the choice of alloying elements and, thus, in the choice of material for the component. In addition, this allows for quicker minor adjustments to the alloy, for example, to strengthen or weaken certain mechanical properties in the component by adding alloying elements or changing the alloy ratio. This rapid adaptation and general

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flexibility mean that, among other things, various prototypes can be manufactured more quickly and at a lower cost. In addition, special fabrications can be made in small batch sizes, where the order size of alloyed powders would make production only marginally profitable [11, 12]. However, the many advantages are also countered by some challenges that complicate the process. These include varying melting temperatures of the individual elemental powders. Also, varying reflectivity and consequent varying energy absorption can negatively affect the alloy outcome, leading to inclusions of solid powder particles and inhomogeneities. Furthermore, uneven behavior during the melting process makes it difficult to produce a uniform density in the component. Overall, all these different properties can cause imperfections, inhomogeneities, and the like in parts, which deteriorate the mechanical properties and render the part unusable in case of doubt [10]. Due to the growing need for cost-effective and beneficial alloys in additive manufacturing, in-situ alloying is gaining increasing importance in research and industry. The mixing of elemental powders offers much greater flexibility in additive manufacturing in terms of designing alloys for industry according to the needs [8]. The above facts are the motivation for this work. This study aims to investigate the processing of an aluminum-bronze powder mixture into an in-situ alloy using low laser powers. The available power of the equipment is many times smaller than the laser beam power used in many experiments [5, 13]. The challenge in this experimental setup is thus in the adjustment of the parameters. For example, the variance of the scanning speed will be used to test the extent to which the energy input can be adjusted so that processing of these powders is possible or whether simply adjusting the parameters to the low laser beam power is not sufficient for successful part fabrication. The results can serve as a basis for further applications in the field of highly reflective powder materials in the “Additive Manufacturing—Metals” working group.

Experiments To create a CuAl12Sn9 alloy in the In Situ process, pre-alloyed CuSn10 powder is mixed with 12% pure aluminum powder. Before the actual tests, the powders are examined with regard to their suitability for the additive manufacturing process. For this purpose, the powders were examined under an optical microscope and the grain fractions were determined. The CuSn10 powder has a diameter of 17.3 ± 7.3 µm, while the aluminum powder has a diameter of 20.3 ± 6.2 µm. The size distribution is shown in Fig. 2. Both powders are gas atomized and therefore have a spherical shape. The CuSn10 powder shows hardly any adhesions or other defects, while the aluminum powder shows clear adhesions. For further investigation of the microstructures, the powders were heat-etched in a graphite-containing epoxy resin and

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Fig. 2 Distribution of powder particle sizes

metallographically prepared. Morphology and microstructure can be seen in Figs. 3, 4. Both powders have a very fine microstructure. Only very few pores could be detected in the CiSn10 powder, while the aluminum powder hardly has any particles without pores. In the flow test, the flow time for the aluminum was 7.1 ± 0.3 s, while the aluminum was not hard-working. For the fabrication, 880 g of CuSn10 powder was filled with 120 g of aluminum powder in a 5 L plastic bottle and mixed for 30 min using a shaker-mixer and filled into the SLM machine for processing. The parameterization was based on the processing of bronze in [14]. Due to the equipment used (Concept Laser MLab R), the laser power was limited to 100 W. A track spacing of 0.065 mm was selected and a layer thickness of 0.02 mm, while the scanning speed varied between 100 and 450 mm/s. Nitrogen was used as the shielding gas.

Fig. 3 SEM images of the CuSn10 powder: (left) the powder as delivered and (right) the microstructure of the particle

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Fig. 4 SEM images of the aluminum powder: (left) the powder as delivered and (right) the microstructure of the particle

In order to assess the homogeneity of the powder bed, some powder layers were grown without exposure, and samples were taken from the powder bed using an adhesive carrier. Figure 5 shows one of the samples taken under the SEM, which allows the powders to be distinguished based on their brightness. Here it is particularly clear that the cusn10 powder is finer and shows fewer defects. A computer-aided graphic examination was carried out on the images using ImageJ. In the process, 25.1 ± 2.2% of the projected particle area was assigned to aluminum, which corresponds to a mass fraction of 9.36 ± 0.7% and is thus below the added 12%. To test parameters, density cubes with dimensions of 10 × 10 × 10 mm3 were manufactured and examined. For this purpose, the density was determined hydrostatically, the Vickers hardness was measured, and a metallographic section was made. A total of two construction jobs with density cubes were carried out. The

Fig. 5 SEM images of the mixed powder

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Fig. 6 Hardness and density as a function of the energy introduced

test components could be built up, but the powder bed showed strong intermixing in some cases. After grinding the surface, the manufactured cubes show visible pores and different color gradients on the surface, so it is assumed that segregation occurred in the manufacturing process. The measured density and hardness values are plotted in Fig. 6. There is no clear trend in the values over the applied energy, but there seems to be a trend that a higher energy density produces a denser material. The highest density determined is 7.85 g/cm3 . To obtain information about the pores contained, the densities of the individual elements are calculated to a total density as a reference value for a dense material. This is then 7.98 g/cm3 for CuAl12Sn9, with which a maximum relative density of 98.36% was achieved. The hardness values lie in a range between 290 and 350 HV30. The samples with the highest density were used for further investigations, as they also gave the best impression in the visual inspection. Metallographic processing of the samples shows evidence of intermixing, as well as pores and other cavities. This is seen as the reason for the fluctuations in hardness and density values. This parameter has a laser beam power of 100 W, a scanning speed of 100 mm/s, a track spacing of 0.065 mm, and a layer thickness of 0.02 mm. Figure 7 shows the metallographically prepared samples. These show clear cracks and pores as well as different colored areas. These are attributed to strong intermixing. Furthermore, clear cracks and pores can be seen. The micrographs shown are exemplary for all the areas examined. EDX measurements on the samples show that the copper content fluctuates between 47 and 86%, which confirms strong segregation. Areas that could be optically assigned to different phases could not be distinguished in the strongly fluctuating measurement results. Flat tensile specimens were produced with the described parameter set. These could be completely built up but show clear differences in color. The built-up samples show clear intermixtures and only have a density of 91.5 ± 0.5%. By means of spark spectral analysis, strong fluctuations in the chemical composition could be detected. The copper content, for example, varies between 28 and 82%, independently of the

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Fig. 7 Metallographically prepared microstructure: Cross grinding (left) and longitudinal grinding (right)

Fig. 8 Fabricated and broken tensile specimen

tin content. The hardness on the ground surface is 421 ± 58 HV10. The samples show a very high brittleness and partly already broke during handling, which is why the tensile test could not be carried out. Figure 8 shows one of the samples.

Conclusion It was shown that the homogeneity of the prepared powder mixture of CuSn10 and aluminum could not be proven. In the measurements, the aluminum content was 10% instead of 12%. It is unclear whether there is a systematic measurement error due to the powder extraction or segregation during powder coating. In addition to the different densities, the segregation could be intensified by the poor flowability of the aluminum powder. During the processing of the powders, there was strong segregation of the two powders, which was also evident in the manufactured samples. There was also strong pore and crack formation. It is assumed that the absorption behavior of the powder mixture varies greatly due to the segregation, which changes the required energy input. The manufacturing process is correspondingly error-prone, resulting in an accumulation of defective spots. The difference in density between the density cubes and the upright tensile specimens is attributed to the fact that the segregation of the powders deteriorates as the manufacturing process continues.

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It is assumed that the different colored areas in the material could not be distinguished in the EDX analysis because the individual areas are too narrow. Thus, the material with different compositions is also excited during the analysis.

References 1. Mosallanejad MH, Niroumand B, Aversa A, Saboori A (2021) In-situ alloying in laser-based additive manufacturing processes: A critical review. J Alloy Compd 872:159567. https://doi. org/10.1016/j.jallcom.2021.159567 2. Clayton R (2013) The use of elemental powder mixes in laser-based additive manufacturing. Masters Theses 3. Kang N et al (2018) In-situ synthesis of aluminum/nano-quasicrystalline Al-Fe-Cr composite by using selective laser melting, 13598368, vol 155, pp 382–390. https://doi.org/10.1016/j.com positesb.2018.08.108 4. Robert Kremer (2022) Experimentelle und simulative Untersuchung der Kristallstruktur und Eigenspannungen an Selektiv Lasergeschmolzenen Bauteilen aus CuSn10, Masterthesis, FH Dortmund 5. Beiss P (2013) Pulvermetallurgische Fertigungstechnik. Berlin, Heidelberg: Springer Berlin Heidelberg 6. Gebhardt A, Kessler J, Schwarz A (2019) Produktgestaltung für die additive fertigung. Hanser; Ciando, München 7. Klahn C, Meboldt M, Fontana F, Leutenecker-Twelsiek B, Jansen J, Eds (2018) Entwicklung und Konstruktion für die Additive Fertigung: Grundlagen und Methoden für den Einsatz in industriellen Endkundenprodukten, 1st ed. Würzburg: Vogel Business Media 8. Dipl.-Ing. Michael Karg, Bhrigu Ahuja M.Sc., Prof. Dr.-Ing. Michael Schmidt, (PDF) In-situLegierungsbildung beim Laserstrahlschmelzen von Metallen aus Mischungen elementar reiner Pulver 9. Skelton JM, Sullivan EJ, Fitz-Gerald JM, Floro JA (2022) Efficacy of elemental mixing of in situ alloyed Al-33wt%Cu during laser powder bed fusion. J Mater Process Technol 299:117379. https://doi.org/10.1016/j.jmatprotec.2021.117379 10. Martinez R, Todd I, Mumtaz K (2019) In situ alloying of elemental Al-Cu12 feedstock using selective laser melting. Virtual Phys Prototyp 14(3):242–252. https://doi.org/10.1080/ 17452759.2019.1584402 11. Katz-Demyanetz A, Koptyug A, Popov VV (2020) In-situ Alloying as a Novel Methodology in Additive Manufacturing. In: 2020 IEEE 10th International conference nanomaterials: applications & properties (NAP), 02SAMA05–1–02SAMA05–4 12. Krakhmalev P, Yadroitsev I, Yadroitsava I, de Smidt O (2017) Functionalization of biomedical Ti6Al4V via in situ alloying by Cu during laser powder bed fusion manufacturing. Mater (Basel, Switz) 10(10):1154. https://doi.org/10.3390/ma10101154 13. Kao S-W, Yeh J-W, Chin T-S (2008) Rapidly solidified structure of alloys with up to eight equalmolar elements—a simulation by molecular dynamics. J Phys: Condens Matter 20(14):145214. https://doi.org/10.1088/0953-8984/20/14/145214 14. Kremer R, Khani S, Appel T, Palkowski H, Foadian F (2022) Selective laser melting of cusn10: simulation of mechanical properties, microstructure, and residual stresses. Mater (Basel, Switz) 15(11):3902. https://doi.org/10.3390/ma15113902

Nanosized Cadmium Selenide Thin Coatings for Possible Utilization in Optoelectronics Ikhazuagbe H. Ifijen and Bala Anegbe

Abstract The performance of energy conversion and storage technologies such as solar cells, supercapacitors, and batteries is the subject of a lot of research. Cadmium selenide (CdSe) thin films are appropriate for the next generation of chalcogenidebased photovoltaic and electrochemical energy storage systems because of their narrow bandgap and high absorption coefficient in the visible range, as well as their low electrical resistivity. This paper provided a concise background on the chemical synthesis of CdSe nanoparticles as well as information on the film properties generated at temperatures that are reproducible, effective, and affordable for optoelectronic applications. Due to the bandgaps, which were established by many evaluated studies, being adequately located in the visible solar energy region, these CdSe thin films are suitable for electrochemical energy storage systems, such as in solar energy harvesting. Keywords Cadmium selenide · Optoelectronic · Films · Nanoparticles

Introduction Compared to their macroscale counterparts, materials at the nanoscale exhibit various characteristics [1–10]. They generally show desired features that are not seen in bulk materials, such as enhanced strength, chemical reactivity, or conductivity due to high surface-to-volume ratios, altered electronic bandgap energy (optical properties), and tailored functionality. The quantum confinement effect, which occurs when particle size is decreased, is what causes such a significant alteration in physical and chemical properties [11–18]. For instance, the development of semiconductor technology over I. H. Ifijen (B) Department of Research Operations, Rubber Research Institute of Nigeria, Iyanomo, Benin City, Nigeria e-mail: [email protected]; [email protected] B. Anegbe Department of Industrial Chemistry, Faculty of Science, Federal University, Oye-Ekiti, Nigeria © The Minerals, Metals & Materials Society 2023 The Minerals, Metals & Materials Society, TMS 2023 152nd Annual Meeting & Exhibition Supplemental Proceedings, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-031-22524-6_33

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the past few decades and the creation of new pathways into the electronics industry have been made possible by tailoring of the nanoparticles and nanostructures made of semiconductor materials. Cadmium selenide nanomaterials, among nanosized semiconductor materials, have been demonstrated to be a part of a commercial revolution that has given rise to an explosion of hundreds of new products due to their diverse physico-chemical properties, which enable their use in a wide range of imaginative applications. A direct bandgap II–VI semiconducting material with the n-type is called cadmium selenide. The 1.74 eV bulk bandgap energy at 300 K, which is quite close to the NIR, can be raised through a number of methods [19]. The reported molecular weight is 191.37 g/mole, with Cd and Se making up 58.74% and 41.26%, respectively, and having a dark red colour [19]. The II–VI group semiconductors, such as CdSe, CdTe, ZnSe, and CdS thin films, have been the subject of intensive research during the past 10 years because of their potential use in optoelectronic devices. Due to its widespread application in numerous industries, including biomedical technology, solar cell technology, chemical sensing, thin film transistors, photoconductors, acousto-optical devices, gas sensors, photoelectrochemical devices, and photoreceptors, CdSe is a significant member of this group of substances [20]. Several fabrication methods, including chemical bath deposition, molecular beam epitaxy, electrodeposition, spray pyrolysis, consecutive ionic layer adsorption and reaction, thermal evaporation, and MOVCD, have been reported to create thin films of CdSe. The structural, electrical, and optical characteristics of CdSe thin films have been the subject of extensive research, but for a number of reasons, more work is still needed in this area. Semiconducting CdSe can be applied to solar cells, electrochemical energy storage systems, and other optoelectronic devices, but their competitiveness and utilization in such devices require a fabrication method that is both dependable, cost-effective, and scalable [21, 22]. Although there are a lot of laboratory-based studies, very little has been done to go from basic or foundational studies to the development of viable prototype devices and potential commercialization [23]. The majority of the time, these fundamental studies require expensive materials and equipment, often at absurdly demanding conditions like extremely high temperatures and ultra-high vacuums [24– 26]. When moving from the lab to industry, these intense manufacturing conditions can be very difficult. In order to modify and improve the properties of CdSe thin films to make them more effective for utilization and use in devices, there is an urgent need for dependable procedures that will make it simple to manipulate various parameters or the deposition circumstances [27–29]. Because of their many benefits, such as homogeneous films, large area deposition, and the ability to produce high-quality thin films with easily controllable parameters, chemical techniques that allow for easy property control have been used by many researchers for the fabrication of semiconducting CdSe thin films. This article provides a brief overview of the synthesis and characteristics of CdSe deposited on thin films, which are dependable, effective, and reasonably priced for potential optoelectronics purposes.

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Synthesis of CdSe NPs General Background on Chemical Synthesis The development of trustworthy synthesis pathways for NPs of controllable size and, consequently, characteristics is always needed given the broad range of technical domains in which NPs have a great deal of promise for application. The significance of the synthesis techniques became apparent when a prior study’s reaction rate, which is incredibly quick for direct separation of microscopic nanocrystals [30], was attained. For the synthesis of CdSe NPs, a number of methods have been developed, which are typically based on the breakdown of appropriate Cd- and Se-containing precursors [31]. The most common source of cadmium is cadmium chloride, or CdCl2 , whereas cadmium oxide, or CdO, is a frequently used substitute in the manufacture of cadmium selenide clusters and other semiconductors. Although many Se-based compounds have been successfully used, there is disagreement about the best source of Se. The breakdown of organometallic compounds with chalcogenides and metals used as precursors in high-boiling organic solvents at relatively high temperatures is one of the several synthesis processes that produces CdSe nanocrystals of varying sizes (B300 1C). CdSe NPs produced using this method have narrow size distributions and high crystallinity. Rapid nucleation at the beginning of the reaction, followed by nucleus expansion and suppression of additional nucleation are essential components for achieving this. The single-source precursor technique, hot-injection-based synthesis, and the solution-liquid solution mechanism are the three methods that are frequently used in this context [32, 33]. By triggering a burst of nucleation upon swift addition of the precursor mixture, the hot-injection approach allows for the rapid production of large quantities of monomer. To put it another way, hot precursors are quickly combined together [34]. Here, one of the two following methods is typically used to regulate NP growth: Either altering the surface energy of a specific aspect of the nano-cluster by offering a suitable surface-stabilizing ligand, or managing the concentration of reactants. Thermal decomposition techniques are widely used, despite certain drawbacks, including the need for air-free reaction conditions, the use of hazardous reactants, and the scarcity of single-source precursors [33]. Alternative techniques used to create nanoclusters include solvothermal and hydrothermal procedures. While the hydrothermal technique entails a particular case employing water as the solvent, the solvothermal approach uses organic solvents as the reaction media. Regardless of the solvent, the reaction is conducted in a closed vessel, such as an autoclave, which allows for an increase in pressure when heated to a particular temperature, increasing the precursor solubility. When employing the solvothermal method, crystallization of CdSe NPs with regulated form is still quite difficult. The Wulff facet argument, which contends that the form is controlled by the surface energy associated with each face or facet of the crystal, is undoubtedly the most popular and prominent characteristic model for controlling the NP shape [35]. Many scientists investigated the production of NPs by changing reaction parameters such as the temperature, capping ligands, and reaction fluids, as well as by using various

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Cd and Se sources. However, numerous applied approaches continue to suffer from the emergence of multiple NP families, and only a small number of studies have successfully synthesized a single NP family [36]. The synthesis of single-family NPs using traditional hot-injection techniques has also been regarded as being rather difficult [33].

Recent Research Studies on Thin Coatings of Cadmium Selenide for Possible Use in Optoelectronic Devices According to several studies, the crystallinity, orientation, grain size, optical bandgap, and electrical resistivity that are impacted by the film thickness are closely related to the distinctive properties of CdSe thin films and their applications in the field of optoelectronics. The synthesis of CdSe thin films using the SILAR method and the impact of immersion cycles on the structural, morphological, optical, and electrical properties of SILAR deposited CdSe thin films are both reported by Chaudhari et al. for potential use in optoelectronic [37]. The sequential ionic layer adsorption and reaction (SILAR) approach was used to deposit CdSe thin films on the glass substrate. The effect of varying the number of immersion cycles on the distinctive structural, morphological, optical, and electrical properties of the films is investigated. Different sets of the film are created by varying the number of immersion cycles as 30, 40, 50, and 60. The deposited films had a hexagonal structure, with the (1 0 1) plane showing the most pronounced reflection, according to the XRD investigations. Furthermore, it is discovered that when the number of immersion cycles increases, the (1 0 1) plane’s peak intensity also increases. In the FESEM image, the thin films appear to cover the entire surface area with a somewhat uniform smoothness (Fig. 1). According to research on the optical characteristics of CdSe thin films for various numbers of immersion cycles (Fig. 2), the absorbance rises as the number of immersion cycles increases. Along with this, it was discovered that as the number of immersion cycles increased, the optical bandgap and electrical resistivity also reduced. An easy way to alter the physical properties of the CdSe material for optoelectronic applications is suggested by a strong correlation between the number of immersion cycles and the physical characteristics. Many studies are being done to improve the efficiency of energy conversion and storage technologies such as solar cells, supercapacitors, and batteries. This makes the next generation of chalcogenide-based photovoltaic and electrochemical energy storage devices perfect for them due to their narrow bandgap, high absorption coefficient, and low electrical resistance in the visible region. Hussain et al. described the reproducible, effective, and cost-effective properties of CdSe thin films produced at temperatures (below 100 °C) using widely accessible precursors [20]. Through the use of XRD, FTIR, RBS, and UV–vis spectroscopy, the samples were analyzed. As the annealing temperature was raised, the grain size of the nanostructures increased

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Fig. 1 FESEM images of CdSe thin films deposited with (a) 30, (b) 40, (c) 50, and (d) 60 immersion cycles [37]

Fig. 2 Plot of absorbance with respect to wavelength for CdSe thin film deposited with (a) 30, (b) 40, (c) 50, and (d) 60 immersion cycles [37]

from 2.23 to 4.13 nm, revealing crystalline cubic structure along the preferred direction (111). The samples’ optical characteristics show a little change in the bandgap energy with decreasing deposition temperature, from 2.20 to 2.12 eV (Fig. 3). These CdSe thin films are excellent for solar energy harvesting because the bandgap is appropriately situated in the range of visible solar energy. It might potentially be employed in electrochemical energy storage systems. The preparation circumstances, deposition methods, heat treatment, substrate, doping, film thickness, and substrate temperature have a significant impact on the physical and chemical properties of CdSe thin films, which can be used in new applications. The heat treatment can be carried out in vacuum, air, and gaseous media like N2 , H2 , and Ar. The quality of the crystal structure has a significant impact on the physical characteristics of thin films. A thorough review of the literature highlights the necessity to look into how thickness affects the physical characteristics of CdSe thin films. In order to investigate the impact of thickness on the physical characteristics of CdSe thin films for potential optoelectronic applications, Purohit et al. conducted a study [38]. The thermal evaporation technique was used to form thin films with thicknesses of 445 nm, 631 nm, and 810 nm on glass and ITO-coated glass substrates. These films were then 300 C annealed in an environment of air.

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Fig. 3 Plot of (ahn)2 vs. (hn) of thin film of CdSe deposited at different temperatures and the film deposited at 80 °C and annealed at 400 °C [20]

The films have a preferred reflection (111) and nanocrystalline character, as shown by the XRD patterns, and contain a cubic zinc-blende structure. Calculations and in-depth discussions of the structural parameters are done. The stacking of smaller grains on the surface of larger grains may be to blame for the observed decrease in grain size of annealed films with increasing thickness. For as-deposited and annealed films, the optical bandgap was found to be minimal for films with a thickness of 631 nm, and it ranged from 1.80 to 1.83 eV and 1.69 to 1.84 eV, respectively (Fig. 4). Calculations of the extinction coefficient and refractive index also reveal that they vary with layer thickness. When compared to other thicknesses, all structural and optical parameters in the as-deposited thin films exhibit the opposite trend due to the observed drop in crystallinity at 631 nm in thickness. Electrical investigations demonstrate that the relationship between current fluctuation and voltage is linear. The resistivity of films that have just been deposited is seen to rise with thickness while falling for films that have been annealed. The as-deposited and annealed films are uniform, smooth, completely coated, and devoid of crystal flaws, according to the SEM analysis. According to the compositional study, the temperature gradient

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Fig. 4 The Tauc plot of (a) as-deposited and (b) annealed CdSe thin films [38]

between the deposited material and substrate surface is reduced as layer thickness increases, which causes the Cd and Se ratio to drop. Optoelectronic applications will benefit greatly from this insight. In a recent work, Giurlani et al. looked into the ideal circumstances for the codeposition of Cd and Se to achieve high efficiency while maintaining a nanometric thickness [39] to evaluate the best conditions for the electrodeposition of a CdSe film on n-Si. A potential of −0.75 V and a mixture of Na2 SeO3 and 3CdSO4 ·8H2 O in an electrolyte of sulfuric acid were found to be the ideal conditions for the deposition of a CdSe film on n-Si. RBS analysis was used to identify an excess of Se at overpotentials less than −0.70 V, whereas electrochemical stripping voltammetry and SEM pictures were used to determine an excess of Cd at overpotentials larger than -0.80 V. Evaluations of the samples’ crystallinity and photoemission revealed that, despite the electrodeposition producing a crystalline deposit, the PL rose by almost an order of magnitude upon annealing at 400 °C in a nitrogen environment. A quick and affordable way of preparation is electrochemical deposition. In order to realize optoelectronic devices, this study paves the way for the use of electrochemical deposition as a quick, low-cost fabrication method that can incorporate additional interesting materials (Fig. 5). The impact of Ag doping on the structural, optical, and electrical characteristics of CdSe thin films was examined by Kaur et al. (2019) [40]. By using the thermal evaporation process in an environment of argon gas, the thin films of Ag-doped CdSe at 1% and 5% doping were created on glass substrates. The hexagonal crystal structure of CdSe and Ag-doped CdSe thin films is shown by XRD analysis, and at Ag 5% doping, there is a drop in crystallinity that is attributable to an increase in discontinuities at grain borders. TEM images show that the nanoparticles in CdSe, CdSe: Ag 1%, and CdSe: Ag 5% thin films are all spherical, with typical particle sizes of 40–45 nm, 25–30 nm, and 55–60 nm, respectively. In PL spectra, the band edge emission peak changes from 531 nm (for CdSe) to 605 nm (for CdSe: Ag 1%) and 610 nm (for CdSe: Ag 5%), as a result of the bandgap narrowing (Fig. 6).

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Fig. 5 Photoluminescence spectra of the sample prepared at 0.75 V before (blue) and after (claret violet) the annealing at 400° C for 4 h, using an excitation laser with a wavelength of 476 nm [39]

Because of non-radiative recombination of the defect levels, CdSe thin films have a trap-related emission peak that is missing from Ag-doped CdSe thin films. Due to charge carriers being trapped at trap centers and dispersion at grain boundaries, Ag-doped CdSe thin films are reported to have lower electrical conductivity than CdSe. Hoping of charge carriers between localized states causes conduction to occur at low temperatures (Fig. 7). Hall measurements show n-type behavior of undoped and Ag-doped CdSe thin films, and these two types of conduction pathways were seen to be involved in the transport phenomenon.

Conclusions This paper provided a concise background on the chemical synthesis of CdSe nanoparticles as well as information on the film properties created at temperatures that are predictable, effective, and affordable for optoelectronics applications. The literature review of numerous research studies that are relevant to this area of study reveals that the as-deposited CdSe films under consideration have the potential to be used in optoelectronics as electrochemical energy storage systems, such as in solar energy harvesting, because the bandgaps found through a variety of evaluated studies are appropriately located in the visible solar energy region.

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Fig. 6 (a) UV–Vis spectra (the insets illustrate the Tauc plots for corresponding thin films) (b) room temperature photoluminescence spectra of undoped and Ag-doped CdSe thin films (a: absorption coefficient; h: Planck’s constant; m: frequency; k: wavelength; kex: excitation wavelength) [40]

Fig. 7 (a) Temperature dependence of electrical conductivity (b) validity of Mott’s relation [40]

References 1. Omorogbe SO, Aigbodion AI, Ifijen HI, Ogbeide-Ihama N, Simo A, Ikhuoria EU (2020) Low temperature synthesis of super paramagnetic Fe3 O4 morphologies tuned using oleic acid as crystal growth modifier. In book: TMS 149th annual meeting & exhibition supplemental proceedings 619–631 2. Characterization of sulphated cellulose nanocrystals as stabilizer for magnetite nanoparticles synthesis with improved magnetic properties. Nig J Mater Sci Eng 7(2): 23–31 3. Ifijen IH, Ikhuoria EU, Maliki M, Otabor GO, Aigbodion AI (2022) Nanostructured materials:

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Optical Properties of Crystalline Silicon in the Infrared Allyson Tarifa and Nuggehalli M. Ravindra

Abstract Silicon has been one of the most well-understood semiconductor materials in the literature. In spite of its mature know-how and technology, there is an absence of reliable values of its wavelength-dependent optical constants, i.e., refractive index and extinction coefficient of monocrystalline silicon in the wavelength range of 1–10 µm, in the literature. These values are critical to fully simulate, model, and understand the optical properties of silicon in the infrared range of wavelengths, as well as to be able to design devices of interest, particularly in the infrared. In this study, the Forouhi–Bloomer dispersion equations have been utilized to predict the functions of the refractive index and extinction coefficient for the entire wavelength spectrum, including the sought 1–10 µm range. The calculated reflectivity and transmissivity are then analyzed and compared to prior findings in the literature. Keywords Silicon · Optical properties · Infrared · Modeling

Introduction Silicon is one of the most ubiquitous semiconductors in the electronics industry, particularly in photovoltaics. Crystalline silicon alone accounts for 85–90% of the world’s solar cell market and holds one of the highest PV cell efficiencies of around 26% [1]. This has led to an abundance of studies surrounding the optimization of silicon properties such as homojunction/heterojunction formation, surface texturing, and surface passivation [2]. However, there remains a gap in understanding the complex refractive index of crystalline silicon completely, particularly in the 1–10 µm wavelength range. A. Tarifa Department of Mechanical and Industrial Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA N. M. Ravindra (B) Department of Physics, New Jersey Institute of Technology, Newark, NJ 07102, USA e-mail: [email protected] © The Minerals, Metals & Materials Society 2023 The Minerals, Metals & Materials Society, TMS 2023 152nd Annual Meeting & Exhibition Supplemental Proceedings, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-031-22524-6_34

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There already exist multiple data sources for values of the extinction coefficient and refractive index of crystalline silicon but their results often vary by differences that exceed their precisions [3]. Additionally, among the values listed in the highly reputable and referenced Handbook of Optical Constants, not only are there disagreeing values of the optical constants for the same wavelengths, but there are also no consistent values in the 1–10 µm wavelength range. This specific wavelength range falls under the category of infrared light, specifically the near-infrared (NIR) and mid-infrared range (MIR). Infrared light from the sun contributes a significant 40% of the wavelength range of solar irradiance received on Earth making this wavelength range an important area of study [4]. Additionally, with regard to solar cells, in order to predict the heat loss between the solar cell and its immediate environment, accurate data of emissivity and its wavelength dependence for the material is necessary. Therefore, in order to properly predict how the solar irradiance in the near-infrared and mid-infrared range affects the reflectivity, emissivity, and transmissivity of crystalline silicon for the benefit of photovoltaic applications, it is crucial to first understand the intrinsic optical properties of silicon. The complex refractive index is what gives the indication of the light-bending response of a material [5]. The complex refractive index comprises a real and imaginary component, the refractive index n and the extinction coefficient k, respectively. Values of the extinction coefficient and the refractive index are both necessary to model the interaction of light with materials [6]. There are multiple factors that lead to the variability of the complex refractive index. These factors include doping densities, change in temperature, and surface morphology; additionally, some of these factors simultaneously affect the bandgap of the material [7]. The established equations that will be discussed in this paper take these factors into consideration by utilizing the experimentally acquired materialspecific constants. Among the established methods that are used to find the theoretical complex refractive index of a material, the Forouhi–Bloomer dispersion equations have shown success in accurately predicting their values [8, 9]. Their first paper on the optical properties of semiconductors presents a set of equations to find functions for the extinction coefficient and refractive index in the 0–20 eV range based on the Principle of Causality and the Kramers–Kronig relation [8]. Upon claims that the original equations were not physically sound, Forouhi and Bloomer developed a new set of equations that directly address these concerns [10]. In this study, both versions of the Forouhi–Bloomer equations were simulated using MATLAB and were used to model the complex refractive index, absorption coefficient, reflectivity, transmissivity, and emissivity of crystalline silicon in the desired 1–10 µm wavelength range. The results of these simulations were compared with each other as well as the results from the Sellmeier Equation and experimental data from the literature to find the best method to determine the optical properties of silicon in the sought-after wavelength range.

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Theoretical Basis Original Forouhi-Bloomer Dispersion Formula The first equations that were utilized to model the complex refractive index of crystalline silicon were the equations proposed in the original Forouhi–Bloomer paper [8]. The complex refractive index consists of the imaginary component k, a measure of the scattering and absorption of light as it enters a material, and the real component n, the ratio of how light propagates through a vacuum to that of a material [5]. These equations were proposed as an alternative method for determining the complex refractive index of specifically crystalline semiconductors; this paper is built off their prior work that proposed a method for finding the complex refractive index of amorphous semiconductors and dielectrics. By first finding the imaginary part of the complex refractive index, k, we can then find the real part, n, by using the Kramers–Kronig analysis. Kramers–Kronig analysis is used in cases where there is a response function triggered by an applied force, which follows the law of causality [11]. Furthermore, the results for the Kramers–Kronig analysis can be applied to physical interpretations by only considering the range [0, ∞] due to the impossibility of negative frequencies. The assumption used for the following equations is a one-electron model with a discrete lifetime of being in an excited state. It may be noted that both the original and updated Forouhi–Bloomer equations assume no shift in the bandgap. By first deducing an expression for the absorption coefficient as a variable of wavelength, and then an expression for the energy absorbed in the frequency range as a variable of wavelength, these expressions can be plugged into the following equation, where α is the absorption coefficient, c is the speed of light in a vacuum, and ω is the wavelength: k=

cα 2ω

This can then be further simplified to consider the final expression in terms of energy with constants A, B, C, and E g (representing the unique Energy Bandgap of the material). k(E) =

q  i=1

 2 Ai E − Eg E 2 − Bi E + Ci

(1)

Once an equation for the extinction coefficient is determined, an equation for the refractive index and its accompanying constants can be derived using the Hilbert transform. n(E) = n(∞) +

B0 E + C0 E2 − B E + C

(2)

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  Bi2 Ai 2 B0i = + E g Bi − E g + Ci − Qi 2    Bi Ai  2 − 2E g Ci E g + Ci C0i = Qi 2 Q 0i =

1 1 4Ci − Bi2 2 2

(3) (4) (5)

The number of terms in the summation (delineated by Q) is directly correlated with the number of peaks in the complex refractive index. Additionally, when examining the results for the theoretical plots of k(E) and n(E), an increase in Q terms led to an increase in the visible minor peaks in the resulting plot [8].

Updated Forouhi–Bloomer Dispersion Formula In 2019, Forouhi and Bloomer proposed an update to their previous work upon suggestions that their original equations were not physically sound. The two major claims were that as the energy tended towards infinity, the extinction coefficient could not be equal to non-zero, and the functions n(E) and k(E) could not both be asymmetric functions of E. This led to a reworking of the equations into the following two new dispersion equations for the full radio-wave to EUV spectral range with new constants: k(E) =

p  j=1

n(E) = n(∞) +

 A j (E − E g ) Aj E + 2 E − Bj E + C j E2 − Bj E + C j i=1 q

p  j=1

 D j E D j + Fj + 2 2 E − Bj E + C j E − Bj E + C j i=1

(6)

q

  Aj Bj Eg − Qj 2   Aj Eg B j Cj − Fj = Qj 2 Dj =

D j = − F j =

Aj Bj 2Q j

AjCj Qj

(7)

(8) (9) (10) (11)

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 Qj = Cj −

B 2j

 21

4

(12)

These dispersion equations follow the two Kramers–Kronig analysis requirements of modeling the expected behavior as E tends to infinity and that n(E) and k(E) be Hilbert transforms of each other. While they do not follow the symmetry constraints, Forouhi and Bloomer argue that the symmetry constraints are not a necessary requirement in the condition of Causality [10].

Sellmeier’s Model Another method used to model the optical properties of semiconducting materials is the Sellmeier equation, developed by Wolfgang von Sellmeier, which models the refractive index as a function of wavelength. The Sellmeier equation is best used for modeling the refractive index in the transparent region when there is negligible absorption; the Sellmeier coefficients are found from a least-square fitting procedure and the experimentally derived coefficients can be found from the refractive index database [13, 14].

 Aj λ2

n(λ) = 1 + λ2 − Bj j

(13)

Emissivity, Reflectivity, Transmissivity Using the results for the complex refractive index and the absorption coefficient, the emissivity, reflectivity, and transmissivity functions for crystalline silicon can be subsequently determined. Emissivity is found by comparing the radiation of a material with that of a blackbody and is a property used in photovoltaics to predict the heat transfer between a solar cell and its environment [6, 15]. By using McMahon’s generalized law for partially transparent bodies, the emissivity, reflectivity, and transmissivity can thus be related using the following equations, where k is the extinction coefficient, n is the refractive index, t is the thickness of the material, K is the absorption coefficient, and λ is the wavelength [16]: ε=

(1 − R)(1 − T ) 1 − RT

(14)

Optical Properties of Crystalline Silicon in the Infrared

R=

369

(n − 1)2 + k 2 (n + 1)2 + k 2

T = exp(−Kt) = exp(−

(15)

4π kt ) λ

(16)

In the case of Sellmeier equation, which is applied to the transparent region of the material, according to Kirchhoff’s law, the emissivity equation will be the following: ε =1− R

(17)

In this paper, the original Forouhi–Bloomer equations, the updated Forouhi– Bloomer equations, and the Sellmeier equations are utilized to model the optical properties of crystalline silicon. Their results are subsequently compared with each other while taking into consideration the conditions where they are anticipated to hold the most accuracy. External experimental results are also used to analyze the results of the simulations.

Methodology and Results Original Forouhi-Bloomer Dispersion Formula The first simulations done on MATLAB were modeled using the original Forouhi– Bloomer Dispersion formulas (1) and( 2) and the experimentally acquired optical constants shown in Table 1 for crystalline silicon [8]. To verify the validity of the program, the complex refractive index was simulated using the original energy range in the paper before simulating the desired 1–10 µm wavelength range, the equivalent of 1.239–0.1239 eV. Then the complex refractive index was modeled in the desired wavelength range using Eqs. (1) and (2) (see Figs. 1, 2 and 3). With the results of the complex refractive index, the behavior of the absorption coefficient, reflectivity, transmissivity, and emissivity were subsequently modeled using Eqs. (14–16). Table 1 Value of the Parameters Ai, Bi, Ci, and n (∞) [8] Ai Si

Bi (eV)

C i (eV2 )

N(∞)

E g (eV)

1.950

1.06

0.00405

6.885

11.864

0.01427

7.401

13.754

0.06830

8.634

18.812

0.17488

10.652

29.841

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(a)

(b)

Fig. 1 Plot a shows the results of the complex refractive index in the original Forouhi–Bloomer paper and the plot b shows the results from the MATLAB program for the refractive index (in orange) and the extinction coefficient (in blue) for the same energy range of 0–7 eV [8]

(a)

(b)

Fig. 2 Once verified, the parameters of the program were changed to only display the complex refractive index between 1.239 and 0.1239 eV. The left plot shows the resulting extinction coefficient function, and the right plot shows the resulting refractive index, both as functions of wavelength for the 1.239 and 0.1239 eV range (1–10 µm)

Updated Forouhi–Bloomer Dispersion Formula After modeling the original Forouhi-Bloomer equations, to maintain consistency, the updated Forouhi–Bloomer dispersion Eqs. (6–12) were modeled using the same optical constants for crystalline silicon as used in the original paper (Table 1). The results of the MATLAB program for the updated equations were then compared with a previous paper [17] that found the results for crystalline silicon in the 0.2–2 eV range. The values for the complex refractive index were modeled first in the 0–7 eV range for comparison with the original Forouhi–Bloomer equations (see Fig. 4). Then, the complex refractive index was modeled in the desired eV range of 1.239 eV to 0.1239 eV. The updated Forouhi–Bloomer equations left the possibility of a resulting negative extinction coefficient; thus the MATLAB program was

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371

(a)

(b)

(c)

(d)

Fig. 3 The above plots show the resulting absorption coefficient, reflectivity, transmissivity, and emissivity in the 1.239 and 0.1239 eV range as functions of wavelength using the original Forouhi– Bloomer equations Fig. 4 The plot shows the complex refractive index for c-Si as a function of eV in the 0–7 eV range using the updated Forouhi–Bloomer equations

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constructed to plot both the original extinction coefficient results and then change the negative extinction coefficient values for zeros in accordance with the suggestion of Forouhi and Bloomer [10] (see Figs. 5, 6, and 7). In the process of comparing the previous results for the refractive index with the results from the program, there was a slight discrepancy between the two [17]. To supplement the theoretical model, the exact results published in the paper were mimicked using a linear regression program on Desmos [18] (see Fig. 8). The approximated complex refractive index results gave the following results for the desired optical properties (see Fig. 9).

(a)

(b)

Fig. 5 Plot a displays the results for the extinction coefficient and the plot b displays the results for the refractive index of silicon in the desired eV range of 1.239–0.1239 eV

Fig. 6 The plot shows the extinction coefficient after being logically indexed to only show positive values. Using the values of the logically indexed complex refractive index, the behavior of the absorption coefficient, reflectivity, transmissivity, and emissivity were subsequently modeled for the updated Forouhi–Bloomer results using Eqs. (14–16)

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(a)

(b)

(c)

(d)

Fig. 7 The above plots show the a absorption coefficient, b reflectivity, c transmissivity, and d emissivity in the 1.239 and 0.1239 eV range for the updated Forouhi–Bloomer equations as functions of wavelength

(a)

(b)

Fig. 8 Plot (a) shows the mimicked results from the Lin and Ravindra paper shown on the right (b). The number of peaks for the refractive index n was approximated to be two and the number of peaks for the extinction coefficient k was approximated to be one [17]

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(a)

(b)

(b)

(d)

Fig. 9 The above plots show the a absorption coefficient, b reflectivity, c transmissivity, and d emissivity in the 1.239 and 0.1239 eV range for the updated Forouhi–Bloomer equations that were approximated using linear regression on Desmos and then simulated on MATLAB [18]

Sellmeier’s Model The Sellmeier equation is another equation that can predict the behavior of the refractive index with respect to wavelength, in the transparent region of a material. Using constants found from the refractive index database for an initial wavelength range of 1.357–11.04 µm, the Sellmeier equation for crystalline silicon was modeled using MATLAB in the 1–10 µm range using Eq. (13) [13]. The resulting refractive index was then used to model the reflectivity and emissivity of crystalline silicon using Eqs. (15 and 17) (see Figs. 10 and 11).

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Fig. 10 The plot shows the refractive index as modeled using the Sellmeier Equation as a function of wavelength in the 1–10 µm range

(a)

(b)

Fig. 11 The above plots show the modeled reflectivity (a) and emissivity (b) of silicon as a function of wavelength in the 1–10 µm range

Discussion When comparing the simulations of the original and updated Forouhi–Bloomer equations with each other, the Sellmeier equations, and experimental results, there are some notable differences.

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(a)

(b)

Fig. 12 The first plot a shows a similar behavior to the refractive index results from both the original Forouhi–Bloomer equations and the Sellmeier equations. The plot at 300 K shows the highest correlation with the results from the original Forouhi–Bloomer equations [19]. The portion of the second plot b which shows the experimental values of the extinction coefficient from the 6.67 to 10 µm wavelength range is closed off by thick blue brackets. In this wavelength range, there are 4 discernable peaks in the extinction coefficient behavior; in the overall range shown, there are closer to 10 [19]

Refractive Index Values When comparing the simulation results to experimental refractive index values n (Fig. 12a), there was a higher correlation with the original Forouhi–Bloomer results (Fig. 2b) and the Sellmeier equations (Fig. 10). The behavior of the experimental refractive index in the 1–10 µm range peaked at values between 3.42 and 3.46 at temperatures ranging from 50 to 300 K and gradually decreased and stabilized as the wavelength increased. The simulated original Forouhi–Bloomer equations and the Sellmeier equation also followed a similar behavior, initially peaking at values of 3.41 and 3.58, respectively.

Extinction Coefficient Values When comparing the results to experimental values of extinction coefficient k, there was not a strong correlation to the simulated values. The experimental extinction coefficient data, used in this comparison are from measurements taken by the laboratory at the University of Reading, which specializes in infraredoptics [19]. The values of the extinction coefficient in the original Forouhi–Bloomer simulation (Fig. 2a) ranged from (0.001 to 0.01) and the values of the updated ForouhiBloomer simulation (Fig. 5a) ranged from (0.03 to (−0.01)), dipping below the x-axis. The experimental extinction coefficient result range falls between (0.00001– 0.0001) with a peak at around 0.0005 [19]. The values of the extinction coefficient in the experimental data for infrared wavelength, in comparison with the simulation results, experienced a 99% and 90% error when comparing the lowest theoretical and experimental values (0.001 and 0.00001, respectively) and when comparing the highest theoretical and experimental values (0.01 and 0.001, respectively).

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Furthermore, the negative values resulting from the simulated extinction coefficient model (Fig. 5a) for the updated Forouhi–Bloomer equations also seemed to suggest that there is an increased chance of anomalous absorption when the incoming photon energy falls below the bandgap energy of silicon. Anomalous absorption is described to be extra absorption in the medium which occurs due to excess energy from down-transitions after emission, which is indicated by negative refractive index values [20]. The experimental data seems to corroborate that when the wavelength starts to enter the infrared range, starting at 0.7 µm, the values of the extinction coefficient start to gradually decrease, which agrees with the behavior of the original Forouhi– Bloomer simulation (Fig. 2a) [21]. This seems to suggest that the theoretical formulas which model the impact of the extinction coefficient in the infrared region overestimate considerations of the extinction coefficient in silicon. For infrared energy levels which are below the bandgap of silicon (the bandgap is accepted to be between values 1.06–1.11 eV [8]) starting at wavelengths of 1.16 µm, there seems to be significantly less absorption and scattering of light in the medium based on experimental values [5]. However, for energy levels that exceed the bandgap eV levels, extinction coefficient considerations become increasingly more important.

Reflectivity Values When comparing the experimental reflectance data with the simulation results, there is a good agreement between the resulting values and behaviors [21]. The behavior of the reflectivity functions resulting from the original Forouhi–Bloomer equations (Fig. 3b), the updated Forouhi–Bloomer equations (Fig. 7b), and the Sellmeier equation (Fig. 11a) all showed a decrease in reflectivity peaking at around 0.3. The greatest difference between the behavior of experimental and simulated values is seen in the results for the approximated updated Forouhi–Bloomer equations (Fig. 9b) (see Fig. 13).

Emissivity Values When comparing simulation results to experimental emissivity data, there was a higher correlation with both the Sellmeier equation (Fig. 11b) and the updated Forouhi–Bloomer equations (Fig. 7d). The updated Forouhi–Bloomer equations, until the 7 µm wavelength (where the extinction coefficient was approximated to be zero) remained in the 0.7–0.8 emissivity range, which is acknowledged to be the intrinsic emissivity of silicon [22]. The Sellmeier equations also accurately simulated crystalline silicon’s intrinsic emissivity values, which substantiated its claims of modeling the optical properties of materials in their transparent regions [12] (see Fig. 14).

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Fig. 13 The reported reflectivity at wavelength 1000 nm (1 µm) falls a little above 0.3, which is similar to the peak reflectivity values in the simulation results for the original Forouhi– Bloomer equations, the updated Forouhi–Bloomer equations, and the Sellmeier equation which at a wavelength of 1 µm displayed values between 0.3 and 0.295, 0.317, and 0.241, respectively [21]

(a)

(b)

Fig. 14 Plot a shows the spectral emittance for p-type doped silicon at 58 °C as a function of wavenumber (cm −1 ). The emittance falls between the 0.8 to 0.6 range which reflects the results of both the updated Forouhi–Bloomer simulations and the Sellmeier simulation [22]. Plot b shows the experimental emissivity results as a function of wavelength for a single-side polished n-Si wafer in a range of temperatures, the general behavior of the emissivity remains to be from 0.6 to 0.8 [6]

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Transmissivity Values The biggest discrepancy between the experimental and theoretical results could be seen in the transmissivity plots. There is a general variability in transmissivity behavior due to material thickness, polishing, and resistivity [23]; additionally, there is a reported unclear relationship between reflectivity and transmissivity behavior unlike other semiconductors such as geranium [19] (see Fig. 15). All the simulated transmissivity plots, the original Forouhi–Bloomer equations (Fig. 3c) and both the updated Forouhi–Bloomer equations (Figs. 7c and 9c), show transmissivity behavior in silicon’s transparent region that peaks at a value of around 1. However, this theoretical behavior does not correlate with the transmittance behavior of experimental results (Fig. 15a–d).

(a)

(b)

(d) (c) Fig. 15 The above plots show varying results for the transmittance of silicon wafers in the infrared region. Plot a shows the transmittance of a silicon wafer with an anti-reflective coating versus one without. Plot b shows the transmittance data for a double-side polished silicon wafer. Plot c shows the transmittance data for a silicon wafer with a range of different polishing finishes and resistivities. Plots a and b display a transmittance that falls between 0.5 and 0.6 in the infrared wavelength spectrum [23]. Plot c shows that a typical (doubled-side polished) silicon wafer will have the transmittance behavior seen in curve 1. Curves 3 and 4 show that the transmittance behavior is directly impacted by higher doping resistivities and by polishing [23]. Plot d shows the transmittance behavior as a function of wavenumber and shows that beyond the band gap absorption, the transmittance stays steadily above 0.5 [19]

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The major difference between the experimental results and simulations seems to be that the models do not consider the effect of temperature on the refractive index. As the energy received by the material increases, its optical properties start to exhibit the material’s intrinsic behavior, as seen in Fig. 14b. The shortcoming of this is that without fitting the equations to experimental data, there is an inability to set parameters to find the optical constants at the desired temperature. The original Forouhi–Bloomer equations mostly reflect the intrinsic optical properties of silicon but cannot seem to be modified to consider a state of low emissivity without refitting the data. This shortcoming is most notable in the resulting emissivity and transmissivity simulations, which are at odds with each other in the infrared wavelength spectrum. Due to the general agreement of the reflectivity behavior for silicon in this range, the variations in transmissivity and emissivity depend on each other. For the transmissivity to increase, the emissivity must decrease and vice versa. Transmissivity and reflectivity are both properties that influence the K constant in the calculation of the efficiency of solar cells; a higher transmissivity is favorable for high solar cell efficiency [24]. Therefore, the dispersion equations are lacking the ability to be considered as a function of temperature.

Conclusion This paper set out to simulate and compare a variety of optical properties for crystalline silicon in the 1–10 µm wavelength range using the original Forouhi–Bloomer equations, the updated Forouhi–Bloomer equations (both calculated and approximated), and the Sellmeier equation. This is to make up for a gap in the literature of a defined complex refractive index for silicon in the infrared region. There is still a large area of uncertainty when simulating the optical properties of silicon in this region due to the differences in doping, the polishing of the wafer, the shifting of the bandgap which has been observed in c-Si [14], and the general difference in properties as a function of temperature. Going forward, when modeling the intrinsic emissivity of silicon in the infrared wavelength range, the best model can be found in the simulated results from the original Forouhi–Bloomer model which reflects a reliable refractive index, silicon’s intrinsic reflectance, and silicon’s intrinsic emissivity values. While the transmissivity results are more variable, the original Forouhi–Bloomer formulas still reflect the best fit with the behavior gradually decreasing after reaching a peak of 1 towards the value of 0.3 but still notably falling between the experimental result ranges of 0.7– 0.5. Going forward, what is needed in the literature are improved dispersion formulas that can consider the steady state temperature of a material and can interchangeably be modeled as functions of temperature and wavelength.

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References 1. El Haj Assad M, Alhuyi Nazari M, Rosen MA (2021) Applications of renewable energy sources. Des Perform Optim Renew Energy Syst, 1–15. https://doi.org/10.1016/b978-0-12-821602-6. 00001-8 2. Singh J, Agrahari A (2019) The progression of silicon technology acting as substratum for the betterment of future photovoltaics. Int J Energy Res 43(9):3959–3980. https://doi.org/10.1002/ er.4402 3. Palik ED, Ghosh G, Prucha EJ (1998) Handbook of optical constants of solids. Academic Press 4. Barolet D (2021) Near-infrared light and skin: why intensity matters. Chall Sun Prot, 374– 384https://doi.org/10.1159/000517645 5. Hecht E (2002) Optics, 5th edn. Pearson Education, Inc. 6. Ravindra NM, Sopori B, Gokce OH, Cheng SX, Shenoy A, Jin L, Abedrabbo S, Chen W, Zhang Y (2001) emissivity measurements and modeling of silicon-related materials: an overview. Int J Thermophys 22(5):1593–1611. https://doi.org/10.1023/a:1012869710173 7. Pawlak BJ, Gregorkiewicz T, Ammerlaan CA, Takkenberg W, Tichelaar FD, Alkemade PF (2001) Experimental investigation of band structure modification in Silicon Nanocrystals. Phys Rev B 64(11). https://doi.org/10.1103/physrevb.64.115308 8. Forouhi AR, Bloomer I (1988) Optical properties of crystalline semiconductors and dielectrics. Phys Rev B 38(3):1865–1874. https://doi.org/10.1103/physrevb.38.1865 9. Poelman D, Smet PF (2003) Methods for the determination of the optical constants of thin films from Single Transmission Measurements: a critical review. J Phys D Appl Phys 36(15):1850– 1857. https://doi.org/10.1088/0022-3727/36/15/316 10. Forouhi AR, Bloomer I (2019) New dispersion equations for insulators and semiconductors valid throughout radio-waves to extreme ultraviolet spectral range. J Phys Commun 3(3):035022. https://doi.org/10.1088/2399-6528/ab0603 11. Kramers-Kronig_relation (nd). https://www.chemeurope.com/en/encyclopedia/Kramers-Kro nig_relation.html. Accessed August 22, 2022 12. Paschotta DR (2021, Aug 13). Sellmeier formula, explained by RP Photonics Encyclopedia; refractive index, Sellmeier equation, dispersion formula. https://www.rp-photonics.com/sellme ier_formula.html. Accessed August 22, 2022 13. Salzberg CD, Villa JJ (2008) Optical constants of Si (Silicon). https://refractiveindex.info/? shelf=main&book=Si&page=Salzberg. Accessed August 22, 2022 14. Bhattacharya S, John S (2019) Beyond 30% conversion efficiency in silicon solar cells: a numerical demonstration. Sci Rep 9(1). https://doi.org/10.1038/s41598-019-48981-w 15. Krauter SCW (2006) Energy yield. In: Solar electric power generation: photovolatic energy system. Essay, Springer, pp 146–162 16. Sa¯o T (1967) Spectral emissivity of Silicon. Jpn J Appl Phys 6(3):339–347. https://doi.org/10. 1143/jjap.6.339 17. Lin L, Ravindra NM (2020) Simulation of optical properties of semiconductor multilayers from extreme ultraviolet to far infrared. Mater Sci Eng Int J 4(5):131–137. https://doi.org/10. 15406/mseij.2020.04.00139 18. Graphing calculator. Desmos (nd). https://www.desmos.com/calculator?lang=en. Accessed July 27, 2022 19. Silicon (SI). University of Reading (nd). https://www.reading.ac.uk/infrared/technical-library/ cadmium-telluride-cdte/silicon-si. Accessed August 29, 2022 20. Chen Y-J, Lee C-C, Chen S-H, Flory F (2013) Extra high reflection coating with negative extinction coefficient. Opt Lett 38(17):3377. https://doi.org/10.1364/ol.38.003377 21. Honsberg C, Bowden S (nd) Optical properties of silicon. PV Education. https://www.pveduc ation.org/ko/%ED%83%9C%EC%96%91%EA%B4%91/materials/optical-properties-of-sil icon. Accessed August 30, 2022

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22. Ravindra NM, Tong FM, Amin S, Shah J, Kosonocky WF, McCaffrey NJ, Manikopoulos CN, Singh B, Soydan R, White LK, Zanzucchi P, Hoffman D, Markham JR, Liu S, Kinsella K, Lareau RT, Casas LM, Monahan T, Eckart DW (1994) Development of emissivity models and induced transmission filters for multi-wavelength imaging pyrometry. SPIE Proceedings. https://doi.org/10.1117/12.171183 23. High transmission silicon for infrared optical applications-Sil’tronix Silicon Technologies. (2019, May 20). https://www.sil-tronix-st.com/en/news/High-transmission-silicon-for-inf rared-optical-applications. Accessed August 30, 2022 24. Backus CE (1976). Solar cells. IEEE Press

Prediction of Grain Size Evolution During Hot Rolling of HSLA Steels Considering Precipitation Goran Kugler, Jan Foder, Boštjan Bradaškja, and David Bombaˇc

Abstract A physical-based model for predicting grain size evolution during multipass hot rolling of HSLA steels has been developed, consisting of two coupled modules. The first is a microstructure module based on modeling the interaction of an ensemble of multiple grains. It considers strain hardening, dynamic recovery, and dynamic recrystallization during plastic deformation, as well as static recovery, static recrystallization, metadynamic recrystallization, and grain growth after straining. In the second module, the KWN multiclass approach was used together with classical nucleation theory for simulations of precipitation kinetics during thermomechanical processing. The parameters of the model were obtained through extensive experiments with the Gleeble-machine, thermodynamic calculations with ThermoCalc software, and microstructural characterizations of selected HSLA steel grades. A user-friendly application for simulating the hot rolling schedule was developed for industrial use. The results of the simulations show good predictability of the simulation system compared to industrial results for different hot rolling schedules. Keywords Hot rolling · Recrystallization · Precipitation · Modelling · Microstructure

Introduction During hot working of metallic materials, the technology needs to be controlled in such a way that in addition to the prescribed final geometry of the product also required microstructure and consequently final properties are obtained. The most important processes that influence and control the evolution of microstructure during G. Kugler (B) · D. Bombaˇc Faculty of Natural Sciences and Engineering, University of Ljubljana, Aškerˇceva 12, 1000 Ljubljana, SI, Slovenia e-mail: [email protected] J. Foder · B. Bradaškja SIJ Acroni d.o.o., Cesta Borisa Kidriˇca 44, 4270 Jesenice, SI, Slovenia © The Minerals, Metals & Materials Society 2023 The Minerals, Metals & Materials Society, TMS 2023 152nd Annual Meeting & Exhibition Supplemental Proceedings, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-031-22524-6_35

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hot rolling of HSLA steels are strain hardening, dynamic recovery, dynamic recrystallization, static recovery, metadynamic recrystallization, grain growth, and/or static recrystallization. The first three listed processes are taking place during plastic deformations, and the second four are taking place after each straining. These processes determine the austenitic microstructure prior to subsequent solid-state phase transformations that usually start to occur during or after the last deformation steps [1]. The growth of recrystallizing grains can be strongly influenced by the presence of small nano sized precipitates and solute atoms. Therefore, when optimizing the final properties, it is necessary to control the formation and dissolution of precipitates, i.e. their volume fraction, density, and size distribution. The precipitates influence the moving grain boundaries by the so-called Zener pinning, which inhibits the velocity of the boundary motion. The strength of the Zener pressure is mainly determined by the proportion and size of the precipitates [2–6]. Both parameters can be influenced by the process parameters of the technology used. For the optimal control of technologies of hot rolling a good knowledge of the kinetics of these processes is necessary and in particular how their kinetics are influenced by the main technological parameters such as strain, strain rate, temperature, stress state, and times between successive plastic deformations. In this contribution, a physical-based model for predicting grain size evolution during multi-pass hot rolling of HSLA steels is introduced which consists of two coupled modules. The first is a microstructure module based on the approach proposed by Orend et al. [7] describing the evolution of microstructure as modeling the interaction of an ensemble of multiple grains. It considers strain hardening, dynamic recovery, and dynamic recrystallization during plastic deformation, as well as static recovery, static recrystallization, metadynamic recrystallization, and grain growth after straining. The second module is the precipitation model based on the modified Dutta et al. [8] approach for the simulation of strain-induced precipitation combined with KWN multiclass approach [9]. The parameters of the model were obtained with thermomechanical testing, thermodynamic calculations, and microstructural characterizations of selected HSLA steel-grade.

The Model The microstructure is represented by an ensemble of N(t) grains, which are characterized by three state variables, i.e. dislocation density ρ i , size r i , and strain εi . Their volumes and surfaces are given as [7] Vi (ri ) = κV,i ri3 and Si (ri ) = κ S,i ri2 ,

(1)

where κV,i and κS,i are geometric constants. The dependence of the dislocation density ρ i , of a particular grain on the strain ε, is given by

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  √ dρ = k1 ρ − k2 ρ , d

(2)

which is the Kocks and Mecking model [10] at the macroscopic level, where k 1 is a constant that represents hardening, and k 2 is the softening parameter that represents the recovery of dislocations and is temperature and strain-rate dependent. The hightemperature flow stress is related to the density of dislocations within the grain by [10] √ σ = αGb ρ

(3)

where α is a dislocation-interaction term, which is 0.5–1.0 for most metals, G is the shear modulus and b is a value of Burger’s vector. The average dislocation density is calculated as N 1  ρ= Vi ρi , VE i

(4)

takes the volume fraction V i of each grain into account. V E is the total volume of the system. The ensemble model proposed by Orend et al. [7] is based on the idea of describing the free energy of the grains in a certain volume and the dissipated power during grain-boundary movement to allow the usage of a Lagrangian formalism for deriving equations for the growth rate r i of each grain. In their approach, the growth rate r i is given as [7]     2γ κV,i   τρi 1 + κV,i + λ − , r˙i = −M 6 κ S,i ri

(5)

where M is grain-boundary mobilty, τ is the dislocation line energy, γ is grainboundary energy, and λ Lagrange multiplier that can be calculated as N λ=−

i

     τρ 1 + κ + λ − κV,i ri2 6 κκV,i i V,i S,i N 2 κV,i i κV,i ri 6 κ S,i

2γ ri

.

(6)

Nucleation during dynamic recrystallization is modeled by adding new grains to the ensemble when an existing grain exceeds the critical dislocation density given by Roberts and Ahlblom criteria [10]  ρcr =

20γ ε˙ 3bl Mτ 2

1/3 (7)

where l is subgrain size and ε˙ is strain rate. For static recrystallization Bailey–Hirsch criteria were employed

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ρcr =

4γ . τl

(8)

Instead of defining the nucleation rate, in our model for modeling nucleation for both static and dynamic recrystallization, we preferred to use a probability, pi , which is proportional to the grain surface area Si (ri ) of a grain where nucleus appears pi = K · S i (ri )˙εk e− RT t, Qn

(9)

where K and k are constants, Qn is the activation energy for nucleation and Δt is the time step. When nucleation occurs, the volume of the initial grain is reduced by the volume of the newly formed nucleus with a critical radius. In this way, we ensure that the volume of the system is maintained even during the nucleation process. For simulation of the kinetic of the strain-induced precipitation of NbC, a simple model was proposed based on Dutta et al. [8] that was improved in a few points, i.e. multiclass approach is employed and all three main processes such as nucleation, growth, and coarsening are coupled together without any additional expression. The driving force for nucleation is given by  Nb C  kB T X ss X ss ln . G v = − VN bC K N bC

(10)

Critical radius is given as R* = –2γ /Gv and activation energy for heterogeneous nucleation as G ∗het =

γ 16 γ 3 − 0.8μb2 . π 3 G 2v G v

(11)

For nucleation, we used equations according to the classical nucleation theory, thus nucleation rate is given as  

  G ∗het −t dN 1 − exp . (t, T ) = N0 Zβ ∗ exp − dt kT τ

(12)

Parameter N 0 , which represents the number of possible heterogeneous nucleation sites and gives the number of dislocation nodes in the dislocation network as N0 = 0.5ρ 1.5 . For the growth of precipitates, we used the effective diffusion coefficient, which takes into account the combination of the volume diffusion coefficient and the diffusion coefficient along the dislocation lines through the density of dislocations as in [8]   2 2 ρ + D 1 − π Rcore ρ . Deff = D p π Rcore

(13)

The growth rate of NbC precipitates in KWN multiclass approach is given as [9]

Prediction of Grain Size Evolution During Hot Rolling of HSLA Steels … Nb C DeNf bf DeCf f − X iN b (R) − X iC (R) X ss X ss dR   = = at at dt R R N b VFe − X N b (R) C VFe − X C (R) X ss X ss i i V at V at N bC

387

(14)

N bC

where X i is the equilibrium concentration on precipitate/matrix boundary which depends on precipitate R, X ss is concentration of elements X and Nb in matrix and X p their concentrations in precipitate. Employing expression that connects solubility product and precipitate radius with concentration on precipitate/matrix boundary  X iN b (R)X iC (R) = K N bC x exp

 R0 , R

(15)

ones obtain a system of two equations with two unknowns from which equilibrium concentrations X iN b in X iC on precipitate/matrix boundary can be determined. R0 is given as R0 = 2γ VN bC k B T . Considering mass balance new content of the elements in the matrix can be determined [9] X = m

X 0m − X mp f V VVatFe

N bC

1 − fV

,

(16)

where X 0m represents the initial proportion of element m in the matrix, f v is the volume fraction of precipitated NbC and X mp is the concentration of element m in the precipitate. The volume fraction of NbC is given as [9] fv =

4 i

3

πri3 Ni ,

(17)

where N i is the number of precipitates and r i is their radius. Mean radius is thus given as Rmean

 r i Ni = i . i Ni

(18)

During thermomechanical processing of metallic materials, the presence of alloying elements in the matrix and precipitates can strongly influence the growth rate of recrystallizing grains. Precipitates affect the moving high-angle grain boundaries through the so-called Zener pinning, which inhibits the movement. The strength of the Zener pressure is mainly determined by the proportion and size of the precipitates. In our approach, we followed the approach of Buken et al. [3, 4] by defining Zenner pressure as N 3γ  f i pZ = 2 i ri

(19)

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where f i is the volume fraction of precipitates in class i and r i is their mean radius. When describing the influence of precipitates on the kinetics of recrystallization, it must be taken into account that the precipitates at the interfaces (boundary pinning) are located on fast diffusion paths (dislocations/grain boundaries), and therefore, grow much faster compared to precipitates located inside the grains. This means that the Zener pressure decreases as the numerator in the above equation decreases due to Oswald ripening and the denominator increases due to faster growth. Due to the decrease in Zener pressure, the grain boundary may locally relax and move into a deformed structure where it again encounters a greater number of finer precipitates and the Zener pressure increases again. On average, the boundary can move faster in such an environment than in the case where only volume diffusion was considered. Therefore, we define the effective mobility M ef , which depends on the magnitude of the Zener pressure pZ , as

Me f =

x M f + (1 − x)M p ; pd > p Z Mp; pd ≤ p Z

(20)

where M f is the mobility of the grain boundary without precipitates at the tilted boundary and M p is the mobility when precipitates maximally impede the movement of the grain boundary. The pressure ratio x is the ratio between the pressure boundary movement pd (surface tension and dislocations) and Zener pressure pZ x=

pd − p Z pd

(21)

In the developed model, for a given time step, we first calculate the new precipitation parameters and determine the Zener pressure and thus the ratio x, which we then use to determine the effective mobility to calculate the growth parameters of the large-angle boundaries in the microstructure under consideration at that time step. A schematic representation of the entire model and the coupling between the microstructure and precipitation modules can be found in Fig. 1.

Results and Discussion The model presented can be used to predict the microstructure during the hot deformation of any steel, provided the required material parameters are available. An attempt was made to validate and evaluate the performance of the model for HSLA steel (S690). The material parameters were determined using hot compression tests performed on the Gleeble 1500D thermomechanical simulator. Cylindrical samples were machined from HSLA steel with the chemical composition given in Fig. 2. Single and double hit compression tests were performed to obtain flow curves and static softening kinetics. After the single-hit hot compression tests, the samples were

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Fig. 1 Scheme of the model where microstructural evolution is included on a basis of thermomechanical processing, the main input parameters, internal state variables, and calculated outputs for optimal microstructural development in the model, where are SP—static recovery, DP—dynamic recovery, SHR—strain hardening, N˙ —new grain nucleation and removal of grains with a resolution set lower than a resolution set in a model, Di , ρi —grain sizes and dislocation densities, Ni —grain size distribution, PG—growth/dissolution and/or coarsening of precipitates (in bulk and on dislocations), N˙ het,hom —precipitate nucleation, ri —sizes of precipitates, Nihet,hom —size distributions of precipitates, and Averages—mean radiuses, volume fractions, number densities of precipitates, solute contents in matrix, etc.

quenched to preserve the developed microstructure. The procedure used to determine material parameters based on Zener–Hollomon parameter and hyperbolic sine equation is shown schematically in Fig. 2, with material parameters given in Table 1. The fine-tuning and testing of the model were carried out through various experiments and metallographic analyses. For example, the simulation of the post-dynamic softening after the prescribed deformation, as shown in Fig. 3a, where the evolution of the mean grain sizes at a temperature of 900 and 1100 °C is compared. Figure 3b shows the influence of temperature on the development of the mean grain size when rolling to a dimension of 51 mm. The rolling schedules here use the same deformations in all passes with varied pass temperatures given in Table 2. The same rolling schedule with temperatures Tprofil-1 was used to evaluate the influence of DRX on the evolution of the calculated mean grain size during hot rolling, as shown in Fig. 4. For that one, simulations were run with and without nucleation of DRX grains.

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Fig. 2 Typical flow curve and procedure for determining thermomechanical parameters (a), estimated kinetics of DRX (b), fitted and measured dependence between steady-state stresses and mean austenite grain size (c), determination of parameters of sinh equation (d), and chemical composition of our steel (e)

The influence of DRX on the calculated mean grain size is small and is more significantly observed only in the last two rolling passes. However, as can be seen in Fig. 4c and d, the consideration of DRX and MDRX has a significant influence on the grain size distribution. When only SRX is considered, the grain distribution is log-normal, whereas when DRX, MDRX, and SRX are considered, the final grain distribution changes to a bimodal distribution and is consistent with the observed bimodal distribution of industrially rolled plates when the same rolling schedule was used.

Conclusions In this paper, a physics-based model for predicting grain size evolution in multi-pass hot rolling of HSLA steels has been developed and evaluated. The model is coupled with two separate models, one used for the precipitation of NbC carbides and the other being a microstructural model for recrystallization during hot rolling of steels, describing the evolution of an ensemble of grains. The model has been parameterised and tested with a S690 HSLA steel and is able to describe the evolution of the average grain size and the grain distribution during hot rolling. With the calculation of the rolling schedule in a reasonable time, the model can be used for industrial monitoring of microstructural evolution during hot rolling or for the development of new rolling schedules with targeted grain size.

α def /Mpa–1

0.0065

Qdef /kJ mol–1

330.7

5.84

ndef /1.1 × 1013

Adef /s–1 75

G/GPa 0.15

α/k 2 /m–2 44

1.5 × 106

k 1 /m–2

Table 1 Material parameters used for hot rolling grain size evolution of S690 HSLA steel 0.8

γ /J m–2

0.0058

pi /-

8.7 × 10–6

M/m4 J–1 s–1

Prediction of Grain Size Evolution During Hot Rolling of HSLA Steels … 391

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Fig. 3 Fine-tuning and testing of the model; effect of post-dynamic softening on mean grain size at temperatures of 900 and 1100 °C (a) and influence of temperature on mean grain size evolution during hot rolling to plate thickness of 51 mm (b) Table 2 Rolling pass temperatures used to study the influence of temperature on the evolution of microstructure Roll pass

1

2

3

4

5

6

7

8

Tprofil-1,° C

1220

1147

1135

1130

1115

1110

1093

935

Tprofil-2, °C

1220

1201

1194

1167

1159

1147

1138

1124

Tprofil-3, °C

1220

1201

1194

1167

1159

1147

1138

870

Tprofil-4, °C

1220

1193

1185

1158

1149

1113

1103

1090

9

10

11

932

928

906

926

930

936

865

860

855

939

948

958

Fig. 4 Influence of DRX on the evolution of the mean grain size during hot rolling. Average grain size as a function of time (a), average grain size after each rolling pass (b), grain size distributions when only SRX was considered (c), and when DRX and MDRX were included (d)

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References 1. Bombac D, Peet MJ, Zenitani S, Kimura S, Kurimura T, Bhadeshia HKDH (2014) Modell Simul Mater Sci Eng 22:045005 2. Nakata N, Militzer M (2005) ISIJ Int 45(1):82–90 3. Buken H, Sherstnev P, Kozeschnik E (2016) Modell Simul Mater Sci Eng 24:035006 4. Buken H, Kozeschnik E (2017) Metall Mater Trans A 48:2812–2818 5. Kaverinsky VV, Sukhenko ZP (2021) Metallofiz Noveishie Tekhnol 43(1):27–45 6. Timoshenkov A, Warczok P, Albu M, Klarner J, Kozeschnik E, Bureau R, Sommitsch C (2014) Comp Mater Sci 94:85–94 7. Orend J, Hagemann F, Klose FB, Maas B, Palkowski H (2015) Mater Sci Eng A 647:191–200 8. Dutta B, Palmiere EJ, Sellars CM (2001) Acta Mater 49(5):785 9. Perez M, Dumont M, Acevedo-Reyes D (2008) Acta Mater 56(9):2119 10. Mecking H, Kocks UF (1981) Acta Metall 29:1865 11. Roberts W, Ahlblom B (1978) Acta Metall 26(5):801–813 12. Mukhopadhyay P, Loeck M, Gottstein G (2007) Acta Mater 55:551–564 13. Schäfer C, Mohles V, Gottstein G (2011) Acta Mater 59:6574–6587

Reduction of Friction and Adhesion in Copper and Brass Extrusion by Application of Boron Containing Surface Modifications Stefan Lechner, Alexander Thewes, and Sören Müller

Abstract Due to extensive abrasion and adhesion, tools for copper and brass extrusion are subject to considerable wear. In the present study, the effect of boron containing surface modifications on friction and adhesion was investigated by means of a high-temperature, high-speed friction test for extrusion. A Ti-Si-B-CN nanocomposite coating and a boridic diffusion layer were applied to hot work tool steel 1.2367 and nickel-based alloy 2.4668, respectively. Using billets made of copper alloy CW024A and brass alloy CW724R, the friction tests were performed at high temperatures and normal pressures typical of the extrusion process. The evaluation of the obtained test data indicates a significant influence of the Ti-Si-B-C-N nanocomposite coating on the friction and adhesion behavior of the investigated material pairings. While friction and adhesion are greatly reduced for the Ti-Si-B-C-N coating, the effect of the boridic diffusion layer is substantially less. Keywords Copper and brass extrusion · Tool wear · Boron containing surface modifications · Axial friction test · Friction and adhesion

Introduction In hot extrusion, the product quality is largely influenced by the performance of the die. Therefore, tool life is highly important to maintain consistent results in terms of dimensional accuracy and surface quality. In copper and brass extrusion, where billet temperatures are 600–1050 °C, the dies are subjected to high thermomechanical stresses [1]. However, since the common operating limit for hot work tool steels is S. Lechner (B) · S. Müller Extrusion Research and Development Center, Technische Universität Berlin, Gustav-Meyer-Allee 25, 13355 Berlin, Germany e-mail: [email protected] A. Thewes Institute for Surface Technology, TU Braunschweig, Field Office Dortmund, Eberhardstr. 12, 44145 Dortmund, Germany © The Minerals, Metals & Materials Society 2023 The Minerals, Metals & Materials Society, TMS 2023 152nd Annual Meeting & Exhibition Supplemental Proceedings, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-031-22524-6_36

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around 500 °C, these stresses often result in deflection, plastic deformation, crackbased fracture and, especially on the die face, wear [2]. In addition to abrasive wear, adhesion between extruded material and tool surface is another significant component of die wear in extrusion. The high adhesive forces between copper and iron lead to the formation of local welds, which are entrained during the extrusion process, and thus lead to washouts on the die face and die bearing [3–5]. Hard phase surface modifications that are capable of withstanding the high operating temperatures of hot extrusion can not only reduce abrasive wear, but also have further favorable tribological properties (e.g. low coefficient of friction, reduced adhesion of workpieces) [6, 7]. Therefore, two boron containing hard phases are investigated in this study with regard to their influence on friction and adhesion in copper and brass extrusion. On the one hand, a nanocomposite Ti-Si-B-C-N coating with possible boridic phases nc/a-TiB2 , a-BN, and a-B4 C incorporated in the nanostructure is applied on hot work tool steel 1.2367 by means of plasma enhanced chemical vapor deposition (PECVD) [8–12]. On the other hand, the nickel-based alloy 2.4668 (alloy 718) was gas borided to form nickel, iron, and chromium borides. Gas and powder pack boriding enhances tribological performance and increases the surface hardness of nickel-based alloys [13–19]. Utilizing a high-speed friction device for extrusion processes [20], surfacemodified test specimens are friction-tested against workpiece materials copper alloy CW024A and brass alloy CW724R. Moreover, an alternative configuration of the friction test rig is used to determine differences in adhesion properties of the surface modifications and billet materials.

Experimental Procedure Axial Friction Device In the extrusion process, hydrostatic conditions are present, which cannot be reproduced by common friction test devices like pin-on-disc, block-on-cylinder, and rotating-disc [21]. To account for this constraint and the main parameters affecting friction in hot extrusion, namely contact normal pressure, temperature, and relative friction speed, an axial friction device was developed by Sanabria et al. The setup is described in detail in [20], however, the principal function is summarized as follows. An assembly of multiple parts (see Fig. 1) is fixed in a universal testing machine (UTS) MTS 810 (MTS Systems Corporation, US), which measures and controls the axial tension–compression force, displacement, and velocity. The main components are the frame (white/light blue), bolt–nut arrangement (white), Belleville springs (navy blue), floating upper stem (orange), and container (magenta). The positioning of the friction partners is shown in the detail of Fig. 1. A billet of workpiece material (grey) is placed in a liner made of tool material (black) and between two stems (green). Furthermore, 13 µm thick graphite paper Grafoil GTB is added on the faces

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of the stems to minimize friction during the upsetting of the billet. Container, liner, billet, and stems are heated using a furnace, and temperature is measured at the billet and container surface with thermocouples. To perform the friction test, a new liner and billet are inserted into the device. After heating to test temperature, the billet is upset with a constant strain rate of 0.0005 s−1 , until a predefined force is reached. The compression force is set according to the normal pressure to be achieved between the liner and billet. Subsequently, the nuts of the bolt–nut arrangement are tightened and the force applied by the UTS is released. Since the floating upper stem is connected to an interchangeable Belleville spring arrangement, compression force on the billet and normal pressure are maintained. By pulling down the container with a constant velocity, the actual friction stroke is carried out and friction force is measured by the load cell of the UTS. To consider the fact that the liner is to be coated on the inside, it has been redesigned. For better accessibility during surface modification, the liner now comprises two half shells. This approach increases the homogeneity of surface treatment on the friction surface due to better accessibility for gaseous precursors during boriding and PECVD coating deposition. In case of PECVD, half shells avoid hollow cathode effects in a glow discharge. This in turn increases kinetic energy and the resulting residual stresses, ionization of precursors, and plasma chemistry, which affects the stoichiometry of the coating. Fig. 1 Model of the axial friction device with detail of container (magenta), liner (black), stems (green), and billet (grey)

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Table 1 Precursors and flow rates used for Ti-Si-B-C-N top layer deposition and gas boriding Precursor flow rate [sccm]

TiCl4

N2

BCl3

Si(CH3 )4

H2

Ar

Ti-Si-B-C-N-coating

77

667

200

67

3333

100

Boriding

–

870

50

–

900

–

Surface Modifications One of the surface modifications adopted in this study is a thin solid nanocomposite film of Ti-Si-B-C-N. The PECVD coating was deposited on liners made of hot work tool steel 1.2367 using a PN 100/150 device (Ruebig GmbH & Co. KG, Austria). First, a plasma etching routine was performed for 1 h, followed by a plasma nitriding process for 16 h and the coating synthesis. For the nitriding, an atmosphere with a low N2 /(N2 + H2 ) ratio of 5% and additional Ar for plasma support was provided. The process parameters pressure, applied bias voltage, and substrate temperature were set to 240 Pa, −480 V, and 530 °C, respectively. As other studies have shown, plasma nitriding improves adhesion between the steel substrate and PECVD hard coating [22, 23]. The nanocomposite film consists of a TiN adhesion layer, a graded interlayer, and a Ti-Si-B-C-N top layer. Formation of the top layer was carried out under the following conditions: pressure of 200 Pa, bias voltage of -560 V, substrate temperature of 530 °C, and a duty cycle of 0.33. The precursor flow rates are listed in Table 1. The other surface modification investigated in the present study is gas boriding of alloy 718. Using a PlaTeG PP20 device (PVA TePla AG, Germany), the liners were borided and precipitation hardened according to AMS5663 in a combined process. During the last 4–6 h of the heat treatment’s first temperature stage at 720 °C, the gaseous atmosphere was changed from H2 to a mixture of N2 , H2, and BCl3 . The atmospheric pressure was 700 Pa for the whole process and the precursor flow rates are presented in Table 1.

Friction Tests The design of the friction tester allows the investigation of friction properties at high contact normal pressures, temperatures, and relative friction speeds. Characterization of the tested surface modifications was performed at varying normal pressures, which will be referred to as normal stresses in the following. These were normalized  to the flow stresses of the billet materials (σn = σn /k f ). Temperature and strain rate-dependent flow stress data of copper and brass was obtained by means of hot compression tests as described by Lechner et al. [24]. As reported by Vidal Sanabria the friction stress remains almost constant above certain normal stresses for a given temperature and relative friction speed [25]. To determine this threshold value, the first series of tests with a variation of the normalized normal stress was performed at

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a relative friction speed of 10 mm/s as well as 700 °C (copper) and 650 °C (brass). Normalized normal stresses of 3 (copper) and 5 (brass) were found to be above the threshold value. Therefore, the main tests were conducted with normalized normal stresses of 1 and 3 for copper and 1 and 5 for brass. Relative friction speed was set to 10 mm/s for all tests and the process temperatures were 700 °C for copper as well as 650 and 750 °C for brass. In addition to the liners with surface modifications, untreated liners made of hot work tool steel 1.2367 were tested as benchmarks.

Adhesion Tests A second main series of tests was carried out to characterize the adhesion between surface modifications and billet materials. In contrast to the friction tests, the nuts were not tightened after the billet was upset and the predefined compressive force was applied. In this way, a specific normal stress was present on the billet’s surface, but not maintained during the whole experiment, since the release of compressive force was not compensated by the Belleville springs. The forces measured during the first part of the friction stroke, therefore, result solely from the adhesion of the billet and liner [25]. The adhesion test parameters were set identically to the friction tests.

Results and Discussion Friction Tests In dry sliding friction between a softer and harder material, three different contact modes can be apparent in dependence on the contact normal stress. At low normal stresses, slip condition as described by Coulomb is prevalent (sliding friction) [25]. With higher normal stresses, the elastic deformation of the asperities on the contact surface changes to plastic deformation as the yield stress of the softer material is exceeded. The friction mode changes to stick–slip condition [26] and in addition to mechanical interaction, chemical adhesion is observed. The third mode is the sticking condition. Due to high contact normal stresses and severe plastic deformation of the asperities, a strong adherence is generated between the contact surfaces. In consequence, the relative friction motion is facilitated by severe shearing of a subsurface layer of the softer material (shear friction) [27]. Considering the high pressures in extrusion, shear friction is the primary friction mode. In order to represent this condition and to ensure that the friction tests are performed at sufficiently high normal stresses, a small series of tests with a variation of the contact normal stress was performed. As shown in Fig. 2a, the friction force   increases with increasing contact normal stress σn , until a threshold (σn < 3) is

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Fig. 2 a Friction force measured for copper and untreated hot work tool steel 1.2367 at three different contact normal stresses, constant temperature, and constant friction speed and b friction force measured for copper in dependence on surface modification

reached. This represents the transition from mixed friction (stick–slip) to complete  shear friction (stick). With even higher contact normal stresses (σn = 5), no significant increase in the peak friction force can be observed. The tests with brass indicated  that this threshold is slightly larger, but less than σn = 5. However, the extrusion pressure and thus the contact normal stresses decrease towards the bearing channel of the die, and therefore, mixed friction is also present in the extrusion process. To  account for this condition, low normal stresses (σn = 1) were also applied for the following tests. Friction forces of the tests on untreated hot work tool steel and the two surface modifications with copper as friction partner are presented in Fig. 2b. The beginning of the curves, which represents the elastic deformation of the billet, is almost identical. But there are also significant differences, both in the level of the peak and in the following curve progression. While a broad force maximum is observed for hot work tool steel 1.2367 and borided alloy 718, a pronounced peak and a considerably smaller friction force are evident for the Ti-Si-B-C-N coating. The slight drop in friction force for hot work tool steel 1.2367 and borided alloy 718 reflects the deformation behavior of copper. After a critical amount of deformation, dynamic recrystallization, and thus softening is initiated and an equilibrium of softening and strain hardening develops [28]. As a result, the shear stress and thus the friction force drops to an almost constant level. The much lower friction force, as observed for the Ti-SiB-C-N coating, originates from a different friction mode. Since the main testing parameters, i.e. contact normal stress, temperature, and relative friction speed, are the same for all tests, the cause must be the friction properties of the PECVD coating. On the one hand, the reduced peak in friction force indicates lesser stiction, which is overcome even at lower forces. On the other hand, the overall reduced friction force suggests mixed friction (stick–slip) instead of pure sticking condition. Since the friction force at elevated normal stresses is quite dependent on the contact area, friction stress is considered in the following instead of friction force.

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For this purpose, the size of the contact area between the billet and liner is derived from the billet dimensions, taking into account thermal expansion during heating and upsetting. Friction stress is then calculated by relating force to the contact area (τ = FF /Ac ). Furthermore, friction stresses are normalized to the corresponding  reference test with untreated hot work tool steel 1.2367 at σn = 1 and only the peak friction stress is used to compare the performance of the surface modifications. The results of the tests with copper are summarized in Fig. 3a. In general, the friction stress increases with higher contact normal stress, irrespective of the surface modification. This is consistent with the results of the first series of tests. In comparison with the untreated hot work tool steel 1.2367, significantly lower friction stresses were measured for the Ti-Si-B-C-N coating. As discussed previously, this is due to the occurrence of mixed friction and can be reasoned with significantly reduced adhesion between copper and Ti-S-B-C-N. This effect can also be seen for borided alloy 718, albeit substantially weakened. Whereas the friction stress is reduced by around 63% and 38% for the Ti-Si-B-C-N coating, it is only lowered by about 12% for the boriding. The normalized friction stresses for the tests with brass are shown in Fig. 3b. Analogous to the experiments with copper, the friction stress increases with higher contact normal stress. In addition, the influence of the temperature becomes visible here. As friction stress is directly proportional to the flow stress of the billet material, it decreases with rising temperature. In this test series by 55% on average, neglecting the outlier of the test with Ti-Si-B-C-N coating at 750 °C and low contact normal stress. The following effects can be observed with regard to the influence of the surface modifications on the friction stress:

Fig. 3 a Normalized friction stresses of copper in dependence on surface modification and normal stress and b normalized friction stresses of brass in dependence on surface modification, normal stress, and temperature

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– Substantial reduction of 86% and 73% at low normal stresses for the Ti-Si-B-C-N coating – Slight reduction of 15% and 12% at high normal stresses for the Ti-Si-B-C-N coating – Considerable reduction of 44% and 62% at low normal stresses for the boriding – Insignificant effect at high normal stresses for the boriding – Negligible influence of the temperature for both surface modifications.

Adhesion Tests In addition to the friction stress, the adhesion component of friction was determined in a separate test series. For this purpose, the force required to apply the contact normal stress was released prior to the friction test. This causes the contact normal stress to relieve and contact between liner and billet, which is solely sustained by adhesion. The effect of this stress relief can be clearly seen in Fig. 4. In case of the untreated hot work tool steel 1.2367 and borided alloy 718, high adhesion force act on the contact surface and the force curve is similar to the one with maintained contact normal stress. The obvious difference is the sudden change of gradient at the maximum and reduced maximum forces. As described below, this is caused by reduced stiction. The friction motion in the corresponding friction tests was facilitated solely by shear, hence the friction stress applied there must be greater than the shear stress of the copper. With regard to the identical test temperature and friction speed, the shear stress of copper is also identical in friction and adhesion tests. The only difference is the reduced mechanical component of the friction due to the relieved contact normal stress. Since the relative motion in the adhesion tests already starts in the range of elastic deformation, stiction is now lower than the shear stress of copper, and for the most part, maintained by adhesion. The test with Ti-Si-B-CN coating is characterized by a major deviation in the force curve. Stiction and friction are overcome and sustained at very low forces, respectively. This indicates a significantly reduced adhesion between copper and the PECVD coating. To calculate the adhesion component of friction Ca for the individual tests, the determined friction stresses of the adhesion tests were divided by the corresponding ones of the friction tests: Ca = τ A /τ F × 100%. The values obtained for copper and the different surface modifications are presented in Fig. 5a. The untreated hot work tool steel and borided alloy 718 exhibit high adhesion components with an average of 80 and 76%, respectively. An influence of contact normal stress is not observed in the investigated parameter range. The values for the Ti-Si-B-C-N coating are considerably lower at 47% and 35%. The decline of the adhesion component of friction with increasing contact normal stress is unexpected, as it should increase or remain constant instead [25]. The reason for this is assumed to be the friction stress  values gained by the friction tests with σn = 1, which are used as reference values. In these tests, the low normal pressure is not sufficient to fully form the adhesion

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Fig. 4 Friction force measured for copper in dependence on surface modification without maintaining contact normal stress

between the liner and billet and achieve sticking friction. Consequently, a reduced friction force is measured, as shown in Fig. 2a. However, since the friction stress obtained by the adhesion tests is related to that of the friction tests, the adhesion component is overestimated. Regardless of this error, the PECVD coating achieves the desired effect of drastically reducing the adhesion of copper. The adhesion components of friction for the tests with brass as billet material are displayed in Fig. 5b. For hot work tool steel 1.2367, high adhesion components of 86% and 89% on average were determined. No noteworthy influence of normal stress is detected. Similarly, high values were determined for borided alloy 718 (74–90%). Since no valid test result could be obtained for this surface modification and the  parameter combination σn = 1 and T = 750 °C, no value is available. As mentioned

Fig. 5 a Adhesion components of friction for copper in dependence on surface modification and normal stress and b adhesion components of friction for brass in dependence on surface modification, normal stress, and temperature

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above, the adhesion component increases with higher normal stress or at least remains constant, so it can be assumed that it is at most 83%. As is the case for copper, a greatly reduced adhesion component for the Ti-Si-B-C-N coating is also evident for brass.   Adhesion components of about 60% were found for σn = 1, and for σn = 5 these were 15% and 29% for 650 and 750 °C, respectively. For both boriding and PECVD coating, the adhesion component appears to diminish with increasing normal stress. As interpreted in the discussion of the results with copper, this apparent decline is due  to an overestimated adhesion component at σn = 1. This is because no fully formed adhesion and thus no sticking was achieved in the corresponding friction tests with brass either. Considering the test temperature, a slight influence can be seen. With the rising temperature, there is also a slight increase in the adhesion component, which can be attributed to stronger interatomic bonding of the intermetallic contacts [29]. In summary, only the PECVD coating had a major effect on the adhesion of brass.

Conclusion Friction tests were carried out at high contact normal pressures, temperatures, and velocities, representing the process conditions of copper and brass extrusion. Two surface modifications, a nanocomposite Ti-SI-B-C-N coating applied on hot work tool steel 1.2367 and boriding of nickel-based alloy 718, were investigated with respect to their influence on friction and adhesion behavior. The results of the study are summarized as follows: – Small but noticeable reduction in friction due to gas boriding of alloy 718 – Great reduction in friction caused by the PECVD coating, especially at moderate contact normal pressures – Apparently no significant effect of the boriding on the adhesion of copper, at most a minor decrease in the adhesion of brass – Substantial reduction in the adhesion of both copper and brass caused by the PECVD coating. The boron containing surface modifications investigated in this study not only have the potential to reduce abrasive wear, but also adhesive wear of extrusion dies. In addition, the reduced adhesion can have a positive effect on the buildup remaining on the dies, thereby simplifying the cleaning of the dies between the individual extrusion cycles. The reduced friction, which was observed particularly at moderate normal pressures, can also benefit extrusion behavior, since the normal stress decisively decreases towards the die orifice. Thus, a higher surface quality of the extruded product may be obtained. Results of extrusion trials demonstrating the effectiveness of the coatings in terms of adhesion reduction during extrusion will be published in a separate study. Acknowledgements The authors are grateful for the financial support of the Arbeitsgemeinschaft industrieller Forschungsvereinigungen (AiF) [grant No. 19862 N].

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References 1. Bauser M, Sauer G, Siegert K (2001) Strangpressen. Aluminium-Verlag, Düsseldorf 2. Schwartz M, Ciocoiu R, Gheorghe D, Ciuca I (2014) Failures of AISI H21 die in copper hot extrusion. Mater High Temp 31(2):95–101. https://doi.org/10.1179/1878641313Y.000000 0001 3. Buckley DH (1981) Surface effects in adhesion, friction, wear, and lubrication. Elsevier, New York 4. Clode MP, Sheppard T (1990) Formation of die lines during extrusion of AA6063. Mater Sci Technol 6(8):755–763. https://doi.org/10.1179/mst.1990.6.8.755 5. Thedja WW, Müller K, Ruppin D (1993) Die Vorgänge im Presskanal beim Strangpressen von Aluminium, Teil 2: Reibung im Presskanal und Matrizenverschleiß. Aluminium 69(7):649–653 6. Lin J, Moore JJ, Mishra B, Pinkas M, Sproul WD (2010) The structure and mechanical and tribological proberties of TIBCN nanocomposite coatings. Acta Mater 58(5):1554–1564. https:// doi.org/10.1016/j.actamat.2009.10.063 7. Behrens BA, Bräuer G, Paschke H, Bistron M (2011) Reduction of wear at hot forging dies by using coating systems containing boron. Prod Eng Res Devel 5:497–506. https://doi.org/10. 1007/s11740-011-0308-z 8. Gissler W (1994) Structure and properties of Ti-B-N Coatings. Surf Coat Technol 68(69):556– 563. https://doi.org/10.1016/0257-8972(94)90217-8 9. Karvankova P, Vepˇrek-Heijmann M, Azinovic D, Vepˇrek S (2006) Properties of superhard nc-TiN/a-BN and nc-TiN/a-BN/a-TiB2 nanocomposite coatings prepared by plasma induced chemical vapor deposition. Surf Coat Technol 200(9):2978–2989. https://doi.org/10.1016/j.sur fcoat.2005.01.003 10. Vepˇrek S, Vepˇrek-Heijman M, Karvankova P, Prochazka J (2005) Different approaches to superhard coatings and nanocomposites. Thin Solid Films 476(1):1–29. https://doi.org/10.1016/j.tsf. 2004.10.053 11. Chen X, Ma S, Xu K, Chu PK (2012) Oxidation behavior of Ti-B-C-N coatings deposited by reactive magnetron sputtering. Vacuum 86(10):1505–1512. https://doi.org/10.1016/j.vacuum. 2012.03.001 12. Seifert HJ (ed) (2005) Refractory and hard materials in the Ti-Si-B-C-N system: phase equilibria, phase reactions and thermal Stabilities. University of Florida, Gainesville 13. Ueda N, Mizukoshi T, Demizu K, Sone T, Ikenaga T, Kawamoto M (2000) Boriding of nickel by the powder-pack method. Surf Coat Technol 126(1):25–30. https://doi.org/10.1016/S02578972(00)00517-X 14. Lou DC, Solberg JK, Akselsen OM, Dahl N (2009) Microstructure and property investigation of paste boronized pure nickel and Nimonic 90 superalloy. Mater Chem Phys 115(1):239–244. https://doi.org/10.1016/j.matchemphys.2008.11.055 15. Makuch M, Kulka M (2014) Microstructural characterization and some mechanical properties of gas-borided Inconel 600-alloy. Appl Surf Sci 314:1007–1018. https://doi.org/10.1016/j.aps usc.2014.06.109 16. Makuch M (2020) Nanomechanical properties and fracture toughness of hard ceramic layer produced by gas boriding of Inconel 600 alloy. T Nonferr Metal Soc 30(2):428–448. https:// doi.org/10.1016/S1003-6326(20)65224-4 17. Deng DW, Wang CG, ALiu QQ, Niu TT (2015) Effect of standard heat treatment on microstructure and properties of borided Inconel 718 T. Nonferr Metal Soc 25(2):437–443.https://doi.org/ 10.1016/S1003-6326(15)63621-4 18. Campos-Silva I, Contla-Pacheco AD, Ruiz-Rios A, Martínez-Trinidad J, Rodríguez-Castro G, Meneses-Amador A, Wong-Angel WD (2018) Effects of scratch tests on the adhesive and cohesive properties of borided Inconel 718 superalloy. Surf Coat Technol 349:917–927.https:// doi.org/10.1016/j.surfcoat.2018.05.086 19. Campos-Silva I, Contla-Pacheco AD, Figueroa-López U, Martínez-Trinidad J, Garduño-Alva A, Ortega-Avilés M (2019) Sliding wear resistance of nickel boride layers on an Inconel 718 superalloy. Surf Coat Technol 378:124862. https://doi.org/10.1016/j.surfcoat.2019.06.099

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Thermal Fatigue of Spheroidal Graphite Cast Iron Primož Mrvar, Mitja Petriˇc, and Milan Terˇcelj

Abstract Using high Si spheroidal graphite cast iron (SGI), thermal fatigue tests at a temperature of 600 ºC were carried out. Surface layer degradation on test samples was studied, i.e., degradation of graphites, initiation and growth of cracks in relation to specific characteristics of graphites as well as oxidation process. Special attention was devoted to the oxidation progress of degenerated graphite nodules (complex structured graphite that contains also small ferrite particles), whereas their oxidation rate is accelerated in comparison to usual graphite nodules. Crack initiation and growth are accelerated in case of the successive arrangement of graphite, by the process of graphite-matrix debonding. The complex process of oxidation is related to the characteristics of graphite particles as well as their distribution in the matrix. Keywords Spheroidal graphite cast iron · Thermal fatigue · Oxidation · Graphites · Surface degradation

Introduction Due to their acceptable price, excellent castability, good thermal conductivity, as well as good combination of mechanical properties (wear resistance, ductility, strength, fatigue,), spheroidal graphite cast irons (SGI) have found their application in various areas such as automotive and agricultural industry (producing of manifolds, motor blocks, brake drums, etc), exploitation of wind energy (turbine components), pipeline components, etc. Due to the above-mentioned advantages, further increase in their application is expected which requires developing of new cast irons with improved properties to sustain increased mechanical, thermal, tribological, and chemical loads in wider temperature ranges as well as revealing degradation mechanisms in specific load and environmental conditions [1–8]. P. Mrvar (B) · M. Petriˇc · M. Terˇcelj Faculty of Natural Sciences and Engineering, University of Ljubljana, Aškerˇceva 12, 1000 Ljubljana, SI, Slovenia e-mail: [email protected] © The Minerals, Metals & Materials Society 2023 The Minerals, Metals & Materials Society, TMS 2023 152nd Annual Meeting & Exhibition Supplemental Proceedings, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-031-22524-6_37

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The microstructure of the matrix can vary from ferrite to perlite in the as cast state as well as from austenite to bainite in which the different graphite morphologies are achieved, i.e., flake, compacted, and spheroidal graphite. The matrix microstructure and characteristics related to graphite decisively influence the relevant properties of cast iron, i.e., static and fatigue strength, thermal (thermal conductivity) and thermal fatigue resistance, oxidation progress, degradation development, etc. [3, 4, 6, 9]. The oxidation resistance of cast iron is very important for high-temperature mechanical fatigue resistance, as oxidation is always involved in such conditions. It was revealed that the addition of Cr, Si, Al, Ce, and La can improve the oxidation resistance of cast iron. The main degradation mechanism, i.e., the occurrence of microcracks and their growth, linking of cracks, creep, and oxidation, as well as their mutual effects, in relation to the above-mentioned loads were revealed, whereas their occurrence is related to the characteristic of loads and their mutual interactions, as well as to microstructural and graphite characteristics. At mechanical fatigue loads, three different damaging mechanisms related to graphite were observed, i.e., matrix– graphite “pure” debonding, “onion like” mechanism, and the “disaggregation” [10– 12]. Graphite particle is desired for the increase of mechanical fatigue properties, they additionally depend on the chemical composition, inoculation method, graphite nodules size, share of ferrite in the matrix, etc. Decreased graphite nodularity reduces high-temperature fatigue life as well as corrosion resistance [13]. On other hand, for achieving better thermal conductivity properties as well as better thermal fatigue resistance compacted, flake graphite is desired. In this contribution, thermal fatigue tests of SGI, with increased content of Si at a maximal test temperature of 600 ºC, were carried out. The degradation mechanism, i.e., initiation and growth of cracks in relation to debonding and oxidation of graphite were studied. In order to observe the degradation processes, the tests were interrupted at a selected number of thermal-cooling cycles. Thus, the initiation of cracks and their growth in relation to characteristics of graphite particles and oxidation, etc., was studied.

Experimental Production of SGI The Base alloy was produced in a medium-frequency induction furnace with a capacity of 5 tons. The metal charge contained circular cast iron, steel scrap, pig iron, carburite, and FeSi75 alloy. The desulfurization of the base alloy was done by adding CaC2 to the flowing ladle. For alloying of molybdenum, the master alloy FeMo65 was used. Then the magnesium treatment according to a sandwich process was done in a ladle. The used nodulizing agent was FeSi50Mg15 and inoculant FeSi60Ba5 was added. The treated SiMo SGI alloy was poured in standard Y-probes-type II with a thickness of 25 mm at a temperature of 1330 °C.

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Table 1 Chemical composition of newly developed ductile cast iron in wt.% C

Si

Mn

S

Cr

Cu

P

Mg

Ni

Mo

Sn

Al

Fe

3,06 4,36 0,37 0,005 0,038 0,020 0,019 0,043 0,01 0,095 0,010 0,012 Rest.

High silicon and low Mo (Table 1) SGI that belongs to SiMo family of iron-based alloys was used for thermal fatigue testing. Tensile tests were also performed on the SGI samples using a universal tensile test machine INSTRON 1255, 500 kN with an extensometer.

Thermal Fatigue Test and Conditions of Testing Thermal fatigue testing was carried out on Gleeble 1500D thermo-mechanical simulator, i.e., in its working cell, where borehole specimens were clamped between two copper anvils on both sides (Fig. 1a). Conductive heating and internal water cooling of samples (occurrence of cracks in sample internal) were computer guided. The temperature of the tested sample was measured by a thermocouple (type K) which was welded in the middle of the sample’s working surface. Approximately, 3 s for heating of samples to the maximum selected test temperature of 600 °C was needed while water cooling and air drying of the inter samples lasted for about 0.6 and 0.5 s, respectively (Fig. 1b). Thermal fatigue bechavior was investigated on samples after selected number of cycles, i.e., after 200, 500, 1000, 2000, and 4000 heating–cooling cycles.

a)

b)

Fig. 1 Test sample and its insertion between cooper anvils in Gleeble 1500D working cells (a) and temperature cycles during testing (b)

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Results and Discussion Description of New Material The microstructure of the SGI material consists of a ferrite matrix (see Fig. 2a with detail on Fig. 2b) and predominately spheroidal graphite (Fig. 2c). Pearlite as well as a carbide network of MxCy which percentages amounted 5 area. % and 0.2 area. %, respectively, were observed (Fig. 2b). Usually, no ferrite or negligibly small amounts (Fig. 2c) were found in graphite. Furthermore, a small amount of degenerate spheroidal graphite was revealed (Fig. 2d); in fact, the structure of both types of graphite (i.e., spheroidal (Fig. 2c) and degenerated (Fig. 2d)) can be complex. On the other hand, also degenerated graphites with an increased amount of ferrite were found (Fig. 2e and f). The graphite/matrix interphase was in some cases serrated as well as hooked. The average measured values for yield strength, tensile strength, and elongation were 485MPa, 610.5 MPa, and 10.5%. respectively.

Degradation of the Surface Layer of Test Samples Appearance of the Degradation Progress of Surface Layer on Cross Sections As mentioned in section “Results and Discussion” (Fig. 2c–f), the graphite nodules can also be degenerated, i.e., composed of ferrite islets inside graphite nodules in various amounts. Several modes of degradation of graphite were observed, they differ in dependence on their position (depth) regarding cooled surface, their composition, and size. In the first stage of their degradation, i.e., at low number of cycles (200 thermal cycles), the oxidation of graphite-matrix boundary (around the nodules) occurs for both normal graphite nodules as well as degenerated ones, this is usually followed by the oxidation of ferrite islets in the degenerated graphite as seen in Fig. 3a, b. The Fig. 3a BSE image shows ferrite islets inside degenerated graphite nodules, while elemental EDS mapping for oxygen is presented in Fig. 3b. Both mentioned oxidation processes can take place simultaneously, as seen on the right side of the graphite in Fig. 3b, while on the left side, the oxidation of ferrite islets has still not begun. Further stages of degradation (progress in oxidation process on previously mentioned spots) of complex structured graphite particle are shown in Fig. 3c and d, i.e., EDS elemental mapping for oxygen, is visible; on both previous figures, crack initiation on the upper side of graphite, a consequence of increased stresses, is also visible. In Fig. 3e and f, the final stage of nodule oxidation for graphite nodules very close to the cooled surface is shown. Whereas the oxidation tongues run on the ferrite islets pathways. The presented figures reveal that the oxidation occurred first on interphase boundary areas between graphite nodules and the matrix,

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a)

b)

c)

d)

e)

f)

Fig. 2 Microstructure of high Si and low Mo spheroidal graphite cast iron (a) with detail showing perlite and eutectic MxCy carbides (b), detail of nodular graphite containing a small amount of ferrite (c) and degenerated graphite (d), detail of spheroidal graphite with an increased amount of ferrite (e) with EDS mapping for Fe (f)

then simultaneously followed by oxidation of ferrite areas in degenerated graphite nodules. The ferrite in the degenerated nodules represents pathways for the internal oxidation of graphite. This is followed by oxidation of the carbon areas in graphite nodules. This way of oxidation of degenerated graphite nodules is also accelerated that due to increased volume of oxidised graphite increases stresses around graphite and consequently additionally accelerates the crack growth around graphite nodules, especially in radial direction as seen in Fig. 3c–f. Similar mode of oxidation of degenerated graphite also in internal, i.e. in relation to crack growth, was observed (see Fig. 3g with detail on Fig. 3h (EDS mapping for oxygen)). From the last figure, it can be observed that an increase in volume and

Thermal Fatigue of Spheroidal Graphite Cast Iron a)

b)

c)

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e)

f)

g)

h)

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Fig. 3 Stages of degradation of degenerated nodular graphite; crack formation and initial oxidation of matrix-graphite boundary around graphite as well as ferrite islets, SEM image (a) and EDS elemental mapping for oxygen at 500 cycles (b), advanced stage of matrix-graphite boundary oxidation around graphite and ferrite islets in graphite; EDS elemental mapping for iron at 1000 thermal cycles (c and d), final stage of complex graphite degradation (oxidation) at 4000 cycles (e and f), and occurrence of several cracks around graphite nodule in relation to oxidation and growth of crack at 4000 cycles (g and h)

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consequently also stresses around graphites due to oxidation can lead to the growth of several cracks on such spots.

Role of Graphites and Their Debonding, Perlite and Porosity at Growth of Cracks As presented in Fig. 3a–d, the initiation of cracks is usually related to graphites located at the cooled surface or close to the cooled surface while their growth (radial direction) is related to their successive arrangement (Fig. 4a). The process of debonding between graphite and matrice (Fig. 4b) observed at the higher number (1000) of cycles is also a relevant phenomenon for the growth of cracks. As presented in Fig. 4c, with detail in Fig. 4d, the process of debonding accelerates the growth of cracks; namely in debonded areas access to oxygen is facilitated. Furthermore, the growth is especially accelerated in case of successive arrangement of graphites as in the case shown in detail in Fig. 4d. The role of perlite in the growth of cracks at a temperature of 600 °C was so far not enough elucidated. In Fig. 4e, the case of oxidation of perlite located at the cooled surface is presented; from the figure occurrence of oxidation tongue (ca 15 µm in depth) is visible that indicate the increased oxidation rate of perlite in comparison to matrice (Fig. 4f) since oxidation depth of matrice, in this case, amounted to ca 5–7 µm. Further on increased oxidation rate of perlite in comparison to matrice at test temperature of 600 °C is presented also on Fig. 4g; in this cases perlite is located at grain boundaries. Thus by diffusion of oxygen on grain boundaries this reach also small perlite areas bellow cooled surface that leads accelerated oxidation of the areas; consequently this increases volume as well as stresses on the spots that accelerates growth of cracks. Figure 4h has presented the influence of porosity on crack growth; despite its location ca 60 µm bellow cooled surface this influence on initiation of crack on the spot as well as on further growth of cracks. Namely, such spots represent increased stress concentration that leads to mentioned crack initiation and accelerated growth of cracks.

Average Length of Cracks The seven longest cracks for a selected number of thermal cycles were measured (values at 200 cycles are given in Fig. 5) and the average value at an interrupted number of cycles was calculated. Thus, the average value of the seven longest cracks at 200 cycles was 76 µm, at 500 cycles 174 µm, 1000 cycles 418 µm, 2000 cycles 558 µm, and at 4000 cycles, the average value was 643 µm.

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Fig. 4 Initiation and growth of cracks on areas with increased density of graphites (a), debonding between matrice and graphite (b), growth of cracks on area of debonding and oxidation (c) with detail (d), accelerated oxidation of perlite areas (e) in comparison to matrice (f), accelerated oxidation of perlite located at grain boundaries below cooled surface (g), and increased influence of porosity on initiation and growth of cracks (h)

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Fig. 5 Lengths of the seven longest cracks and average length at 200 cycles

Conclusions In this study, thermal fatigue tests at a test temperature of 600ºC for SGI with increased content of Si were carried out whereas the tests at 200, 500, 1000, 2000, and 4000 thermal cycles were interrupted. From the obtained results, the following conclusions can be derived: – Regarding the composition of graphite particles, i.e., presence or non-presence of ferrite particles in graphite, two types of graphite can be distinguished, i.e., degenerated (with the presence of ferrite particles) and normal (without the presence of ferrite particles). – Graphite particles present spots for stress concentration, especially on spots of their serrated surface, as well as on their sharp-edged spots. On both mentioned spots, thermal stresses are additionally increased, and consequently, micro cracks occurred first on the spots. – Occurrence of cracks and oxidation of graphite, ferrite, and perlite are the main degradation mechanisms. – Degradation mode of graphite particles depends on their location, i.e., their location at the cooled surface or below the surface, their distance from the surface (depth), their characteristics (size, shape, composition), and microstructure characteristics. – First step of degradation of graphite bellow surface is occurrence of cracks up to the located graphite and oxidation of interphase area between graphite and matrice. Oxidation of ferrite spots in graphites is second stage of degradation of degenerated graphites which serve for oxygen penetration and consequently accelerated oxidation of graphites. – Cracks grow further in case of the successive arrangement of graphite and the growth rate is accelerated at their lower mutual distance. – Presence of porosity also accelerates the growth of cracks. In case of in one line in radial direction successive arrangement of graphite, grain boundary orientation, porosity, etc. longest cracks occur. – Debonding between graphite and matrix was observed at 1000 cycles. In case of a lower mutual distance between graphite and their successive arrangement, the growth of cracks increased at the spots.

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– Perlite areas contribute to the degradation of the surface layer. The oxidation rate of perlite islets is slightly increased in comparison to matrice. Oxidation of perlite occurs in its entire area. Perlite islets, due to the increased volume of the oxidized area, serve as spots for increasing stresses that accelerate the growth of cracks.

References 1. Davis JR (ed) (1996) ASM specialty handbook—cast irons. ASM International, Metals Park, OH 2. Pan Sining, Gang Yu, He Xiuli, Li Shaoxia, Zhang Yue, Li Qingyu (2019) Collective evolution of surface microcrack for compacted graphite iron under thermal fatigue with variable amplitude. Int J Fatigue 118:139–149. https://doi.org/10.1016/j.ijfatigue.2018.09.005 3. Kim S, Cockcroft SL, Omran AM (2009) Optimization of the process parameters affecting the microstructures and properties of compacted graphite iron. J Alloys Compd 476:728–732 4. Méndez S, Arenas MÁ, Niklas A, González R, Conde A, Sertucha1 J, de Damborenea JJ (2019) Effect of silicon and graphite degeneration on high-temperature oxidation of ductile cast irons in open air. Oxid Metals 91:225–242. https://doi.org/10.1007/s11085-018-9875-0 5. Sudhakar AN, Markandeya R, Srinivasa Rao B, Pandey AK, Kaushik D (2022) Effect of alloying elements on the dry sliding wear behavior of high chromium white cast iron and Ni-hard iron. Mater Today Proc. https://doi.org/10.1016/j.matpr.2021.09.295 6. Weitao S, Bin W, Xiaoliang L, Yuqian W, Jian Z (2021, in print) Controlling the tribology performance of gray cast iron by tailoring the microstructure. Tribol Int. https://doi.org/10. 1016/j.triboint.2021.107343 7. Wollmann Daniela, Pintaude Giuseppe (2021) Tribological performance of high-strength cast iron in lubricated contact containing carbon black. Wear 476:203743. https://doi.org/10.1016/ j.wear.2021.203743 8. Lin Meng-Bin, Wang Chaur-Jeng, Volinsky Alex A (2011) High temperature oxidation behavior of flake and spheroidal graphite cast irons. Oxid Met 76:161–168. https://doi.org/10.1007/s11 085-011-9244-8 9. Liu Yangzhen, Li Yefei, Xing Jiandong, Wang Shaogang, Zheng Baochao, Tao Dong, Li Wei (2018) Effect of graphite morphology on the tensile strength and thermal conductivity of cast iron. Mater Charact 144:155–165. https://doi.org/10.1016/j.matchar.2018.07.001 10. Iacoviello Francesco, Di Cocco Vittorio, Bellini Costanzo (2019) Fatigue crack propagation and damaging micromechanisms in Ductile Cast Irons. Inte J Fatigue 124:48–54. https://doi. org/10.1016/j.ijfatigue.2019.02.030 11. Cavallini M, Di Bartolomeo O, Iacoviello F (Feb–Mar 2008) Fatigue crack propagation damaging micromechanisms in ductile cast irons. Eng Fract Mech 75(3–4):694–704. https:// doi.org/10.1016/j.engfracmech.2007.02.002. 12. Di Cocco V, Iacoviello F (Dec 2017) Ductile cast irons: microstructure influence on the damaging micromechanisms in overloaded fatigue cracks. Eng Fail Anal 82:340–349. https:// doi.org/10.1016/j.engfailanal.2017.06.039 13. Xiang Shengmei, Hedström Peter, Zhub Baohua, Linder Jan, Odqvist Joakim (2020) Influence of graphite morphology on the corrosion-fatigue properties of the ferritic Si-Mo-Al cast iron SiMo1000. Int J Fatigue 140:105781. https://doi.org/10.1016/j.ijfatigue.2020.105781

Utilization of Plant Oil-Based Fatliquor in the Processing of Leather I. H. Ifijen, I. O. Bakare, E. O. Obazee, O. C. Ize-Iyamu, N. U. Udokpoh, A. O. Ohifuemen, F. U. Mohammed, E. A. Fagbemi, and P. O. Ayeke

Abstract The chemical and mechanical steps in the leather-producing process— soaking, unhairing/liming, deliming/bating, pickling, tanning, neutralization/dyeing, fatliquoring, drying, and finishing—transform hides and skin into leather. The fatliquor, which is often injected into the collagen fibers to lubricate them without leaving an oily residue on the surface of the leather, is composed of many plants sulfonated oils. Plant sulfonated oil has been shown to improve the tensile strength, flexibility, and softness of leathers, as well as their lubricating capabilities. This investigation focused on the lubricating qualities of leather that had been treated with various fatliquored oils. The physicochemistry, difficulties, and potential uses of fatliquor in the manufacturing of leather were also emphasized. Keywords Fatliquor · Plant oil · Leather

Introduction Plant oils are extracted from plants and used in a variety of industrial and food goods [1–13]. This practice dates back hundreds of years. Among these industrial uses is the usage of lubricants in the leather sector. Leather tanning is the sequential mechanical and chemical transformation of hides or skins into leathers [14]. In the processing of leather, tanning is essential. Vegetable tannins, chrome tanning agents, or syntans are used to tan the purified matrix. Animal hides lose the majority of their natural oils after the tanning process is complete. Because there is inadequate lubrication inside the fibrils, the resulting leather is hard, stubborn, and challenging to work with [15]. As a result, a neutral oil and emulsifier mixture known as leather lubricant (also known as leather fatliquor) is added to the leather matrix to stop fiber attachment. I. H. Ifijen (B) · I. O. Bakare · E. O. Obazee · O. C. Ize-Iyamu · N. U. Udokpoh · A. O. Ohifuemen · F. U. Mohammed · E. A. Fagbemi · P. O. Ayeke Department of Research Operations, Rubber Research Institute of Nigeria (RRIN), Km. 19, Benin-Sapele Road, Iyanomo, Edo State, Nigeria e-mail: [email protected]; [email protected] © The Minerals, Metals & Materials Society 2023 The Minerals, Metals & Materials Society, TMS 2023 152nd Annual Meeting & Exhibition Supplemental Proceedings, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-031-22524-6_38

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Either adding surfactants to the oil [16] or chemically altering the oil molecules by attaching a water-soluble group (polar group), such as a sulfate or phosphate group, produces the emulsion. The fact that each plant fatliquor has a different set of features that are dependent on the characteristics of the vegetable oil utilized is quite interesting to note. Fatliquors have an impact on the matrix of the leather by lowering frictional forces, enhancing tensile strength, adding softness, and protecting the leather from cracking [14, 15]. The type of fatliquors employed typically affects the physical properties of leather, such as flexibility, feel, and stitch tear resistance. The quantity, current use, odor, and color of an oil define its potential as a key raw material in the expanding commercial fatliquor sector [17]. Various plant oils have been employed in numerous trials to create fatliquor for use in the leather processing industry. But there are still hardly any reviews in the literature that deal with this field of research. As a result, this study gave an incisive picture of how plant oil is used as a fatliquor in applications for leather production. The chemistry, physicochemistry, challenges, and possibility of employing fatliquor in the production of leather were also covered.

Chemistry and Physicochemistry of Fatliquoring Structure of the Hide and Fatliquoring Animal skins include certain non-collagenous elements as well as collagen in the form of a porous matrix. The cohesiveness of the fibers is lessened by tanning. The triple helix structure of protein-collagen is destroyed during denaturation, and a coiled structure is created instead. The ability of the fatliquoring agents to oxidize is connected to the decline in skin denaturation temperature brought on by aging [18]. The application of fatliquor enables the neutral fraction of the fibrils to be lubricated and kept from sticking at a time when the diameter of the collagens is reduced during drying [19]. The emulsions’ particle kinds and sizes may have an impact on the characteristics of leather; the higher the penetration, the smaller the particle size [20]. Since all linkages are non-covalent, they dissolve at a specific temperature and cause the collagen structure to shrink [20]. Since the fatliquoring component penetrates and spreads through the collagen fibers, a drop in pH could cause the emulsion to become unstable. The contraction brought on by tanning leads the fibrils in the leather to become fixed before the pH is lowered. Many current studies concentrate on altering leather by attaching various monomers (such as styrene and acrylate derivatives) to collagen molecules [21]. With rising temperatures, the percentage of leather shrinkage gradually rises [22]. In leather that has been fatliquored with less, the pH percentage and shrinkage caused by water vapor are both higher. However, the effect is less noticeable in leather that has been fatliquored heavily [23]. Leather shrinkage increases as drying temperature rises. The primary factor influencing the

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qualities of fatliquoring agents’ UV resistance is their number of double bonds, and prolonged radiation can cause chain scission, which can reduce mechanical performances [24, 25].

Recent Research Studies on the Use of Plant Oil in Producing Fatliquor for Leather Processing Applications In order to determine the oil’s potential for use in leather fatliquoring, Nkwor et al. [9] looked into the synthesis, characteristics, characterization, and sulphonation of Afzelia africana aril cap oil [26]. In order to confirm the oil’s modification, the Afzelia africana aril cap oil was analyzed both before and after the sulphonation procedure was complete. The melting point, acid value, free fatty acid, iodine value, saponification value, and percent SO3 of the unsulphonated/sulphonated oil were found to be 6.39 °C, 19.90 °C, 12.99 mg KOH/g, 0.50 mg KOH/g, 6.50; 5.25, 77 g iodine/100 g; 21 g iodine/100 g, 185 mg KOH; 176 mg KOH and nil; 3.92% respectively. Using standard techniques, the fatliquoring potential was evaluated and contrasted with commercial fatliquor. Sudan stain, tensile strength, double edge tear, and elongation at break test results, respectively, showed a considerable improvement in the lubricating and mechanical properties of the leather treated with the sulphonated Afzelia africana aril cap oil (Table 1, Fig. 2). The treated leather’s enhanced lubricating and mechanical qualities were superior to those of commercial fatliquor. This study demonstrates that A. africana aril caps, which have little commercial value, can serve as a source of fatliquor for the leather industry (see Fig. 1). Before hides or skins are converted into leather, there are various steps in the production process where microbes might attack. The exceedingly delicate hides and skins must be preserved due to travel and long-term storage in order to prevent losses from bacterial degeneration. Given that they are made of water, protein, fatty substances, and mineral salts, hides and skins are also excellent substrates for the growth of microbes [27]. On leather, microbial growth can result in protein degradation, color changes, strength loss, and disagreeable odors [28]. During the procedure, biocides can be used as antimicrobial agents to shield the leather fibers against bacterial or fungal attack. However, the organic toxicants employed in the manufacturing of leather, like phenolic active compounds, would be harmful to the environment [29]. Chemical biocides are frequently used in the leather industry, which raises problems because their primary purpose is to preserve hides during processing rather than to give finished goods antibacterial qualities [30]. Additionally, due to issues with human health and the environment, several of these biocides used in the leather-processing have been restricted or banned [27]. The introduction of a perfect, ecologically safe, antibacterial leather treatment offers a potential chance to solve these challenges. There are certain benefits to using plant-based antibacterial substances as preservatives over these toxic biocides [31].

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Table 1 Strength Properties of Sulphonated Afzelia africana aril cap oil [26] Properties Tensile strength (N/mm2 )

NC

PC

PF

Parallel

22.38

32.83

24.03

Perpendicular

12.93

17.11

15.08

17.66

24.97

19.56

28.88

31.04

39.98

Average tensile strength (N/mm2 ) Elongation at break (%)

Parallel Perpendicular

Average elongation at break (%) Double edge tear load (N)

Parallel Perpendicular

Average double edge tear load (N) Grain crack strength (N)

Parallel Perpendicular

Average grain crack strength (N) Distention at grain crack (mm)

Parallel Perpendicular

Average distention at grain crack (mm) Ball burst strength (N)

Parallel Perpendicular

Average ball burst strength (N) Distention at burst (mm)

Parallel Perpendicular

Average distention burst (mm)

26.14

46.10

40.86

27.51

38.57

40.42

30.03

45.03

47.07

38.94

59.63

54.20

34.49

52.48

50.64

35.0

37.0

50.0

33.0

36.0

34.0

34.0

36.5

42.0

7.56

8.95

10.09

8.23

8.02

8.26

7.90

8.49

9.18

35.0

37.0

50.0

34.0

46.0

54.0

34.5

41.5

52.0

7.56

8.95

10.09

8.23

9.08

10.54

7.90

9.02

10.32

To be employed in the processing of leather, Yorgancioglu et al. [32] produced an antibacterial fatliquor emulsion from castor oil containing thymol (1, 2, 4, and 8% w/w). Zeta sizer was used to determine the fatliquors’ zeta potential and average particle size. To calculate the weight losses when fatliquor emulsions are exposed to constant heating rates, the thermogravimetric behaviors of the emulsions in dry air were examined. After the emulsification procedure, fatliquor emulsions were applied to cattle leathers that had been chrome tanned. When compared to the control group sample (Fig. 2a), the collagen fiber bundles of the fatliquored leather’s SEM analysis demonstrate that they were clearly separated. After being treated with a thymol-loaded fatliquoring emulsion, the collagen fibers of fatliquored leather were successfully lubricated, making it more likely that they would be orientated under external stresses, increasing the leather’s strength qualities. The fatliquored leather’s antibacterial properties were examined in accordance with a recognized test procedure for evaluating antimicrobial activity in dynamic contact situations with Gram-positive and Gram-negative bacteria. A study of the emulsion’s minimum inhibitory concentration (MIC) was also conducted. 99.9% of

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Fig. 1 Staining test results showing cross section of chrome tanned goatskin processed (a) without fatliquor, negative control (NC); (b) with sulphonated Afzelia africana aril cap oil (SACO); (c) with commercial sulphated fatliquor, TRUPON DXV (PC) [26]

Fig. 2 SEM images of cross sections of a control group leather and b fatliquored leather

S. aureus and 98.6% of E. coli bacteria were reduced, respectively, by the microorganisms. 6.25 and 12.5 lL/mL were found to be the MIC values from emulsions against bacteria, respectively. Thymol was extremely effective in the fatliquor emulsion against both Gram-positive and Gram-negative bacteria, according to the findings of antibacterial testing. With the best fatliquor emulsion (4% thymol w/w), physical

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characterization of leather was also done, and the results showed that the fatliquored leathers had adequate physical qualities when compared to identical leather manufactured with commercially available fatliquors. The study’s findings suggested that thymol-loaded fatliquor emulsions with small particle sizes could be a promising solo fatliquor to give leather functional properties like physical, strength, morphological, and antibacterial properties as an effective fatliquor when compared to conventional fatliquors. For usage in the small-scale leather industry, leftover bovine fat was converted into fatliquor in a prior study [33]. Before being sulphated with sulfuric acid and then neutralized with ammonia to create fatliquor, the physico-chemical characteristics of bovine fat are determined. Physical and chemical analyses of the fat liquid are performed. 90% of the fatliquor’s surface-active groups are found to be sulphated, and these groups are seen to be anionic in nature. On light leather, the fatliquor has been applied, and the fixed leathers are put through physical testing. The physical tests on fixed leather produced findings that are consistent with the requirements for standard leather. It has been noted that synthetic fatliquor may be used instead of natural fat to cure leather. In order to lubricate leather for goatskins that had been chrome tanned, Nkwor et al. [34] sulphonated oil from the mesocarp of Canarium schweinfurthii [34]. The oil’s ability to fatliquor was evaluated by contrasting it with an imported fatliquor frequently used in Nigerian tanneries after the oil had been characterized and the sulphonated fatliquor had been created. By using GC–MS, the fatty acid composition of mesocarp oil from Canarium schweinfurthii was identified. The FT-IR, 1H NMR, 13C NMR, and DSC measurements were used to describe the produced sulphonated fatliquor. In the production of leather shoe uppers and physical tests on the fixed leather, fatliquor was added to light leather. Images taken using scanning electron microscopy (SEM) reveal that the leather treated with the generated sulphonated oil has greatly opened up structurally (Fig. 3). The sulphonation of CSO was validated by structural characterizations done using FT-IR, 1H NMR, and 13C NMR studies. According to DSC findings, neither the CSO nor the SCSO would likely degrade when processed or used as leather because they were both comparatively thermally stable at the temperatures examined. The tensile strength, double edge rip, and grain strength of the leather produced by the sulphonated C. schweinfurthii fatliquor were comparable to those of commercial/imported fatliquor. The stain test outcome demonstrated that the C. schweinfurthii fatliquored leather was completely lubricated and was equivalent to leather from commercial fatliquor. This suggests that commercial products for the manufacture of leather shoe uppers may compete with the sulphonated C. schweinfurthii fatliquor. In order to be taken into consideration as a replacement for imported fatliquor in Nigeria [8], Nkwor et al. [8] synthesized, characterized, and analyzed sulfonated sesamum indicum oil. The major findings in the results of the FT-IR, 1H NMR, 13C NMR, and 13C NMR DEPT investigation revealed that the oil had undergone sulfonation. Both unsulfonated and sulfonated oils showed a striking variation in their physicochemical outcomes. Using industry-standard techniques, the

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Fig. 3 Scanning Electron Microscopy (X2000) of Chrome Tanned Goatskins Processed with: ANC; B-PC; C-A1 (PURE); D-A2 (BLEND) [34]

sulfonated sesame fatliquor was applied to goatskin and contrasted with a commercial sulfated fatliquor in the production of shoe upper leather. SEM images of leather bundles containing PC and SSO are shown in Fig. 4. On drying, all of the leather samples processed with commercial sulfated fatliquor, PC, and leather processed with sulfonated sesame oil, SSO, showed opened up structures. This suggests additionally that the leather shoe upper fibers, which did not cling together due to the good lubrication, can easily slide over one another and may be employed in the production of shoes. For the commercial and synthetic fatliquors, the average results for tensile strength, double edge tear, elongation, and softness are as follows: 14.27 N/mm2 , 13.77 N/mm2 , 50.61 N, 60.11 N, 38.06%, 54.28%, 25.2; 25.0 (Table 2). The Sudan IV stain test and results from scanning electron microscope analysis showed that the leather treated with the sulfonated Sesamum indicum oil and that treated with the commercial leather fatliquor had equivalent levels of lubrication. Thus, according to experimental evaluations, imported fatliquor in the leather sector could be replaced by the synthesized sulfonated sesamum indicum oil. Power ultrasonography, a powerful and non-polluting means of activation, has recently gained prominence in the chemical and physical activities of the process industries. A sound wave with an ultrasonic frequency is one that is above the human auditory range (16 Hz–16 kHz). Power ultrasound, which has a frequency range of 20–100 kHz, is frequently used to speed up or carry out chemical reactions as well as to improve the efficiency of physical processes like cleaning, emulsification, degassing, crystallization, extraction, etc. Diagnostic ultrasound is utilized in nondestructive testing and the medical field and has a frequency range of 1–10 MHz. The

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Fig. 4 Scanning electron microscopy results for the leather test pieces, PC, and SSO. (Magnification: X 2000) [8]

Table 2 Tensile strength, elongation at break and double edge tear results [8] Properties Tensile strength

S1 (N/mm2 )

Parallel

15.50

14.10

Perpendicular

13.03

13.44

14.27

13.77

Parallel

34.72

48.00

Perpendicular

41.38

60.54

38.06

54.28

Mean tensile strength (N/mm2 ) Elongation at break (%) Average elongation at break (%) Tear load (double edge tear) (N) Average tear (double edge tear) load (N)

S2

Parallel

423.2

Perpendicular

588.9

614.3 587.9

506.1

601.1

fundamental benefit of using physical techniques to activate reactions, such as using power ultrasound, over chemical ones is that they don’t add to the pollution burden in the form of chemical entities. The potential use of ultrasound to process industries like leather in order to enhance quality, increase diffusion rate, shorten process time, and reduce pollution load has been thoroughly studied [37]. For instance, Sivakumar et al. [23] employed ultrasound as a technique to prepare fatliquor emulsion with the least amount of emulsifying ingredient for use in the fatliquoring process of leather [37]. Less chemical contamination is generated as a result of this procedure. Studies on the effects of process variables, such as ultrasonic output power and oil usage, were conducted. The ultrasound-prepared fatliquor emulsion was used in the fatliquoring process of leather, and the strength characteristics of the leathers were evaluated. Laser Diffraction Technique has been used to assess the size of emulsion particles, another crucial factor for diffusion through leather. The fatliquor emulsion that was made using ultrasound has been proven to be stable. The strength qualities of leather treated with the ultrasonically created fatliquor emulsion “US LIQ” are either superior to or on par with those of leather treated using a conventional approach.

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Additionally, ultrasound offers more stable and evenly distributed emulsion particle sizes, which may aid in the penetration and dispersion of fat during the fatliquoring process. As a result, the current study suggests that using ultrasound to prepare a fatliquor emulsion for application to leather could be a practical cleaner production option.

Challenges and Future Prospects The conventional method of “oiling” the leather involves adding sulfated oils to it by diluting sulfuric acid; however, this method has disadvantages. It’s crucial to examine how salt is created in the final product when sulfation. Because of this, the fat contents are removed before oiling the leather; however, this step is not required for fatliquoring. A good fatliquoring penetration is necessary to stop the fibers from sticking together while the leather dries. The hydrophilic and hydrophobic groups need to be powerful enough. The generated fatliquors ought to be easily emulsifiable in hot or cold water at any dilution, ought to include at least 3% fatty content, and ought to be stable for at least half an hour [38]. The prepared fatliquors only need a few potentials: • There shouldn’t be any offensive odors. • The emulsion’s moisture content should be 35% by mass and its pH should range from 6 to 8, accordingly. • Sulfate (SO3 ) in the form of sulfuric esters should make up a total of 3% of the mass. • Avoid overstabilizing the fatliquors by adding extra emulsifying agent since this can lead to inadequate bath exhaustion and ultimately result in the fatliquors being wasted. The need for excellent leather is evolving along with the times. Appropriate fatliquoring has improved a variety of properties, including suppleness, feel-touch, hydrophobicity, tear strength, color quality, fire and heat resistance, moisture resistance, and sweat resistance [39]. There are rumors that the use of fat-liquefying chemicals throughout the crucial production step may make the leather unstable when heated. Fatty spew is produced as a result of using natural fat in fatliquoring. Fatty lipid formation is primarily caused by the palmitic and stearic acids [40]. In addition to being poisonous, the waste produced during fatliquoring is also obsolete in nature [41]. During the fatliquoring, chlorinated organic compounds could be released. Fatliquors occasionally contain benzidine, along with other illegal aromatic amines and halogenated oils; high levels of exhaustion of these chemicals should be taken into account. Chemicals that are detrimental to the environment should be avoided, especially those that contain benzidine, halogenated fatliquors, and other restricted aromatic amines [42–44].

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Conclusion This study provided a thorough assessment of the usage of plant oil based fatliquor in the leather processing industry as well as their physicochemistry, challenges, and future prospect. According to the numerous research examined in the literature, rubbing fatliquor into leathers enhances their lubricating, physicochemical and mechanical properties. Additionally, it was observed that due to the various types of plant oils used, the qualities acquired in each fatliquor made from plant oils varied from one another. Leather that is supple and soft is frequently produced when a fatliquoring agent is used. The results of this study show that plant oil with little to no commercial value could be used as a less expensive and more efficient leather fatliquor substitute for the production of leather. Therefore, in order to avoid paying for imports, it is generally preferred to produce fatliquor domestically. Therefore, plant oils with hardly any commercial benefit can be used as an import replacement for fatliquors globally.

References 1. Maliki M, Ifijen IH (2020) Extraction and characterization of rubber seed oil. Int’l J Sci Eng Sci 4(6):24–27 2. Ogbiede OK, Omorotionmwan EA, Igenumah OD, Ifijen HI, Akhigbe IU (2022) Comparative analysis on physicochemical properties and chemical composition of coconut and palm kernel oils 13(1):70–75 3. Ifijen IH, Maliki M, Omorogbe SO, Ibrahim SD (2022) Incorporation of metallic nanoparticles into alkyd resin: a review of their coating performance. In: The Minerals, Metals & Materials Society (eds) TMS 2022 151st annual meeting & exhibition supplemental proceedings. The Minerals, Metals & Mater. Series. Springer, Cham, pp 338–349 4. Ifijen IH, Maliki M, Odiachi IJ, Aghedo ON, Ohiocheoya EB (2022) Review on solvents-based alkyd resins and water borne alkyd resins: impacts of modification on their coating properties. Chem Afri 5:211–225 5. Ifijen IH, Nyaknno UU, Onaiwu GE, Jonathan EM, Ikhuoria EU (2022) Coating properties of alkyd resin, epoxy resins and polyurethane based nanocomposites: a review. Momona Ethiop J Sci (Accepted) 6. Otabor GO, Ifijen IH, Mohammed FU, Aigbodion AI, Ikhuoria EU (2019) Alkyd resin from rubber seed oil/linseed oil blend: a comparative study of the blend properties. Heliyon 5(5)15:e01621 7. Ifijen IH, Odi HD, Maliki M, Omorogbe SO, Aigbodion AI, Ikhuoria EU (2020) correlative studies on the properties of rubber seed and soybean oil based-alkyd resins and their blends. J Coat Technol Res 18:459–467 8. Nkwor AN, Ukoha PO, Ifijen HI (2021) Synthesis of sulfonated Sesamum indicum L. seed oil and its application as a fatliquor in leather processing. J. Leather Sci Eng 3(16):1–13 9. Nkwor AN, Ukoha PO, Ifijen HI, Ikhuoria EU (2020) The use of sulfonated jatropha curcas oil for the processing of mechanically improved leather. Chem Afri 3:911–925 10. Ifijen HI, Nkwor AN (2020) Selected under-exploited plant oils in Nigeria: a correlative study of their physiochemical properties. Tanz JScience 46(3):817–827 11. Ifijen IH, Ikhuoria EU, Omorogbe SO, Agbonlahor OG (2018) Comparative studies on the use of palm kernel and coconut oil as biodiesel fuel sources. Int’l J Green Chem 4(1):19–24

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12. Zhou Y, Zhao W, Lai Y, Zhang B, Zhang D (2020) Edible plant oil: global status, health issues, and perspectives. Front Plant Sci 11:1315 13. Malik M, Ikhuoria EU, Ifijen IH (2020) Extraction and physiochemical characterization of oils obtained from selected under-utilized oil-bearing seeds in Nigeria. ChemSearch J 11(1):110– 117 14. Wang C, Li T, Feng S(2012) Synthesis of fatliquor from palm oil and hydroxylterminated organosilicon. Asian J Chem 24:63–67 15. Quadery A, Uddin T, Azad A, Chowdhury M, Deb A, Hassan N (2015) Fatliquor preparation from Karanja seed oil (Pongamia pinnata L.) and its application for leather processing. IOSR J Appl Chem 8(1):54–58 16. Affiang S, Ggamde G, Okolo V, Olabode V, Jekkada J (2018) Synthesis of sulphated fatliquor from neem (Azadirachta indica) seed oil for leather tannag. Am J Eng Res 7(4):215–221 17. Zarłok J, Smiechowski K, Mucha K, T˛ecza A (2014) Research on application of flax and soya oil for leather fatliquoring. J Clean Prod 65:583–589 18. Bajza Z, Vrˇcek VI (2001) Fatliquoring agent and drying temperature effects on leather properties. J Mater Sci 36:5265–5270 19. Huang X, Kong X, Cui Y, Ye X, Wang X, Shi B (2018) Durable superhydrophobic materials enabled by abrasion-triggered roughness regeneration. Chem Eng J 336:633–639 20. Luo Z, Xia C, Fan H, Chen X, Peng B (2011) The biodegradabilities of different oil-based fatliquors. J Am Oil Chem Soc 88:1029–1036 21. Gao D, Wang P, Shi J, Li F, Li W, Lyu B, Ma J (2019) A green chemistry approach to leather tanning process: cage-like octa(aminosilsesquioxane) combined with Tetrakis (hydroxymethyl)phosphonium sulfate. J Cleaner Prod 229:1102–1111 22. Valeika V, Širvaityt˙e J, Beleška K (2010) Estimation of chrome-free tanning method suitability in conformity with physical and chemical properties of leather. Mater Sci 16(4):330 23. Sivakumar V, Prakash RP, Rao PG, Ramabrahmam BV, Swaminathan G (2008) Power ultrasound in fatliquor preparation based on vegetable oil for leather application 16(4):549–553 24. BajzaI ZZ, Vrˇcek IV (2001) Fatliquoring agent and drying temperature effects on leather properties. J Mater Sci 36(21):5265–5270 25. Gutterres M, Dos Santos ML (2009) Study of fatliquoring parameters using experimental design. J Soc Leather Technol Chem 93:171–175 26. Nkwor AN, Ukoha PO (2020) Evaluation of the leather fatliquoring potential of sulphonated Afzelia africana aril cap oil. Heliyon 6:e03009 27. Yorgancioglu A, Bayramoglu EE, Renner M (2019) Preparation of antibacterial fatliquoring agents containing zinc oxide nanoparticles for leather industry. J Am Leather Chem Assoc 114:171–179 28. Koizhaiganova M, Yasa I, Gulumser G (2015) Assessment of antibacterial activity of lining leather treated with silver doped hydroxyapatite. Int Biodeter Biodegr 105:262–267 29. Sirvaityte J, Siugzdaite J, Valeikac V, Dambrauskien˙e E (2012) Application of essential oils of thyme as a natural preservative in leather tanning. Proc Estonian Acad Sci 61:220–227 30. Lkhagvajav N, Koizhaiganova M, Yasa I, Çelik E, Sari Ö (2015) Characterization and antimicrobial performance of nano silver coatings on leather materials. Braz J Microbiol 46:41–48 31. Yorgancioglu A, Bayramoglu EE (2013) Production of cosmetic purpose collagen containing antimicrobial emulsion with certain essential oils. Ind Crop Prod 44:378–382 32. Yorgancioglu A (2021) Emulsification and application of a thymol loaded antibacterial fatliquor for leather industry. J Industr Textile 51(3):470–485 33. Nyamunda BC, Moyo M, Chigondo F (2013) Synthesis of fatliquor from waste bovine fat for use in small scale leather industry. Indian J Chem Technol 20:116–120 34. Nkwor AN, Ukoha PO, Wise WR, Nwaji NN, Flowers K (2019) Fatty Acid Profile and Production of Fatliquor from Canarium schweinfurthii Mesocarp Oil. Pertanika J Sci Technol 27(4):2221–2243 35. Contamine F, Faid F, Wilhelm AM, Berlan J, Delmas H (1994) Chem Eng Sci 49(24B):5865– 5873

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36. Ando T, Kimura T (1990) Reactivity and selectivity in organic sonochemical reactions involving inorganic solids. Ultrasonics 28:326–332 37. Sivakumar V, Prakash RP, Rao PG, Ramabrahmam BV, Swaminathan G (2008) Power ultrasound in fatliquor preparation based on vegetable oil for leather application. J Cleaner Prod 16:549–553 38. Zhang Y, Wang L (2009) Recent research progress on leather fatliquoring agents. Polym Plast Technol Eng 48:285–291 39. Devikavathi G, Ramamoorthy U, Sundar V, Muralidharan C (2010) Influence of fatliquor on ageing characteristics of leather. Revista de Pielarie Incaltaminte 10:31–42 40. Tournier R (2015) Diagnosis, prevention and treatment of fatty spew in the tannery. J Am Leather Chem Assoc 110:260–276 41. Adzet J (2010) Transformation of lime split trimmings into different collagen materials. J Am Leather Chem Assoc 105:254–271 42. Kamely N (2022) Fatliquors for leathers: an application of microemulsion-a review. Polym Bull 79:1977–2002 43. Kalyanaraman C, Kanchinadham SBK, Devi LV, Porselvam S, Rao JR (2012) Combined advanced oxidation processes and aerobic biological treatment for synthetic fatliquor used in tanneries. Ind Eng Chem Res 51:16171–16181 44. Kalyanaraman C, Kameswari KSB, Varma VS, Tagra S, Rao JR (2013) Studies on biodegradation of vegetable-based fatliquor-containing wastewater from tanneries. Clean Technol Environ Policy 15:633–642

Part XI

Advanced Joining Technologies for Automotive Lightweight Structures

Joint Strength Optimization of Single-Lap Al 5052-H36 Adhesively Bonded for Off-Road Vehicle Chassis Components M. Nodeh, A. Maslouhi, and A. Desrochers

Abstract The main objective of this paper is to study the effect of the key parameters on the mechanical strength of adhesively bonded Al 5052-H36 joints. The key parameters are adhesive type, curing temperature, and geometrical parameters. To evaluate the effect of these parameters on the performance of bonded joints, single lap joints (SLJ) coupons were prepared and tested under tension. The adhesives 852/25 GB and Ep 5089 were studied to analyze the effect of the polymer type on the mechanical behavior of bonded joints. The effect of geometrical parameters and curing temperature of adhesive were investigated for joints assembled with 852/25 GB. The distribution of stresses within the bond area was assessed numerically using a linear model and validated with an analytical model. 852/25 GB makes weaker joints and shows cohesive failure, while EP 5089 adhesive resulted in stronger joints and interface failure. Samples exposed to heating cycles generated the strongest joints compared to those cured @ RT and 35 °C. Increasing the length of the overlap, decreasing the adhesive thickness, and the presence of spew fillet improved the joint resistance. The stress distributions from the numerical modeling showed very good agreement with the results of the analytical model. The numerical and analytical models were used to interpret the experimental results. Keywords Aluminium and alloys · Stress analysis · Lap-shear · Curing temperature

M. Nodeh (B) · A. Maslouhi · A. Desrochers Department of Mechanical Engineering, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada e-mail: [email protected] A. Maslouhi e-mail: [email protected] A. Desrochers e-mail: [email protected] © The Minerals, Metals & Materials Society 2023 The Minerals, Metals & Materials Society, TMS 2023 152nd Annual Meeting & Exhibition Supplemental Proceedings, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-031-22524-6_39

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Introduction In recent decades, the growing demand for lightweight vehicles due to environmental requirements and increasing customer requests for more safety, greater efficiency, and more luxury appearance has persuaded the automotive industry to develop lightweight and energy-efficient vehicles. To achieve this aim, efforts have been conducted to substitute steel for aluminum in chassis and body structures [1]. In addition, over the past few decades, joining techniques for aluminum alloys have developed and resulted in the emergence of new assembly methods such as adhesive bonding [2]. Adhesive bonding of aluminum presents numerous advantages over conventional joining techniques. These include bonding dissimilar materials with various thicknesses, making continuous joints with uniform stress distribution, absorbing energy, and reducing noise and vibration while diminishing the weight of the structure. Adhesive technology helps produce joints without any distortion or residual stress caused by heating [3, 4]. The single lap joint (SLJ) is a typical adhesive joint that is deeply studied in the literature experimentally, numerically, or analytically [5]. The strength of single lap joints, bounded by adhesive, can be improved by changing the joint geometrical parameters, selecting the proper adhesive and curing temperature of the adhesive. The main objective of this study is to investigate the effect of the mentioned parameters on the strength of AA 5052-H36 adhesive joints. The static strength of bonded joints with EP 5089 (one component heat curing adhesive) and 852/25 GB (two component structural acrylic adhesive) was studied and compared. The proper adhesive was selected based on the joint strength and failure mode. The effect of geometrical parameters on the joint performance was investigated comprehensively for the preferred adhesive. The chosen parameters are overlap length, adhesive thickness, and presence of spew fillet. Three levels of temperatures such as room temperature, 35 °C, and cyclic high temperature, were used to cure the adhesive to study the effect of curing temperature on the joint performance. A 3D linear finite element model of single lap joints was created. The stresses distribution in the adhesive layer was generated, and the obtained stress values were validated with an analytical model. The influence of geometrical parameters on the stress distribution was examined and used to interpret the experimental results.

Materials and Methods Materials The mechanical properties of adherend and adhesives are given in Table 1.

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Table 1 Mechanical properties of adherend and adhesives Materials

Young’s modulus (MPa)

Yield strength (MPa)

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Joint Geometry The geometrical dimensions of the single lap joint (SLJ) are presented in Fig. 1. To investigate the effects of the overlap length (L), the values of 12.5, 15, 17.5, and 20 mm were selected. The SLJ samples manufactured in an aluminum jig, and specific shims were used to control the value of adhesive thickness (t). The bond line thicknesses used were 0.25, 0.4, 0.55, and 0.7 mm. Samples were prepared with square-ended and 45° spew fillets, at the joint extremities, to study the effect of spew fillets on the shear strength of the SLJ. Alignment tabs were also bonded to the joints to reduce eccentric loading and bending.

Samples Preparation A bonding jig was designed and fabricated to ensure correct bond length, accurate alignment, and uniform bond line thickness. The surface of the samples was cleaned with alcohol and Kim wipe. The adhesive joints were cured under three different temperatures; 24 h at room temperature, 24 h at 35 °C, and heating cycles which is applied to the chassis of recreational vehicle in the production line with the aim of drying and baking paint.

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Fig. 2 Geometry of the SLJ and applied boundary conditions

Lap Joint Mechanical Testing The SLJ specimens were loaded onto an Instron testing machine with a load cell of 100 KN. The lap joints were tested at a rate of 0.5 mm/min at room temperature. To check the repeatability of the tests, three specimens were tested for each investigated parameter.

Finite Element Modeling In this work, a 3D linear FE model of a single lap joint was created using the ANSYS Workbench software. Figure 2 shows the geometry of SLJ and boundary conditions, which has the same status as the experimental specimens. The obtained distribution of shear and peel stresses within the mid-layer of adhesive in the FE model was validated with the model of Goland and Reissner (G&R) [6].

Results and Discussion Validation of the FE Model with the Goland and Reissner Model The distribution of shear and peel stresses along the overlap as a function of normalized distance from the center of the joint (x/c, c is the half-length of the overlap) is shown in Fig. 3. There is a good agreement between the numerical model and G&R analytical model. In the G&R model, it is considered that there is a shear stress concentration at the ends of the overlap, which violates the stress-free conditions.

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Analysis that ignores the stress-free state overestimates the stress at the ends of the overlap and tends to yield conservative predictions [7].

Experimental and Numerical Results Type of the Adhesive Influence The experimental load–displacement curves of SLJs bonded with EP 5089, and 852/25 GB are presented in Fig. 4. The joints bonded with EP 5089 show higher strength and a lower ductility compared to 852/25 GB. Figure 5 shows the distribution of shear and peel stresses in the middle layer of adhesives through the overlap. Generally, the adhesive with a higher elastic modulus makes more stress concentration at the ends of the overlap as EP 5089 does in

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this study, while the 852/25 GB shows a more uniform stress distribution. This phenomenon can be explained by the fact that, for brittle adhesives, the failure takes place at the end of the overlap. Once a crack has initiated, the adhesive has no longer enough ductility to absorb the fracture energy. The failure mode of samples bonded with 852/25 GB is cohesive, conversely, the EP 5089 shows an adhesive (interface) failure.

Geometrical Parameters Influence Overlap Length The load–displacement curves of the 852/25 GB SLJ for different overlap lengths can be seen in Fig. 6. The joint strength of SLJs increases almost proportionally to the overlap length. For all specimens with different geometrical parameters, the maximum peel stresses were higher than the maximum shear stresses. This behavior shows that the failure of bonded SLJs is mainly a result of the peel stress and initiates from where it has its maximum value (bonded joint edges). The obtained stresses belong to the middle layer of the adhesive. According to Fig. 7a, increasing the length of the overlap leads to a decrease in the value of concentrated stress at the edge of the bonded joints. Increasing the overlap length showed an almost similar effect on the distribution of peel stresses (Fig. 7b).

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Adhesive Thickness Influence Figure 8 compares the failure load and displacement of tested samples with different adhesive thicknesses. Increasing the adhesive thickness from 0.25 to 0.7 mm reduces failure load by 17%. The FE model shows that the maximum shear and peel stresses along the overlap decrease as the adhesive thickness increases, meaning that the joint strength improves

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accordingly. However, the experimental results do not corroborate FE analysis corresponding outcomes. The main cause of this error is considering the middle layer of adhesive as a suitable region for studying the distribution of stresses. At the interface between adhesive and adherend or at the region close to the interface, both shear and peel stresses tend to increase with increasing bond line thickness while the stresses at the middle layer of the adhesive decreases [8]. Indeed, stress singularities complicate the interpretation of the results at the points located at the ends of the overlap, owing to the geometry of sharp edges. Besides, according to the fact that the peel stress is more critical and sensitive to the adhesive thickness [8] than the shear stress, the distribution of peel stress was studied along a line at 0.045 mm from the interface (Fig. 9). According to Fig. 9, within around ±0.75 of the normalized overlap, the peel stress rises sharply, and this is the region where the effects of bond line thickness become important, where the thicker bond line shows higher peel stress. Increasing the adhesive thickness increases interface stresses, particularly the peel stress, which is the main reason for the overall joint strength reduction.

The Shape of the Joint Influence Figure 10 presents the load–displacement response of typical SLJ samples with square ends and spew fillet shapes. Mechanical resistance of single lap joints increases by 33% with the presence of a spew fillet at the ends of the joint. This behavior is attributed to the ability of the spew fillet to carry the shear force and transfer some part of the longitudinal load from one adherent to the other. Figure 11 compares the distribution of stresses for samples with a 45˚ spew fillet and squared end. The presence of a spew fillet not only reduces the value of maximum

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stresses but also provides a more uniform stress distribution along the bond length resulting in stronger joints.

Curing Temperature Influence Figure 12 presents the load–displacement curves for different curing conditions. Figure 12 shows that increasing the curing temperature from RT to 35 °C leads to increasing the joint strength by 13%. Carbas et al. [9] have reported that below the glass transition temperature (Tg ), the performance of the adhesive in terms of strength 8

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and stiffness, increases along with the curing temperature. The Tg of the applied adhesive is 81 °C. Therefore, below this temperature, elevating the temperature increases the adhesive performance, which leads to improving the joint strength. Joints with adhesive cured under heating cycles show the highest resistance and the largest displacement at failure. The hardness of AA5052-H36 was decreased under heating cycles and this reduction was accompanied by a decrease in the resistance of the Al tested after heating. This was verified by tensile tests, which showed a reduction in the elastic limit and the maximum resistance of the material tested after heating. Therefore, the adherend will yield, and the load-bearing capacity of the SLJ will increase accordingly before joint failure.

References 1. Carle D, Blount G (1999) The suitability of aluminium as an alternative material for car bodies. Mater Des 20:267–272. https://doi.org/10.1016/s0261-3069(99)00003-5 2. Pizzi A, Mittal KL, Mittal KL (2017) Handbook of adhesive technology, Third Edition, CRC Press. https://doi.org/10.1201/9781315120942 3. Schroeder KJ (1996) Structural adhesives for aluminum vehicles. https://doi.org/10.4271/ 960166 4. Kalpakjian S, Schmid SR, Sekar KSV (2018) Manufacturing engineering and technology, n.d. https://cds.cern.ch/record/2318298. Accessed November 16, 2018 5. Grant LDR, Adams RD, Da Silva LFM (2009) Experimental and numerical analysis of T-peel joints for the automotive industry. J Adhes Sci Technol 23:317–338. https://doi.org/10.1163/156 856108X383529 6. Goland REM (n.d.) The stresses in cemented joints | Scinapse. https://scinapse.io/papers/941 22711. Accessed February 11, 2020 7. Shaikh S, Anekar N, Kanase P, Patil A, Tarate S (2017) Single lap adhesive joint (SLAJ): a study. Int J Curr Eng Technol IJCET INPRESSO Spec, 2277–4106. http://inpressco.com/cat egory/ijcet 8. Gleich DM, Van Tooren MJL, Beukers A, Gleich DM (2001) Analysis and evaluation of bondline thickness effects on failure load in adhesively bonded structures, Adhes. Sci Technol 15:1091– 1101. https://doi.org/10.1163/156856101317035503 9. Carbas RJC, Marques EAS, Da Silva LFM, Lopes AM (2014) Effect of cure temperature on the glass transition temperature and mechanical properties of epoxy adhesives. J Adhes 90:104–119. https://doi.org/10.1080/00218464.2013.779559

Recent Advances in the Transformative Non-fusion Weld-Brazing Process Used to Join Thin-Gauge Alloys Used in the Automotive Industry M. Shehryar Khan , Y.-H. Cho, F. Goodwin, and Y. Norman Zhou

Abstract As we rapidly move towards the electrification of modern vehicles, making them lighter and stronger has become as important as ever. This means that the welding and joining techniques we use to build these structures need to evolve to be able to successfully join the advanced materials used for these applications. Weld-brazing (WB) is a novel non-fusion joining technique that has shown excellent promise in the joining of similar and dissimilar metallic alloys. However, existing literature on WB has either treated this process as a form of traditional torch brazing or a form of fusion welding which has made the optimization of the process a significant challenge. Researchers at the University of Waterloo have shown that WB is a joining technique that is fundamentally different from the traditional joining techniques it has been derived from. This study investigates recently discovered critical factors that control the joint integrity for WB applications. Keywords Characterization · Process technology · Mechanical properties · Advanced high strength steels · Hot-dip galvanized · Hot-dip galvannealed · Wettability · Gas metal arc brazing · Arc-brazing · Si-Bronze filler material

Introduction Lightweight vehicle design has been the governing factor in improving vehicle performance and fuel efficiency of modern vehicles. The manufacturing of automotive structures with the desired combinations of strength, ductility, toughness, and fatigue resistance at an affordable cost has been achieved by using thin-gauge advanced high-strength steels (AHSSs) [1–3]. However, due to the unacceptably low M. Shehryar Khan (B) · Y.-H. Cho · Y. N. Zhou Department of Mechanical and Mechatronics Engineering, Centre for Advanced Materials Joining, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L3G1, Canada e-mail: [email protected] F. Goodwin International Zinc Association, Durham, NC, USA © The Minerals, Metals & Materials Society 2023 The Minerals, Metals & Materials Society, TMS 2023 152nd Annual Meeting & Exhibition Supplemental Proceedings, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-031-22524-6_40

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corrosion resistance of uncoated AHSSs, they must be coated with various types of metallic coatings (Al–Si, galvanized, galvannealed, Zn–Al–Mg, etc.) to offer barrier and/or sacrificial protection. Nevertheless, the protective coatings are problematic during fusion welding, which leads to serious issues that impact the mechanical integrity of the welded joints. Heat-affected zone and fusion zone softening during welding, liquid metal embrittlement (LME) cracking, and the formation of several types of welding defects like weld concavity, undercut, and porosity are some of the major issues associated with the fusion welding of coated AHSSs [4–13]. A non-fusion joining method called weld-brazing (WB) has been proposed as a viable alternative to effectively join AHSSs. The process has several subcategories based on the heat source used to achieve WB like arc brazing which is more specifically known as gas metal arc (GMA) brazing (GMAB) or cold metal transfer (CMT) brazing, laser brazing (LB), and laser-arc hybrid brazing. The significantly lower heat input (HI) associated with WB comes mainly from using lower melting temperature (e.g., CuSi3Mn −980 °C) brazing filler wires, which require much lower HI settings to be used without changing existing welding infrastructure [14]. Khan et al. [15] stated that in WB, “workpieces are joined by the wetting of the molten filler material onto the surface of the substrate (without any significant melting of the substrate),” similar to what has been shown in several different independent studies that either used a laser heat source [16–18], or an arc heat source [19, 20] for their respective investigations. Non-fusion WB has also been proposed as an effective method to join dissimilar materials made up of different types of metallic alloys. Since WB is a relatively new technology, the literature on the subject is limited. The literature that does exist views the WB process either as a form of traditional torch brazing or a form of fusion welding which has made the optimization of the process a significant challenge. However, it is abundantly clear that the joining mechanism of the WB technology must be fundamentally different from fusion welding or torch brazing since the joining in WB relies on a low-melting temperature filler material to join a higher melting temperature and higher strength substrate, while having a “minimal reliance on capillary action for the spreading of the molten filler material to create a joint,” [21] which is primarily due to the extremely high cooling rate associated with the WB process. In fact, it is well-known that in traditional torch brazing, the wettability of the molten filler material is improved by ensuring a tight fit-up between the workpieces (i.e., 99.999%) aluminum is thinkable, and would

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reduce the costs of AABs significantly. Combined with the hydrogen evolution, this question will be in the focus of the planned extensive tests at the Extrusion Research and Development Center Technical University of Berlin. Acknowledgements The authors kindly acknowledge the funding by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) Project No:426183735.

References 1. Elia GA, Marquardt K, Hoeppner K, Fantini S, Lin R, Knipping E, Peters W, Drillet J-F, Stefano P, Hahn R (2016) An overview and future perspectives of aluminum batteries. Adv Mater 28:7564–7579. https://doi.org/10.1002/adma.201601357 2. U.S. Geological Survey (2020) Mineral commodity summaries 2020: U.S. Geological Survey, 200 p, https://doi.org/10.3133/mcs2020 3. OPEC Annual Statistical Bulletin 2021, 56th edn, Section 3: Oil data: upstream, p 22 4. Wilcke W, Girishkumar G (2010) Lithium-air battery: promise and challenges. J Phys Chem Lett 1:2193–2203. https://doi.org/10.1021/jz1005384 5. Liu Y, Sun Q, Li W, Adair KR, Li J, Sun X (2017) A comprehensive review on recent progress in aluminum–air batteries. Green Energy Environ 2:246e277. https://doi.org/10.1016/j.gee.2017. 06.006 6. Hopkins BJ, Shao-Horn Y, Hart DP. Suppressing corrosion in primary aluminum-air batteries via oil displacement. Science 362(6415):658–661. https://doi.org/10.1126/science.aat9149 7. Egan DR, de León CP, Wood RJK, Jones RL, Stokes KR, Walsh FC (2013) Developments in electrode materials and electrolytes for aluminum–air batteries. J Power Sources 236:293e310. https://doi.org/10.1016/j.jpowsour.2013.01.141 8. Cho Y-J, Park I-J, Lee H-J, Kim J-G (2015) Aluminum anode for aluminum–air battery—Part I: influence of aluminum purity. J Power Sources 277:370–378. https://doi.org/10.1016/j.jpo wsour.2014.12.026 9. Fan L, Lu H, Leng J (2015) Performance of fine structured aluminum anodes in neutral and alkaline electrolytes for Al-air batteries. Electrochim Acta 165:22–28. https://doi.org/10.1016/ j.electacta.2015.03.002 10. Moghanni-Bavil-Olyaei H, Arjomandi J (2015) Performance of Al–1Mg–1Zn–0.1Bi–0.02In as anode for the Al–AgO battery RSC Adv 5:91273–91279. https://doi.org/10.1039/c5ra15 567c 11. Ma J, Wen J, Gao J, Li Q (2014) Performance of Al–0.5 Mg–0.02 Ga–0.1 Sn–0.5 Mn as anode for Al–air battery in NaCl solutions. J Power Sources 253:419e423. https://doi.org/10.1016/j. jpowsour.2013.12.053 12. Ma J, Wen J, Gao J, Li Q (2014) Performance of Al−1Mg−1Zn−0.1Ga−0.1Sn as anode for Al-air battery. Electrochimica Acta 129:69–75. https://doi.org/10.1016/j.electacta.2014.02.080 13. Ma J, Wen J, Zhu H, Li Q (2015) Electrochemical performances of Al–0.5Mg–0.1Sn–0.02In alloy in different solutions for Al–air battery. J Power Sources 293:592–598. https://doi.org/ 10.1016/j.jpowsour.2015.05.113 14. Mokhtar M, Talib MZM, Majlan EH, Tasirin SM, Ramli WMFW, Daud WRW, Sahari J (2015) Recent developments in materials for aluminum–air batteries: a review. J Ind Eng Chem 32:1– 20. https://doi.org/10.1016/j.jiec.2015.08.004 15. Paramasivam M, Jayachandran M, Venkatakrishna Iyer S (2003) Influence of alloying additives on the performance of commercial grade aluminum as galvanic anode in alkaline zincate solution for use in primary alkaline batteries. J Appl Electrochem 33:303–309. https://doi.org/ 10.1023/A:1024141918663

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16. Park I-J, Choi S-R, Kim J-G (2017) Aluminum anode for aluminum–air battery—Part II Influence of In addition on the electrochemical characteristics of Al-Zn alloy in alkaline solution. J Power Sources 357:47e55. https://doi.org/10.1016/j.jpowsour.2017.04.097 17. Smoljko I, Gudi´c KS, Kuzmani´c N, Kliški´c M (2012) Electrochemical properties of aluminum anodes for Al/air batteries with aqueous sodium chloride electrolyte. J Appl Electrochem 42:969–977. https://doi.org/10.1007/s10800-012-0465-6 18. Tang Y, Lu L, Roesky HW, Wang L, Huang B (2004) The effect of zinc on the aluminum anode of the aluminum–air battery. J Power Sources 138:313–318. https://doi.org/10.1016/j. jpowsour.2004.06.043 19. Wilhelmsen W, Arnesen T, Hasvold Ø, Størkersen NJ(1991) The electrochemical behaviour of Al-In alloys in alkaline electrolytes. Electrochimica Acta 36(1):79–85. https://doi.org/10. 1016/0013-4686(91)85182-7 20. Fan L, Lu H (2015), The effect of grain size on aluminum anodes for Al–air batteries in alkaline electrolytes. J Power Sources 284:409e415. https://doi.org/10.1016/j.jpowsour.2015.03.063 21. Liang S, Zhang Y, Guan D, Tang Y, Mao Z (2010) Effect of rolling processing on microstructure and electrochemical properties of high active aluminum alloy anode. Trans Nonferrous Metals Soc China 20:942–949. https://doi.org/10.1016/S1003-6326(09)60240-5

Triple-Cation Perovskite Photoabsorbers and Solar Cells Mahdi Temsal, Sujan Aryal, and Anupama B. Kaul

Abstract We present our results on the photoabsorber characterization of triplecation perovskite and their integration into solar cells. The photabsorbers were fabricated using the spin coating approach and constructed into two-terminal devices. After outlining the fabrication process of our three-dimensional perovskite photodetectors, their photo response to incoming radiation was measured using broadband illumination through temperature-dependent measurements. We also discuss our efforts on the integration of the triple-cation absorbers into solar cells in the n-ip architecture and compare the response of the triple-cation solar cells with those fabricated using MAPbI3 absorbers. Our results presented here provide a fabrication and characterization framework for the three-dimensional perovskite structures in photoabsorber and solar cell devices. Keywords Triple-cation · Three-dimensional perovskite · MAPbI3 · Two-terminal measurement

Introduction Photovoltaics or in other words, solar cells, have become a viable alternative for electrical power generation over the past several decades. With mounting challenges and issues associated with climate change, we should move toward the use of solar energy that is vastly available for harnessing instead of our heavy reliance on fossil fuel-based sources which lead to harmful carbon emissions in order to preserve the environment and enhance sustainability [1]. Perovskite solar cells (PSCs) have sharply progressed from 2.6% power conversion efficiency (PCE) in 2012 to close to M. Temsal · S. Aryal · A. B. Kaul (B) Department of Electrical Engineering, University of North Texas, Denton, TX 76207, USA e-mail: [email protected] A. B. Kaul Department of Materials Science and Engineering, PACCAR Technology Institute, University of North Texas, Denton, TX 76207, USA © The Minerals, Metals & Materials Society 2023 The Minerals, Metals & Materials Society, TMS 2023 152nd Annual Meeting & Exhibition Supplemental Proceedings, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-031-22524-6_43

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25.7% [2–5]. Besides glass substrates, perovskite solar cells have also been formed on SiO2 substrates and in this design, the top sub-cell absorbs high energy (short wavelength) photons while transmitting low energy (long wavelength) photons to the bottom sub-cell [6, 7]. In this work, we present our results on the temperature and power-dependent studies on three-dimensional (3D) triple-cation perovskites which were also integrated into a solar cell architecture. The perovskite photodetectors were contacted with Au which served as the top contact metal. The photo response of the triple-cation perovskite photodetectors was measured over a wide range of temperatures from ∼4 to 300 K using broadband white light illumination. As noted, the optical power of the illumination source was also varied. The dark current and the current under illumination of the triple-cation photoabsorber was measured using a broadband illumination source and the time constants of the switching cycle were also measured over multiple cycles where rise times and a fall times in the switching characteristic were tabulated. The solution processable approach for the photoabsorber with spin coating also allowed us to integrate this absorber into a solar cell n-i-p architecture. The response of the triple-cation solar cells was compared to our baseline 3D perovskite absorber, MAPbI3 . We believe that our temperature-dependent response of the triple-cation absorber allows us to map the photoresponse which has previously not been explored over a wide temperature range, providing a proof-of-concept validation of the temperature and power-dependent photoabsortion characteristics, where switching dynamics were also determined. The PCE of our triple-cation device was higher than the MAPbI3 absorber, due to the enhanced stability and greater ruggedness of the triple-cation absorber.

Results and Discussion Material Characterization and Temperature-Dependent Optoelectronic Transport Measurements of the Triple-Cation Photoabsorbers First, we characterized our triple-cation photoabsorbers on SiO2 substrates using twoterminal measurements. The photocurrent Iph was calculated using the following relation, Iph = Ilight− Idark , where Ilight and Idark are the light and dark currents, respectively. The current–voltage characteristic of the device in the dark and under illumination is shown in Fig. 1a and the temperature-dependent response of the photocurrent is shown in Fig. 1b. From the data in Fig. 1a, it is clear that the current under illumination is unequivocally higher than in the dark at power P0 ~ 3.32 mW/cm2 , indicating that our films are responsive to incoming radiation by exhibiting a photoresponse. We then proceeded with the photoresponse measurements as a function of temperature using broadband white light illumination, as shown by the data in Fig. 1b where the device was kept at a voltage of 2.4 V, again with P0 ~ 3.32 mW/cm2 3.

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The photocurrent fluctuated with temperature, but was largely confined to be below 320 pA, though a peak is evident at ~200 K, which could be indicative of a phase transition within the crystal lattice of the triple-cation absorber. The time-resolved Iph measurements were conducted on the triple-cation photoabsorber under illumination using broadband white light with P0 = 3.32 mW/cm2 at 4 K over two cycles and the data are shown in Fig. 2a, b, respectively, where the latter is on an expanded scale for determining the switching time constants in greater detail over a single cycle. The incoming ON/OFF pulses of radiation have a duration of 1000 ms and the device was biased at Vds = 35 V. As shown by the magnified plot for the rise time, τrise and fall time, τfall , we measured these to be τrise ∼ 80.74 ms and τfall ∼ 50 ms for our triple-cation absorber. The τrise and τfall values are calculated from the 90 to 10% of the maximum Iph on the rising and falling edges, respectively, when the light is pulsed.

Fig. 1 a The current plots for the triple-cation device in the dark and under broadband illumination at a P0 ~ 3.32 mW/cm2 . b Variation of Iph as a function of temperature for triple-cation from 4 to 300 K

Fig. 2 a The Ids response of triple-cation plotted as function of time in two cycle, b The Ids response of triple-cation plotted as function of time in one cycle

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Shown in Fig. 3 is the photocurrent as a function of the input power, P0 which was in the range between 0.307 mW/cm2 and 2.5 mW/cm2 . The photocurrent generally increased with increasing power, as expected, and it was observed that at 2.35 mW/cm2 the highest Iph resulted which was measured to be ~300 pA.

Fig. 3 Variation of Iph as a function of broadband light P for triple-cation

Solar Cell Fabrication and Characterization Using Triple-Cation and MAPbI3 Photoabsorbers We then proceeded to integrate our triple-cation photoabsorbers into solar cells. Perovskite solar cells (PSCs) have various properties which make them attractive materials for solar cell applications, including efficient light absorption, tunable band gap, long charge-carrier lifetimes, and high defect tolerance [8, 9]. One of the main challenges towards having efficient perovskite photovoltaics is the fabrication of every layer of the PSCs by scalable deposition methods and demonstration of high-performance devices on a large area. At present, the perovskite layer of the most efficient PSCs is fabricated by spin coating and using an antisolvent dripping method to obtain compact, pinhole-free perovskite layers of high optoelectronic quality [10, 11]. In our current work, we have also used a spin coating approach to form our 3D perovskite absorbers. For the triple-cation perovskite, the precursors used were FAI, MABr, PbBr2 , PbI2, and CsI which were weighed out into a vial with the stoichometric composition of Cs at 0.05, FA at 0.81, MA at 0.14, PbI at 2.55, and Br at 0.45. For each 1 mL of precursor solution, the following quantities of powder were used:

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FAI (171.97 mg), PbI2 (507.11 mg), MABr (22.39 mg), and PbBr2 (73.4 mg). The powders were then dissolved in a mixture of DMF and DMSO at a ratio of 4:1 (800 μL and 200 μL) to form the perovskite precursor solution, and stirred at 65 °C for 120 min. In another vial, we used CsI (389.71 mg) and 1 mL DMSO, and then added 50 μL of CsI to the first vial, and then the solution was mixed together to form the triple-cation solution. The triple-cation perovskite solution was stirred overnight at room temperature. Incidentally, in order to create the triple-cation perovskite absorber on SiO2 substrates, the spinning conditions used were 1000 rpm for 10 s, followed by 4000 rpm at 20 s with 1 mL of triple-cation solution, where 0.1 mL of chlorobenzene was slowly dripped as the antisolvent on the rotating substrate in the last 5 s. The substrate with triple-cation solution and above the absorber Au electrodes deposition were used with a shadow mask to electrically contact the 3D perovskite which was placed in a cryogenic stage for the 2-terminal measurements. The solar cell devices with the triple-cation were fabricated on pre-etched FTO substrates. For comparative purposes, we also formed MAPbI3 solar cell devices, which is a cornerstone and historically significant material for the perovskite community. In order to form the MAPbI3 perovskite, we used PbI2 and CH3 NH3 I or methylammonium iodide (MAI). A mixture of DMF and DMSO was used at a ratio of 4:1 (800 μL and 200 μL) to dissolve the PbI2 . Then the MAPbI3 perovskite was stirred overnight at room temperature. For the cleaning process, the FTO glass substrates were sequentially cleaned by detergent, deionized water, and acetone and isopropanol for 30 min each under sonication. Finally, FTOs were treated using UV-O3 in a UV-ozone oven for 20 min. For the n-i-p solar cell device fabrication process, a compact (c)-TiO2 served as the electron transport layer (ETL), and the Spiro-OMeTAD served as the hole transport layer (HTL), while an 80 nm thick Au layer was used as the collector electrode by e-beam evaporation. For forming the c-TiO2 layer, FTO substrates were spin coated a 0.15 M solution using titanium diisopropoxide bis (acetylacetonate) in ethanol at 4000 rpm for 20 s, and then the films were sintered in a muffle furnace at 510 °C for 30 min. The c-TiO2 coated substrates were transferred into the glove box where the triple-cation and a MAPbI3 absorber layers were deposited, as mentioned earlier. After cooling down the FTO substrates, the spiro-OMeTAD (86 mg mL−1 in chlorobenzene) was doped with three solutions to form the HTL, and all these were used in conjunction. These comprised of 34 μL of TBP, 20 μL of Lithium bis (trifluoromethanesulfonyl) imide (Li-TFSI) and 11 μL FK209. For the latter two solutions, i.e., Li-TFSI, a mother solution of 500 mg/mL in acetonitrile was used, while the FK209 required a mother solution of 300 mg/mL in acetonitrile. This solution for the HTL was spin coated at 4000 rpm for 20 s on top of the MAPbI3 and triple-cation layer. Finally, to deposit the collector electrode, we used e-beam evaporation at a pressure of ~10−5 Torr for 80 nm of Au for both the MAPbI3 and triple-cation solar cell devices. Here, the n-i-p architecture was used for both the triple-cation absorber (Fig. 4-left) and MAPbI3 (Fig. 4-right) was used as a reference material for comparative purposes. The electrical characteristics of the fabricated PSCs were measured under onesun optical illumination, i.e., 100 mW-cm−2 , using the Oriel LSH-7320 LED solar

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Fig. 4 Device architecture of (left) triple-cation and (right) MAPbI3

simulator connected to a source meter unit from Ossila (Model: X200). The light was calibrated with a standard mono-Si solar cell (PVM-396, PV Measurements Inc., Boulder, CO, USA) certified by the US National Renewable Energy Laboratory (NREL). The J-V characteristic is shown for the triple-cation absorber in Fig. 5a, while the equivalent data is represented in Fig. 5b for the MAPbI3 absorber.

Fig. 5 a The J-V curves of triple-cation with 15.99% PCE, b The J-V curves of MAPbI3 with 11.93% PCE

The triple-cation PSC resulted in a maximum PCE of ~15.99%, with an open circuit voltage Voc = 1.02 V, short circuit current density Jsc of 28.48 mA-cm−2 , and a fill factor of FF = 54.69%. From the J-V Characteristic for the MAPbI3 in Fig. 5b, the PCE is ~11.93%, with a Voc = 0.96 V, Jsc ~ 22.76 mA-cm−2 and FF = 54.69%. The PV figures of merit for both absorber devices are summarized in Table 1.

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Table 1 Optimized photovoltaic parameters of n-i-p PSCs based on MAPbI3 and triple-cation as absorbers PSC absorbers

Voc (V)

Jsc (mA-cm−2 )

FF (%)

PCE (%)

Rsh (-cm2 )

Rsh (-cm2 )

MAPBI3

0.96

22.76

54.96

11.93

991.01

14.88

Trible-cation

1.02

28.48

55.05

15.99

176.43

10.99

Conclusions In summary, we have reported on the fabrication and characterization of photoabsorbers based on triple-cation perovskite absorbers which were also integrated into a solar cell architecture and the response was compared to MAPbI3 absorbers. Acknowledgements We thank the Office of Naval Research (grant number ONR N00014-20-12597) that enabled us to pursue this work.

References 1. Masuda T M, Araki K, Ota Y, Nishioka K (2022) Impact and recent approaches of highefficiency solar cell modules for PV-powered vehicles. Jpn J Appl Phys 61(SC):SC0802 2. Chung J, Shin SS, Hwang K, Kim G, Kim KW, Kim W, Ma BS, Kim YK, Kim TS, Seo J (2020) Record-efficiency flexible perovskite solar cell and module enabled by a porous-planar structure as an electron transport layer. Energy Environ Sci 13(12):4854–4861 3. Yang L, Xiong Q, Li Y, Gao P, Xu B, Lin H, Li X, Miyasaka T (2021) Artemisininpassivated mixed-cation perovskite films for durable flexible perovskite solar cells with over 21% efficiency. J Mater Chem A 9(3):1574–1582 4. Zeng P, Deng W, Liu M (2020) Recent advances of device components toward efficient flexible perovskite solar cells. Solar RRL 4(3):1900485 5. Moghadamzadeh S, Hossain IM, Gharibzadeh S, Abzieher T, Pham H, Hu H, Fassl P, Lemmer U, Nejand BA, Paetzold UW (2020) Triple-cation low-bandgap perovskite thin-films for highefficiency four-terminal all-perovskite tandem solar cells. J Mater Chem A 8(46):24608–24619 6. Raza E, Ahmad Z (2022) Review on two-terminal and four-terminal crystallinesilicon/perovskite tandem solar cells; progress, challenges, and future perspectives. Energy Rep 8:5820–5851 7. Min M, Sakri S, Saenz GA, Kaul AB (2021) Photophysical dynamics in semiconducting graphene quantum dots integrated with 2D MoS2 for optical enhancement in the near UV. ACS Appl Mater Interfaces 13(4):5379–5389 8. Unger EL, Kegelmann L, Suchan K, Sörell D, Korte L, Albrecht S (2017) Roadmap and roadblocks for the band gap tunability of metal halide perovskites. J Mater Chem A 5(23):11401–11409 9. Herz LM (2017) Charge-carrier mobilities in metal halide perovskites: fundamental mechanisms and limits. ACS Energy Lett 2(7):1539–1548 10. Jiang Q, Zhao Y, Zhang X, Yang X, Chen Y, Chu Z, Ye Q, Li X, Yin Z, You J (2019) Surface passivation of perovskite film for efficient solar cells. Nat Photonics 13(7):460–466 11. Jeong J, Kim M, Seo J, Lu H, Ahlawat P, Mishra A, Yang Y, Hope MA, Eickemeyer FT, Kim M, Yoon YJ (2021) Pseudo-halide anion engineering for α-FAPbI3 perovskite solar cells. Nature 592(7854):381–385

Part XIII

Advances in Magnetic Materials

Incisive Review on Magnetic Iron Oxide Nanoparticles and Their Use in the Treatment of Bacterial Infections Muniratu Maliki, Stanley O. Omorogbe, Ikhazuagbe H. Ifijen, Oscar N. Aghedo, and Augustine Ighodaro

Abstract Magnetic nanoparticles (MNPs) have shown great promise in a variety of biomedical applications, including magnetic hyperthermia, improving MRI data, augmenting tissue engineering efforts, and boosting medication delivery to difficultto-reach microniches. Their integration in diverse illnesses’ treatment pathways demonstrates an exponential increase in trend toward the incorporation of innovative biotechnologies in medical and pharmaceutical systems. Clinicians can use superparamagnetic nanoparticles (SPNs) to create a localized thermo-ablative impact that destroys bacterial biofilms. SPNs can also sensitize resistant bacterial cells to antibacterial chemicals by physically disrupting bacterial membranes. IONPS have also enhanced the transport of bactericidal chemicals to microniches, and could thus be used to treat disorders that require therapeutic intervention that must be able to pass through the blood–brain barrier. This Review carried out an incisive study on magnetic iron oxide nanoparticles and their use in the treatment of bacterial infections. This study also focused on the mechanisms underlying the antibacterial action of magnetite iron oxide (IONPS) against microorganisms. Keywords Magnetic nanoparticles · Superparamagnetism · Infectious disease · Iron oxide

M. Maliki Department of Chemistry, Edo State University Uzairue, Edo State, Nigeria S. O. Omorogbe · I. H. Ifijen (B) Department of Research Operations, Rubber Research Institute of Nigeria, Iyanomo, Benin City, Nigeria e-mail: [email protected]; [email protected] O. N. Aghedo Department of Science Labouratory Technology, Faculty of Life Sciences, University of Benin, P.M.B. 1154, Benin City, Nigeria A. Ighodaro Depatment of Aseptic Quality, Quantum Pharmaceuticals, Quantum House, Durham, UK © The Minerals, Metals & Materials Society 2023 The Minerals, Metals & Materials Society, TMS 2023 152nd Annual Meeting & Exhibition Supplemental Proceedings, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-031-22524-6_44

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Introduction Significant progress has been made in the diagnosis and creation of novel medications for the treatment of infectious disorders brought on by bacteria, viruses, and parasites throughout the past century. But in the developing world, they are the main causes of mortality and morbidity [1]. Infectious diseases have presented enormous problems throughout human history, particularly in underdeveloped nations where povertyrelated infectious diseases are the main cause of morbidity and mortality [2]. In 2012, the World Health Organization (WHO) estimated that microorganism-related diseases claimed 15 million lives globally, with bacterial and viral infections playing a significant role [3]. The prevention, control, and treatment of infectious diseases have required significant efforts from doctors and researchers, and these tasks call for precise instruments for pathogen identification and appropriate medications for treatment [1]. Together, the development of new therapies due to technology and the lack of suitable therapeutic or preventative medications are crucial. In order to enable the avoidance of these types of diseases and to provide a more effective therapeutic, safe, and of high quality, new materials and technologies have been studied. Nanomaterials have been discovered to have increased surface to volume ratio, reactivity, strength, electrical characteristics, and optical qualities because of their incredibly small sizes [4–20]. Using nanomaterials is now one of the most promising strategies to combat bacterial antibiotic resistance [21, 22]. It has been proven that nanoparticles (NPs) of a number of metals and their oxides, including Ag, ZnO, Fe2 O3 , Fe3 O4 , Al2 O3 , TiO2 , and CuO, exhibit antibacterial and antifungal effects on both Gram-positive and Gram-negative bacteria [23, 24]. Due to their magnetic properties, iron oxide-based magnetic nanoparticles (MNPs) have been widely researched as helpful bacterial detection platforms over the past few years [25, 26]. These MNPs have also been extensively utilized as bacterial separation agents, drug delivery agents, bioimaging contrast agents, and agents for magnetic hyperthermia to identify and treat bacterial illnesses [27–29]. For bacterial concentration and separation, MNPs can be functionalized with target molecules such aptamers, bacteriophages, different antibodies, antibiotics, antimicrobial peptides, and antibiotics [30]. MNPs conjugated with various metals enable the development of numerous bacterial detection techniques, such as colorimetric, fluorescent, and surface-enhanced Raman detections, on the basis of the surface modification [31]. In addition to in vitro detection techniques, magnetic resonance imaging (MRI) contrast agents for in vivo bacterial imaging have also been demonstrated for superparamagnetic iron oxide-based NPs [32]. Additionally, MNPs have demonstrated considerable potential in antibacterial applications [29] thanks to their distinct magnetic characteristics and high specific surface area. As a result, numerous studies have been conducted on magnetic iron oxide nanoparticles and their application in the management of bacterial infections. This work offers a sharp overview of current advancements in the utilization of iron oxide magnetic nanoparticles as an antibacterial agent against various bacterial species. The mechanisms underpinning

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magnetite iron oxide’s (IONPS) antibacterial effect against microbes were another area of emphasis in this work.

Prior Investigations into the Antibacterial Capabilities of Magnetic Iron Oxide Nanoparticles MNPs have been used in medical and pharmaceutical areas as drug delivery and hyperthermia agents for bacteria killing since the late 1970s [33]. In their research published in 2015, Arakha et al. examine how the interaction pattern between bacteria and iron oxide nanoparticles (IONP) influences IONP’s capability to combat pathogens [34]. To do this, n-IONP, an IONP with an atomic configuration similar to magnetite and a negative surface potential was produced by co-precipitation. n-IONP, also known as positive surface potential IONP, had its surface potential altered by a coating formed of molecules of the positively charged chitosan (p-IONP). The IONP surface had been successfully coated with chitosan molecule, according to comparison results from the XRD, zeta potential analyzer, and fourier transform infrared spectroscope. The generated nanocrystals were also found to be spherical in shape with a diameter of 10–20 nm. Insignificant antibacterial efficacy was found for n-IONP (50 M) against Escherichia coli and Bacillus subtilis in the BacLight fluorescence test, bacterial growth kinetics, and colony forming unit investigations. However, coating with the chitosan molecule significantly increased the IONP’s antibacterial tendency. Additionally, the bacteria were treated with pIONP, which seemed to produce more reactive oxygen species (ROS) than usual. Overall, the data showed that the chitosan coating of IONP produced an interface that increased ROS generation, which led to antibacterial activity. The primary factor causing implants to fail is biofilm buildup on the implant surface. Because exopolymeric compounds shield the organisms in a matrix that is impervious to most antibiotics and immune cells, biofilms on implant surfaces are challenging for antibiotics to eradicate. Biofilm development is thought to be resolved by the application of metals at the nanoscale. The influence of iron-oxide nanoparticles on the development of biofilms on various biomaterial surfaces and pluronic coated surfaces was investigated by Thukkaram et al. [35]. Bacterial adherence on pluronic coated surfaces was significantly reduced after 30 min as compared to other surfaces. The presence of various quantities of iron-oxide nanoparticles was then allowed to develop bacteria for 24 h. The presence of the highest concentration of iron-oxide nanoparticles among surfaces coated with pluronic was found to significantly inhibit the establishment of biofilm. Thus, a combination of polymer brush coating with iron-oxide nanoparticles may demonstrate a considerable decrease in the production of biofilms.

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In a study published in 2019, Gabrielyan et al. evaluated the impact of iron oxide (Fe3 O4 ) nanoparticles on the growth of Gram-positive Enterococcus hirae ATCC 9790 and Gram-negative Escherichia coli BW 25,113 [36]. E. coli’s growth specific rate decreased, demonstrating the bactericidal action of Fe3 O4 NPs. The concentration was a factor in how the NPs’ inhibitory impact worked. The singlet oxygen produced by Fe3 O4 NPs and other reactive oxygen species, such as superoxide radicals, may be the cause of the inhibition. Depending on the NPs concentration employed, Fe3 O4 NPs had opposing effects on E. hirae, causing either stimulation or inhibition of growth. In E. coli, the addition of NPs changed the redox potential kinetics and reduced H2 yield, but there was no discernible change in intracellular pH. Even in the presence of DCCD, Fe3 O4 NPs reduced H + -fluxes through bacterial membrane in E. coli more than E. hirae and enhanced ATPase activity in E. hirae more than E. coli. The findings of this study indicated that the varied effects of Fe3 O4 NPs on Gram-positive and Gram-negative bacteria are most likely caused by variations in the composition of bacterial cell walls and their unique metabolic characteristics. Different quantities of Fe3 O4 NPs do not have any hemolytic (cytotoxic) effect toward erythrocytes. As a result, in biomedicine, biotechnology, and pharmaceutics they can be suggested as antibacterial agents. The size-dependent antibacterial activity of the biogenic Fe NPs was investigated by Irshad et al. [37]. The SEM study of bacteria (Pseudomonas aeruginosa) before and after exposure to biogenic Fe NPs generated under optimal conditions is shown in Fig. 1. The biogenic Fe NPs generated at extract concentrations of 40 mL demonstrated the greatest antibacterial activity against Pseudomonas aeruginosa, measuring (22 (±0.5) mm as opposed to FeNPs at extract concentrations of 20 mL and 60 mL, measuring 18 (±0.4) mm and 14 (±0.3), respectively. The 40 mL peel extract-optimized Fe NPs are not only very effective in producing ROS, but also exhibit negligible hemolytic activity. Thus, significant antibacterial activity and biocompatibility are reported in Fe NPs manufactured utilizing a greener method. Small size and a big surface area can be attributed to this high antibacterial activity. The antibacterial efficacy of FeNPs produced using an aqueous extract of Phoenix dactylifera against four bacterial species, including Klebsiella pneumonia, Bacillus

Fig. 1 SEM analysis of bacteria (Pseudomonas aeruginosa) a before treatment with biogenic Fe NPs synthesized at optimized condition i.e., 40 mL peel extract and b after treatment with biogenic Fe NPs synthesized at optimized condition i.e., 40 mL peel extract whereas, c on treatment with Punica granatum peel extract only [37]

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Fig. 2 A Antimicrobial activity of synthesized Fe NPs against (a) Bacillus subtilis, (b) Escherichia coli, (c) Micrococcus leutus, (d) Klebsiella pneumonia. B Antimicrobial activity of standard drug and extract of Phoenix dactylifera leaves against (a) Bacillus subtilis, (b) Escherichia coli, (c) Micrococcus leutus, (d) Klebsiella pneumonia [38]

subtilis, Micrococcus leutus, and Escherichia coli, was examined by Batool et al. in 2021 [38]. Figure 2 depicts the antibacterial activity of the produced Fe NPs against Klebsiella pneumonia, Escherichia coli, Bacillus subtilis, and Micrococcus leutus. Maximum antibacterial effectiveness against Escherichia coli Escherichia coli (25 ± 0.360) and Klebsiella pneumonia (25 ± 0.519) was noted for FeNPs (synthesised from 13 mM salt concentration). Phoenix dactylifera aqueous extract provides a costand environmentally-friendly means to create FeNPs, which could open the door to a variety of uses, particularly as antibacterial agents. According to Prabhu et al. [39], ferric nitrate serves as the precursor material, urea serves as the fuel, and Tween 80, a non-ionic surfactant, helps the chemical combustion process burn up the Fe3 O4 nanoparticles [39]. Fe3 O4 nanoparticles’ antibacterial activity was examined using the well diffusion method against grampositive and gram-negative strains of Proteus vulgaris, Xanthomonas, Escherichia coli, and Staphylococcus aureus. On both gram-positive and gram-negative bacterial strains, the Fe3 O4 nanoparticles demonstrated good antibacterial capabilities. We can infer that Fe3 O4 is a highly potent antibacterial agent given the size of the zone of inhibition (Figs. 3 and 4). Iron oxide nanoparticles (IONPs) were synthesized using Penicillium spp. that was isolated from soils to study the antibacterial and antioxidant properties of the IONPs by Zakariya et al. [40]. This work demonstrated that Penicillium sppfungal‘s filtrate was a successful starting point for the IONPs’ synthesis. A peak at 350 nm, which denoted the generation of IONPs, was visible on the UV-spectrophotometer. With sizes ranging from 3.31 to 10.69 nm, TEM and SEM examination revealed that IONPs had a spherical form. The IONPs’ FTIR spectra exhibited bands at 3313 cm−1 and 1636 cm−1 , which demonstrated the protein’s role in the production

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Fig. 3 Antimicrobial activity of different extracts with Fe3 O4 at low concentration [39]

Fig. 4 Antimicrobial activity of different extracts with Fe3 O4 at high concentration [39]

and capping of nanoparticles. The biosynthesized IONPs contained iron components, as revealed by the EDX, and good stability was also shown by a zeta potential study (+33.9 mV). In addition to having strong antioxidant action, biosynthesized IONPs demonstrated effective antibacterial activity against pathogenic bacteria. At the maximum dose (250 g), the IONPs showed greater inhibitory activity against S. aureus (12 ± 0.6 nm), E. coli (11.3 ± 1.2 nm), K. pneumonia (11.3 ± 0.6 nm), S. sonnie (11.3 ± 0.6 nm), and P. aeruginosa (11.3 ± 0.6 nm). With IC50 values of 12.2 g/mL, IONPs also demonstrated antioxidant efficacy against the DPPH radical

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Fig. 5 Antibacterial effect of synthesized iron oxide nanoparticles against a Staphylococcus aureus, b E. coli, c Klebsiella pneumonia, d Shigella Sonnie and e Pseudomonas aeruginosa [40]

in comparison to ascorbic acid. The biosynthesized IONPs from Penicillium spp. have shown the promise for medicinal applications as antibacterial and anticancer drugs in the future (Fig. 5). In order to create a photothermal-enzyme combination antibacterial therapy platform, Guo et al. [41] created iron oxide (Fe3 O4 ) nanoparticles (IONPs) with outstanding biosafety, great photothermal conversion ability, and peroxidase-like enzymatic activity [41]. By inducing H2 O2 to catalyze the creation of ·OH in a mildly acidic environment, IONPs with peroxide-like catalytic activity can provide specific bactericidal effects and increase the sensitivity of bacteria to heat. The photothermal effect could break down bacterial cell membranes when triggered by near-infrared light, leading to the cleavage and inactivation of bacterial protein, DNA, or RNA. In the meantime, it can enhance the peroxidase-like enzyme’s catalytic activity and encourage IONPs to catalyze the formation of additional ·OH for bacterial eradication. Escherichia coli and Staphylococcus aureus’ antibacterial rates reached about 100% after receiving IONPs’ synergistic treatment. It also clearly kills germs in mice with infected wounds and significantly speeds up the healing of wounds with S. aureus, which has considerable potential for use in clinical anti-infection therapy. Magnetic iron oxide nanoparticles (MNPs) and their derivatives (Au coated, Ag coated, Co doped, or cationic polymer modified) have been extensively studied for their potential to penetrate into bacterium cells and biofilm mass, which may inactivate bacteria and antibiotic-resistant bacteria [42]. Highly ordered methicillinresistant biofilms were created by Geilich et al. on glass coverslips, and they were

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then treated with MNPs both with and without an external magnetic field [43]. After 24 h of incubation, the MNPs were able to enter the strong biofilms when a magnet was present, however little iron was found to permeate the biofilms when there was no magnetic field. In addition, they showed how the MNPs loaded with methicillin had a deep penetration and an antimicrobial effect. It was demonstrated using laser scanning confocal microscopy of the bacterial biofilms stained with the Live/Dead Biofilm Viability kit that the antibiotic delivery system made with MNPs could deepen drug penetration and deliver high concentrations of antibiotics into the multiple layers of the biofilms, whereas the antibiotic alone could only control planktonic bacteria without having the ability to penetrate biofilms [43]. As a result, the magnetic drug delivery system using MNPs demonstrated significant promise for treating biofilms quickly and effectively by controlling the movement and position of antibiotics.

The Mechanisms of Antibacterial Magnetite Iron Oxide (IONPs) Activity ROS production, whether via photocatalysis, Fenton reactions, or other related processes, is one of the primary causes of IONP toxicity [44]. In turn, ROS have a genotoxic effect that harms DNA molecules. The antioxidant system enzymes (SOD, catalase, and glutathione reductase) can become less active, increasing the concentration of ROS [24]. Mecapto (-SH), amino (-NH), and carboxyl (-COOH) groups of proteins, including those in enzymes, can bind to metal ions, causing deactivation or partial inhibition [45]. The integrity of the bacterial cell wall is also compromised by IONPs, as demonstrated in the reference. SEM was used to demonstrate that IONPs are directly bound to the Staphylococcus aureus cell wall [46]. In antibiotic-resistant bacteria present in operating rooms, IONPs can reduce the expression of antibiotic resistance genes (ARGs) [46]. IONPs have the power to impair F0/F1-ATPase activity, decrease the rate of H+ flow through the membrane, and alter the redox potential. Based on their size and similarities to other types of metal oxide nanoparticles, the antibacterial effect of IONPs has been hypothesized in various investigations [24]. Small nanoparticles have been shown to have the ability to prevent DNA replication by inactivating topoisomerase [47]. The ability of Fe2 O3 NPs to connect directly with the E. coli cell wall was demonstrated using the electron microscopy technique. Additionally, IONPs have the ability to enter the cytoplasm, where they can gather and damage cell walls and form vacuoles [47]. Due to their affinity for the inner membrane’s FHL complex, Fe2 O3 IONPs can concentrate between the outer and inner membranes of bacterial cells. As a result, Fe2 O3 IONPs were found to have more pronounced antimicrobial and antibiofilm properties. Streptococcus mutans biofilms were reduced more quickly by positively and neutrally charged IONPs than by negatively charged IONPs [24]. Oleic acidcoated IONPs can stop S. aureus and P. aeruginosa from forming biofilms. IOPNs have the physicochemical properties, such as a surface charge, hydrophobicity, and a

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high surface area ratio by volume that allow them to adsorb and permeate into bacterial biofilms. Both magnetic and paramagnetic characteristics can be found in iron oxide nanoparticles [24]. Superparamagnetic iron oxide nanoparticles (SPIONs) are another term for Fe2 O3 O4 NPs with significant paramagnetic activity [48]. As a result of vibration damage, localized hyperthermia, and the production of ROS, SPIONs in the presence of alternating magnetic fields cause cell death and biofilm destruction. The combination of all of the aforementioned variables causes membrane rupture, bacterial cell wall destruction, bacterial dissociation from a biofilm, cell fusion, and death [48]. Since IONPs bind to the FHL complex in the inner membrane of Gramnegative bacteria, they can concentrate between the outer and wall. Fe2 O3 IONPs therefore have stronger antibacterial effects against Gram-negative bacteria [36]. In Fe2 O3 IONPs, bactericidal and antibiofilm actions were demonstrated. In comparison to negatively charged IONPs, positively and neutrally charged IONPs generated a greater decrease of Streptococcus mutans biofilms [89]. Oleic acid-coated IONPs can stop S. aureus and P. aeruginosa from forming biofilms [44]. IOPNs have physicochemical properties, such as a surface charge, hydrophobicity, and a high surface area ratio by volume that allow them to adsorb on and enter bacterial biofilms [24]. Both magnetic and paramagnetic characteristics can be found in iron oxide nanoparticles [23]. As a result of vibration damage, localized hyperthermia, and the production of ROS, SPIONs in the presence of alternating magnetic fields cause cell death and biofilm destruction. The combination of all of the aforementioned factors causes membrane rupture, bacterial cell wall destruction, bacterial dissociation from a biofilm, cell fusion, and death [24].

Conclusions The utilization of magnetic iron oxide nanoparticles in the treatment of bacterial infections was the subject of a thorough investigation by this Review. The processes underpinning magnetite iron oxide’s (IONPS) antibacterial effect against microbes were also examined. Although there is nothing particularly novel about iron oxide magnetic nanoparticles in the pharmaceutical and medical fields, and many researchers are using them in various therapies, there are some theories about how they behave in vivo, but these are not yet fully developed. For the treatment of illnesses or to combat germs, other formulations including iron magnetic nanoparticles have also been suggested. These nanoparticles may operate as membrane permeability enhancers, cause cell wall damage, or produce reactive oxygen species, which can all be beneficial in combating bacterial infections. IONPs can therefore be viewed as possible new generation antibacterial agents.

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Part XIV

Advances in Multi-principal Element Alloys II

Data-driven Search and Selection of Ti-containing Multi-principal Element Alloys for Aeroengine Parts Tanjore V. Jayaraman and Ramachandra Canumalla

Abstract There is rapidly growing interest in Ti-containing multi-principal element alloys (MPEA), due to their distinct combination of the room- and elevatedtemperature mechanical properties and corrosion resistance for a wide range of potential applications. This has motivated us to analyze the literature data of the Ticontaining MPEAs to unearth the composition-processing-microstructure-property relationships for aeroengine applications. We synergistically applied advanced statistical analyses—including principal component analysis (PCA) and hierarchical clustering (HC)—and multiple-attribute decision making (MADM) to hear the voice of the data. The ranks assigned by several MADMs, including ARAS (additive ratio assessment), ROVM (range of value method), and MEW (multiplicative exponent weighing), were consistent. However, the ranks of the alloys varied upon varying the relative weights of various properties, which revealed several MPEAs’ potential to substitute superalloys for a range of aeroengine parts. The analyses suggest potential replacement substitutes and provide possible directions for the design and improvement of Ti-containing MPEAs. Keywords Superalloy 718 · Ti-containing multi-principal element alloys · Multiple attribute decision making · Material selection

T. V. Jayaraman (B) Department of Mechanical Engineering, University of Michigan-Dearborn, Dearborn, MI 48128, USA e-mail: [email protected] R. Canumalla (B) Weldaloy Specialty Forgings, Warren, MI 48089, USA e-mail: [email protected] © The Minerals, Metals & Materials Society 2023 The Minerals, Metals & Materials Society, TMS 2023 152nd Annual Meeting & Exhibition Supplemental Proceedings, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-031-22524-6_45

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Introduction and Background Superalloy 718 (also called Inconel 718) and other superalloys have been extensively used in aeroengine parts owing to their excellent combination of properties. For the past several decades, there have been prominent efforts to reduce the weight of the aeroengines by using stronger and lighter materials [1–8]. Specifically, the sustained research over more than a decade in the new class of alloys, the multiprincipal element alloys (MPEA), has presented opportunities for relatively lighter Ti-containing MPEAs having an excellent combination of properties [1, 9–20]. Therefore, it is imperative to analyze the literature data of Ti-containing MPEAs to unearth the composition-processing-microstructure-property relationships for aeroengines parts. More importantly, sort and select the Ti-containing MPEAs in the literature and compare them with the current industry benchmark of Inconel 718 [21–23]. Subsequently, identify and focus on a few top-ranked MPEAs with equivalent or superior properties compared to the benchmark and pursue further development for the intended applications. Material selection is a holistic approach to selecting an optimal material from a list of materials, which typically involves trade-offs among availability, cost, environmental effects, various properties, etc. [24]. Multiple attribute decision making (MADM) finds wide applications in many industries, including manufacturing, logistics, construction, and transportation, which involves making preference decisions over the available alternatives characterized by multiple and usually conflicting attributes [25–27]. In the present investigation, we applied multiple attribute decision making (MADM) coupled with advanced statistical analyses to rank the Ti-containing MPEAs in the literature and identify the competitors to superalloy 718 for aeroengine parts. The paper ranks the Ti-containing MPEAs in the literature by MADM, consolidates the ranks evaluated by diverse MADMs by basic and advanced statistical analyses, and identifies/recommends the top three Ti-containing MPEAs for further evaluation for the potential replacement of superalloy 718 in aeroengine parts. The ranks of the alloys varied upon varying the relative weights of various properties, which revealed several MPEAs’ potential to substitute a range of aeroengine parts.

Materials and Methodology We applied a novel methodology of data-driven sorting and selection of Ti-containing MPEAs from the literature, which comprised of the following steps: (i) compile literature data, (ii) apply five multiple attribute decision making (MADM) methods, viz., ARAS (additive ratio assessment), OCRA (operational competitiveness ratio), etc., (iii) consolidate the ranks by advanced statistical techniques, and (iv) identify Ti-containing MPEAs that can potentially replace superalloy 718. Figure 1 presents the flowchart of the novel methodology for data-driven sorting and selection of Ti-containing MPEAs from the literature.

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Fig. 1 The flowchart of data-driven sorting and selection of Ti-containing MPEAs

Literature Data We compiled a list of Ti-containing MPEAs and their properties from the literature, including conference proceedings and peer-reviewed journals [1, 9–20]. Table 1 presents the alloy chemistry (in at.%), processing conditions, resulting microstructures of the alloys, and unique identifier assigned for the current study—alloy designation, while Table 2 presents their properties. The properties identified for the investigation were density (ρ), yield strength at room temperature (0.2% YS-RT ), and yield strength at 800 °C (0.2% YS-800 °C). For the targeted aeroengine turbine parts, a combination of low density and high yield strengths at ambient and elevated temperatures is desirable. Hence, in the jargon of MADM, ρ is a minimizing attribute (lower the better), while 0.2% YS-RT and 0.2% YS-800 °C are maximizing attributes (higher the better). Thus, the alternatives (Alloy designation) and the attributes (ρ, 0.2% YS-RT, and 0.2% YS-800 °C) form the data matrix for the study.

Ranking We evaluated the ranks of the decision matrix (columns Alloy designation, ρ, 0.2% YS-RT, and 0.2%YS-800 °C in Table 2) by several MADM methods. Making preference decisions over the available alternatives that are often characterized by multiple and usually conflicting attributes is MADM [25, 26]. Distinct components of MADM are (i) the decision matrix, which comprises alternatives and attributes, and (ii) attribute weights that quantify the relative importance of the attributes [25, 26, 28]. The attribute weights are of two types: (a) objective—applies a mathematical model to quantify the relative weights of the attributes, and (b) subjective—takes experts’

Alloy chemistry in at. %

Ni47.9 -Al10.2 -Co16.9 -Cr7.4 -Fe8.9 -Mo0.9 -Nb1.2 -W0.4 -C0.4 -Ti5.8

Al6.25 -C1 -Co15 -Cr13 -Fe4.5 -Mo1.75 -Nb0.6 -Ni48 -V5 -Ti5

Al10 -Co25 -Cr8 -Fe15 -Ni36 -Ti6

Al10 -Co25 -Cr8 -Fe15 -Ni36 -Ti6

Al20.4 -Mo10.5 -Nb22.4 -Ta10.1 -Ti17.8 -Zr18.8

Al21.9 -Nb32 -Ta9 -Ti26.7 -Zr10.3

Al7.9 -Hf12.8 -Nb23 -Ta16.8 -Ti18.9 -Zr20.6

Sl#

1

2

3

4

5

6

7

Vac. arc melting-remelted 5 times

Vac. arc melting-remelted 5 times

Vac. arc melting-remelted 5 times

Vac. induction melted and solidified directionally

Vac. induction melted and solidified directionally

Vac. arc melting (5 times) and suction casting

Vac. arc melting followed by DS to produce columnar microstructure

Processing step 1

AC, HIP at 1200 °C/207 MPa/2 h, 1200 °C/24 h in Ar

AC, HIP at 1400 °C/207 MPa/2 h, 1400 °C/24 h in Ar

AC, hot isostatic pressing (HIP) at 1400 °C/207 MPa/2 h, 1400 °C/24 in Ar

Homogenized at 1220 °C/20 h/FC-900 °C/50 h/AC

Homogenized at 1220 °C/20 h/Furnace Cooled (FC)-900 °C/5 h/Air Cooled (AC)

ST 1175 °C/2 h-850°C/8 h-650°C/8 h-Water Quenched (WQ)

Solution treated (ST) at 1210 °C/10 h to Homogenize; Aging at 800 °C/20 h

Processing step 2

ONS-BCC-Ti17.8

ONS-BCC-Ti26.7

ONS-BCC-Ti18.9

BCC1 + BCC2; 75 μm avg. grain size; nanolamellar structures of the two phases BCC; 2000 μm avg. grain size; nanophases BCC; 140 μm avg. grain size; nanophases

(continued)

[11, 12]

[11, 12]

[11, 12]

[10]

HMD-HESA3-FCC-Ti6-50H

L12 γ ’-Ni3 (Ti,Al)(46% Vf/460 nm) in γ FCC solid sol. & B2/NiAl (needle like, up to 50 μm long) ( t . We then compare the grain size distribution between the Fokker-Planck and the SPPARKS model. Note that using this predicted grain size distribution, we could reconstruct an ensemble of statistically equivalent microstructures. The Fokker-Planck model predictions agree very well with the SPPARKS kinetic Monte Carlo simulations

Exploiting the fact that the log-normal distribution is one of the most commonly used distributions to characterize the grain size, as well as the fact that in the kinetic Monte Carlo method, time-step is an exponentially distributed random variable, we apply the log transformation to both grain size and time, i.e. X → log X , t → log t, before modeling the QoI (i.e. X after transformation) using the Fokker-Planck equation. The drift and diffusion coefficients are calibrated by simple linear regression, which minimizes the 2 error, based on Corollaries 1 and 2. The initial and training pdfs are constructed using the kernel density estimation method with the normal kernel distribution. The selected bandwidth is optimal for the normal kernel density [2]. The Tikhonov regularization is applied to the initial pdf to reduce the chance of numerical divergent for the Fokker-Planck solver. The Monte Carlo events from 46.5 to 599.5 mcs are used as the training dataset, while the Monte Carlo events from 744.375 to 16681.1 mcs are used as the testing dataset. Specifically, the training dataset includes snapshots at t ∈ {46.5, 60., 77.5, 100.00, 129.25, 166.875, 215.5, 278.375, 359.5, 464.25, 599.5} mcs. The testing dataset includes snapshots at t∈ {774.375, 1000.00, 1291.62, 1668.12, 2154.5, 2782.62, 3593.88, 4641.62, 5994.88, 7742.75, 10000.00, 12915.5, 16681.1} mcs. The time t = 599.5 mcs is used to split the training and testing datasets. By simple linear regression, we obtained the

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Fig. 3 Evolution of grain area pdf. Before (a) and after (b) transformation

Fig. 4 Evolution of log of grain area distribution by a kinetic Monte Carlo simulations and b FokkerPlanck equation with calibrated coefficients at three different snapshots: beginning of training (a), end of training and beginning of testing (b), and end of testing (c)

constant drift and diffusion coefficients, respectively, as D (1) (t) = 0.7320, whereas D (2) (t) = −0.02931. Figure 3a shows the evolution of grain area pdfs before transformation, while Fig. 3b shows the evolution of grain area pdfs after transformation. Figure 4 shows the solution of the Fokker-Planck equation after the log transformation at three different snapshots: Fig. 4a at the beginning of the training dataset, Fig. 4b at the end of the training dataset, and Fig. 4c at the end of the testing dataset. Excellent agreement with the testing dataset is obtained. From the solution of the Fokker-Planck equation shown in Fig. 4, we apply rejection sampling algorithm to draw samples and reconstruct the pdf of the grain size, by inverting the log transformation, i.e. X → exp(X ). Figure 5 shows the comparison between the reconstructed pdfs from Fokker-Planck solution and the original pdfs from SPPARKS. It is observed that even though the Fokker-Planck solution has a longer tail distribution, in general, the last testing pdf at 16,681.1 mcs agrees relatively well with the reconstructed pdf from the Fokker-Planck solution. This demonstrates if the Fokker-Planck coefficients are well-trained, a prediction about

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Fig. 5 Evolution of grain area distribution reconstructed by rejection sampling algorithm from the Fokker-Planck solutions

the evolution of the microstructural descriptor using the trained ROM can be made with a good level of accuracy.

Discussion and Conclusion In this paper, we propose and successfully demonstrate the application of a SROM, formulated by Fokker-Planck equation, to a normal grain growth with kinetic Monte Carlo simulations using SPPARKS. Specifically, we demonstrate that extrapolating the grain size pdf in long time agrees very well with the SPPARKS simulations. The proposed SROM has also been applied to molecular dynamics and phase-field

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simulation, but SPPARKS remains the most successful application so far [37]. It is noteworthy that even though in this particular example, the simple linear regression is used to calibrate the drift and diffusion coefficients in 2 loss function, a more generalized black-box optimization approach, e.g. Bayesian optimization method [31–36], could be used for a more generalized loss function. Analytically, the solutions to some microstructure descriptors are well-known in the field of materials science. In the case study of kinetic Monte Carlo for norml grain-growth problem, it is known that the average grain size (isotropic grain boundary energies and mobilities) grows as t 1/2 , as described in Ng [21] and Breithaupt et al. [3]. Interested readers are referred to the works of Friedrich et al. [7–9, 17, 27] for various applications of stochastic method in time-series and estimation the drift and diffusion coefficients of FokkerPlanck equations in low- and high-sampling rates. It remains questionable to us as to how the log t transformation yields a uniformly distributed random variable, even though mathematically, it is that e−t = r e1/k should be uniformly distributed, because r ∼ U[0, 1], k is the rate constant [38], and t is supposed to be an exponentially distributed random variable. The issue remains an open question for future study. This manuscript provides a numerical evidence that the grain growth in 2d, through SPPARKS, are perhaps related to a geometric Brownian motion, where the logarithm of the grain can be modeled by a Brownian motion or Wiener process under Itô interpretation. The relationship between Langevin equation and Anderson-Kubo process, Ornstein-Uhlenbeck process, Black-Scholes process or geometric Brownian motion is fairly known (cf. Chap. 8 [30]), yet little has been brought into the materials science community. Practically, it validates the numerical implementation of SPPARKS, which agrees with the theoretical mathematical modeling results that have been long established in the literature [3, 21]. For those who wonder what this manuscript is related to data assimilation, there is a direct connection between ensemble FokkerPlanck equation and ensemble Kalman filter; more specifically, ensemble Kalman filter applies the sequential Markov chain Monte Carlo to solve the Fokker-Planck equation [4, 5], by viewing the dynamical model as a stochastic differential equation with Itô calculus [5], as done in this study. Acknowledgements This article has been authored by an employee of National Technology & Engineering Solutions of Sandia, LLC under Contract No. DE-NA0003525 with the U.S. Department of Energy (DOE). The employee owns all right, title, and interest in and to the article and is solely responsible for its contents. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this article or allow others to do so, for United States Government purposes. The DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan https://www.energy.gov/downloads/doe-public-access-plan. This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.S. Department of Energy or the United States Government.

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Proofs Here, we present a short proof for Corollary 1. Assume vanishing boundary condi2 tions at an exponential rate of the pdf, i.e. f (X, t) ∝ e−X ⇒ lim X →±∞ f (X, t) = 0. Proof Here, we integrate by part and utilize the vanishing boundary conditions of the pdf f (X, t) → 0 as X → ±∞. ∞

∂ ∂ E[X (t)] = ∂t ∂t

∞ X f (X, t)d X =

−∞

−∞

∞

−X

= −∞

∞ =− −∞

∂ (1)  X D f dX + ∂X

−∞

∞

−∞

=

X

∂ 2 (2)  D f dX ∂ X2

∞ ∂ D (1) f d X + X (D (2) f ) ∂X −∞

∂ (D (2) f )d X ∂X

= −[X D (1) f ]∞ −∞ + ∞

∞

−∞

∞

−∞

−

∂f dX ∂t

∂ 2 (2)  ∂ (1)  D f dX D f +X ∂X ∂ X2

= −[X D (1) f ]∞ −∞ + ∞

X

(18) ∞ ∂ D (1) f d X + X − [(D (2) f ]∞ (D (2) f ) −∞ ∂X −∞

D (1) (X, t) f (X, t)d X

−∞

If the drift coefficient is a temporal function D (1) (X, t) = D (1) (t), then the last equation simplifies to ∂ (19) E[X (t)] = D (1) (t), ∂t as

∞ −∞

f (X, t)d X = 1.



Here, we present a short proof for Corollary 2 by integrating by part and utilizing the vanishing boundary conditions of f (X, t).

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Proof First, consider ∂ ∂ E[X (t)2 ] = ∂t ∂t

∞ X 2 f (X, t)d X −∞

∞ =

X

2∂f

∂t

−∞ ∞

dX = −∞



=

X2 − −∞ ∞

∂ 2 (2)  ∂ (1)  D X2 − f dX D f + ∂X ∂ X2

∞

∂ 2 (2)  ∂ (1)  D f dX D f + ∂X ∂ X2

∂ (1)  −X D f dX + ∂X

=

∞

2

−∞

−∞

∞

= −[X 2 D (1) f ]∞ −∞ + 2

−∞

∞ −2

X

−∞

= −[X D 2

+

X2

∂ 2 (2)  D f dX ∂ X2

∞ ∂ X D (1) f d X + X 2 (D (2) f ) ∂X −∞

∂ (D (2) f )d X ∂X

(1)

f ]∞ −∞

∞ +2

X D (1) f d X

−∞

∞

∂ (D (2) f ) X ∂X 2

∞ =2

−∞

X D (1) f d X + 2

−∞

− 2[X D

∞

(2)

f ]∞ −∞

∞ +2

D (2) f d X

−∞

D (2) (X, t) f (X, t)d X

(20)

−∞

Observe that if the drift coefficient is a temporal function D (1) (X, t) = D (1) (t), then by Corollary 1, ∂t∂ E[X (t)] = D (1) (t). Thus, 2

∞ −∞

X D (1) f d X = 2

∞ −∞

X D (1) (t) f (X, t)d X = 2D (1) (t)

= 2D (1) (t)

∞ −∞

∞ −∞

X f (X, t)d X

X f (X, t)d X = 2D (1) (t)E[X (t)]

(21)

(t)] = 2 ∂E[X E[X (t)]. ∂t

If the diffusion coefficient is also a temporal function D (2) (X, t) = D (2) (t), then Eq. (20) becomes

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∂ ∂E[X (t)] E[X 2 (t)] = 2 E[X (t)] + 2D (2) (t). ∂t ∂t

(22)

After a few algebraic manipulation, we obtain ∂ ∂ V[X (t)] = (E[X (t)2 ] − [E[X (t)]]2 ) ∂t ∂t ∂E[X (t)] ∂ = E[X (t)2 ] − 2 E[X (t)] ∂t ∂t = 2D (2) (t).

(23)

References 1. Anvari M, Tabar M, Peinke J, Lehnertz K (2016) Disentangling the stochastic behavior of complex time series. Sci Rep 6(1):1–12 2. Bowman AW, Azzalini A (1997) Applied smoothing techniques for data analysis: the kernel approach with S-Plus illustrations, vol 18. Oxford University Press, Oxford 3. Breithaupt T, Hansen LN, Toppaladoddi S, Katz RF (2021) The role of grain-environment heterogeneity in normal grain growth: a stochastic approach. Acta Materialia 209:116699 4. Evensen G (1994) Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J Geophys Res: Oceans 99(C5):10143–10162 5. Evensen G (2003) The ensemble Kalman filter: theoretical formulation and practical implementation. Ocean Dyn 53(4):343–367 6. Frank TD (2005) Nonlinear Fokker-Planck equations: fundamentals and applications. Springer Science & Business Media 7. Friedrich R, Peinke J, Sahimi M, Tabar MRR (2011) Approaching complexity by stochastic methods: from biological systems to turbulence. Phys Rep 506(5):87–162 8. Friedrich R, Renner C, Siefert M, Peinke J (2002) Comment on “Indispensable finite time corrections for Fokker-Planck equations from time series data”. Phys Rev Lett 89(14):149401 9. Friedrich R, Siegert S, Peinke J, Siefert M, Lindemann M, Raethjen J, Deuschl G, Pfister G et al (2000) Extracting model equations from experimental data. Phys Lett A 271(3):217–222 10. Gille ST (2005) Statistical characterization of zonal and meridional ocean wind stress. J Atmos Oceanic Technol 22(9):1353–1372 11. Giuggioli L, McKetterick TJ, Kenkre V, Chase M (2016) Fokker-Planck description for a linear delayed Langevin equation with additive Gaussian noise. J Phys A: Math Theor 49(38):384002 12. Giuggioli L, Neu Z (2019) Fokker-Planck representations of non-Markov Langevin equations: application to delayed systems. Philos Trans Royal Soc A 377(2153):20180131 13. Gottschall J, Peinke J (2008) On the definition and handling of different drift and diffusion estimates. New J Phys 10(8):083034 14. Gradišek J, Govekar E, Grabec I (2002) Qualitative and quantitative analysis of stochastic processes based on measured data, II: applications to experimental data. J Sound Vibr 252(3):563– 572 15. Gradišek J, Grabec I, Siegert S, Friedrich R (2002) Qualitative and quantitative analysis of stochastic processes based on measured data, I: theory and applications to synthetic data. J Sound Vibr 252(3):545–562 16. Homer ER, Tikare V, Holm EA (2013) Hybrid Potts-phase field model for coupled microstructural-compositional evolution. Comput Mater Sci 69:414–423 17. Honisch C, Friedrich R (2011) Estimation of Kramers-Moyal coefficients at low sampling rates. Physi Rev E 83(6):066701

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18. Kleinhans D, Friedrich R, Nawroth A, Peinke J (2005) An iterative procedure for the estimation of drift and diffusion coefficients of Langevin processes. Phys Lett A 346(1–3):42–46 19. Lin WT, Ho CL (2012) Similarity solutions of the Fokker-Planck equation with time-dependent coefficients. Ann Phys 327(2):386–397 20. Mousavi S, Reihani S, Anvari G, Anvari M, Alinezhad H, Tabar M (2017) Stochastic analysis of time series for the spatial positions of particles trapped in optical tweezers. Sci Rep 7(1):1–11 21. Ng FS (2016) Statistical mechanics of normal grain growth in one dimension: a partial integrodifferential equation model. Acta Materialia 120:453–462 22. Pesce G, McDaniel A, Hottovy S, Wehr J, Volpe G (2013) Stratonovich-to-Itô transition in noisy systems with multiplicative feedback. Nat Commun 4(1):1–7 23. Plimpton S, Thompson A, Slepoy A (2008) Stochastic parallel particle kinetic simulator. Technical report, Sandia National Lab.(SNL-NM), Albuquerque, NM (United States) 24. Plimpton S, Battaile C, Chandross M, Holm L, Thompson A, Tikare V, Wagner G, Webb E, Zhou X, Cardona CG et al (2009) Crossing the mesoscale no-man’s land via parallel kinetic Monte Carlo. Sandia Report SAND2009-6226 25. Renner C, Peinke J, Friedrich R (2001) Experimental indications for Markov properties of small-scale turbulence. J Fluid Mech 433:383–409 26. Risken H (1989) The Fokker Planck equation, Methods of solution and application, 2nd edn. Springer, Berlin, Heidelberg 27. Siefert M, Kittel A, Friedrich R, Peinke J (2003) On a quantitative method to analyze dynamical and measurement noise. EPL (Europhys Lett) 61(4):466 28. Sura P, Gille ST (2003) Interpreting wind-driven Southern Ocean variability in a stochastic framework. J Marine Res 61(3):313–334 29. Tabar MRR (2019) The Langevin Equation and Wiener Process. Springer International Publishing, Cham, pp 39–48. https://doi.org/10.1007/978-3-030-18472-8_5 30. Tabar R (2019) Analysis and data-based reconstruction of complex nonlinear dynamical systems, vol 730. Springer 31. Tran A, Eldred M, Wildey T, McCann S, Sun J, Visintainer RJ (2022) aphBO-2GP-3B: a budgeted asynchronous parallel multi-acquisition functions for constrained Bayesian optimization on high-performing computing architecture. Struct Multidisc Optim 65(4):1–45 32. Tran A, Mitchell JA, Swiler LP, Wildey T (2020) An active-learning high-throughput microstructure calibration framework for process-structure linkage in materials informatics. Acta Materialia 194:80–92 33. Tran A, Sun J, Furlan JM, Pagalthivarthi KV, Visintainer RJ, Wang Y (2019) pBO-2GP-3B: a batch parallel known/unknown constrained Bayesian optimization with feasibility classification and its applications in computational fluid dynamics. Comput Methods Appl Mech Eng 347:827–852 34. Tran A, Tran M, Wang Y (2019) Constrained mixed-integer Gaussian mixture Bayesian optimization and its applications in designing fractal and auxetic metamaterials. Struct Multidisc Optim 59:2131–2154 35. Tran A, Tranchida J, Wildey T, Thompson AP (2020) Multi-fidelity machine-learning with uncertainty quantification and Bayesian optimization for materials design: application to ternary random alloys. J Chem Phys 153:074705 36. Tran A, Wildey T, McCann S (2020) sMF-BO-2CoGP: a sequential multi-fidelity constrained Bayesian optimization for design applications. J Comput Inform Sci Eng 20(3):1–15 37. Tran A, Wildey T, Sun J, Liu D, Wang Y (2022) A stochastic reduced-order model for statistical microstructure descriptors evolution. J Comput Inform Sci Eng, pp 1–18 38. Voter AF (2007) Introduction to the kinetic Monte Carlo method. In: Radiation effects in solids. Springer, pp 1–23

Towards Machine Learning of Crystal Plasticity by Neural Networks Christoph Hartmann

Abstract The use of crystal plasticity models in macroscopic numerical analysis still poses challenges. Component and crystal structures have different scales by several orders of magnitude. For this reason, a discretization of the crystal structure within the framework of the component is not reasonably possible. Therefore, for each integration point its microstructure is represented by a representative volume element. Due to the computational effort associated with this coupling, the use of crystal plasticity models has been limited. They are mainly used in academia and with little application in component and process design. The approach taken here is to decouple computational effort through machine learning by training a neural network that eventually serves as a material model in macro-scale analysis. Based on the deformation gradient and the microstructure, the trained neural network reproduces the resulting stress response, where the conducted investigations also cover consecutive deformation patterns and general stress states. Keywords Crystal plasticity · Machine learning · Neural networks · Computational efficiency

Introduction The finite element method (FEM) is one of the most frequently used methods in structural mechanics for designing complex components and processes. Originating from its continuum mechanics foundation, the behavior of each material point of a body is described by a set of constitutive equations. Such a material model links all variables describing the macroscopic continuum, such as stresses and strains. In reality, however, the properties of metals are directional at the atomic level. This results from the activation of different deformation mechanisms in the underlying C. Hartmann (B) Chair of Metal Forming and Casting, Technical University of Munich, Walther-Meissner-Strasse 4, Garching near Munich 85748, Bavaria, Germany e-mail: [email protected] © The Minerals, Metals & Materials Society 2023 The Minerals, Metals & Materials Society, TMS 2023 152nd Annual Meeting & Exhibition Supplemental Proceedings, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-031-22524-6_51

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crystal structure [1]. As a consequence, the macroscopic properties show anisotropy. This is especially true for materials which have a distinct preferred direction at the atomic level, i.e. a clear arrangement of the crystals along certain directions, or whose processing results in such an arrangement. This is the case, for example, for rolled materials, e.g. sheet metals [2]. A well-known approach to describe the anisotropic plastic behavior are crystal plasticity (CP) methods. Their basis are phenomenological, or physical constitutive equations, which describe the behavior of single crystals. Multicrystal materials can be modeled by full-field simulations or homogenization. In a full-field simulation, a single crystal is resolved by several points, whereas in a homogenization one point contains several crystal orientations. The resulting boundary value problem can be solved either by means of FEM or spectral methods, the CPFE or CPFFT, respectively [3]. The use of CP methods in the context of macroscopic FE simulations presents a two-layered problem. The component and the crystal structure show different scales by several orders of magnitude. For this reason, a discretization of the crystal structure within the framework of an FE simulation at the component scale is not feasible. Therefore, for each integration point its microstructure is represented by simulation of a representative volume element (RVE) [3]. Machine learning, such as vector machines or neural networks (NN), is becoming increasingly important in industrial application, research, and science [4]. First applications of machine learning in materials science date back to the 1990s. Reference [5] gives a summary of various applications of NN in this field. In the field of CP, approaches exist to partially replace simulations by neural networks. Mainly, these aim to obtain fast predictions for the stress-strain curve [6, 7], material properties [8], or texture evolution [9, 10] based on a simple stress state.

Approach This work aims to evaluate further the use of machine learning models, in particular NNs, to represent material behavior at the microscopic level for CP simulations. The idea is to train NNs in such a way that it reproduces the resulting stress response (first Piola-Kirchhoff stress tensor P), based on the deformation gradient F and the microstructure of a RVE. To predict the stress response to consecutive deformation steps with a FE simulation, the input data of the NN must either be output by the NN itself or be computable from its predictions. Consequently, the NN can take over the role of the material model in the simulation. In contrast to previous investigations, a general stress state is considered. Starting from a RVE, a suitable set of simulations is to be generated, which should represent the considered strain and stress space best possible. The CPFFT method is used for data generation due to its efficiency for periodic RVEs [11]. The DAMASK toolkit [3] is used for simulation, which is integrated into an overall Python framework that allows parallel computation and automated customization of new analysis based on previous results. This framework handles the start as well

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as pre- and post-processing of the conducted simulations, where one goal was to create an effective code for training data generation in a reasonable time against the background of meaningful future application.

Data Generation In order to be able to replace a material model with a NN, the training data set should represent the expected strain-stress space to be simulated as good as possible. Hence, the choice of training samples may respect the future problem at hand, i.e. if mainly tensile loads are to be simulated, higher data resolution in this stain-stress domain should be aimed for. Also, the step size of the predictor of the FE simulation is important. In the context of this work, general stress states are considered. The approach used is to simulate different load paths based on certain deformation patterns applied to a RVE. For each deformation step, an updated RVE with modified microstructure results, from which further simulations may originate in a tree style fashion. For the 2D case, for example, eight different load step scenarios are possible, represented by Fstep . The baseline 2D RVE for data generation has a 32 × 32 × 1 grid and N = 20 grains, which was determined by means of sensitivity analysis. A load step is calculated over a time of one second. The number of increments is 21 and the frequency for writing the restart environments and reading the results is 20 increments. The deformation gradient tensor F and the first Piola-Kirchhoff stress tensor P as well as the orientation in quaternions and their IPF values are stored. Altogether 156,856 simulations result. The computing time with four processes running in parallel amounts to approximately 167 h, i.e. just under one week, on ordinary CPU and GPU hardware.

Neural Network Models The NN should replace a CP model for general stress states, and hence, predict the first Piola-Kirchhoff stress tensor P for given predictor steps of the deformation gradient Fstep . To take the deformation history into account, the instantaneous deformation gradient Finit is passed to the NN as an additional input. Furthermore, it is investigated to what extent the specification of the texture of the RVE affects the accuracy of the stress prediction. For this purpose, two NN architectures are investigated, see Fig. 1, where one NN receives the orientation of each node as an additional input represented by an image. In order to use this in a FE simulation, the NN also output the updated orientation of the grains for further processing. NN1 has two input layers with nine nodes for the components of the deformation gradient variables Fstep and Finit . Each input layer is followed by a fully connected layer (layer 1) with N 1 1 and N 1 2 nodes, respectively. Then the layers are merged and fed to another fully connected layer (layer 2) with N 2 nodes. The third hidden layer

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layer 1

layer 2

dropout 1

layer 3

NN2 convolution 1

max-pool 1 convolution 2

layer 1

max-pool 2

flaen

flatten layer 2

dropout 1

layer 3

Fig. 1 Model architecture of NN1 and NN2

(dropout 1) is a dropout layer, followed by a final fully connected layer (layer 3) with N 3 nodes. Layers 1, 2, and 3 each have the ReLU function for activation. The output layer consists of nine nodes and uses a linear activation function to output negative values as well. All fully connected layers use L2-regularization. NN2 represents a combination of NN1 with a CNN. This consists of two convolutional-max-pooling layers and receives as input an image of 32 × 32 pixels with four channels for the real values of the quaternions. The first layer is a convolutional layer with 16 filters, a kernel size of 4 × 4, and a step size of 2 × 2 in combination with a max-pooling layer with kernel 2 × 2. The second convolutional layer has 32 filters, a kernel size of 2 × 2, and a step size 1 × 1 with applied zero-padding. This is followed by another max-pooling layer with kernel size 4 × 4. The 32 4 × 4 filters are then converted to a layer with 512 nodes (flatten layer). The flattened layer is passed to a network with the same architecture as NN1 as an additional input.

Model Training and Hyperparameters For a selected RVE, a total of 156,856 simulations produces the data set for the NN training. The entire data set is divided into 80% training data, 10% validation data, and 10% testing data. This results in a total of 125,484 simulations for actual training and 156,856 simulations each for validation and testing of the NN predic-

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Table 1 NN training settings and hyperparameters by NM algorithm [12] optimization NN N11 N12 N2 N3 lr l2 dr BS epoch NN1 NN2

980 1010

1020 1925

900 925

1600 1250

0.000546 0.000611

8 · 10−6 3 · 10−6

0.23 280 0.0663 353

Table 2 MAE of the first Piola-Kirchhoff stress tensor P in MPa for the two NNs NN P11 P21 P31 P12 P22 P32 P13 P23 P33 NN1 NN2

22.53 11.54

28.67 8.31

5.24 1.52

28.66 8.07

26.55 11.97

5.51 1.30

5.27 1.54

5.51 1.30

0.049 0.039

462 633

Average 14.22 5.07

tion. In order to work with the same data for both NNs, this division is performed once. The error of the validation set MAEval achieved via the training is used as the objective function. The NN hyperparameters are also optimized using the NM algorithm [12]. The considered hyperparameters are the learning rate lr , the factor of L2-regularization l2, the dropout rate dr , the mini batch size B S, and the number of neurons in each layer. Learning takes place over a maximum of 1,000 epochs. The training is terminated, if after 80 epochs no improvement of MAEval is to be recognized. Table 1 summarizes the results of the hyperparameter optimization for both NNs.

Results For both NN architectures, the quality of the first Piola-Kirchhoff stress tensor P prediction is evaluated; see Table 2. For NN2, significantly smaller deviations can be seen for all components of the first Piola-Kirchhoff stress tensor P. The value for eal test averaged by the network NN2 over all components is less than half of MAErtest the value for the network NN1. In this respect, the additional use of the orientation in quaternions provides more accurate results.

Simple Loading The creation of the entire data set is based on the eight boundary conditions. For the simple loading scenario testing, each load step contained therein is calculated six times in succession within a simulation. The same simulation parameters are used for training data generation. For each load step, the actual deformation gradient tensor step Fstep can be calculated, which is required as input for both NNs. Additionally, the orientations at the beginning of a load step obtained by the simulation are used

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in kN/m2

tension 11

shear 21 in kN/m2

in kN/m2

shear 12

NN1

NN2

simulation

training data limit

Fig. 2 Comparison of stress-strain curves and the predictions of NN1 and NN2 for tension and positive shear loading

as additional input for the NN2. Figure 2 shows the simulated stress-strain curves in comparison to the predictions of both NNs for tension and positive shear loading. During data generation, a maximum of five consecutive steps of the same load step are allowed. This limit is indicated by the gray line. On average, the prediction error of both NNs increases for decreasing strain levels and is largest for the first load step. NN2 is able to predict the stress curve somewhat better, although significant deviations of the stress curve can also be seen for the first two load steps. For NN1, the course of the stress-strain curve appears almost constant. It is suspected that the poor predictions for early load steps are related to the algorithm used for data generation. For the first load step, only eight simulations of the boundary conditions are available in total. For the second load step, the number increases to 8 · 7 = 56, for the third to 8 · 7 · 7 = 392, and grows approximately exponentially until the fifth load step. From the sixth step on, this behaviour is interrupted by the specified maximum deformation values. Overall, the NNs thus have significantly more training data available for load steps 5 and 6 than for earlier load steps. The slightly better results for NN2 may be explained by the additional information of orientation that allows the NN to better distinguish between individual load steps.

Combined Loading In the following, four test scenarios of simulations with different combinations of tension, compression, and shear loading by 0.02 each are considered: • S1: three times tension in x-direction and four times tension in y-direction; • S2: three times xy-shear and four times yx-shear loading; • S3: alternating tension in x-direction and xy-shear loading;

582 S2

S3

S4

in kN/m2

S1

in kN/m2

Fig. 3 Comparison of stress responses and the predictions of NN1 and NN2 for the different load step combinations S1, S2, S3, and S4

C. Hartmann

number of load step

number of load step NN1

NN2

simulaon

training data limit

• S4: two times tension in x-direction, two times tension in y-direction, and three times xy-shear loading. The von Mises equivalent stresses obtained by simulation and the NN predictions after each load step are given in Fig. 3. The overall picture is similar to that for the simple loading. Again, for the last load step (gray line) the stresses predicted by the NNs agree well. Regarding the number of load steps, the mesh NN2 can follow the stress curve again better in the direction of earlier load steps.

Summary and Conclusion The aim of the work was to investigate further the possibility to replace a CP material model with NNs. In order to be able to perform different CP simulations for data generation of a RVE, the software package DAMASK was integrated into a Python simulation environment. For a 2D RVE, a data set of 156,856 simulations was created, which was used for the training of two different NN architectures. The quality of the NNs was evaluated by comparing the predictions with the results of simulations of different load steps. It is concluded that the used algorithm for building a simulation data tree is promising for generating large data sets, however, until now it is still not suitable to represent a general stress space, since the developing data density presents as inhomogeneous. By using the crystal orientation as an additional input, the prediction quality improves, but a good agreement within the general stress-strain space could also not be achieved. Regarding the prediction of the stress response for a mixed load condition, no similarity between the stresses obtained by simulation and NN could be found. The reason for this is assumed to be the different load conditions on which the algorithm, and hence the training data, is based, from which the NN was not able to learn correlations between the simple and mixed loads.

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References 1. Hartmann C, Lechner P, Volk W (2021) In-situ measurement of higher-order strain derivatives for advanced analysis of forming processes using spatio-temporal optical flow. CIRP Ann 70(1):251–254 2. Roters F, Eisenlohr P, Hantcherli L, Tjahjanto DD, Bieler TR, Raabe D (2010) Overview of constitutive laws, kinematics, homogenization and multiscale methods in crystal plasticity finite-element modeling: Theory, experiments, applications. Acta Mater 58(4):1152–1211 3. Roters F, Diehl M, Shanthraj P, Eisenlohr P, Reuber C, Wong SL, Maiti T, Ebrahimi A, Hochrainer T, Fabritius H-O, Nikolov S, Friák M, Fujita N, Grilli N, Janssens KGF, Jia N, Kok PJJ, Ma D, Meier F, Werner E, Stricker M, Weygand D, Raabe D (2019) Damask - the düsseldorf advanced material simulation kit for modeling multi-physics crystal plasticity, thermal, and damage phenomena from the single crystal up to the component scale. Comput Mater Sci 158:420–478 4. Hartmann C, Opritescu D, Volk W (2019) An artificial neural network approach for tool path generation in incremental sheet metal free-forming. J Intell Manuf 30(2):757–770 5. Bhadeshia HKDH (1999) Neural networks in materials science. ISIJ Int 39(10):966–979 6. Yamanaka A, Kamijyo R, Koenuma K, Watanabe I, Kuwabara T (2020) Deep neural network approach to estimate biaxial stress-strain curves of sheet metals. Mater Design 195:108970 7. Koenuma K, Yamanaka A, Watanabe I, Kuwabara T (2020) Estimation of texture-dependent stress-strain curve and r-value of aluminum alloy sheet using deep learning. Mater Trans 61(12):2276–2283 8. Herriott C, Spear AD (2020) Predicting microstructure-dependent mechanical properties in additively manufactured metals with machine- and deep-learning methods. Comput Mater Sci 175:109599 9. Usman A, Waqas M, Abhijit B, Oxana S, Kaan I (2019) Application of artificial neural networks in micromechanics for polycrystalline metals. Int J Plast 120:205–219 10. Pandey A, Pokharel R (2021) Machine learning based surrogate modeling approach for mapping crystal deformation in three dimensions. Scripta Mater 193:1–5 11. Prakash A, Lebensohn RA (2009) Simulation of micromechanical behavior of polycrystals: finite elements versus fast fourier transforms. Model Simul Mater Sci Eng 17(6):064010 12. Nelder JA, Mead R (1965) A simplex method for function minimization. Comput J 7(4):308– 313

Part XVIII

Algorithm Development in Materials Science and Engineering

Differential Property Prediction: A Machine Learning Approach to Experimental Design in Advanced Manufacturing Loc Truong, WoongJo Choi, Colby Wight, Elizabeth Coda, Tegan Emerson, Keerti Kappagantula, and Henry Kvinge

Abstract Advanced manufacturing techniques have enabled the production of materials with state-of-the-art properties. In many cases however, the development of physics-based models of these techniques lags behind their development in the lab. This means that material and process development proceeds largely via trial and error. This is sub-optimal since experiments are cost-, time-, and labor-intensive. In this work, we propose a machine learning framework, differential property classification (DPC), which enables an experimenter to leverage machine learning’s unparalleled pattern matching capability to pursue data-driven experimental design. DPC takes two possible experiment parameter sets and outputs a prediction which will produce a material with a more desirable property specified by the operator. We demonstrate the success of DPC on AA7075 tube manufacturing process and mechanical propL. Truong (B) · W. Choi · C. Wight · E. Coda · T. Emerson · H. Kvinge Pacific Northwest National Laboratory, Seattle, USA e-mail: [email protected] W. Choi e-mail: [email protected] C. Wight e-mail: [email protected] E. Coda e-mail: [email protected] T. Emerson e-mail: [email protected] H. Kvinge e-mail: [email protected] K. Kappagantula Pacific Northwest National Laboratory, Richland, USA e-mail: [email protected] H. Kvinge University of Washington, Seattle, USA © The Minerals, Metals & Materials Society 2023 The Minerals, Metals & Materials Society, TMS 2023 152nd Annual Meeting & Exhibition Supplemental Proceedings, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-031-22524-6_52

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erty data using shear-assisted processing and extrusion (ShAPE), an emerging solid phase processing technology. We show that by focusing on the experimenter’s need to choose between multiple candidate experimental parameters, we can reframe the challenging regression task of predicting material properties from processing parameters, into a classification task on which machine learning models can achieve good performance. Keywords Advanced manufacturing · Machine learning · Shear assisted processing and extrusion (ShAPE)

Introduction Despite impressive progress in tasks ranging from object recognition to speech-totext, to games such as Go [1], there are many scientific domains where machine learning (ML) is just beginning to have a significant impact. While advanced manufacturing also has many challenges that would benefit from the strong patternmatching capabilities of ML systems, the intersection of these two fields is still in its infancy [2]. In this work, we propose a ML-based framework to aid in experimental design in advanced manufacturing. Because of the physical regimes in which they process materials, advanced manufacturing techniques are frequently associated with nascent physics-based models which may be useful for process and material development. This is a significant limitation because without such models as a guide, trial-and-error methods have to be used to manufacture samples with the desired performance metrics which results in less efficient research and development. Thus, there is a significant need to develop predictive methods that can aid in reducing research and development delays. Specifically, it is critical to develop approaches that can assist with identifying processing parameters that will result in a specific desired set of properties in the components manufactured. We call our framework differential property classification (DPC). A DPC model is designed to distinguish between two sets of process parameters, identifying which (if any) will result in a material with a larger property value. For example, the process parameters for some manufacturing processes may be the temperature at which a material is heated or the pressure that is exerted on it during manufacturing. A property of the resulting material may be ultimate tensile strength (UTS). In such an example, DPC would help the experimenter identify those temperature and pressure values that will result in a material with high (or low) UTS. Of course, a DPC model is specific to a particular manufacturing technique, a particular material system, and a particular property Y . It takes as input two sets of manufacturing processing parameters A and B and as output, it provides a prediction of whether (1) processing parameters A will yield a material with higher property Y than processing parameters B, (2) processing parameters B will yield a material with higher property Y than processing parameters A, or (3) the processing parameters A and B will yield a

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Fig. 1 A schematic of the DPC model. DPC helps an experimenter choose between possible processing parameters for a manufacturing process

material with approximately the same value for property Y (see Fig. 1). The idea is that when deciding between a range of possible experiments to run, the experimenter can use DPC to select the set of processing parameters that optimizes for the desired property. The motivation for translating what might otherwise be a standard regression problem (“what is the value of property Y for sample produced using process parameters A?”) into a 3-way classification problem comes from two observations. The first observation is that there is frequently only a limited amount of data associated with advanced manufacturing processes. Classification problems often require less data to achieve an acceptable level of accuracy than regression problems do. If one can solve a problem in an easier classification setting as opposed to a more challenging regression setting, then one should choose the former as a starting place for developing a framework to eventually address the latter. The second related observation is that in designing experiments in the materials and manufacturing domain, identifying the relative performance of materials produced from a range of candidate process parameters is of good value even though identifying the exact material properties that will result from the process regime is the ultimate goal for a model. This is especially true in the case where the former can be done with strong accuracy, while the latter cannot due to the size of the dataset. This means building a DPC model that achieves high accuracy instead of a regression model whose performance is less satisfactory. We demonstrate the effectiveness of DPC on an advanced manufacturing dataset consisting of the process conditions/mechanical property measurements from 20 experiments performed to synthesize AA7075 tube synthesis using Shear Assisted Processing and Extrusion (ShAPE) [3, 4]. We explore a range of different model types and training regimes, highlighting those that result in the best performance. We also analyze our model with respect to variable amounts of training data, showing that DPC models are relatively robust even when only small amounts of data are available. This is an important property since the purpose of DPC is to guide experimentation and thus our assumption should always be that DPC will be used in situations where little data currently exists.

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The DPC Framework and Model The DPC framework involves translating a regression problem into a classification problem on pairs of process parameters. Suppose that X is the set of all possible process parameters for a given manufacturing process, Y = R is the set of all posk1 is a process sible material property values for a given property, Dt = {(xit , yit )}i=1 k 2 parameter/property regression training set, and De = {(xie , yie )}i=1 the corresponding regression test set. We choose some t ∈ R which will be the threshold we use to identify whether two property values y1 and y2 are “different”. The DPC test set associated with this task is e = {(xie , xie , z i1 ,i2 ) | 1 ≤ i 1 , i 2 ≤ k2 , z i1 ,i2 ∈ Z } D 1 2

(1)

where Z = {0, 1, 2} are the classes and

z i1 ,i2

⎧ e e ⎪ ⎨1 if yi1 − yi2 > t, e = 2 if yi2 − yie1 > t, ⎪ ⎩ 0 if |yie1 − yie2 | < t.

(2)

The latter case, where the absolute difference between yi1 and yi2 is less than t can be interpreted as describing when yi1 and yi2 are sufficiently close so as to be treated as the “same”. This could be because property measurements are noisy or because two measurements might as well be the same from a practical standpoint. We can build a validation or training set in a manner analogous to that described above. e , has been constructed, we choose an ML model capable of Once a test set, D doing 3-way classification. The DPC framework is agnostic to the particular model architecture and different model types may be preferable depending on the nature of the data. Since we were working with relatively low-dimensional data, our experiments in this paper used eXtreme Gradient Boosting (XGBoost) [5], a tree-based boosting algorithm, and a simple feed-forward neural network. Training can be done by training a backbone model to do regression and then inserting it into the DPC framework, by training a DPC model to do classification directly, or by some combination of the two. The choice of t should largely be driven by the application. If t is too small, pairs of process parameters that do not actually result in meaningfully different material properties will be labelled as if they do. If t is too large, legitimately different property values may be grouped as if they were the same. Furthermore, as t changes the class balances will shift. In the experiments below, we frequently chose t to be some fraction of the standard deviation of property values, for example 1% of standard deviation.

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Experiments We trained and evaluated our DPC models on the process and mechanical property data obtained from the synthesis of AA7075 tubes using ShAPE [3, 4]. Detailed description of the manufacturing approach and property characterization can be found elsewhere.

The Training and Test Sets The dataset that we used for training and testing is comprised of 20 distinct ShAPE experiments, each resulting in a single AA7075 tube. Some process parameters such as power, torque, tool position with respect to billet, extrusion force, and extrusion temperature were measured continuously (every .01 seconds) over the course of the ShAPE experiment resulting in time series data. Others such as heat treatment time are available as discrete data points. Mechanical properties were measured for samples obtained from (on average) 10 locations along the length of an extruded tube. Since there are in general many more process parameter measurements than material property measurements, the size of our dataset is limited by the number of material properties that were measured. We split our dataset at the level of an individual experiment into 75% (15 experiments) for the training set Dt and 25% (5 experiments) for the test set De . Note that since process parameters and properties measured across the tube produced in a single experiment are frequently similar, if we were to mix measurements from a single experiment between training and test sets we would risk the models memorizing characteristics particular to each experiment. We constructed a corresponding clase following description (1). This involved generating all possible sification test set D pairs of process parameter/property data points from De resulting in 1600 pairs in e . We also generated the new labels from Z . For one of our models, we generated a D t from Dt for training. For all experiments in the paper, we used a classification set D threshold t equal to 1% of the standard deviation of measurements for the particular property value.

Models and Training The backbone models we used in our experiments differed along two dimensions: model architecture and model type. By model architecture, we mean the base learning algorithm underlying the DPC model. We explored two of these. The first is a multilayer perceptron (MLP), i.e., a vanilla feed-forward neural network with fully connected layers and nonlinearities. All of our MLPs were trained using the Adam optimizer with a learning rate of 0.009. While we experimented with other network

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architectures, the primary one that we used across several experiments has 3 layers including a hidden layer of dimension 35. We used ReLU nonlinearities in all cases. The second model architecture we tested was an XGBoost decision tree model that was trained with a max depth of 6 and 1000 estimators at a 0.1 learning rate. We used Pytorch [6] to implement the MLP. We explored three different backbone model types. The first, which we call a direct regression model, takes a regression model f : X → Y that has been trained e , on Dt and use it to predict values from Z . That is, for input pair (x1 , x2 , z) ∈ D we calculate f (x1 ) and f (x2 ) and predict z based on their values in accordance with (2). The second backbone model type we explored, which we call the difference regression model, is trained so that for given input (x1 , y1 ) ∈ Dt and (x2 , y2 ) ∈ Dt , model f : X × X → Y predicts the difference y1 − y2 . This difference prediction can again be used to predict a value from Z via (2). The final model type that we explored was a direct classification model. Models of this type take concatenated pairs of process parameters from (x1 , y1 ) and (x2 , y2 ), and predict the corresponding label from Z directly. Note that all of these model types use different forms of the training set. Direct regression models are trained on Dt . On the other hand, difference regression models are trained on a derivation of Dt which is constructed from pairs of process parameters. The target value in this case is material property differences. The direct t , which is constructed from Dt analogously to classification models are trained on D what is outlined in (1) and (2). Direct regression and difference regression models are trained with respect to mean squared error (MSE), while direct classification models are trained with cross entropy.

Results and Discussion We begin by evaluating the performance of the two different architectures underlying our DPC models (MLPs and XGBoost models). Table 1 contains the accuracies for e . We include a direct regression backbone version of each model on the test set D 95% confidence intervals for the MLP which had more variable performance based

Table 1 The accuracy of both DPC models (MLP and the XGBoost model) on the test sets for different material properties. We include 95% confidence bounds which are calculated over 5 random weight initializations of the MLP MLP XGBoost Max load UTS Yield strength

77.00 ± 3.0 88.00 ± 1.0 79.00 ± 1.0

87.81 89.00 82.94

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Table 2 DPC accuracy values for different backbone model types: direct regression, difference regression, and direct classification. The first two models were trained for a regression task, while the last was only trained for DPC prediction. All backbone models use XGBoost, our best performing architecture (see Table 1) Max load UTS Yield strength Direct reg. Difference reg. Classification pred.

86.12 84.56 87.81

90.00 86.50 89.00

79.00 77.00 82.00

on the random weight initialization. These intervals were calculated over 5 different random initializations. We see that the XGBoost model achieves consistently better performance than the MLP for each of the three material properties that we evaluated. Particularly striking is the comparison between the XGBoost and MLP model performance predicting which process parameters would result in a material with greater max load. In this case, the XGBoost model achieves accuracy almost 10% better than the MLP. We hypothesize that the XGBoost model’s superior performance arises from it being a simpler model that is less likely to overfit the small training sets that were used. We next compared the different backbone model types (direct regression, difference regression, and direct classification) that were described in the Models and Training section. Results from our experiments are shown in Table 2. We see that overall, direct regression and direct classification appear to perform similarly with both methods delivering comparable accuracy on the three different properties. Also, difference regression consistently underperformed relative to the other two methods. We believe that there are two factors at play here. On the one hand, models trained on the regression task are exposed to additional information that models trained only on classification are not. On the other hand, the direct classification model has been optimized for the final task that it will be evaluated on, whereas the direct regression model is optimized for a different (though related) task. Finally, given that DPC was developed to be able to work in low-data environments, we wanted to explore how DPC accuracy changes as the number of experiments available for training changes. In Fig. 2, we plot the accuracy of a DPC model that uses an XGBoost direct regression backbone model on the fixed test set as a function of the number of experiments in the training set. Recall that each experiment contributes (roughly) 10 process parameter/property pairs to the training set. We see that even in the ultra-low data regime of 5 experiments, the model still achieves reasonable accuracy of 80%. The model’s performance continues to improve, reaching 90% at 15 experiments. The amount of variability also decreases significantly as can be seen by the error bars that represent multiple runs over random subsets of the training set.

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Fig. 2 A comparison of DPC accuracy (for an XGBoost direct regression backbone model) on the test set based on the number of experiments in the training set. Recall that there are 15 experiments, and each experiment provides around 10 process parameter/property pairs for the training set. We created error bars by randomly sampling and then training on 5 different size k subsets for each of k = 3, 5, . . . , 15

Conclusion In this work, we presented a new framework, differential property classification (DPC), to aid in experiment planning in advanced manufacturing. DPC is designed to handle one of the persistent challenges of working with machine learning in the field of advanced manufacturing: limited amounts of data. Through our experiments using real ShAPE data, we showed that DPC can yield helpful predictions even when very few experiments have already been run. We believe that this represents another step toward the larger goal of leveraging data-driven methods to improve the efficiency of the advanced manufacturing research and development cycle. Declarations The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements KSK thanks Scott Whalen, Md. Reza-E-Rabby, Tianhao Wang, Scott Taysom, and Timothy Roosendaal for their insights into ShAPE AA7075 tube manufacturing and property determination. KSK is grateful for the support from Luke Gosink, Elizabeth Jurrus, and Sam Chatterjee for this project. This work was performed using the resources available at the Pacific Northwest National Laboratory (PNNL) and funded by the Mathematics for Artificial Reasoning in Science (MARS) Initiative as a Laboratory Directed Research and Development Project. PNNL is a multi-program national laboratory operated by Battelle Memorial Institute for the U.S. Department of Energy under contract DE-AC05-76RL01830.

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References 1. Silver D, Schrittwieser J, Simonyan K, Antonoglou I, Huang A, Guez A, Hubert T, Baker L, Lai M, Bolton A et al. (2017) Mastering the game of go without human knowledge. Nature 550(7676):354–359 2. Arinez JF, Chang Q, Gao RX, Xu C, Zhang J (2020) Artificial intelligence in advanced manufacturing: current status and future outlook. J Manufact Sci Eng 142(11). https:// asmedigitalcollection.asme.org/manufacturingscience/article-pdf/142/11/110804/6594922/ manu_142_11_110804.pdf. https://doi.org/10.1115/1.4047855.110804 3. Whalen S, Reza-E-Rabby M, Wang T, Ma X, Roosendaal T, Herling D, Overman N, Taysom BS (2021) Shear assisted processing and extrusion of aluminum alloy 7075 tubing at high speed. In: Light metals 2021, pp 277–280 4. Whalen S, Olszta M, Reza-E-Rabby M, Roosendaal T, Wang T, Herling D, Taysom BS, Suffield S, Overman N (2021) High speed manufacturing of aluminum alloy 7075 tubing by shear assisted processing and extrusion (shape). J Manuf Process 71:699–710. https://doi.org/10.1016/ j.jmapro.2021.10.003 5. Chen T, Guestrin C (2016) Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd international conference on knowledge discovery and data mining, pp 785–794 6. Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga L et al (2019) Pytorch: an imperative style, high-performance deep learning library. Adv Neural Inf Process Syst 32:8026–8037

Multi-faceted Uncertainty Quantification for Structure-Property Relationship with Crystal Plasticity Finite Element Anh Tran, Pieterjan Robbe, and Hojun Lim

Abstract The structure-property linkage is one of the two most important relationships in materials science besides the process-structure linkage, especially for metals and polycrystalline alloys. The stochastic nature of microstructures begs for a robust approach to reliably address the linkage. As such, uncertainty quantification (UQ) plays an important role in this regard and cannot be ignored. To probe the structure-property linkage, many multi-scale integrated computational materials engineering (ICME) tools have been proposed and developed over the last decade to accelerate the material design process in the spirit of Material Genome Initiative (MGI), notably crystal plasticity finite element model (CPFEM) and phase-field simulations. Machine learning (ML) methods, including deep learning and physicsinformed/-constrained approaches, can also be conveniently applied to approximate the computationally expensive ICME models, allowing one to efficiently navigate in both structure and property spaces effortlessly. Since UQ also plays a crucial role in verification and validation for both ICME and ML models, it is important to include UQ in the picture. In this paper, we summarize a few of our recent research efforts addressing UQ aspects of homogenized properties using CPFEM in a big picture context. Keywords ICME · CPFEM · Uncertainty quantification · Machine learning · Microstructure · Monte Carlo · Materials variability · Polynomial chaos expansion · Stochastic collocation · DAKOTA · DREAM.3D · DAMASK

A. Tran (B) Scientific Machine Learning, Sandia National Laboratories, Albuquerque, NM, USA e-mail: [email protected] P. Robbe Plasma and Reacting Flow Science, Sandia National Laboratories, Livermore, CA, USA e-mail: [email protected] H. Lim Computational Materials and Data Science, Sandia National Laboratories, Albuquerque, NM, USA e-mail: [email protected] © The Minerals, Metals & Materials Society 2023 The Minerals, Metals & Materials Society, TMS 2023 152nd Annual Meeting & Exhibition Supplemental Proceedings, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-031-22524-6_53

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Introduction The structure-property linkage is one of the two most important relationships in materials science besides the process-structure linkage, especially for metals and polycrystalline alloys. The stochastic nature of microstructures begs for a robust approach to reliably address the linkage. As such, uncertainty quantification (UQ) plays an important role in this regard and cannot be ignored. To probe the structureproperty linkage, many multi-scale integrated computational materials engineering (ICME) tools have been proposed and developed over the last decade to accelerate the material design process in the spirit of Material Genome Initiative (MGI), notably crystal plasticity finite element model (CPFEM) and phase-field simulations. Machine learning (ML) methods, including deep learning and physics-informed/constrained approaches, can also be conveniently applied to approximate the computationally expensive ICME models, allowing one to efficiently navigate in both structure and property spaces effortlessly. Since UQ also plays a crucial role in verification and validation for both ICME and ML models, it is important to include UQ in the picture. In this paper, we summarize a few of our recent research efforts addressing UQ aspects of homogenized properties using CPFEM in a big picture context. Figure 1 describes an seamless integration UQ/optimization workflow for crystal plasticity finite element with microstructure reconstruction pipeline, featuring DAKOTA [6], DREAM.3D [10], and DAMASK [21] with PETSc/TAO [4] as the spectral solver. Given a set of microstructure descriptors, for example, grain size and orientation distribution functions with crystallographic texture, one can employ DREAM.3D [10] to generate a statistically equivalent ensemble of representative volume elements (RVEs). DAMASK [21] can then be used, along with PETSc/TAO [4], to perform massively parallel CPFEM simulations on high-performance computers. It is worthy to note that all of these packages are publicly available, as of the moment, to support a wide range of research activities. Using the available computational CPFEM results, the parameters-to-observables map is then constructed in a data-driven manner, where the parameters are associated with microstructure descriptors, and the observables are associated with unbiased homogenized materials properties. Finally, DAKOTA [6] wraps the integrated DREAM.3D and DAMASK to efficiently sample the parameters space and provide a robust estimator for the observables, tailoring specific problems of interest. Depending on the problem, different UQ algorithms can be utilized for the optimal performance in terms of computational cost so that the problem of interest can be solved appropriately. To this end, in this paper, we summarize some of our recent research efforts in addressing homogenization problems in CPFEM, with an emphasis on efficiency and robustness of the UQ algorithms to investigate homogenized materials property while accounting for variability in microstructures. The organization of the paper is as follows. Section “Inferring Grain Distribution from Property Observables” describes

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Fig. 1 A publicly available seamless UQ/optimization integrated workflow with DAKOTA [6], DREAM.3D [10], and DAMASK [21] with PETSc [4] as the spectral solver

a stochastic inverse approach to infer a grain size distribution given a deterministic or stochastic parameters-to-observables map in a data-consistent manner. In this case, the observable is the yield strength, which subsequently brought us to the remarkable Hall-Petch relationship. The parameters-to-observables map is constructed using a ML approach known as the Gaussian process regression, which allows efficient rejection-sampling scheme. Section “Toward Mesh-Independent Robust Estimator of Homogenized Materials Properties” describes a multi-level/multi-index Monte Carlo approach that efficiently provide an unbiased estimator for the observables with multiple mesh-resolution. The main idea of this approach is to estimate the observables at high-fidelity levels (i.e. fine mesh) with a cheaper computational cost at low-fidelity levels (i.e. coarse mesh), by exploiting the correlation between high-fidelity and low-fidelity observables. Section “Constitutive-Model-Form Error” describes a stochastic collocation approach to quantify the uncertainty associated with commonly used constitutive models in CPFEM, namely phenomenological and dislocation-density-based constitutive models, which is the stochastic forward problem (as opposed to the stochastic inverse problem in section “Inferring Grain Distribution from Property Observables”). This approach relies on a combination

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of Smolyak sparse grid and polynomial chaos expansion, also known as stochastic collocation approach, to efficiently provide the probability density function (pdf) of the observables, given the pdf of the parameters. Sobol indices can also be applied to rank the influence of different constitutive model parameters. This approach is arguably efficient on high-dimensional spaces with many constitutive model parameters thanks to the nested structure of Smolyak sparse grid. Section “Discussion and Conclusion” discusses and concludes the paper.

Inferring Grain Distribution from Property Observables In this study [28], we solve the stochastic inverse problem for structure-property linkage. The problem statement is as follows: given a deterministic structure-property linkage, for instance, Hall-Petch relationship, and given a distribution of the observables, we are interested in inferring the distribution of parameters, so that the pushforward map of the parameters distribution are consistent in the materials properties space. Figure 2 illustrates the concept of the stochastic inverse and stochastic forward problems in structure-property linkage. In order to solve the stochastic inverse problem, we need an efficient parametersto-observables approximation Q(λ) that allows a large number of sampling. ML models come as a natural choice, and in this work, we employed the Gaussian process regression with heteroscedastic noise to account for different variances at different grain sizes. To construct Q(λ), we vary the grain size parameter, μ D , in DREAM.3D and adjust the average grain size D in the DAMASK constitutive model accordingly. Figure 3 illustrates the workflow coupling DAMASK and DREAM.3D, first demonstrated by Diehl et al. [7]. The observable is the offset yield stress σˆ Y at ε = 0.002 under uniaxial tension with ε˙ 11 = 0.001 s−1 . An ensemble of 25 RVEs, each representing a 64 µm × 64 µm × 64 µm physical domain, is generated through DREAM.3D, where the CPFEM simulation is performed on a 64 × 64 × 64 grid. The ensemble average of the observable is estimated in a Monte Carlo manner, −1  NRVE =25 (i) σˆ Y . σY ≈ NRVE i=1

Fig. 2 Schematic illustrating the relationship between the stochastic forward and stochastic inverse problems. In the context of structure-property relationships, the stochastic forward problem assumes that the distribution of inputs is known and seeks to solve for the distribution of outputs, while the inverse problem assumes a target distribution on the outputs is known and seeks a distribution on input that propagates forward to the target

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Fig. 3 Constructing machine learning models for structure-property relationship for homogenization problems

up

init (λ) = U (0.25, 2.75), and updated density, π (λ), on Fig. 4 On the left: The initial density, π  obs = the microstructure feature λ = μ D . On the right: The target density on material properties, πD N (540, 10), and push-forwards of the initial and updated densities. (Reprinted with permission from [28] ©2020 The Minerals, Metals & Materials Society)

Figure 4a shows a uniform prior and updated prior (obtained after solving the stochastic inverse problem). Figure 4b shows a comparison between the push-forward of initial density (black dot dashed line), the target distribution N (540, 10) (red dashed line), and the push-forward of the updated density (blue solid line). It is observed that the push-forward of the updated density agrees very well with the target density, and this verifies the solution of the stochastic inverse problem. Additionally noted in our previous study, since the Hall-Petch relationship is monotonic, bijective (i.e. one-to-one and onto), the stochastic inverse solution is unique in this case.

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Fig. 5 A sequential mesh coarsening for a fixed microstructure ω at various  mesh-resolution or mesh level of fidelity (h-refinement), with  = 0 being the lowest level of fidelity and  = 4 being the highest level of fidelity

Toward Mesh-Independent Robust Estimator of Homogenized Materials Properties The second research thrust focuses on multiple mesh-resolution and multiple constitutive models underpinning CPFEM. It is often the case that the size and meshresolution of the RVE is limited due to prohibitive computational cost, especially when one needs an ensemble of RVEs to estimate a quantity of interest, which further exacerbates the problem. Imposing periodic boundary conditions on the RVE is a convenience modeling assumption, but it also widens the gap between simulation and reality. To this end, a class of advanced Monte Carlo methods [8, 9, 12, 13, 19, 20] seems to be a naturally good fit for two reasons. First, the dimensionality of microstructure is high-dimension: one can imagine each cell is associated with a set of three Euler angles, a specific phase, and the number of cells scales as N 3 , as shown in Fig. 5. As such, Monte Carlo approach is an appropriate numerical tool because it is dimensionally independent and only depends on the number of samples. Indeed, it turns out that the classical ensemble average, which has been widely used in the CPFEM literature, is the vanilla Monte Carlo estimator, which is the simplest in this case. Second, by interpreting the coarse-mesh as low-fidelity and fine-mesh as high-fidelity, one can enumerate the fidelity, and fuse the information across multiple fidelity levels accordingly. Figure 5f shows an example of multi-level Monte Carlo estimator with five different mesh-resolution, 83 (Fig. 5a), 163 (Fig. 5b), 203 (Fig. 5c), 323 (Fig. 5d), and 643 (Fig. 5e). Numerous mesh studies have been conducted in the CPFEM literature, yet we are not aware of any idea regarding adaptively fusing multiple predictions across a wide range of mesh.

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Fig. 6 Required number of samples N at each level  = 0, 1, . . . , 4 to reach a target accuracy of ε(k) , k = 1, 2, . . . , 10, in the MLMC experiment. On levels  = 0, 1 and 2, a minimum number of warm-up samples N = 10 is imposed to get an initial estimate of the variance of the multi-level differences V[ Q  ]. For the target tolerance εtarget , only two evaluations of the high-fidelity model are required

Figure 6 shows the number of RVE samples for α-Ti required at different fidelity levels  = 0, 1, . . . , 4 to reach a user-defined tolerance. In this case, the observable is the effective homogenized Young modulus with the phenomenological constitutive models proposed in [33, 34].

Constitutive-Model-Form Error This research effort [29] focuses on solving stochastic forward problem with a specific microstructure RVE, where constitutive model parameters are treated as random variables. In this study, we impose a uniform prior for 16 constitutive parameters, where the phenomenological constitutive model and corresponding default parameters are adopted from [22, 23] (Tables 7 and 8), [2, 31, 32]. The stochastic collocation method is used with a Smolyak sparse grid of level 2 and polynomial chaos expansion with Legendre polynomial basis, which results in 577 simulations. An example of nested Smolyak sparse grid in 2-dimensional is shown in Fig. 7 with Gaussian abscissas. Figure 8 shows the equivalent stress-strain curve with 577 sets of constitutive model parameters. Using the stochastic collocation approach, the pdf’s of εY and σY are quantified in Fig. 9a and b, respectively. Based on the stochastic collocation results, Sobol indices global sensitivity analysis is conducted to investigate the sensitivity of 16 constitutive model parameters. Ranking from the most influential parameters to the least influential parameters for εY from the Sobol indices for main effects, Tτ0,basal = 0.5668, Tτ0,C2 =

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Fig. 7 Comparison of 2D Smolyak nested sparse grids at various level , 1 ≤  ≤ 5, with the number of abscissas varies at 5, 17, 49, 97, 161, respectively, using Gaussian abscissas for quadrature

Fig. 8 Equivalent εvM − σvM plots for hcp Mg

0.4772, Tn tw = 0.2439, Th s−s = 0.1021, Tτ∞,basal = 0.07249, Tτ0,pyra = 0.6131, Tn s = 0 0.02082, Tτ∞,pyra = 0.01091. Ranking from the most influential parameters to the least influential parameters for σY from the Sobol indices for main effects, Tτ0,C2 = = 0.3729, Tn tw = 0.3684, Tτ0,basal = 0.3566, Tτ0,pyra = 0.1181, Tτ∞,basal = 0.1064, Th s−s 0 0.1061, Tτ0,pris = 0.03861. Compared to Sedighiani et al. [22, 23], our analysis shows some agreements, but mostly differ in the set of sensitive parameters. Possible explanations are due to (1) different quantities of interest and (2) methodological approach: Sedighiani et al. [22, 23] studies are conducted based on ANOVA, whereas our approach relies on global sensitivity analysis with Sobol’ indices.

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Fig. 9 Quantifying uncertainty associated with εY and σY for hcp Mg

Discussion and Conclusion In this paper, we summarize some of our recent efforts in quantifying uncertainty with CPFEM for structure-property linkage. Because microstructure embeds nearly all the information, including manufacturing, thermo-chemical-mechanical [3], it can be considered as one of the most complicated yet exciting topics in materials science. Intrinsically noisy, sparse/scarce, dynamically stochastic, and highdimensional, microstructure is arguably an ultimate advanced testbed for state-ofthe-art UQ techniques. Comprehensive reviews of UQ within the ICME and materials design context can be found in McDowell [17], Panchal et al. [18], Kalidindi et al. [15], Honarmandi and Arróyave [14], Acar [1]. While there have been numerous UQ research done in the past for materials science, there remains numerous challenges and opportunities in the future, especially with the emerging trend of machine learning and computer vision. In this big picture, CPFEM has been and remains an important computational tool to link structure and property. Because of the inherent uncertainty associated with microstructure, including both aleatory and epistemic uncertainty, it is our envision that UQ would play an integrated capability to solve stochastic forward and inverse problem for structure-property linkage. Furthermore, the stochastic nature of microstructures makes it appropriate to treat variables associated with microstructures or microstructure descriptors as random variables that would be described by a pdf, e.g., grain size, grain area, chord-length, instead of deterministic variables. As such, inverse problems in process-structure linkage more often can be cast as deterministic optimization with noisy observables [26, 27] using, for example, Gaussian process and Bayesian optimization [5, 16, 24, 25, 30, 36], while inverse problems in structure-property linkage may require a Bayesian inference [28] or variational inference approach [35] along with Markov chain Monte

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Carlo sampling [11]. However, there are numerous technical challenges remain to be solved in future works. Acknowledgements This article has been authored by an employee of National Technology and Engineering Solutions of Sandia, LLC under Contract No. DE-NA0003525 with the U.S. Department of Energy (DOE). The employee owns all right, title and interest in and to the article and is solely responsible for its contents. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this article or allow others to do so, for United States Government purposes. The DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan https://www.energy.gov/downloads/doe-public-access-plan. This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.S. Department of Energy or the United States Government.

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Prediction of Cutting Surface Parameters in Punching Processes Aided by Machine Learning A. Schenek, M. Görz, M. Liewald, and K. R. Riedmüller

Abstract Punching represents one of the most frequently used manufacturing processes in the sheet metal processing industry. As an important quality criterion for shear cutting processes, the geometric shape of the cutting surface is considered. In this regard, the edge draw-in height, the clean cut proportion, the fracture surface height, and the burr are relevant parameters for monitoring the production quality in punching processes. These parameters can easily be measured in shear cutting processes with an open cutting line (e.g. using laser triangulation). For processes with a closed cutting line, however, such a measurement is often not possible due to the limited accessibility. The present paper therefore proposes a machine learning approach, which enables a data-driven prediction of cutting surface parameters based on measurable process data. The new approach presented in this paper is to pre-train a neural network on numerically determined cutting force curves. As an output, the neural network predicts the mentioned quality parameters of punched sheet metal component edges. The output of the numerically pre-trained neural network is evaluated for numerically and experimentally determined process data and cutting surface parameters. Keywords Cutting surface quality · Machine learning · Punching force

A. Schenek (B) · M. Görz · M. Liewald · K. R. Riedmüller Institute for Metal Forming Technology, University of Stuttgart, Holzgartenstr. 17, 70174 Stuttgart, Germany e-mail: [email protected] M. Liewald e-mail: [email protected] © The Minerals, Metals & Materials Society 2023 The Minerals, Metals & Materials Society, TMS 2023 152nd Annual Meeting & Exhibition Supplemental Proceedings, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-031-22524-6_54

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Introduction and State of the Art Almost every sheet metal component needs to be trimmed and punched along its production chain [1]. Here, important features to evaluate the quality of the trimmed and punched components are the edge draw-in height (he ), clean cut height (hs ), fracture surface height (hb ), and burr height (see Fig. 1) [2]. For a better comparability between different sheet metal thicknesses, these parameters are additionally specified as relative quantities in relation to the sheet metal thickness (t). These relative quantities are the edge draw-in height proportion (KEA), clean cut proportion (GSA), fracture surface height proportion (BFA), and burr height proportion (Burr). While these surface parameters can easily be measured in shear cutting processes with an open cutting line (e.g. using optical measurement systems) [3], a direct measurement is often not possible for punching processes with a closed cutting line due to the limited accessibility. For such inaccessible processes, however, the origin of the characteristic cutting surface parameters can be qualitatively explained on the basis of the punch force–displacement curve [4]. In general, the (pure) shearing of sheet metal materials can be divided into three typical phases referring to its punch force–displacement curve (see Fig. 2) [4]. Phase 1 mainly includes not only the elastic deformation of the sheet metal material, but also the elastic deformation of the punch, the cutting tool, and the elastic expansion of the press. Phase 2 contains the plastic flow of the sheet material. In this phase, the cutting force reaches its maximum Fs max . The edge draw-in is completely formed out in phase 2 and the plastic flow of the sheet material causes the formation of the clean cut proportion. In the beginning of phase 3, the slug and the sheet metal grid are separated from each other due to progressive punch movement. Due to the material separation, phase 3 is characterised by an instant decrease in the cutting force–displacement curve. A commonly known feature to detect the moment of material separation from punching force curves is the cutting length ls . In addition to the maximum cutting force (Fs max ) and the cutting length (ls ), the cutting work (W) summarizes information on all three phases by integrating the punch force over the punch stroke (S). The cutting surface features obtained at the end of the punching process and the corresponding force–displacement curves depend on a large variety of parameters.

hE

t

hS hB hG

KEA [%] = 100% * h E /t (Edge draw-in height proportion) GSA [%] = 100% * h S /t (Clean cut proportion) BFA [%] = 100% * h B /t (Fracture surface height proportion) Burr [%] = 100% * h G /t (Burr height proportion) t [mm]

= Sheet metal thickness =[

,

,

,

]

Fig. 1 Cutting surface appearance (left) [2] and feature vector of normalized cutting surface parameters (right)

Prediction of Cutting Surface Parameters in Punching Processes Aided … Force F Phase 1

Phase 2 Downholder F

Phase 1 Phase 2

Phase 3

Punch DownPunch F holder Sheet metal

Punch

Die

Elastic deformation Plastic deformation Stroke S

=[

Phase 3 Punch

Downholder

F

Sheet metal Die

W = ∫0

609

, ,

Sheet metal

Die

Fracture

, ]

Fig. 2 Phases of a punching process according to [2] and feature vector of measurable process data

Regarding the sheet metal material, the sheet metal thickness and the mechanical properties of the sheet metal material show a major effect on the cutting force curve and the producible cutting surface quality [5]. In previous investigations, it was therefore possible to inversely determine material parameters such as tensile strength, yield strength, uniform strain, elongation at break, and hardening exponent from cutting force curves using an artificial neural network [6]. In terms of tool parameters, the clearance and the edge geometry of the punch and die significantly influence the punching process. In this regard, sensor data from cutting force curves [7–10] or strain measurements from the machine or the punch [11, 12] have already been considered for detecting the wear condition of a cutting tool. Asahi et al. [13], for example, show that modern machine learning algorithms enable a classification of different tool wear states on the basis of measured displacement and cutting force curves. From an analysis of the sensor data, it turned out that autoencoders can distinguish between the three defined wear states “new”, “half worn”, and “worn”. Regarding the cutting surface features, Djavanroodi et al. [14] as well as Stanke et al. [15] predicted the edge draw-in on a fine blanked workpiece depending on the V-ring geometry, the blank holder force, and the counterpunch force using an artificial neural network model [14]. Since artificial neural networks require big amounts of training data, they used numerical simulation data for the training of the neural network. Al-Momani et al. [16] developed a model for the prediction of burr height in a shearing process using artificial neural networks and multiple regression analysis. Based on numerically determined punch and counterpunch forces, Mao et al. successfully predicted clean cut heights of fine blanked parts also using an artificial neural network [17]. Summarizing these findings, an analysis of cutting force curves seems to be a promising approach to indirectly determine cutting surface features from punching forces. Since the mentioned literature is either limited to single cutting surface features or investigates only a limited amount of different sheet metal materials or sheet metal thicknesses, the objective of this paper is to clarify the following research question: Do cutting force–displacement curves provide sufficient information for a data-based prediction of multiple cutting surface parameters?

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Approach for Developing a Data-Driven Prediction Model for Shear Cutting Surface Parameters The proposed approach to predicting cutting surface features from punching force features is to first (numerically) generate a large variance of different cutting force curves by finite element simulation. Afterwards, the corresponding cutting force features and the sheet metal thickness (= measurable process data) are used as inputs for a neural network. As a result, the neural network is trained on the numerically determined cutting surface features. The numerically pre-trained neural network subsequently has to be tested for its applicability to real measured process data. For the present investigations on this approach, a newly developed concave punch nose design (CPND) was used to generate a sufficiently large variance of cutting force curves (see Fig. 3). The idea of the CPND is to vary the punch tip geometry in order to induce compressive stresses in the shear-affected zone [18]. The increase in compressive stresses is achieved by a small web width (SB) attached to the cutting punch as well as a corresponding web angle (SW) and leads to an increase of clean cut proportions and decrease of the edge draw-in height (see Fig. 3). CPND seemed to be a promising process to investigate the formerly formulated research question, since these changes of cutting surface features are commonly considered as a quality increase of cutting surface quality and since CPND shows mayor effects on cutting force curves. Furthermore, CPND offers the possibility to generate totally different cutting force curves and cutting surface features even if other tooling parameters such as sheet metal thickness or clearance are kept constant. Figure 3 summarizes the chosen approach. The prediction strategy was developed for measurable process data, whereby the feature vector of measurable process data consisted of the maximum punching force, cutting length, cutting work, and the sheet metal thickness. Tooling parameters

Measurable Process Data & Features

affecting

Force

Stroke

DP600 – Simulation • t = 1mm • u = 15% • SB = 0.15mm • SW = 75°

w

Die

SB

DP600 – Simulation • t = 1mm • u = 15% • SB = 5.0mm • SW = 0° (Conv. Punching)

w affecting

SW

Downholder

Punch

Cutting Surface Features

Force

d/2

Stroke =[

, ,

, ]

=[

,

,

,

]

Prediction possible?

Fig. 3 Approach to investigate the impact of measurable process data on the features of cutting surfaces

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611

Investigated Sheet Metal Materials and Simulation Model Setup Three different sheet metal materials (DC03, DP600, and DP800) were considered for the investigations presented in this paper. The yield curves of these sheet metal materials were determined from experimental tensile tests and subsequently extrapolated using the Hockett–Sherby approach. Figure 4 summarizes the Hockett–Sherby fitting parameters and the tensile strengths of the investigated sheet metal materials. Figure 3 (left) shows the geometric structure of the simulation model with the correspondingly varied cutting parameters. Mesh windows were used for a local mesh refinement along the shear-affected zone. In order to keep the calculation times short, the punch, die, and down holder were modelled as rigid bodies. Simulation setup was performed using the simulation software DEFORM 2D, which has been proven to be suitable for precise numerical calculation of cutting and punching processes [19]. The fracture modelling necessary for a punching simulation was realized by the “Normalized Cockroft Latham” (NCL) criteria, which is commonly used for scientific research [20] and industrial investigations on shear cutting processes [21]. This damage model is defined as a function of maximum principal stress (σ∗) normalized with effective stress (σv). The material constant C (see Eq. 1) represents the amount of ductile energy that can be applied to the sheet metal material until fracture occurs, where dϕ is the increment of plastic strain and ϕ f is the limiting fracture strain. As soon as the integral exceeds a material-specific limit value, finite elements are deleted in order to simulate fracture formation. ϕ f C= 0

σ∗ dϕ σv

(1)

A constant punch diameter of 10 mm was chosen for all numerical and experimental investigations. The cutting-edge radii of the investigated punches were kept constant at 50 μm in all simulations and experiments. The chosen size of the cutting-edge radii corresponds to current industrial recommendations for punching high-strength

Fig. 4 Experimentally determined flow curves (left) and Hockett–Sherby fitting parameters (right)

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Table 1 Investigated parameter sets (overview) Parameter description Punch diameter

D

Parameter set 1 (used for evaluation purposes)

Parameter set 2 (used for neural network training)

10 mm

10 mm mma

Web width

SB

0.15 & 5.0

Web angle

SW

75° & 0.0°a

0.1–0.5 mm (0.1 mm steps) 45° bis 75° (10° steps)

Punch radius

Rs

50 μm

50 μm

Die radius

RM

20 μm

20 μm

Clearance

u

15%

5, 10, 15%

Sheet thickness

t

1.0 mm

0.5, 1.0, 1.5 mm

Sheet metal material

WS

DC03, DP600, DP800

DC03, DP600, DP800

Height

H

0.8 mm

0.8 mm

a=

conventional punching process

sheet materials [22]. The web width, the web angle, the clearance, the sheet metal thickness, and the sheet metal material were varied within two different parameter sets (see Table 1). Parameter set 1 was initially used for an experimental evaluation of the chosen fracture model (NCL). Two different web widths (0.15 mm and 5.0 mm) and two different web angles (75° and 0°) were investigated within parameter set 1 in order to compare simulation results with experimentally determined cutting surface features and force curve features (section “Correlation Analysis to Identify the Influence of Measurable Process Data on Cutting Surface Features (Parameter Set 2)”). Afterwards, parameter set 2 was used for a full factorial parameter study. The goal of this parameter study (5*4*3*3*3 = 540 simulations) was to generate a sufficiently large database for training the neural network. Only the data from gained from dataset 2 were used to numerically pre-train a neural network. Finally, the experimentally determined data from dataset 1 were applied to the neural network for evaluation purposes.

Evaluation of the Simulation Model Accuracy (Parameter Set 1) Since the prediction accuracy of numerically pre-trained neural networks depends on the quality of the simulation model, the simulation model used in the research work presented here was validated using a conventional shear cutting process (SB = 5.0 mm, SW = 0°). In this context, Fig. 5 shows a comparison of the numerically (Sim.) and experimentally (Exp.) determined cutting surface features and force curve features. The cutting surface features of the experimental specimens were measured by a digital microscope (Alicona Infinite Focus). The cutting force curves were determined by a direct force measurement using a piezo-electric force washer. A

Prediction of Cutting Surface Parameters in Punching Processes Aided … 17.54% DC03 – Experiment • t = 1mm • u = 15% • SB = 5.0mm • SW = 0.0°

41.44% 41.02%

8.78% DP600 – Experiment 26.77% • t = 1mm • u = 15% 64.45% • SB = 5.0mm • SW = 0.0°

DP800 – Experiment • t = 1mm • u = 15% • SB = 5.0mm • SW = 0.0°

DC03 – Simulation • s = 1mm • u = 15% • SB = 5.0mm • SW = 0.0° = 2.2 •

DP600 – Simulation • s = 1mm • u = 15% • SB = 5.0mm • SW = 0.0° = 2.9 •

9.06% 22.58% 68.36%

DP800 – Simulation • s = 1mm • u = 15% • SB = 5.0mm • SW = 0.0° = 2.8 •

613

Feature

Simulation

Experiment

Fsmax [N]

7742.5

8643.16

ls [mm]

0.644

0.742

W [J]

4.484

5.525

Feature

Simulation

Experiment

Fsmax [N]

12954.4

13315.7

ls [mm]

0.389

0.470

W [J]

4.654

5.257

Feature

Simulation

Experiment

Fsmax [N]

16460.6

15903.3

ls [mm]

0.375

0.424

W [J]

5.630

5.562

Fig. 5 Accuracy of the simulation model for conventional punching processes

detailed description of the punching tool setup and the force measuring method was already published in a prior contribution [6]. The left side of Fig. 5 shows a good agreement between the numerically and experimentally determined cutting surfaces under conventional punching conditions. The comparison of the numerically and experimentally force features also shows satisfying results. Since the proposed approach for a prediction of cutting surface features is based on an optimized punch nose design, the accuracy of the simulation model and the chosen fracture model (NCL) also was experimentally evaluated for CPND. Figure 6 shows a comparison of numerically and experimentally determined cutting surface features and force curve features for a CPND (SB = 0.15 mm, SW = 75°). For each investigated parameter constellation, a high agreement between numerically calculated and experimentally determined values could be found. Furthermore, the influence of a CPND becomes clear, when comparing the results from Fig. 5 with the results from Fig. 6. In all investigated cases, a significant increase in clean cut proportions can be observed for CPND. For the investigated sheet metal material DP800, for example, the GSA raises from 22.58 to 50.69%. Knowing that the simulation model and the chosen fracture model (NCL) provide accurate results, parameter set 2 was used for a full factorial parameter study. The goal of this parameter study (540 simulations) was to generate a sufficiently large database for the training of the neural network. In order to summarize the results of this parameter study, a corresponding correlation analysis of the simulation results is presented below at first.

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A. Schenek et al. 7.81% DC03 – Experiment • t = 1mm 65.51% • u = 15% • SB = 0.15mm • SW = 75° 26.68%

DC03 – Simulation • s = 1mm • u = 15% • SB = 0.15mm • SW = 75° = 2.2 •

4.68% DP600 – Experiment 49.00% • t = 1mm • u = 15% • SB = 0.15mm • SW = 75° 46.33%

DP600 – Simulation • s = 1mm • u = 15% • SB = 0.15mm • SW = 75° = 2.9 •

5.36% DP800 – Simulation DP800 – Experiment s = 1mm 50.69% • • t = 1mm • u = 15% • u = 15% • SB = 0.15mm • SB = 0.15mm • SW = 75° • SW = 75° = 2.8° 43.94% •

Feature

Simulation

Experiment

Fsmax [N]

6445.8

6922.1

ls [mm]

0.832

0.909

W [J]

4.746

5.227

Feature

Simulation

Experiment

Fsmax [N]

10463.3

9935.6

ls [mm]

0.678

0.651

W [J]

6.453

5.468

Feature

Simulation

Experiment

Fsmax [N]

13245.2

13561.4

ls [mm]

0.675

0.683

W [J]

8.163

8.231

Fig. 6 Accuracy of the simulation model for punching with a concaved punch nose design

Correlation Analysis to Identify the Influence of Measurable Process Data on Cutting Surface Features (Parameter Set 2) The Pearson correlation matrix is the most common way of analysing linear correlations between multiple variables. The Pearson correlation coefficient (r) varies between −1 and +1 with 0 implying no correlation. Correlation values of −1 or +1 imply an exact linear relationship between two variables. Positive correlation values mean that as variable 1 increase, variable 2 also increases. Negative correlations imply that as variable 1 increases, variable 2 decreases. After a numerical parameter study for parameter set 2, which was used to train a neural network, the correlation matrix shown in Fig. 7 could be determined. Regarding the features of measurable process data (Fsmax , ls , W, t), the following conclusions can be drawn from the correlation matrix: • Higher maximum punching forces (Fsmax ) lead to smaller edge draw-in proportions KEA (r = −0.62), smaller clean cut proportions GSA (r = −0.46), bigger fracture surface height proportions BFA (r = 0.62), and smaller burr height proportions (r = −0.57). • Longer cutting force lengths (ls ) lead to smaller edge draw-in proportions KEA (r = −0.28), increased clean cut proportions (r = 0.68), and smaller fracture surface height proportions (r = 0.62). • Higher cutting works (W) lead to smaller edge draw-in height proportions KEA (r = −0.57). • Bigger sheet metal thicknesses t lead to smaller edge draw-in height proportions KEA (r = −0.59).

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Fig. 7 Correlation matrix for analyzing the numerically determined results for parameter set 2

Since none of these correlations indicates an exact linear relationship, analytical modelling might be difficult for determining cutting surface features from punching force features. A suitable approach for modelling non-linear relations between multiple input and output variables is artificial neural networks. In this regard, the following section shows that artificial neural networks provide good capabilities for predicting cutting surface features from punching forces.

Neural Network Setup, Model Training, and Model Evaluation (Parameter Set 2) For the studies on the predictability of cutting surfaces features from measurable process data features, a fully connected feedforward neural network (FFNN) was used. The neural network setup and the network training were implemented in Python using the machine learning libraries, tensorflow and keras. Figure 8 summarizes the neural network setup used. The neural network consisted of one input layer, seven hidden layers, and one output layer. The features of measurable process data were considered as inputs of the neural network. The output layer of the neural network consisted of four neurons, representing the cutting surface features KEA, GSA, BFA, and Burr. The rectified linear activation function (ReLU) was used for each layer. The activation function, the shown number of hidden layers, and the number of neurons for each hidden layer were determined by a systematical hyperparameter tuning using keras hyperband tuner.

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Hidde n Laye rs

Activation Function

Layer 1: 30 Neurons Layer 2: 45 Neurons Layer 3: 20 Neurons Layer 4: 25 Neurons Layer 5: 15 Neurons Layer 6: 45 Neurons Layer 7: 30 Neurons

reLu

O utputs KEA GSA BFA Burr

KEA GSA BFA Burr

Fig. 8 Neural network setup and activation function

For the training cycles (200 epochs) of the created FFNN, 80% of the data gained from parameter set 2 were used as training dataset. 10% of the data were used as validation dataset, in order to improve the model training throughout the 200 training epochs. The remaining 10% finally were used to test the trained neural network on unseen data (test data). The errors calculated during the training cycles (loss function: mean squared error) were used to incrementally improve the previously randomly initialized weights of the ANN using the Adam optimization algorithm. The progression of the loss function and the mean squared error throughout the training epochs as well as the final prediction results are displayed in Fig. 9. The model shows no overfitting, since the blue and black curves do not diverge over the entire training process of 200 epochs. Accuracy

Prediction based on train data

Mean absolute error

Prediction based on test data

Fig. 9 Learning process and prediction results for the numerically determined data of parameter set 2

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With respect to the training dataset, it is observed that the neural network adapts to the data with a mean absolute error (mae) of 1.8%, which is a satisfying result for a machine learning task (see Fig. 9). For the (unseen) test data, the mae for the prediction of KEA, GSA, BFA, and Burr is 2.0%. The comparatively similar mae for the training and the test dataset indicates a good generalizability of the numerically pre-trained neural network. In order to finally determine the prediction quality of the numerically pre-trained neural network also for experimental data, the response of the network was verified for the experimental data gained from parameter set 1, as described in the following section.

Evaluation of the Neural Network Predictions for Experimentally Determined Data (Parameter Set 1) The applicability of the numerically pre-trained network to real measured process data (parameter set 1) was finally verified for the experimental data described in section “Evaluation of the Simulation Model Accuracy (Parameter Set 1)”. For this purpose, the process data features shown in Figs. 5 and 6 were used as input for the neural network. The predicted cutting surface features were subsequently compared with the experimentally determined KEA, GSA, BFA, and Burr. Figure 10 shows the result of this comparison. The mean absolute error for the prediction of the neural network based on measured experimental data is 3.1%. Considering the research question posed at the beginning, the contents presented in the sections “Neural Network Setup, Model Training, and Model Evaluation (Parameter Set 2)” and “Evaluation of the Neural Network Predictions for Experimentally Determined Data (Parameter Set 1)” show that cutting force curves have a high potential for indirect determination of cutting surface characteristics. Using a numerically pre-trained neural network, relatively accurate predictions can be made regarding the cutting surface features of shear cut sheet metal component edges based on numerically and/or experimentally determined process data features. In order to further improve the prediction accuracy of Fig. 10 Prediction result for the experimentally determined data of parameter set 1

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the presented approach, additional experimental investigations are planned to extend the existing dataset.

Summary and Outlook The quality of shear cut sheet metal component edges is determined by the edge draw-in, the clean cut, the fracture surface, and the burr. For shear cutting processes with closed cutting lines, the problem arises that the produced cutting surface characteristics cannot be directly measured by optical measurement technologies. For this reason, this paper investigated a new approach for predicting cutting surface characteristics from measurable process data. Besides the sheet metal thickness, these measurable process data includes the maximum cutting force, the cutting work, and the length of the cutting force curve. The proposed approach to predicting geometric cutting surface features (outputs) from measurable process data (inputs) is to first numerically generate a large variance of different cutting force curves by finite element simulation and afterwards pre-train a neural network to the numerically determined cutting surface features. Using an optimized punch design, the neural network showed satisfying results for the prediction accuracy of numerically (mae = 2.0%) and experimentally (mae = 3.1%) determined cutting surface features. Since a basic principle in data science is that more training data leads to an improved accuracy of machine learning models, future research will consist of expanding the existing datasets by further numerical punching simulations and punching experiments.

References 1. Demmel P, Golle R, Hoffmann H, Petry R (2015) Schneiden. In: Siegert K (ed) Blechumformung – Verfahren, Werkzeuge und Maschinen; Springer, Berlin Heidelberg, pp 223–240. https://doi.org/10.1007/978-3-540-68418-3 2. Verein Deutscher Ingenieure (1994) VDI2906 Blatt 2: Quality of cut faces of (sheet) metal parts after cutting, blanking, trimming or piercing; general introduction, characteristic values, materials. In: VDI-Gesellschaft Produktionstechnik (ADB) VDI-Handbuch Betriebstechnik, Teil 2; Düsseldorf 3. Lorenz M, Menzl M, Donhauser C, Layh M, Pinzer B (2022) Otical inline monitoring of the burnish surface in the punching process. Int J Adv Manuf Technol 118:3585–4360. https://doi. org/10.1007/s00170-021-07922-6 4. Hoffmann H, Neugebauer R, Spur G (2012) Handbuch Umformen – Handbuch der Fertigungstechnik. Carl Hanser Verlag, München. https://doi.org/10.3139/9783446430044.fm 5. Lange K (1985) Handbook of metal forming. McGraw-Hill, New York 6. Schenek A, Goerz M, Liewald M, Riedmüller KR (2022) Data-driven derivation of sheet metal properties gained from punching forces using an artificial neural network. Eng Mater 926:2174–2182. https://doi.org/10.4028/p-41602a 7. Jin J, Shi J (2000) Diagnostic feature extraction from stamping tonnage signals based on design of experiments. J Manuf Sci Eng 122(2):360–369. https://doi.org/10.1115/1.538926

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8. Klingenberg W, Singh UP (2004). Principles for on-line monitoring of tool wear during sheet metal punching. In: Hinduja S (ed) Proceedings of the 34th international MATADOR conference. Springer, London. https://doi.org/10.1007/978-1-4471-0647-0_25 9. Lee WB, Cheung CF, Chiu WM, Chan LK (1997) Automatic supervision of blanking tool wear using pattern recognition analysis. Int J Mach Tools Manuf 37(8):1079–1095. https://doi.org/ 10.1016/S0890-6955(97)88104-7 10. Breitling J, Pfeiffer B, Altan T, Siegert K (1997) Process control in blanking. J Mater Process Technol 71(1):187–192. https://doi.org/10.1016/S0924-0136(97)00167-2 11. Doege E, Meiners F, Mende T, Strache W, Yun JW (2002) Sensors for process monitoring: metal forming. Sens Manuf 1:172–202. https://doi.org/10.1002/3527600027.ch4b 12. Li X, Bassiuny AM (2008) Transient dynamical analysis of strain signals in sheet metal stamping processes. Int J Mach Tools Manuf 48(5):576–588. https://doi.org/10.1016/j.ijmach tools.2007.06.010 13. Asahi S, Karadogan C, Tamura S, Hayamizu S, Liewald M (2021) Process data based estimation of tool wear on punching machines using TCN-autoencoder from raw time-series information. IOP Conf Ser Mater Sci Eng 1157(1):012078. https://doi.org/10.1088/1757-899X/1157/ 1/012078 14. Djavanroodi F, Pirgholi A, Derakhshani E (2010) FEM and ANN analysis in fine-blanking process. Mater Manuf Processes 25(8):864–872. https://doi.org/10.1080/10426910903367444 15. Stanke J, Feuerhack A, Trauth D, Mattfeld P, Klocke F (2018) A predictive model for die roll height in fine blanking using machine learning methods. Procedia Manuf 15:570–577. https:// doi.org/10.1016/j.promfg.2018.07.279 16. Al-Momani ES, Mayyas AT, Rawabdeh I, Alqudah R (2012) Modeling blanking process using multiple regression analysis and artificial neural networks. J Mater Eng Perform 21(8):1611– 1619. https://doi.org/10.1007/s11665-011-0079-x 17. Mao H, Chen H, Liu Y, Ji K (2022) A novel force variation fine-blanking process for the high-strength and low-plasticity material. Metals 12(3):458. https://doi.org/10.3390/met120 30458 18. Senn S, Liewald M (2019) Investigation of a new sheet metal shear cutting tool design to increase the part quality by superposed compression stress. IOP Conf Ser: Mater Sci Eng 651(38):3–7. https://doi.org/10.1088/1757-899X/651/1/012088 19. Uhlmann E, Von der Scheulenburg M, Zettier R (2007) Finite element modeling and cutting simulation of Inconel 718. CIRP Ann 56(1):61–64. https://doi.org/10.1016/j.cirp.2007.05.017 20. Han D, Hörhold R, Wiesenmayer S, Merklein M, Meschut G (2018) Investigation of the influence of tool-sided parameters on deformation and occurring tool loads in shear-clinching processes. Procedia Manuf 15:1346–1353. https://doi.org/10.1016/j.promfg.2018.07.349 21. Neugebauer R, Bouzakis KD, Denkena B, Klocke F, Sterzing A, Tekkaya AE, Wertheim R (2011) Velocity effects in metal forming and machining processes. CIRP Ann Mauf Technol 60:627–650. https://doi.org/10.1016/j.cirp.2011.05.001 22. Thyssenkrupp AG (2022) Dualphasen-Stahl – maßgeschneidertes Portfolio für modernen Leichtbau. https://www.thyssenkrupp-steel.com/de/produkte/feinblech-oberflaechenverede lte-produkte/mehrphasenstahl/dualphasenstahl/. Accessed 5 May 2022

Part XIX

Alloy Development for Energy Technologies: ICME Gap Analysis

Molecular Dynamics Study of Gradient Energy Coefficient and Grain-Boundary Migration in Aluminum Foam Chaimae Jouhari, Yucheng Liu, and Doyl Dickel

Abstract Aluminum foam is one of the widely known metallic foams that has recently attracted many researchers’ attention due to its unique combination of properties derived from its cellular structure. Previous studies have shown that the foaming process is responsible for the resulting microstructure, which in turn determines the properties of the metal foams and affects their applicability in industry. In order to facilitate the understanding of process-structure–property-performance relations of metal foams, a phase-field (PF) model predicting the microstructural evolution of these materials during the foaming process, must be developed. And to develop such a PF model, the gradient energy coefficient and grain boundary (GB) mobility of foaming materials must be obtained. In this paper, a series of molecular dynamics (MD) simulations were performed on a system of aluminum and silicon (Al–Si) atoms in order to determine those parameters. The obtained results will be used to parametrize the PF model. Keywords Metallic foam · Capillary fluctuation method · Molecular dynamics · Gradient energy · Grain boundary mobility

Introduction Aluminum foam combines exceptional mechanical, thermal, and acoustic properties, such as low density, high stiffness to weight ratio, thermal insulation and high acoustic, impact energy absorption [1]. These properties make it valuable to many diverse technological applications, especially energy storage applications. However, despite growing interest in metal foams, the use of such materials is considerably C. Jouhari · Y. Liu (B) Department of Mechanical Engineering, South Dakota State University, Crothers Engineering Hall 221, Brookings, SD 57007, USA e-mail: [email protected] D. Dickel Department of Mechanical Engineering, Mississippi State University, Starkville, MS 39762, USA © The Minerals, Metals & Materials Society 2023 The Minerals, Metals & Materials Society, TMS 2023 152nd Annual Meeting & Exhibition Supplemental Proceedings, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-031-22524-6_55

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limited by the lack of sufficient knowledge about the regulations for their production methods, process parameters, and resulted microstructures and properties. Therefore, an enhanced understanding of the process-structure–property-performance relations of metal foams is required to enable the development of top-down design strategies. It is well known that the foaming process governs the microstructure evolution and decides the microstructure of a metal foam, which in turn determines its properties. One decisive step in metal foaming is solidification and the solidification process is strongly affected by solid–liquid interfacial properties including the interfacial energy. Moreover, the grain boundary (GB) mobility markedly affects both recrystallization and grain growth within metal foam materials. In another word, the interfacial energy and GB mobility are indispensable parameters for numerical models that can correctly predict the microstructure evolution of metal foams during foaming processes. However, due to the inherent difficulties associated with direct experimental measurements, numerical techniques such as molecular dynamics (MD) simulation have become the method of choice in determining such material properties at the atomic scale. The capillary fluctuation method (CFM) [2] is a popular tool that has been used extensively to calculate the interface properties such as interfacial free energy, stiffness, and anisotropy of various material systems. For instance, Brown et al. [3] used the CFM to determine the interfacial free energy and stiffness of aluminum in a rapid solidification system. Ueno and Shibuta [4] and Qi et al. [5] used the same method to calculate the solid–liquid interfacial energy of Fe–Cr and Cu–Ni binary alloys, respectively. Many other simulation techniques such as the semi-grand canonical Monte-Carlo (SGCMC) simulation [6] and the Metropolis Monte-Carlo (MMC) simulation in conjunction with the atom swap technique [7] are commonly used to calculate the solid–liquid interfacial properties for binary alloys. Meanwhile, the GB mobility has been calculated by various methods via MD simulations. For example, Schönfelder et al. [8] determined the mobility of high angle [001] twist boundary in Cu by imposing an anisotropic elastic strain on a bicrystal to generate a GB driving force; while Rahman et al. [9] extracted the low-angle grain boundary mobility of [112] tilt boundaries in pure aluminum using two different methods separately: the artificial driving force method (ADF) and the random walk method (RW), and both methods resulted nearly the same magnitude of mobility. In this study, we employ MD simulations to determine the solid–liquid free interfacial energy of an Al–Si system by using the CFM with three orientations. In addition, we describe the grain boundary mobility of the [100] tilt boundary as a function of temperature by applying the random walk method. The results obtained from this study will be further used to parametrize modeling frameworks for metal foams and other metallic material systems previously developed by the authors [10–14].

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Atomistic Simulations Molecular dynamics (MD) simulation is an essential computational technique and has been widely used to find molecular atomic interactions of Al–Si systems in terms of the angular dependent potential (ADP) developed by Starikov et al. [15]. It has also been used to determine the effects of materials’ properties at the atomic scale (e.g., crystal orientation, size scale, and strain rate) on their mechanical behaviors at the macroscopic scale [16]. In this study, the OVITO (open visualization tool) [17] is employed for the visualization of the simulation results and the post-analyses. An Al–Si system is chosen for this analysis because (1) silicon is a popular element in Aluminum foams and almost all Aluminum foams have different silicon content; and (2) the mechanical properties of Ai-Si alloys have been well studied by the authors [18, 19].

The Capillary Fluctuation Method The interfacial energy of the Al–Si system is calculated via MD simulations in conjunction with the CFM. A quasi-3D solid–liquid biphasic system was developed first, which was based on a pure aluminum crystal surrounded by Al–Si alloying liquid, as shown in Fig. 1a. The simulation cell contains a total of 54,044 atoms including 2,844 Si atoms of silicon that were placed randomly in the liquid regions of the system. The boundary conditions are periodic, and the system was modeled with a size of 160 × 16 × 340 Å, as shown in Fig. 1b. The system was firstly energy minimized to remove any unfavorable atomic contacts, then an NPT (constant number of atoms, constant pressure, and constant temperature) ensemble was applied to heat the system to a fixed temperature of 800 K for 5 ps at zero pressure. The solid region was then kept at 800 K while the upper and lower Al–Si regions were heated to a temperature of 2000 K and maintained for 5 ps to get fully melted and homogenized before they were cooled down back to 800 K (close to the melting temperature). Finally, the system was equilibrated by using an NPH (constant number of atoms, constant pressure, and constant enthalpy) ensemble for a period of 215 ps to ensure that the interfaces were stable. The time step of the simulations was 1 fs, and the snapshots of the system were saved every 0.1 ps. The same MD simulation was run for three different solid–liquid interfaces with (100) [010], (110) [001], and (110) [110] orientations. Following the CFM, the position of the interface fluctuates is related to the interfacial stiffness  γ via the following equation: γ (k) = 

k B Teq bL|A(k N )|2 k 2N

(1)

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Fig. 1 A snapshot of the system with two (100) [010] interfaces that illustrates: a a pure aluminum crystal (colored in green) between two melted Al–Si regions (upper and lower red colorful regions); b Al atoms (in yellow) and Si atoms (in red)

where k B is the Boltzmann’s constant, Teq is the equilibrium temperature at which the soli-liquid interface is stable, and the wave number takes values k N = 2πLN . b and L denote the thickness (along y-axis) and length (along x-axis) of the interface, respectively. A(k N ) is the Fourier amplitude of the step height fluctuation and it defined as: 1 A(k) = L

L h(x)exp(ikx)d x

(2)

0

where h(x) is the function that represents the fluctuation of the interface along the x-axis. By calculating the interfacial stiffness for each interface orientation using Eq. (1), the solid–liquid interfacial energy can be derived based on the following relations [20]:   80 18 (3) γ = γ0 1 − 1 − 2 for (100)[010] orientation  5 7   155 39 (4) γ = γ0 1 + 1 +  2 for (110)[001] orientation 10 14     365 21 ¯ orientation γ = γ0 1 − 1 +  2 for(110) 110 (5) 10 14

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where γ0 is the average solid–liquid interfacial energy and 1 and 2 represent the anisotropy parameters. All those variables can be obtained by assigning values of interfacial stiffness into Eqs. (3)–(5). The cubic harmonic expression proposed by Fehlner and Vosko [21] can then be used to obtain the interfacial free energy γ including its anisotropy, which is expressed as:  γ (n) = γ0 1 + 1





n i4

i

2 − 5



+ 2 3



n i4

+

n 21 n 22 n 23

i

17 − 7

 (6)

where n i is the orientation indices of the solid–liquid interface.

The Random Walk Method In order to extract the GB mobility of the Al–Si system we use the random walk method, which is similar to the CFM since it is dependent on the fluctuations analysis of the GB position. In this method, the analysis of the temperature-dependent fluctuations leads to the extraction of the GB mobility through MD simulations of an Al–Si system whose temperatures range from 350 to 850 K. A symmetric [100] tilt boundary was used with a misorientation angle of 36.87° between two crystals. The bicrystal system was set up such that the first crystal has an orientation of [100] along x-axis, [013] along y-axis and 031 along z-axis, while     the second crystal has an orientation of [100] x , 013 y , and 031 z and the direction of the y-axis is normal to the GB plane. The system was modeled within a simulation cell with a size of 12 × 384 × 140 Å. It contains a total of 43,600 atoms including 4,000 Si atoms placed randomly within the bicrystal system. The boundary conditions of the system were kept periodic in the plane of the GB (the x–z plane) while the boundaries in the direction normal to that plane (along y-axis) were set as two free surfaces, as shown in Fig. 2. During MD simulations, the Al–Si bicrystal system was firstly energy-minimized by allowing it to expand and contract perpendicular to the GB while the stress along the y-axis was set to 0. An NPT ensemble was next used to gradually heat the system from 350 to 850 K for 1 ns. Afterward an NVT ensemble was applied to the system and the fluctuations of the GB were considered along the y-axis direction.

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Fig. 2 A bicrystal system that contains Al (blue) and Si atoms (green). The system has a [100] symmetric tilt 36.87° boundary and free surfaces at both ends (along y-axis). The solid white line marks the position of the GB

Results and Discussion Solid–Liquid Interfacial Energy The results of the CFM are highly dependent on the numerical definition of the interface h(x). In order to capture the fluctuations on the interface, the centrosymmetry parameter φC S P defined by Kelchner [22] was used to filter out atoms that belong to a perfect lattice and the atoms that are part of a local crystal defect. This parameter measures the loss of local symmetry by looking at opposite nearest neighbor pairs. The centrosymmetry parameter takes a value of zero (φC S P = 0) for an ideal structure and it increases for structures with defects or in amorphous states. In this study, we calculated the centrosymmetry parameter for each atom along the z-axis on each interface orientation in order to locate the atoms that belong to the interface and extract their positions. Figure 3 shows the centrosymmetry parameter for each atom in the system along the z-axis on the interface with orientation (100) [010]: From Fig. 3, it can be seen that the atoms in the upper and lower liquid regions of the system have an order parameter of φC S P > 7.5, while the atoms that belong to the middle solid region have an order parameter of φC S P < 5.5 and the remaining atoms that have an order parameter between 5.5 and 7.5 are the ones that form the interface. Therefore, the location of the interface can be defined as the position where the atoms have a centrosymmetry parameter of φC S P ∼ = 6.5. By localizing the positions of the interface atoms, we can determine the fluctuation function h(x) for each interface. Next, the Fourier transform is conducted to determine the amplitudes of the Fourier modes k N using Eq. (2) and the interfacial stiffness is calculated from Eq. (1). After obtaining the interfacial stiffness for each interface orientation, Eqs. (3)–(5) are used to define the averaged solid–liquid interfacial energy γ0 and the anisotropy parameters 1 and 2 , which can be substituted into Eq. (6) to determine the anisotropy dependent interfacial energy based on the interface orientation. The resulted solid–liquid interfacial energy at an equilibrium

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350 300

Posion Z (Å)

250 200 150 100 50 0 0

2

4

6

8

10

12

14

16

18

Centrosymmetry parameter Fig. 3 Variation of the centrosymmetry parameter φC S P along the z direction on the interface with orientation (100) [010]

temperature of 800 K was estimated as 0.864 J/m2 which is close to the interfacial energy of Al-13%Si at 803 K, which was estimated as 0.79 J/m2 in a previous experimental study [23].

Grain Boundary Mobility To calculate the GB mobility, the average position of the GB along the y-axis should be represented as a function of time. Thus, the centrosymmetry parameter is used to identify the defect in the system, which is in this case the GB. The centrosymmetry parameter takes the value of zero for each atom surrounded by atoms that belong to a perfect lattice, while the structure with defects is defined by an increase in the centrosymmetry parameter. Therefore, the GB position can be determined as the place where a peak centrosymmetry parameter value along the y-axis appears, as illustrated in Fig. 4. By calculating the mean-squared displacement of the GB position at a time interval of 100 ps, the GB mobility is found to be M = 6.12 × 10−6 m/Pa · s at 800 K.

Conclusion In this paper, the solid–liquid interfacial energy of an Al–Si system is determined by using the CFM with three interface orientations. The GB mobility of a [100] symmetric tilt 36.87° boundary is calculated applying the random walk method. The

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Centrosymmetry parameter

25

20

15

10

5

0 -200

-150

-100

-50

0

50

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150

200

Posion Y (Å)

Fig. 4 Variation of the centrosymmetry parameter along the y-axis for the bicrystal system

centrosymmetry parameter was used in these two methods to track the fluctuation positions of both the solid–liquid interface and the graine boundary. The obtained results will be further used to parametrize an existing phase-field model developed by the authors, which will be used to understand the process-structure–property relationship of aluminum foams. Acknowledgements This project was supported by Mississippi Space Grant Consortium. The authors would like to acknowledge Mississippi State University’s High-Performance Computing Collaboratory for providing the supercomputing facilities required for the computational analysis involved in the present study.

References 1. Peroni L, Avalle M, Peroni M (2008) The mechanical behaviour of aluminium foam structures in different loading conditions. Int J Impact Eng 35:644–658 2. Hoyt JJ, Asta M, Karma A (2001) Method for computing the anisotropy of the solid-liquid interfacial free energy. Phys Rev Lett 86:4 3. Brown NT, Martinez E, Qu J (2017) Interfacial free energy and stiffness of aluminum during rapid solidification. Acta Mater 129:83–90 4. Ueno K, Shibuta Y (2019) Composition dependence of solid-liquid interfacial energy of Fe–Cr binary alloy from molecular dynamics simulations. Comput Mater Sci 167:1–7 5. Qi C, Xu B, Kong LT, Li JF (2017) Solid-liquid interfacial free energy and its anisotropy in the Cu–Ni binary system investigated by molecular dynamics simulations. J Alloy Compd 708:1073–1080 6. Ueno K, Shibuta Y (2019) Semi-grand canonical Monte Carlo simulation for derivation of thermodynamic properties of binary alloy. Mater Sci Eng 529:012–037 7. Ueno K, Shibuta Y (2018) Solute partition at solid-liquid interface of binary alloy from molecular dynamics simulation. Materialia 4:553–557

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8. Schönfelder B, Wolf D, Phillpot SR, Furtkamp M (1997) Molecular-Dynamics Method for the Simulation of Grain-Boundary Migration. Interface Sci 5:245–262 9. Rahman MJ, Zurob HS, Hoyt JJ (2014) A comprehensive molecular dynamics study of low angle grain boundary mobility in a pure aluminum system. Acta Mater 74:39–48 10. Wang X, Liu P-W, Ji Y-Z, Liu Y-C, Horstemeyer MF, Chen L (2019) Investigation on microsegregation of IN718 alloy during additive manufacturing via integrated phase-field and finite element modeling. J Mater Eng Perform 28(2):657–665 11. Liu P-W, Wang Z, Xiao Y-H, Lebensohn RA, Liu Y-C, Horstemey MF, Cui X-Y, Chen L (2020) Integration of phase-field model and crystal plasticity for the prediction of process-structureproperty relation of additively manufactured metallic materials. Int J Plast 128:102670 12. Yenusah CO, Ji Y-Z, Liu Y-C, Stone TW, Horstemeyer MF, Chen L (2020) “Investigation of precipitation kinetics and hardening effects of γ” in Inconel 625 using a combination of meso-scale phase-field simulations and macro-scale precipitates strengthening calculations. In: IMECE2020–23328, Proceedings of ASME 2020 international mechanical engineering congress & exposition, virtual conference, November 16–19, 2020 13. Chen L, Yenusah CO, Ji Y-Z, Liu Y-C, Stone TW, Horstemeyer MF, Chen L-Q (2021) Threedimensional Phase-field simulation of γ precipitation kinetics in Inconel 625 during heat treatment. Comput Mater Sci 187:110–123 14. Yenusah CO, Stone TW, Morgan NR, Robey RW, Liu Y-C, Chen L (2022) Incorporating performance probability and data-oriented design in phase-field modeling. In: IDETC2022– 89513, ASME 2022 international design engineering technical conferences and computers and information in engineering conference (IDETC/CIE 2022), August 14–17, 2022, St. Louis, MO, USA 15. Starikov S, Gordeev I, Lysogorskiy Y, Kolotova L, Makarov S (2020) Optimized interatomic potential for study of structure and phase transitions in Si–Au and Si–Al systems. Comput Mater Sci 184:109891 16. Dou Y-Q, Liu Y-C, Huddleston B, Hammi Y, Horstemeyer MF (2020) A molecular dynamics study of effects of crystal orientation, size scale, and strain rate on penetration mechanisms of monocrystalline copper subjected to impact from a nickel penetrator at very high strain rates. Acta Mech 231:2173–2201 17. Stukowski A (2010) Visualization and analysis of atomistic simulation data with OVITO–the Open Visualization Too. Modell Simul Mater Sci Eng 18:015012 18. Yang W-H, Wang Z, Yenusah CO, Liu Y-C (2020) An integrated model for predicting the porosity effect on the mechanical behavior of additively manufactured Al–10Si–Mg ally. In: 2020-001-1075, Proceedings of SAE 2020 world congress experience, Detroit, MI, USA, April 21–23, 2020 19. Perkins RA, Yang W-H, Liu Y-C, Chen L, Yenusah CO (2019) Finite element analysis of the effect of porosity on the plasticity and damage behavior of Mg AZ31 and Al 6061 T651 alloys. In: IMECE2019-10672, Proceedings of ASME 2019 international mechanical engineering congress & exposition, Salt Lake City, UT, USA, November 11–14, 2019 20. Ueno K, Shibuta Y (2020) Molecular dynamics study of composition dependence of solid-liquid interfacial energy of Fe–Ni binary alloy. Mater Sci Eng 861:012064 21. Fehlner WR, Vosko SH (1976) A product representation for cubic harmonics and special directions for the determination of the Fermi surface and related properties. Can J Phys 54:2159 22. Kelchner CL, Plimpton SJ, Hamilton JC (1998) Dislocation nucleation and defect structure during surface indentation. Phys Rev B 58:11085 23. Abbott TB (1987) The solidification structures and mechanical properties of high siliconaluminum alloys. PhD dissertation, Department of Materials Engineering, Monash University, Clayton, Victoria, Australia, August 1987

Phase-Field Modeling of Aluminum Foam Based on Molecular Dynamics Simulations Chaimae Jouhari, Yucheng Liu, and Doyl Dickel

Abstract This paper presents a phase-field model that is consistent with the multiphase system of aluminum foam to predict the microstructural evolution involved in the foaming process of the aluminum foam and its final microstructure. The phasefield model characterizes the microstructure of the foam material with a set of material constants calibrated through experiments and molecular dynamics (MD) calculations. A series of MD simulations were performed on a group of aluminum and silicon (Al–Si) atoms, whose potentials were defined using the angular dependent potential (ADP). The MD results such as diffusion and specific heat capacity are used as input parameters for the developed phase-field model. The developed phase field model will predict the microstructural evolution of metal foams during foaming processes and will be further used to establish a multiscale computational framework that bridges the process, structure, property and performance of metal foams. Keywords Phase-field model · Multiphase system · Aluminum foam · Molecular dynamics · Angular dependent potential

Introduction Metal foams represent a large family of lightweight structural and functional materials. They exhibit a unique combination of properties derived from their base material and cellular structure, which is controlled by their manufacturing process. One of the widely known metallic foams is aluminum foam [1], which has recently attracted many researchers’ attention due to its outstanding properties that make it an ideal material for thermal insulation, acoustic, vibration and impact energy absorber [2]. C. Jouhari (B) · Y. Liu Department of Mechanical Engineering, South Dakota State University, Brookings, SD 57007, USA e-mail: [email protected] D. Dickel Department of Mechanical Engineering, Mississippi State University, Starkville, MS 39762, USA © The Minerals, Metals & Materials Society 2023 The Minerals, Metals & Materials Society, TMS 2023 152nd Annual Meeting & Exhibition Supplemental Proceedings, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-031-22524-6_56

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Foaming process is responsible for the resulting microstructure, which in turn determines the properties of the metal foams and affects their applicability in industry. Therefore, an enhanced understanding of the process–structure–property–performance relations of the metal foams will help selecting and optimizing appropriate manufacturing processes to produce metal foams according to their expected applications. On the other side, it will enable evaluating the potential applicability of a metal foam and assess its performance based on its manufacturing process. An experimental study was conducted by Rhee et al. [3] to investigate five types of biologically inspired aluminum foams processed differently to study their structure– property relations and examine their impact energy absorption performance. The energy absorption abilities of the tested aluminum foams have were proved to be strongly dependent on their physical bulk properties. Moreover, that study showed that the differences in morphology, porosity and density are the main reasons to the different mechanical responses of the tested aluminum foams. The experimental data generated from study characterized the structure–property relations required for accurate modeling of the aluminum foams. In comparison with experimental studies, numerical investigations of the process– structure–property relations in materials are becoming more attractive due to the accelerated results they can potentially offer at a cost- and time-effective manner. Considerable numerical investigations have been conducted recently to understand the process–structure–property relationship of different materials. Wang et al. [4] have combined finite-element method (FEM) and phase-field method (PFM) to investigate the influence of solidification conditions on the solidification microstructure of IN718 alloy during additive manufacturing (AM). Liu et al. [5] also integrated FEM, temperature-dependent PFM and a fast Fourier transform-based elasto-viscoplastic (EVP-FFT) model to investigate the process–microstructure–property relationship for the AM metallic material. Yenusah et al. [6] and Chen et al. [7] both used phasefield method to study γ precipitation kinetics and morphology evolution of in Inconel 625 during heat treatment. Yang et al. constructed a process–structure–property map for AM-produced Al–Si–Mg alloys [8] and other Mg and Al alloys [9] utilizing an integrated multi-physics model with special focus on the effects of microporosity on the mechanical behavior of those alloys. In this study, we present a series of molecular dynamics simulations to calculate diffusion coefficients and specific heat coefficients that will be used to parametrize the phase-field modelling framework for the metal foams previously developed by the authors [4–7]. The fully parametrized phase-field model presented in this study will aim to describe microstructural evolution of the metal foams during their foaming process.

Phase-Field Method Phase-field modeling is a relatively new paradigm in material science and physics, which has recently become the method of choice for modeling and simulating

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microstructural evolutions under different driving forces such as temperature, stress, strain, compositional gradients, etc. [10–12]. A metal foam is a multiphase and multicomponent material system, in which each phase is represented by a non-conserved field variable. For example, φs, φl, and φg represent the solid, liquid, and gaseous phase in a metal foam phase field model, respectively. Moreover, a phase field model also includes a set of conserved component field variables ci to represent the concentration (weight fraction) of different chemical compositions in the parent material of the metal foam, therefore ci = 1. Both {φ} and {c} are functions of position (x) and time (t). The total free energy of metal foam material system can be represented as E total = E chem + E mech . The local chemical free energy for the metal foam material system can be represented as [13, 14]:  E chemical =



f ({φ}, {c}, T ) +

1

λ ∇φs 2 sl

· ∇φl +

1

κ ∇ci 2 ij

 · ∇c j d V

(1)

V

where λsl and κ ij are the gradient energy coefficients. And the mechanical energy contribution to the total free energy can be estimated as:  1 σi j εielj d V (2) E mech = 2 V

where εielj is the local elastic strain and σi j is the local elastic stress in a microstructure:   φ {c} el σi j = λi jkl εkl = λi jkl εkl − εkl {c} − εklg φg2

(3)

With the derived total free energy, the phase field evolution can be obtained by solving the Allen–Cahn equation [15], it can be called also the time-dependent Ginzburg–Landau equation: δ E total ∂φs = −L sl ∂t δφl

(4)

represents the driving force. L sl is related to interface mobility and δ Eδφtotal l The dynamics of component diffusion is governed by the Cahn–Hilliard nonlinear diffusion equation [16] as: δ E total ∂ci = ∇ Mi j ∇ ∂t δc j where M ij is related to atom mobility.

(5)

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In order to completely solve phase field evolution Eqs. (4) and (5), the material parameters (diffusion coefficient, specific heat capacity, gradient energy coefficients and grain mobility) are to be determined. In this paper, we have determined the liquid diffusion coefficients and the specific heat capacity of the Al–Si system by employing molecular dynamics simulations.

Determination of Material Parameters Molecular dynamics (MD) simulation is an essential computational technique and has been used extensively to find molecular properties of various material systems [17]. Some properties, such as the liquid diffusion coefficient and specific heat capacity, are required to initiate and solve phase-field models. The Large-scale Atomic/Molecular Massive Parallel Simulator (LAMMPS) code was employed in all the MD simulations performed in this study. The atomic interactions of the Al–Si system are described by the angular dependent potential (ADP) developed by Starikov [18]. The developed model of Al–Si system contains 90 wt.% of aluminum and 20 wt.% of silicon, according to the chemical compositions of aluminum foams presented in the experimental study [3]. The boundary conditions are periodic.

Liquid Diffusion Coefficients By using MD simulations, the diffusion coefficients of both aluminum and silicon were determined by calculating the mean-squared displacement of the Al and Si atoms. The simulation cell contains a total amount of 35,555 atoms including 3,555 Si atoms of silicon that were placed randomly in the system. The cell was modeled as an 81 Å cubic cell, as shown in Fig. 1. The system was firstly energy minimized to remove any unfavorable atomic contacts, then it was melted by heating it to a temperature of 2000 K for 1.5 ns. Next, the equilibration MD runs were then performed at a fixed temperature of 1000 K, where an NPT (constant number of atoms, pressure, and temperature) ensemble was applied to the system. After an equilibration period of 10 ns, the mean-squared displacement was calculated for aluminum and silicon, which would be used to determine the diffusion coefficients of both compositions. Two more similar MD simulations were run on the same system but at higher temperatures: 1300 and 1600 K, to examine how the change in temperature affects the diffusion of the Al and Si atoms.

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Fig. 1 A simulation cell at solid state, containing 90 wt% of Al atoms (in yellow) and 10 wt% of Si atoms (in red)

Specific Heat Capacity Specific heat capacity of a material represents the amount of heat required by one unit mass to raise its temperature by one degree and differs from a state of matter to another. Therefore, both the solid and liquid states of Al–Si system were taken into consideration. The simulation cell used to determine the specific heat has the same dimensions and number of atoms as the one used for diffusion simulations. In order to determine the specific heat capacity at solid state, the developed Al– Si system was energy-minimized. Afterwards, the MD simulations were performed using an NPT ensemble, where the system was heated to a temperature of 200 K and was held at that temperature for 10 ns. After that, the temperature increased gradually from 200 to 1000 K within a time period of 500 ps. For the liquid state, the model was likewise energy-minimized and simulated in another NPT ensemble, where it was heated to a higher temperature of 2000 K and held for 1 ns to melt the system before it was cooled down to 800 K (close to its melting temperature) and held at that temperature for 1 ns. Afterwards, the temperature was gradually increased from 800 to 1400 K in 100 ps by employing the NPT ensemble.

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Results and Discussion About Diffusion Coefficients In MD simulations, the mean-squared displacement represents the displacement of an atom with respect to its reference position [19] and it can be defined by: MSD =

N 2 1  c xi (t) − xic (0)   N i=1

(6)

where N is the number of particles to be averaged and x is the position of the particle. Through further Eq. (6) [20], the mean-squared displacement in a 3D space is expressed as: M S D = 6Dt

(7)

where D is the diffusion coefficient. Therefore, the diffusion coefficient of the Al-Si system is estimated as 1/6 of the slope of MSD- time curves. The mean-squared displacements of both aluminum and silicon are calculated at three different temperatures (1000, 1300 and 1600 K) as described in the following plots. Figures 2 and 3 shows that the mean-squared displacement curves of both compositions (Al and Si) exhibit linear growth with respect to time and the lower the temperature the flatter the curve, in other words, as the temperature increases the self-diffusivity of the atoms also increases. Table 1 lists the slope of each MSD curve displayed on Figs. 2 and 3, and D is the diffusion coefficient derived from each curve as one sixth of its slope, and it is represented in Fig. 4 for Al and Si at different temperatures.

700

MSD of Al

500 400 300

1000

200

1300

100

1600

0

5.75 8.24 10.74 13.24 15.74 18.24 20.74 23.24 25.74 28.24 30.74 33.24 35.74 38.24 40.74 43.24 45.74 48.24 50.74 53.24 55.74

MSD (Å2)

600

Time (ps)

Fig. 2 Mean-squared displacements of aluminum atoms versus time at 1000, 1300 and 1600 K

C. Jouhari et al. 800 700 600 500 400 300 200 100 0

MSD of Si

1000 1300 1600 5.75 8.24 10.74 13.24 15.74 18.24 20.74 23.24 25.74 28.24 30.74 33.24 35.74 38.24 40.74 43.24 45.74 48.24 50.74 53.24 55.74

MSD (Å2)

638

Time (ps)

Fig. 3 Mean-squared displacements of silicon atoms versus time at 1000, 1300 and 1600 K

Table 1 Diffusion coefficients of aluminum and silicon at various temperatures Aluminum

T (Kelvin)

1000

(Å2 /ps)

4.83

D 10–9 (m2 /s)

8.05

Slope

Fig. 4 Temperature dependence of the diffusion coefficient of aluminum and silicon

Silicon 1300 8.395 13.99

Diffusion coefficient (10-9 m2/s)

Material

1600

1000

12.73

5.38

21.21

8.96

1300

1600

8.82

13.34

14.7

22.33

25 20 15 10

Al

5

Si

0 1000

1300

1600

Temperature (K)

From Fig. 4, it can be found that the self-diffusivities of both aluminum and silicon atoms increase as the temperature of the system increases. Moreover, the diffusion of silicon is noticed to be higher than the diffusion of aluminum at each temperature.

About Specific Heat Capacity The specific heat capacity of the system can be defined in terms of energy fluctuations with respect to temperature as:

cp =

∂E ∂T

 (8) p

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Fig. 5 Temperature dependence of the energy of the Al–Si system at solid and liquid states

Therefore, the specific heat coefficient is represented as the slope of the curve of the total system energy with respect to temperature, as shown in Fig. 5. MD simulations were first performed for a solid-state system, which was gradually heated from 200 to 1000 K during simulation; and then for a liquid-state system whose temperature raised from 800 to 1400 K. The evolutions of the energy for both the solid- and liquid-state systems as temperature increases were obtained from those simulations and combined to display the temperature dependence of the Ali-Si’ system energy in both the solid and liquid state (Fig. 5). The system contains 32,000 atoms of aluminum and 3555 atoms of silicon. By deriving the slope of the energy with respect to temperature, the specific heat capacity is estimated to be c pSolid = 0.0104T + 4.9583 eV/K in the solid state and c pLiquid = −0.0014T + 12.647 eV/K in the liquid state. It is also noticed from Fig. 5 that the melting temperature of the Al–Si system obtained from MD simulations is in the range of 750–850 K. This result is close to the melting temperature of Al–Si system (at 10 wt. % of silicon) measured from an experiment, which is about 860 K [21].

Conclusion In this paper, the diffusion coefficients of aluminum and silicon were determined at different temperatures and the specific heat capacity was calculated in both solid and liquid states of the Al–Si system. These obtained results will be used to equilibrate next MD simulations, which will be performed to derive the gradient energy coefficients and the grain boundary mobility of the metal foam system. All these material coefficients will be further employed to parametrize the existing phase-field model, which will be used to understand the process–structure–property relationship of metal foams. A multiscale computational framework then can be established by integrating

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this phase-field model with existing internal state variable (ISV) and finite element analysis (FEA) models to investigate the process–structure–property–performance relations of metal foams produced from different foaming processes. Acknowledgements This project was supported by Mississippi Space Grant Consortium. The authors would like to acknowledge Mississippi State University’s High-Performance Computing Collaboratory for providing the supercomputing facilities required for the computational analysis involved in the present study.

References 1. Simancik F, Jerz J, Kovacik J, Miná P (1997) Aluminium foam-a new light-weight structural material. Met Mater 35:C4 2. Peroni L, Avalle M, Peroni M (2008) The mechanical behaviour of aluminium foam structures in different loading conditions. Int J Impact Eng 35:644–658 3. Rhee H, Tucker MT, Whittington WR, Horstemeyer MF, Lim H (2015) Structure-property responses of bio-inspired synthetic foams at low and high strain rates. Sci Eng Compos Mater 22(4):365–373 4. Wang X, Liu P-W, Ji Y-Z, Liu Y-C, Horstemeyer MF, Chen L (2019) Investigation on microsegregation of IN718 alloy during additive manufacturing via integrated phase-field and finite element modeling. J Mater Eng Perform 28(2):657–665 5. Liu P-W, Wang Z, Xiao Y-H, Lebensohn RA, Liu Y-C, Horstemey MF, Cui X-Y, Chen L (2020) Integration of phase-field model and crystal plasticity for the prediction of process–structure– property relation of additively manufactured metallic materials. Int J Plast 128:102670 6. Yenusah CO, Ji Y-Z, Liu Y-C, Stone TW, Horstemeyer MF, Chen L (2020) “Investigation of precipitation kinetics and hardening effects of γ” in Inconel 625 using a combination of meso-scale phase-field simulations and macro-scale precipitates strengthening calculations. In: IMECE2020–23328, Proceedings of ASME 2020 international mechanical engineering congress & exposition, virtual conference, November 16–19, 2020 7. Chen L, Yenusah CO, Ji Y-Z, Liu Y-C, Stone TW, Horstemeyer MF, Chen L-Q (2021) Threedimensional phase-field simulation of γ precipitation kinetics in Inconel 625 during heat treatment. Comput Mater Sci 187:110–123 8. Yang W-H, Wang Z, Yenusah CO, Liu Y-C (2020) An integrated model for predicting the porosity effect on the mechanical behavior of additively manufactured Al–10Si–Mg ally. In: 2020-01-1075, Proceedings of SAE 2020 world congress experience, Detroit, MI, USA, April 21–23, 2020 9. Perkins RA, Yang W-H, Liu Y-C, Chen L, Yenusah CO (2019) Finite element analysis of the effect of porosity on the plasticity and damage behavior of Mg AZ31 and Al 6061 T651 alloys. In: IMECE2019–10672, Proceedings of ASME 2019 international mechanical engineering congress & exposition, Salt Lake City, UT, USA, November 11–14, 2019 10. Yenusah CO, Stone TW, Morgan NR, Robey RW, Liu Y-C, Chen L (2022) Incorporating performance probability and data-oriented design in phase-field modeling. In: IDETC2022– 89513, Proceedings of the ASME 2022 international design engineering technical conferences and computers and information in engineering conference, August 14–17, 2022, St. Louis, MO, USA 11. Chen L, Wang Z, Yang W-H, Xiang L-Y, Wang X, Zhao Y-J, Xiao Y-H, Liu P-W, Liu Y-C, Banu M, Zikanov O (2022) Multi-input convolutional network for ultrafast simulation of field evolvement. Patterns 3(6):100464

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12. Yang W-H, Wang Z, Yang T-N, He L, Song X, Liu Y-C, Chen L (2021) Exploration of the underlying space in microscopic images via deep learning for additively manufactured piezoceramics. ACS Appl Mater Interf 13:53439–53453 13. Chen L (2002) Phase-field models for microstructure evolution. Ann Rev Mater Res 32:113– 140 14. Steinbach I, Pezzolla F, Nestler B, Seeselberg M, Prieler R, Schmitz GJ, Rezende JLL (1996) A phase field concept for multiphase systems. Physica D 94:135–147 15. Cahn JW (1961) On spinodal decomposition. Acta Metall 9:795–801 16. Cahn JW, Allen SM (1977) A microscopic theory of domain wall motion and its experimental verification in Fe–Al alloy domain growth kinetics. Journal de Physique 38:C7-51 17. Dou YQ, Liu YC, Huddleston B, Hammi Y, Horstemeyer MF (2020) A molecular dynamics study of effects of crystal orientation, size scale, and strain rate on penetration mechanisms of monocrystalline copper subjected to impact from a nickel penetrator at very high strain rates. Acta Mech 231:2173–2201 18. Starikov S, Gordeev I, Lysogorskiy Y, Kolotova L, Makarov S (2020) Optimized interatomic potential for study of structure and phase transitions in Si–Au and Si–Al systems. Comput Mater Sci 184:109891 19. Kowsari MH, Alavi S, Ashrafizaadeh M, Najafi B (2008) Molecular dynamics simulation of imidazolium-based ionic liquids. I. Dynamics and diffusion coefficient. J Chem Phys 129:224508 20. Wang J, Hou T (2011) Application of molecular dynamics simulations in molecular property prediction II: diffusion coefficient. J Comput Chem 32:3505–3519 21. Miao Q, Wu D, Chai D, Zhan Y, Bi G, Niu F, Ma G (2020) Comparative study of microstructure evaluation and mechanical properties of 4043 aluminum alloy fabricated by wire-based additive manufacturing. Mater Des 168:108–205

Part XX

Alloys and Compounds for Thermoelectric and Solar Cell Applications XI

Stability Study of Cesium-Based Triple Cation Perovskite Solar Cells in Elevated Environmental Ambients Sujan Aryal, Mahdi Temsal, Ehsan Ghavaminia, and Anupama B. Kaul

Abstract For the hybrid organic–inorganic systems, cesium-based triple cation perovskite solar cells (Cs0.05 FA0.79 MA0.16 PbI2.45 Br0.55 ) have recently received a great deal of attention in view of their greater stability compared to the historically significant methyl ammonium lead iodide (MAPbI3) absorber, given the vulnerability of the latter to moisture, oxygen, and ultraviolet radiation. In this work, we have studied the long-term stability of Cs0.05 FA0.79 MA0.16 PbI2.45 Br0.55 under various stress conditions to accelerate degradation which provides clues into enhancing their stability further. The cesium-based triple cation absorber is integrated into the n-i-p solar cell architecture with gold as the collector electrode, and the stability was gauged using in-use testing with maximum power point tracking, as well as in elevated thermal ambients. Keywords Triple cation · Solar cells · Perovskite · Cesium · Stability · Thermal · Humidity · MPPT

Introduction The demand for electricity is increasing but still most of the energy consumption is supplied from various depleting and polluting sources. Therefore, the renewable energy sources such as hydropower, wind and solar photovoltaics (PV) need to expand expeditiously in the near future in order to overcome this challenge. Solar energy, harnessed from the sun, is one of the most promising ways in which to fulfil our high energy demand, while at the same time providing a clean energy source. Renewable PV based on perovskite photo absorbers is gaining momentum due to the increased S. Aryal · M. Temsal · E. Ghavaminia Department of Electrical Engineering, University of North Texas, Denton, TX 76207, USA A. B. Kaul (B) Department of Materials Science and Engineering, Electrical Engineering, PACCAR Technology Institute, University of North Texas, Denton, TX, USA e-mail: [email protected] © The Minerals, Metals & Materials Society 2023 The Minerals, Metals & Materials Society, TMS 2023 152nd Annual Meeting & Exhibition Supplemental Proceedings, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-031-22524-6_57

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power conversion efficiency (PCE) achieved with these complex materials in such a short time. The PCE achieved with perovskites has increased from ~ 3.8% to above 25% over the past decade [1, 2]. Moreover, these materials allow for low processing cost and simple fabrication, compared to what is possible with the current dominant silicon based solar cells in the PV community [3]. However, the long-term stability of perovskites solar cells (PSCs) remains a critical hurdle for commercialization [4]. Inevitably, vulnerability of the perovskite to moisture is one of the dominant reasons for the instability of PSCs [5]. In addition to the moisture, light exposure, and the heat also make the perovskite absorber prone to material degradation pathways [6]. Researchers have conducted studies on pure archetypical perovskites, such as MAPBX3 , FAPBX3 and CsPBX3 (X = Cl, Br or I) as absorbing materials in solar cells because of their good photovoltaic performance. However, pure perovskites fall short owing to thermal and structural instability due to their structural phase transitions evident at low temperatures, such as ~55 °C and their hydrophilic nature, which has led to the incorporations of multiple cations, and halides [7–9]. The incorporation of multiple cations (MA/FA/Cs) and halides (I/Br) have led the PSC towards higher stability and efficiency [10]. Partial replacement of MA (CH3 NH3 + ) with FA (CH(NH2 )2 + ) and Cs+ has gained a lot of momentum for increased PCE and higher stability [11]. Thus, with the focus towards stable and high PCE PSCs, the mixed triple cation Cs0.05 FA0.79 MA0.16 PbI2.45 Br0.5 is a well-accepted approach within the PV community for further study. Though triple cation absorbers, through the incorporation of Cs+ and Br− , is intrinsically more stable than MAPbI3 , it still suffers from various degradation pathways and there is still more room for improvement towards commercialization. Thus, it is imperative to continue developing new perovskite materials capable of achieving high performance with intrinsically more stability. Besides, it is also important to study the stability of the existing perovskite absorbers and their degradation mechanisms, including for the triple cation based absorbers, which is the focus of this work. The stability related to moisture and oxygen can be optimized using encapsulation approaches but not the stability related to the intrinsic thermally driven mechanisms. Incidentally, the operating temperature of solar cells can reach up to 65 °C or greater, when solar concentrators are utilized. Thus, thermal studies of PSCs are essential to overcome the stability limitations. Hence, in this work we have conducted a thermal stability study and light exposure study in our triple cation PSC devices, which we report on here. To do this, we fabricated the triple cation-based PSCs in an ni-p device architecture, where the cells were stored at ~25 and ~65 °C, considering the extreme temperatures possible in environmental and device operational conditions, through light-soaking tests conducted with and without the flow of ambient gas over the course of several hours.

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Results and Discussion Electrical Characterization of the Device Herein, we fabricated a cesium-based triple cation PSC in a planar n-i-p structure, constituting a mesoporous layer (m-TiO2 ) over a compact layer (c-TiO2 ), where the latter was deposited using a spray-coating approach. Figure 1a depicts the n-i-p structure of the fabricated devices comprising of the following layer stack: FTO/cTiO2 /m-TiO2 /Triple Cation/Spiro-OMeTAD/Au. For the solar cell device measurements, the fabricated device was then exposed to the Oriel LSH-7320 LED solar simulator under one-sun optical illumination. The optimized J-V Characteristics of the triple cation PSC obtained in reverse bias are shown in Fig. 1b, where the device PCE was measured to be ~14.32%, with fill factor (FF) of ~69.75%, photocurrent density Jsc of ~18.85 mA-cm−2 and open circuit voltage Voc of ~1.09 V.

Fig. 1 a The n-i-p architecture of PSCs and b The representative J-V characteristics for the triple cation of the fabricated device

Thermal Stability of the Device To gain insights into the degradation paths related to exposure to elevated ambient temperatures, we subject the devices to temperatures ranging from ambient to ~65 °C. First, we measured the J-V Characteristics of the device in room temperature (RT) of ~25 °C and subsequently the device was kept at elevated temperatures within the glove box environment for 1 h. Figure 2a illustrates the solar cell performance metrics with the figures of merit, i.e. PCE, FF, Jsc, and Voc on a normalized scale. Note that the J-V parameters have a continuous degradation after 30 °C which clearly exhibits that though triple cation is intrinsically more stable than pure archetypical

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Fig. 2 a Normalized PCE, Jsc, FF and Voc when device is exposed to elevated temperatures and b the equivalent J-V curve

perovskites, it still suffers from the degradation. In addition, Fig. 2b depicts the equivalent J-V Characteristic of the measured devices. We also exposed the device to the extreme temperature of 65 °C as a function of time, where the data are exhibited in Fig. 3. The device measurements were taken after every hour of exposure in a vacuum environment when the temperature was 65 °C for 4 h as a preliminary experiment to learn the stability regarding temperature. From Fig. 3a, we clearly see that the J-V parameters are degrading but slowly. However, after 3 h of exposure, all the parameters started going down which shows that thermal exposure is a crucial factor which influences the degradation of the perovskite when the perovskite is thermally exposed for longer hours, impacting the solar cell behavior. The degradation is presumed to originate from decreased charge mobility, increased trap density and generation of PbI2 charge recombination centers near the interface [12]. The equivalent J-V Characteristic of the degradation is also plotted in Fig. 3b.

Fig. 3 a Normalized J-V parameters measured every hour when device was exposed to extreme temperature of 65 °C for up to 4 h. The equivalent J-V Characteristics are shown in (b)

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Maximum Power Point Tracking Not only is the thermal exposure important from our environmental storage testing, but the continuous light exposure of the device also degrades the solar cells, since UV light causes the perovskites to decompose and increase the non-radiative degradation rate [6]. To validate this through our empirical set up, we observed the role of light exposure in our triple cation-based devices, by performing an in-use device operation test for these ageing and stability tests through Maximum Power Point Tracking (MPPT). We exposed our solar cell device for 5 continuous hours under 1 Sun, with and without N2 flow into our test jig. Figure 4a demonstrates the normalized J-V parameters of the triple cation device without N2 flow, where we observe the degradation of the solar cells as a function of time; here the device retained 60% of the initial PCE after 5 h. However, when N2 gas is flowed during these time-dependent device operational measurements, the device performance did not degrade as rapidly, as exhibited by the data in Fig. 4b. This clearly shows that the flow of N2 during the measurements aids in increasing the stability of the devices. We have also measured the J-V Characteristic of the same device before and after the time- dependent measurements conducted in Fig. 4. These results are depicted in Fig. 5a and b, for both devices without and with the flow of N2 , respectively. Here the PCE increased from what was noted at the end of the MPPT testing, where the device without N2 flow has a PCE of ~12.23% before light exposure which decreased to 7.16% after 5 h of MPPT testing which ultimately increased to 9.79% when device was kept at rest from the light exposure. Similarly, the device with N2 flow has an initial PCE of 13.41% which decreased to 11.80% after 5 h of MPPT testing. However, when the same device was measured after providing resting it from continuous exposure, the devices retained its behavior which is shown by increase in PCE to 12.29%. These results are summarized in Table 1.

Fig. 4 Normalized J-V parameters of triple cation devices when exposed to continuous light a with N2 flow and b without N2 flow for ageing testing at maximum power. The PCE dropped to ~60% when N2 is not flown and dropped to ~90% when N2 is flown

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Fig. 5 J-V curve of triple cation devices before and after MPPT testing a when N2 was not flown during MPPT testing (PCE before = 12.23% and PCE after = 9.79%) and b when N2 was flown during MPPT testing (PCE before = 13.41% and PCE after = 12.29%)

Table 1 PCE analysis before and after MPPT Without N2 flow PCE (%)

With N2 flow PCE (%)

Initial

Last PCE in MPPT

After MPPT

Initial

Last PCE in MPPT

After MPPT

12.23

7.16

9.79

13.41

11.80

12.29

Conclusions In conclusion, we have fabricated triple cation perovskite solar cells with spraycoating of c-TiO2 and spin coating of m-TiO2 . The fabricated device was then exposed to temperature-dependent storage testing where we clearly observed degradation in the J-V parameters of the PSCs driven by temperature. In addition, we also performed ageing tests on the devices with continuous exposure of light for 5 h with and without N2 flow during the measurement at maximum power (MPPT), where we clearly observe the flow of N2 assists in enhancing the stability of the devices during device operation. Acknowledgements We thank the Office of Naval Research (grant number ONR N00014-20-12597) that enabled us to pursue this work. A.B.K. is also grateful to the support from the PACCAR Technology Institute at UNT and the endowed Professorship support.

References 1. Best research-cell efficiencies. www.nrel.gov/ncpv/images/efficiencychart.jpg 2. Kojima A, Teshima K, Shirai Y, Miyasaka T (2009) Organometal halide perovskites as visiblelight sensitizers for photovoltaic cells. J Am Chem Soc 131(17):6050–6051

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3. Noel NK, Stranks SD, Abate A, Wehrenfennig C, Guarnera S, Haghighirad AA, Sadhanala A, Eperon GE, Pathak SK, Johnston MB, Petrozza A (2014) Lead-free organic–inorganic tin halide perovskites for photovoltaic applications. Energy Environ Sci 7(9):3061–3068 4. Huang HH, Shih YC, Wang L, Lin KF (2019) Boosting the ultra-stable unencapsulated perovskite solar cells by using montmorillonite/CH3 NH3 PbI3 nanocomposite as photoactive layer. Energy Environ Sci 12(4):1265–1273 5. Ogunniran KO, Martins NT (2021) Humidity and moisture degradation of perovskite material in solar cells: effects on efficiency. In: IOP conference series: earth and environmental science, vol 655, no 1, p 012049. IOP Publishing 6. Khenkin MV, Katz EA, Abate A, Bardizza G, Berry JJ, Brabec C, Brunetti F, Bulovi´c V, Burlingame Q, Di Carlo A, Cheacharoen R (2020) Consensus statement for stability assessment and reporting for perovskite photovoltaics based on ISOS procedures. Nat Energy 5(1):35–49 7. Stoumpos CC, Malliakas CD, Kanatzidis MG (2013) Semiconducting tin and lead iodide perovskites with organic cations: phase transitions, high mobilities, and near-infrared photoluminescent properties. Inorg Chem 52(15):9019–9038 8. Conings B, Drijkoningen J, Gauquelin N, Babayigit A, D’Haen J, D’Olieslaeger L, Ethirajan A, Verbeeck J, Manca J, Mosconi E, Angelis FD (2015) Intrinsic thermal instability of methylammonium lead trihalide perovskite. Adv Energy Mater 5(15):1500477 9. Misra RK, Aharon S, Li B, Mogilyansky D, Visoly-Fisher I, Etgar L, Katz EA (2015) Temperature-and component-dependent degradation of perovskite photovoltaic materials under concentrated sunlight. J Phys Chem Lett 6(3):326–330 10. Saliba M, Matsui T, Seo JY, Domanski K, Correa-Baena JP, Nazeeruddin MK, Zakeeruddin SM, Tress W, Abate A, Hagfeldt A, Grätzel M (2016) Cesium-containing triple cation perovskite solar cells: improved stability, reproducibility and high efficiency. Energy Environ Sci 9(6):1989–1997 11. Zhang H, Liu H, Lu W, Zhang W, Hao Y, Wang P, Yu W (2019) Analysis and suppression of metal-contact-induced degradation in inverted triple cation perovskite solar cells. Mater Lett 236:736–738 12. Yang J, Liu X, Zhang Y, Zheng X, He X, Wang H, Yue F, Braun S, Chen J, Xu J, Li Y (2018) Comprehensive understanding of heat-induced degradation of triple-cation mixed halide perovskite for a robust solar cell. Nano Energy 54:218–226

Part XXI

Biological Materials Science

A Concise Review of the Antibacterial Action of Gold Nanoparticles Against Various Bacteria Ikhazuagbe H. Ifijen, Muniratu Maliki, Nyaknno U. Udokpoh, Ifeanyi J. Odiachi, and Best Atoe

Abstract Gold nanoparticles (AuNPs) have been proven to be a remarkable choice for utilization as an antibacterial agent. AuNP has been demonstrated to have a satisfactory performance against several types of pathogens, and analysis of its antibacterial action has also become a trending subject in contemporary times. In terms of the toxicity effects, results are sometimes ambiguous and contradictory due to the lack of a standardized experimental methodology; different research have utilized diverse techniques, delivery routes, and doses, and comparable tests may provide different results. This study describes the antibacterial action of gold nanoparticles against several types of bacteria in order to give a concise overview of and insight into the existing knowledge for researchers committed to this field. The potential of gold nanoparticles as an antibacterial agent and the toxicity of gold nanoparticles, both in vitro and in vivo, were both emphasized as important topics that need additional research. Keywords Gold nanoparticles · Antibacterial · Drug resistance · Toxicity

I. H. Ifijen (B) · N. U. Udokpoh Department of Research Operations, Rubber Research Institute of Nigeria, Iyanomo, Benin City, Nigeria e-mail: [email protected]; [email protected] M. Maliki Department of Chemistry, Edo State University, Uzairue, Edo State, Nigeria I. J. Odiachi Department of Science Laboratory Technology, Delta State Polytechnic, Ogwashi-Uku, Nigeria B. Atoe Department of Daily Need, Worldwide Healthcare, 100, Textile Mill Road, Benin City, Edo State, Nigeria © The Minerals, Metals & Materials Society 2023 The Minerals, Metals & Materials Society, TMS 2023 152nd Annual Meeting & Exhibition Supplemental Proceedings, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-031-22524-6_58

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Introduction Antibiotics frequently lose their potency over time as a result of the formation and spread of drug resistance in bacterial infections [1]. According to Rossolini et al. (2014), the so-called “antibiotic resistance crisis” and iatrogenic diseases brought on by drug-resistant bacteria increase annual medical expenses by up to billions of euros [2]. It is vitally important to find new antibacterial drugs and therapeutic approaches in light of this dire condition [3, 4]. Nanomaterials have emerged as a viable and effective replacement for traditional materials in the majority of applications across all branches of science and technology [5–15]. Due to the ultra-small size of nanomaterials, they have an enhanced surface to volume ratio and more active atoms at the router surfaces [3, 16]. Silver, gold, and zinc are a few metallurgic nanomaterials that have been approved for use as bactericidal and bacteriostatic agents and each has a unique set of characteristics and spectrum activities [17–21]. The special qualities of gold nanoparticles, including their adaptable size, shape, surface properties, optical properties, biocompatibility, low cytotoxicity, high stability, and multi-functional potential, make them interesting in many medical domains [22, 23]. Gold nanoparticles (AuNP) have already been used in studies on cancer diagnostics, tissue engineering, gum disease, dental caries, and implantology [24]. AuNP has antifungal and antibacterial qualities; thus, it can be added to some biological materials to give them antibacterial capabilities. This increases the material’s practicality for various applications [25]. Using gold nanoparticles as carriers for antibacterial medications, antibacterial drugs can connect to nanoparticles via noncovalent or covalent bonding, enhancing the antibacterial effects of the pharmaceuticals by enhancing their ability to reach the site of action. Under continuous laser irradiation, gold nanoparticles produce photothermal effects that can serve as sterilant [21]. The effectiveness of gold nanoparticles as antibacterial agents against a broad range of bacteria was highlighted in this article, which provided a succinct overview of recent work in that area. We also give a brief discussion of the potential of the toxicity and the restrictions of using gold nanoparticles as an antibacterial agent.

Antibacterial Effects of Gold Nanomaterials The antibacterial properties of gold nanoparticles (AuNPs) against various harmful bacterial isolates (Pseudomonas aeruginosa and Staphylococcus aureus) were studied by Abdulazeem et al. [22]. At 500, 250, 125, 62.5, and 31.25 g/ml concentrations, disk diffusion assays were utilized to gauge the antibacterial effectiveness of AuNPs. According to the study’s conclusions, clinical MDR bacteria might be killed by AuNPs with a 25 nm size (P. aeurogenosa and Staph. aureus). When P. aerurogenosa and Staph. aureus was compared, the biggest inhibition zones for P. aerurogenosa and Staph. aureus, respectively, were averaged at 19 mm and 62.5 g/ml, respectively.

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The enhanced use of NPs as self-therapeutic agents is also made possible by the presence of functional ligands on their surfaces, which can directly interact with biological molecules on a multivalent basis [26]. This method can get around the use of current antibiotics in nanocarrier systems as well as their possible limitations. Gold is a desirable core material since it is basically inert and nontoxic, which makes it suitable for assembly of such self-therapeutic NPs. In order to achieve this, we created many self-healing gold nanoparticles (AuNPs) that act as an antibacterial agent. In order to tackle MDR bacteria, Li et al. (2014) describe an antibacterial method utilizing self-therapeutic AuNPs. Gram-negative and Gram-positive bacteria from 11 clinical MDR isolates, which were efficiently inhibited by cationic and hydrophobic functionalized AuNPs (Fig. 1) [27]. The link between NP ligand structure and activity showed that surface chemistry had a significant impact in AuNP’s antimicrobial capabilities, offering a design factor for forecasting and logically designing novel antibiotic NPs. Considering the effective antibacterial impact on MDR bacteria, the high biocompatibility, and the gradual emergence of resistance, cationic hydrophobic nanoparticles, such as NP 3, present a promising approach for the long-term eradication of (MDR) bacteria, a significant problem in healthcare. Infections linked to medical devices pose a severe health risk, particularly for elderly and/or patients with limited mobility [28]. Despite numerous antimicrobial coatings for medical devices having been researched up to this point, only few of them have been made available for clinical use. It is vital and necessary to conduct research into new bactericidal agents that can eliminate germs, prevent the formation of biofilms, and have acceptable biocompatibility. A variety of different morphology of gold nanoparticles were synthesized by Piktel et al. (2021) and thoroughly tested Fig. 1 PI staining showing NP 3 (C10-AuNP)-induced bacterial cell membrane damage. Scale bar is 5 µm [27]

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against a range of clinical strains of Candida albicans, Pseudomonas aeruginosa, Staphylococcus aureus, as well as a range of uropathogenic Escherichia coli isolates [29]. The development of nanomaterials that are proven to be much more effective against tested bacteria than the gold nanoformulations reported so far was made possible by the optimization of the production of gold nanoparticles. Their antimicrobial spectrum, it should be noted, includes strains with various drug resistance mechanisms. Gold nanoparticles’ potential for development as novel coatings is highlighted by their easy and inexpensive manufacture, exceptional bactericidal efficacy at nanogram concentrations, and minimal toxicity, as shown by the case of urological catheters. The reported research closes a gap in non-spherical gold nanoparticle microbial studies and advances the development of antimicrobial coatings that specifically target multidrug-resistant microorganisms that cause nosocomial infections linked to medical devices. Human health has always been significantly impacted by illnesses like tuberculosis (TB). In 2008, there were reportedly 9.4 million incident cases of TB and 11.1 million prevalent cases, according to a recent WHO report. Every year, 1.3 million people die as a result of TB [30]. Bacillus Calmette-Guérin (BCG), a commonly used tuberculosis vaccine, was created from an attenuated strain of the causative agent of bovine tuberculosis, Mycobacterium bovis. After being grown in potato medium for a long time, BCG lost its virulence. It has been utilized as a TB substitute in biosafety level 2 labs for the creation of anti-TB medications. Since monotherapy fails to eradicate infections and hastens the emergence of resistance, combination medication treatments are typically employed in the treatment of TB. Antibacterial metallic nanoparticles are used as a result of the shortcomings of conventional therapies, such as drug-induced illness and the rising prevalence of multiple-drug-resistant tuberculosis (MDR-TB) [22]. For instance, Zhou et al. (2012) showed that gold and silver nanoparticles exhibit high antibacterial capability for both the Gram-positive bacteria Bacillus Calmette-Guérin (BCG) and the Gram-negative bacteria E. coli (Fig. 2) [3]. When aggregation is not present in significant amounts, these NPs work at their best. Gold nanoparticles of the same size and shape showed various inhibitory effects depending on the surface modification agents used. Mechanistic investigations showed that citrate-capped gold NPs did not result in cell lysis, while PAH-capped gold NPs did. Silver nanoparticles were found to have potent antibacterial capabilities because of their inherent elemental characteristics. This work implies that NPs may be promising candidates for the development of anti-TB medications, but further research is necessary to assure their maximal bactericidal action and minimal host damage. Researchers have suggested a quick and easy way to fabricate gold nanoparticles (CGA-AuNPs) with various particle sizes by employing trisodium citrate (TSC) as the first reducing agent and chlorogenic acid (CGA) as the second reducing agent. Excellent antibacterial action is shared by CGA-AuNPs and CGA. The synthetic CGA-AuNPs can continue to function well even after 26 days without the addition of any additional stabilizers [30]. Algae were used by Murphin et al. to produce scattered cuboid gold nanoparticles in a green manner [31]. An antibacterial test revealed that the produced gold nanoparticles had some potential against E. coli

A Concise Review of the Antibacterial Action of Gold Nanoparticles … Fig. 2 Schematic representation of the interactions between NPs and bacterial cells. a Bacterial cells take up single citrate Au NPs or aggregations of Au NPs complexes. b PAH facilitates Au NPs uptake into bacterial cells followed by lysis. c Most of Ag NPs were trapped in cell walls [3]

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and Staphylococcus aureus, two human pathogens. Tinospora cordifolia stems were utilized by Ali et al. to create gold nanoparticles (AuNPs), and they investigated the impact of AuNPs on the biofilm of Pseudomonas aeruginosa PAO1 [32]. The findings demonstrate that Pseudomonas aeruginosa-related biofilm-related illnesses can be treated with green synthesized AuNPs acting as efficient nanoantibiotics [32]. Gold nanoparticles created using conventional wet chemical methods were stimulated by light by Mocan et al. before being added to MRSA bacteria to speed up the MRSA necrosis rate in a brief incubation period [33]. The aqueous stem of the Cannabis sativa plant was employed by Singh et al. to create gold nanoparticles without the use of any additional reducing, stabilizing, or capping chemicals [34]. Pseudomonas aeruginosa and Escherichia coli were both susceptible to the bactericidal effects of the produced nanoparticles, which were crystalline and had an average diameter between 12 and 18 nm [34]. AuNP thermosensitive gels have been shown by Arafa et al. to have specific bioavailability, skin permeability, and antibacterial and anti-inflammatory properties [35].

Toxicity of Gold Nanoparticles (Au NPs) Due to their potential uses in a variety of sectors, including the chemical, biological, and optical fields, AuNPs have garnered a lot of attention. They are frequently utilized as medication carriers, cosmetic materials, medicinal fillings, and antibacterial materials, and are generally thought to be biosafe [36]. Numerous medication studies have examined AuNPs as drug transporters while taking negative effects into account. Before considering the use of AuNPs in clinical settings, the toxicity of

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AuNPs to organisms must be clearly elucidated. Different harmful effects have been described and have been connected with the AuNP size, shape, dosages, sampling points, surface coating and functionalization, cell lines, or animal models. In vitro and in vivo studies on the cytotoxic effects of AuNPs have produced a variety of inconsistent findings. The biomedical profession has paid significant attention to the finding that AuNPs’ toxicity and distribution are determined by particle size [37]. Cell penetration is possible at small diameters, and smaller particles are more toxic than larger ones [38], especially 1.4–1.5 nm. In particular, Chen et al. (2009) showed that mice were severely harmed by AuNPs of 8–37 nm, but were unaffected by particles of 3, 5, 50, and 100 nm [39]. This is a clear illustration of the size effect of nanoparticles on cells. AuNPs display toxicity at particular diameters, both in vitro and in vivo, according to a number of toxicity studies focusing on AuNPs of 15– 35 nm [40]. In mice, high concentrations of 20-nm AuNPs encouraged the growth of neural precursor cells generated from human neurospheres and caused inflammation, and chronic manifestation included considerable fat loss and suppression of inflammatory effects [41]. It is evident that different investigations have found disparate outcomes, and it’s possible that the outcomes of in vivo and in vitro experiments are not similar. In fact, while some research suggests that AuNPs are not hazardous to some taxa, some data suggests that they are in some of them [42]. Because toxicity studies are not usually methodical, the results are often inconsistent [43]. In addition, the majority of research have only focused on toxicity evaluations without taking into account the toxicity mechanisms [44]. Additionally, the dosage of nanoparticles is crucial because many medications that are safe at low dosages are harmful at high levels. However, dose varies significantly between studies, and the number of cells exposed to nanoparticles at a given concentration is not consistently reported [42– 44]. Future research should undertake comprehensive evaluations of the impact and the mechanism underlying the impacts of AuNPs on microorganisms. In order to shed light on ecotoxicological risks, it is also crucial to examine the ecotoxicity of AuNPs in both functionalized and unfunctionalized forms.

Prospects of Gold Nanoparticles The antibacterial efficacy of formulations prepared at nine distinct concentrations using toothpaste with active components made of zinc citrate, silver, and gold particles, as well as eight standard microbiological cultures were investigated by Juneviius et al. The outcomes demonstrated that gold was less efficient than silver [45]. The acute cytotoxicity of the silver ions generated by silver nanoparticles may limit their potential practical applications, even if silver nanoparticles are thought to be promising antibacterial agents due to their antibacterial activity. Irradiated porous Ag–Au nanoplates, according to the research of Zhu et al. had decreased in vitro cytotoxicity and comparable antibacterial activity on Staphylococcus aureus strains when compared to Ag nanoplates [46]. In addition, research by Chlumsky et al. revealed that

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even though AuNPs, AgNPs, and CS and their mixtures did not eliminate biofilms, they also significantly decreased the metabolic activity of every investigated strain by at about 80% [47]. The results of Saravanakumar and colleagues’ experiments demonstrated the multi-functional biological functions of CS-AuNPs, including antioxidant, antibacterial, antidiabetic, and anticancer effects, which call for more study [48]. Different sizes and surface chemistry of the nanoparticles-controlled cell absorption and nanoparticle toxicity, which adversely affected their clinical applicability [49]. The mononuclear phagocytic system is capable of removing nanoparticles, according to studies. Although changing particle shapes will impact how they are distributed on target organs and eventually their biological toxicity, no appreciable sublethal effects have been seen [50]. Nanoparticles can enter the systemic circulation by skin contact, inhalation, or oral delivery due to their small size. Once in the bloodstream, nanoparticles come into touch with many blood components and may interfere with proper platelet activity, leading to bleeding or thrombosis. The compatibility of nanoparticles with blood components is still up for debate [51]. Nanodrug delivery technologies have created new opportunities for combating multidrug resistance (MDR), which has become the focus of antibiotic improvement. The impact of flavonoid-coated gold nanoparticles (FAuNPs) on the colonization of Enterococcus faecalis in mouse liver and kidney was examined by Riaz et al. The number of microorganisms in mouse organs was considerably lower when free flavonoids were used [52]. Gold nanoparticles have many benefits, including easy and precise fabrication, tiny size, good biocompatibility, and surface plasmon resonance. However, it is unclear whether these benefits might be impacted by in vivo or intracellular variables. The small size and various forms of gold nanoparticles enhance their antibacterial properties, but more study is required to determine whether organisms can survive for a long time and receive a high dose [53]. Studying the impact or variations in the use of gold nanoparticles over time in organisms or a certain physiological state in particular diseases is also crucial. Gold nanoparticles are easily altered, and functionalized gold nanoparticles have a wide range of potential applications in the fight against germs. Gold nanoparticles can be altered to enhance their antibacterial capabilities for use in therapeutic antibacterial applications.

Conclusion This study describes the antibacterial action, potential and toxicity both in vitro and in vivo of gold nanoparticles against several types of bacteria. The review’s findings demonstrate that Au NPs have adequate characteristics that make them suitable as antibacterial agents for preventing a wide range of pathogens. Despite the fact that the properties and uses of AuNPs have been gradually identified, the lack of universal detection and evaluation criteria has led to inconsistent findings and a lack of clarity on the antibacterial activity and toxicity. To make comparisons between studies easier, additional research should establish common standards.

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A Review of Nanovanadium Compounds for Cancer Cell Therapy Ikhazuagbe H. Ifijen, Nyaknno U. Udokpoh, Muniratu Maliki, Esther U. Ikhuoria, and Efosa O. Obazee

Abstract Several vanadium compounds have shown promise as chemotherapeutics during the last few years. Vanadium compounds’ rapid elimination from the body and potential toxicity have, nevertheless, impeded their ongoing expansion. In addition to circumventing these constraints, vanadium-based nanomaterials benefit from the intrinsic photics and magnetic properties of vanadium, which make them a multimodal platform for the detection and treatment of cancer. This review outlined the numerous studies that looked into the prospect of treating cancer cells with nanovanadium compounds over the years. The essential biological and pharmacological activities of vanadium-nanobased materials in cancer treatment are also highlighted. The numerous studies that looked into the prospect of treating cancer cells with nanovanadium compounds found a novel alternative channel for cancer-fighting medicinal techniques. Keywords Nanovanadium · Cancer · Therapy · Nanoparticles

Introduction Nanomaterials have been found to exhibit improved attributes because of their extremely small sizes, including surface to volume ratio, reactivity, strength, electrical characteristics, and optical properties [1–17]. Metal anticancer medicines have been employed extensively as the primary chemotherapeutics in recent years; however, substantial drug resistance and adverse effects, such as nephrotoxicity and I. H. Ifijen (B) · N. U. Udokpoh · E. O. Obazee Department of Research Operations, Rubber Research Institute of Nigeria, Iyanomo, Benin City, Nigeria e-mail: [email protected]; [email protected] M. Maliki Department of Chemistry, Edo State University Uzairue, Edo State, Nigeria E. U. Ikhuoria Department of Chemistry, University of Benin, P.M.B. 1154, Benin City, Nigeria © The Minerals, Metals & Materials Society 2023 The Minerals, Metals & Materials Society, TMS 2023 152nd Annual Meeting & Exhibition Supplemental Proceedings, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-031-22524-6_59

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neurotoxicity, severely limit their usage in clinical practice [18, 19]. It is crucial to research new transition metal anticancer medications that are low in toxicity, highly effective, and have a good bioavailability. In particular, several metal complexes with different ligands exhibit greater potential for the development of high-efficiency and low-toxicity anticancer medicines because of their good water solubility and low toxicity to healthy organisms [20]. Most developments throughout the years have involved metal complexes, such as those containing ruthenium, tin, titanium, germanium, copper, palladium, and vanadium [21]. Vanadium has been thoroughly investigated from its structural makeup to its many physiological activities. It is an ultratrace element with intriguing pharmacological effects that is found in animals and higher plants. Many cancer cell lines have been used to assess the anticancer efficacy of vanadium compounds in vitro and in vivo. An important class of chemicals called VO (oxidovanadium) flavonoids selectively inhibit bone cancer cell growth [22, 23]. Leukemia cells, multiple myeloma cells, and solid tumor cells (brain, prostate, breast, ovarian, etc.) are just a few examples of the human tumor cell lines that are responsive to the vanadium complex metvan (VIVO(SO4) (4,7-Mephen)2 anticancer)’s activity [23, 24]. Additionally, vanadocene derivatives have significantly inhibited the growth of human cancer cell lines, primarily those from malignancies of the liver and testes. Vanadium compounds containing heterocycles and Schiff bases are an additional intriguing class with anticancer characteristics [25]. Their compounds have demonstrated anticancer effects on breast, colon, and bone cancer cells [25]. However, there are still few published evaluations of trials involving cancer cells that vanadium compounds have been shown to target. This study summarized the many studies conducted over the years that investigated the possibility of treating cancer cells with nanovanadium compounds. Additionally highlighted are the vital biological and pharmacological functions of vanadium-nanobased materials in the treatment of cancer.

Vanadium’s Involvement in the Treatment of Cancer One of the deadliest diseases in the world has been and continues to be cancer. Deep understanding of the mechanisms triggering and shaping the chemical and biological phenotypes of cancer is necessary for its cure. According to one theory, the “hallmarks” of cancer consist of eight biological abilities: maintaining proliferative signaling, dodging growth inhibitors, enabling replicative immortality, resisting cell death, causing angiogenesis, triggering invasion and metastasis, reprogramming of energy metabolism, and dodging immune system eradication [26]. The study of these ideas is anticipated to have a growing impact on the creation of cancer treatments. Therefore, important targets include (a) metabolic abnormalities and perturbations in the energy generation process through respiration, and (b) problems in the structure and function of the mitochondria (glycolysis or the “Warburg effect”) [27].

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Furthermore, Reactive Oxygen Species (ROS) cellular levels are revealed as biological factors that determine a cell’s fate. The oxidation of several components, such as nucleic acids, proteins, and lipids, is facilitated by the buildup of intracellular ROS in healthy cells. These diverse oxidative responses produce significant harm, often encouraging apoptosis in the case of severe harm, but they can also result in aberrant growth and transformation [28]. An emerging metal ion that has been suggested as a treatment for both cancer and diabetes is vanadium. Numerous similarities exist between the intracellular cascade mechanisms that hormones like insulin use to promote growth and apoptotic cell death. Although mRNA encoding for proteins that are known to be controlled by vanadium can be produced in response to low amounts of ROS, excessive levels of ROS are harmful to cells and cause apoptotic processes. Regarding this, vanadium compounds that have been suggested for use against cancer have demonstrated cytotoxic effects against cancer cell lines, with ROS and Reactive Nitrogen Species (RNS) emerging during treatment of vanadium as an anti-diabetic medication. Therefore, it is inevitable that the similarities in these metabolic processes must be taken into account when assessing the potential therapeutic application of this metal for cancer [29]. In a similar vein, autophagy, or the lack of it, can influence the onset and progression of a number of pathological disorders, including cancer. By limiting waste buildup and eliminating damaged and outdated organelles, autophagy functions as a process that maintains cellular homeostasis. According to some evidence, autophagy appears to be involved in tumor suppression in normal cells while stimulating tumor growth in tumors that have already formed [30]. Vanadium appears to be crucial in this situation as well, influencing the autophagic pathway in cancerous cells. For many years, it has been known that some cancers, such as some varieties of myeloid leukemia, can differentiate into children with low proliferative potential while still retaining the oncogenic mutations of their malignant progenitors [30]. The use of metal nanoparticles in the field of medical oncology has shown considerable promise in the fight against cancer during the past few years (diagnosis-therapeutics). To achieve this, tailored drug delivery, improved bioavailability, and preservation of their release for systemic distribution can be achieved using customized vanadium nanoparticle drug delivery systems [31]. The following families of vanadium compounds have been identified in an effort to examine the many vanadium forms that have shown promise as cancer therapeutics: (a) organometallic vanadiumcenes, and (b) nonorganometallic vanadium compounds. V(V)-peroxido vanadates, V(V)-peroxido-betaines, and V(V) polyoxovanadates are among the latter group. Below, we investigate the synthetic, structural, and spectroscopic characteristics of these compounds, laying the groundwork for further investigation into their potential as anticancer vanadodrugs.

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Prior Investigations into the Anti-cancer Abilities of Nanoparticles Based on Vanadium Vanadium-based compounds have been extensively studied for their chemopreventive and anticancer properties using experimental animal models and a wide range of cancer cell lines. For instance, it has been shown that oxyvanadium complexes, especially vanadyl (IV) derivatives with hybrid ligands of Schiff base and polypyridyl, have excellent anticancerous therapeutic activity. Animal investigations in vivo are incredibly rare, and the majority of research on the function of these oxovanadium complexes has been conducted in vitro. On VO (hntdtsc)(NPIP), which possessed the best inhibitory action in vitro on various tumor cell proliferation, Bai et al. (2021) recently completed a thorough anticancer activity investigation in vitro and in vivo [32]. According to the study on cellular mechanisms, VO(hntdtsc)(NPIP) prevented the growth of HeLa cells by stopping the cell cycle at the G0/G1 phase via the p16-cyclin D1-CDK4-p-Rb pathway and by triggering cell apoptosis via the mitochondrial-dependent apoptosis pathway. According to the outcomes of nude mice in vivo image detection, H&E pathological examination, and immunohistochemical detection of p16/Ki-67 protein expression, VO(hntdtsc)(NPIP) significantly inhibited the growth of tumor and induced the apoptosis of cancer cells in mice xenograft models, which was inconsistent with the effects in vitro (Fig. 1). Overall, the findings, especially those from in vivo tests, showed that VO(hntdtsc)(NPIP) has the potential to be the lead molecule and a possible anticervical cancer medication. In a study conducted by Guerrero-Palomo et al. [33], sodium metavanadate (NaVO3 [V(+5)]) and vanadyl sulfate (VOSO4 [(+4)]), both of which have been reported to have apoptosis-inducing activities, were used to study the mechanisms of cell death. The scientists examined the A549 cell line’s survival and expression of caspases, reactive oxygen species (ROS) generation, Bcl2, Bax, FasL, and NO after exposing it to varied concentrations (0–100 M) and exposure durations of each chemical. The study’s findings demonstrated that neither substance altered the production of caspases or pro- and anti-apoptotic proteins at baseline. Only the considerable 12 and 14-fold increases in ROS generation caused by NaVO3 and VOSO4, respectively, at 100 M concentrations after 48 h were noticed as a change. The results demonstrated that the larger ROS production was caused by the [(+4)] valence compound and suggested that the cell death induced by the vanadium compounds tested here is not related to standard apoptotic processes. The results demonstrated that the larger ROS production was caused by the [(+4)] valence compound and suggested that the cell death induced by the vanadium compounds tested here is not related to standard apoptotic processes. It’s probable that the difference results from its higher oxidative status, which results in higher ROS production and greater cell damage. The findings indicate that the valence of the chemical affects how well vanadium compounds cause cell death mechanisms to occur. In a different work, Das et al. [34] presented a simple method for the production of irregular dumbbell-shaped vanadium pentoxide nanoparticles (V2 O5 NPs: 30–60 nm) by polyol-induced microwave irradiation procedure in conjunction with calcination

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Fig. 1 a H&E staining was performed on heart, liver, kidney, and lung tissue sections of mice after a dose of 4.0 mg/kg of VO(hntdtsc) NPIP treatment (×400). b Nude mice bearing HeLa cells at the 28th day after transplantation. c Nude mice xenograft specimens at the 28th day after inoculation of HeLa cells. d Growth curves of the average body weight and tumor volume within each experimental group on the 0, 7th, 14th, 21st, and 28th days after tumor transplantation. Tumor volume was calculated using the following formula: width 2 × length × 0.5. *p < 0.05 or **p < 0.01 compared to the control group [32]

[34]. The cell viability analysis of the as-synthesized nanoparticles demonstrated that V2 O5 NPs could effectively limit the proliferation of various cancer cells (B16F10, A549, and PANC1), demonstrating their anti-proliferative action. However, V2O5 NPs did not significantly affect the viability of the normal cells (CHO, HEK-293, and NRK-49F), indicating their biocompatibility. Intriguingly, these nanoparticles damaged the blood vessels in a chick embryo model and prevented the growth and migration of endothelial cells (HUVECs and EA.hy926), demonstrating their antiangiogenic capabilities. According to the mechanistic study, successful internalization of V2O5 NPs produced intracellular reactive oxygen species (ROS), which in turn up-regulated p53 protein and down-regulated survivin protein in cancer cells, resulting in the apoptotic process. Additionally, giving V2O5 NPs to C57BL6/J mice that had melanoma dramatically enhanced their survival when compared to untreated

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control mice, demonstrating the nanoparticles’ therapeutic potential for melanoma. Additionally, the in vivo toxicity investigation showed that sub-chronic exposure to V2O5 NPs had no deleterious effects on mice. Overall, the scientists came to the conclusion that using V2O5 NPs’ anti-angiogenic capabilities in the future, different therapeutic treatment approaches for melanoma and other diseases could take a new turn. Three oxidovanadium (IV) complexes have been shown to have anticancer effects on the MG-63 human osteosarcoma cell line, according to León et al. [35]. The three complexes, VO(oda), VO(oda)bipy, and VO(oda)phen (oda = oxodiacetate), inhibited cell viability in a concentration-dependent manner. While VO(oda)bipy and VO(oda) demonstrated a decrease in cell viability only at higher concentrations (at 50 and 75 M, respectively), VO(oda)phen’s antiproliferative activity could be observed across the whole concentration range (at 2.5 M) (pb 0.01). Additionally, at 2.5 M, VO(oda)phen reduced lysosomal and mitochondrial activity, while at 50 M, VO(oda) and VO(oda)bipy had an impact on neutral red uptake and mitochondrial metabolism (pb0.01). However, in MG-63 cells treated with VO(oda) at 2.5–10 M, no DNA damage examined by the Comet test could be seen. However, at 2.5 and 10 M, VO(oda)phen and VO(oda)bipy, respectively, caused DNA damage (p b 0.01). At 10 M of VO(oda)phen, but only at 100 M of VO(oda) and VO(oda)bipy, reactive oxygen species production increased (p b 0.01). Additionally, as shown by the externalization of the phosphatidylserine, VO(oda)phen and VO(oda)bipy caused apoptosis (Fig. 2). Agarose gel electrophoresis was used to measure DNA cleavage, and the results revealed that VO(oda)(bipy) and VO(oda)(phen) have corresponding DNA cleavage capacities, with VO(oda)(phen) exhibiting the greatest nuclease activity in this series. Since VO(oda)phen had the strongest anticancer action on human osteosarcoma cells, followed by VO(oda)bipy and subsequently by VO(oda), our results generally demonstrated a good relationship between the bioactivity of the complexes and their structures. These experimental experiments collectively demonstrated the following effects of vanadium compounds: (a) chemopreventive implications against chemicallyinduced carcinogenesis, primarily by inactivating carcinogen-derived active metabolites, through the use of regulation of the composition and action of several liver xenobiotic cellular metabolism enzyme and the substrate (P450), and/or by increasing the antioxidant capacity of the target organs of the carcinogens; they act primarily in phase I (initiation) and conversely phase II (promotion) of chemical carcinogenesis; (b) Vanadium accumulation in areas rich in nucleic acids, which prevents DNA and RNA synthesis, or increasing the production of deadly reactive oxygen species in tumor cells, can both have anticancer effects on animals with tumors. Additionally, there is evidence that vanadium compounds may have particular systemic effects on cancer-bearing animals that promote survival and/or control tumor growth. Additionally, extensive research on both normal and cancerous cell lines has provided comprehensive knowledge on the various mechanisms through which vanadium compounds may exert their anticarcinogenic effects [36].

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Fig. 2 Effects of VO(oda), VO(oda)bipy and VO(oda)phen on MG-63 human osteosarcoma cell linemorphology by Giemsa staining. Tumoral osteoblasts were incubated for 24 h without drug addiction (basal, first panel), or with different concentrations (10, 25 or 50 µM) of VO(oda), VO(oda)bipy and VO(oda)phen. After incubation, the cells were stained with Giemsa and observed under the microscope [35]

Current Perspective The biggest challenges facing contemporary cancer treatment plans are comparable to combating “Hydra”. Different cellular activities, which collaborate in the early stages of carcinogenesis, must be “combatted” by a variety of vanadoforms, ranging from straightforward inorganic anions to clearly characterized binary and ternary complexes. The multi-step process of developing cancer involves the stimulation of cells with many stimuli, which ultimately causes unchecked cell growth and division, aberrant cellular differentiation, and cell death evasion. Long-term epidemiological studies, controlled clinical trials, and careful examination of the availability, selectivity, and specificity of toxic well-defined forms of the metal ion vanadium that encompass specific physicochemical characteristics (vide supra) are required before vanadium compounds can be considered effective. The chemistry underlying the

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biological activity of vanadium in its many forms should also be studied, and investigations on the identification of particular sites of interaction of vanadium with biomolecular targets in the cell should be carried out concurrently [37].

Conclusion The research that has been done in recent years has enhanced our understanding of the utilization of nanovanadium compounds for cancer cell treatment. Because of its low toxicity, anticarcinogenic qualities, and human administration of the metal, vanadium is a potential antineoplastic agent for treating human cancer. This point of view is in favor of new complexes since they are more potent and beneficial. More research is necessary, nevertheless, if vanadium compounds are to become a new class of effective anticancer drugs.

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Biodegradation of Petroleum-Based Plastic Using Bacillus sp. Rahulkumar Sunil Singh, Eddie Bryan Gilcrease, Ramesh Goel, Michael L. Free, and Prashant K. Sarswat

Abstract Over the last few decades, petroleum-based plastics have been used in an uncontrolled manner due to their attractive characteristics, posing severe environmental challenges that need to be immediately addressed. Literature indicates some microorganisms for plastic degradation. Bacillus sp. was found capable of degrading various polymers such as polypropylene, polyethylene, polystyrene, and polyurethane. This study investigated the growth and morphological features of Bacillus sp. culture, and its ability to decompose untreated polypropylene (PP) plastics using a mineral salt medium in an incubator shaker at 37 ºC with 120 rpm for 4 weeks. The bacterial growth was spectrophotometrically measured at OD595nm . pH and weight loss measurement provided the extent of plastic degradation. The significant increment in pH of the media towards alkalinity confirmed the degradation of the PP plastics. The preliminary results of 1-week and 4-weeks long incubation suggest that Bacillus sp. assisted plastic degradation might be a feasible approach for diminishing this environmental challenge. Keywords Polymers · Sustainability · Environmental effects · Plastic · Recycling · Biodegradation

Introduction More than 8300 million tons of plastic have been manufactured worldwide since 1950 [1–2]. Out of which 4600 million tons of plastic have been discarded in the environment, 700 million tons are incinerated, and 500 tons of them are recycled, R. S. Singh (B) · M. L. Free · P. K. Sarswat Department of Metallurgical Engineering, University of Utah, Salt Lake City, UT 84112, USA e-mail: [email protected] E. B. Gilcrease · R. Goel Department of Civil and Environmental Engineering, University of Utah, Salt Lake City, UT 84112, USA © The Minerals, Metals & Materials Society 2023 The Minerals, Metals & Materials Society, TMS 2023 152nd Annual Meeting & Exhibition Supplemental Proceedings, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-031-22524-6_60

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while the remaining amount is still in use [3–4]. The majority of currently manufactured plastics are petroleum-based synthetic polymers derived from coal, natural gas, oil, and other petrochemical compounds [5]. In today’s world, plastic has become a necessity in everyone’s life, and it seems impossible to survive without it. The plastic waste generation per capita is significantly higher in developed countries such as USA (68 kg/year), and Europe (50 kg/year) than in developing countries like India (8 kg/year), Sri Lanka (6 kg/year), and Bangladesh (4 kg/year) [6]. The key reason behind the exponential rise in the usage of plastics is their attractive features such as easy accessibility, low cost, high durability, lightweight, stable chemical, and physical properties, weathering resistance, low toxicity, and transparency [7–9]. These features facilitate its industrial and domestic applications in various sectors, primarily in packaging (~40%) followed by construction, textiles, transportation, electronics, food, and medical products [1–2]. Due to this versatile nature, plastics use has increased day by day, and it has also become a threat to the environment [5]. The improper disposal of plastics like incineration, landfilling, and releasing in natural environment causes global warming, soil degradation, groundwater contamination, plastic toxins in food chain, human diseases, and endangered marine life [9]. The most extensively used plastic types are polyethylene (PE), polypropylene (PP), and polyvinylchloride (PVC), polyethylene terephthalate (PET), polyurethane (PU), and polystyrene (PS) with the market share of 36.3, 21, 11.8, 10.2, 8.2, and 7.6% respectively [9–10]. The effective natural decomposition of these petroleum-based plastics often requires hundreds or thousands of years [7]. The degradation of plastic in environment occurs through the physicochemical degradation and biodegradation processes. In nature, plastic degradation initiates with physicochemical degradation, which consists of photodegradation, hydrolysis, and thermal degradation processes, results into the decomposition of plastic wastes to low molecular mass compounds that are further metabolized by microbial processes [9]. The rate of degradation, however, is extremely slow [5]. Thus, in addition to widespread manufacturing of plastics, slow natural degradation is another factor contributing to the accumulation of plastics [4]. This plastic accumulation causing severe environmental issues raises the interest in the scientific community to develop the new type of polymers or mechanisms for degradation. As an alternative to plastics made from petrochemicals, biodegradable materials have been developed using cellulose or starch. However, biodegradable plastics are currently being used in very limited quantities because of their poor durability, lack of necessary infrastructure, and high production cost [8, 11]. Since the later decades of the twentieth century, researchers have been carrying out extensive study to figure out how to modify plastics so that they are more susceptible to microbial attack. The new era of plastic waste management has begun with plastic biodegradation. Biodegradation is the process in which microbes including bacteria, algae, and fungi break down polymer materials through their metabolic activity. This occurs in either anaerobic or aerobic conditions, and the microbes use the organic material as a source of carbon and nutrients [12]. Polymer properties (molecular weight, Size, Crystallinity, Functional groups, Cross-linking) and exposure conditions (Temperature, pH, Hydrophobicity, UV radiation, Biosurfactants) are the controlling factors of the biodegradation process [13]. In addition to the

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aforementioned controlling factors, the biodegradation mechanisms are also very affected by different bonds in the backbone chains of the polymers, because these specific bonds are important to enzymes attack [4]. Consequently, the biodegradation of petroleum-based plastics can be categorized into two types: (1) plastics with C–C backbones, such as PE, PS, PP, and PVC; (2) plastics with C-O backbones, such as PET, and PU [4, 9]. In recent years, several microorganisms and enzymes have been reported to decompose synthetic polymers [14]. Among all those microorganisms, only Bacillus sp. was found capable of degrading all traditional petroleum-based plastics (PE, PVC, PP, PS, and PU) except PET [4]. Additionally, Bacillus sp. can also be utilized to degrade kraft-lignin [15] and crude oil [16]. Numerous plastic biodegradation processes have been carried out using Bacillus sp. to decompose PP [17–18], PE [19–21], PVC [22–23], PS [24], and PU [25]. However, of all the polymers discussed above, Polypropylene (PP) is the one for which no enzymes have yet been discovered that can degrade PP effectively, and there are very few research studies available on its biodegradation mechanism [14]. Most of the investigations aiming for microbial degradation of PP are carried out with the support of pretreatments such as thermo-oxidation, UV irradiation, and using polymer blends with starch, cellulose, or other degradable additives [17, 26]. Therefore, the prime purpose of this work was to systematically investigate the capability of the Bacillus sp. to degrade synthetic PP plastic without any prior pretreatment and an external additive. The study includes the growth process and morphological features of Bacillus sp. culture, and its ability to decompose PP beads in optimized incubation conditions using a mineral salt medium with/without centrifuging. The pH, OD595 , and weight loss measurements were done to demonstrate the plastic degradation with microbial treatment. Based on the experimental outcomes and existing knowledge, this research paper also highlights the challenges associated with the biodegradation of PP in the absence of pretreatment.

Material and Methods Material, Microorganisms, and Chemicals Plastic Sample Collection Polypropylene (PP) plastic pellets of 4–5 mm size were purchased from Amazon to use in the experiments of the current study. The PP pellets were washed with de-ionized (DI) water, and then they were dipped into 70% (v/v) solution of ethanol for 10 min to sterilize them. After sterilization, it was air dried for 15 min.

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Bacterial Species and Culture Media The Bacillus sp. (Type strain. Genome sequenced strain.; Strain designation: VT-13– 104) bacteria was selected based on its ability to degrade petroleum-derived plastics obtained from American Type Culture Collection (ATCC). The culture growth mediums (Tryptic Soy Agar/Broth) were procured from Fisher Scientific. Inoculation of Bacillus sp. was done using the prepared tryptic soy broth (Tryptone: 17 g, Soytone: 3 g, Dextrose: 2.5 g, NaCl: 5 g, K2HPO4: 2.5 g). The inoculum is streaked over the agar plate surface using a sterilized loop and incubated at 37 ºC for 24 h. This results in the formation of discrete, isolated colonies.

Minimal Salt Media Preparation Minimal media for biodegradation study was prepared with composition (per 1000 ml of DI water) of 0.02 g CaCl2 , 0.05 g FeCl3 , 1 g KH2 PO4 , 1 g K2 HPO4 , 1 g NH4 NO3 , 1 g Na2 HPO4 , 0.2 g KCl, 0.2 g NaCl, 0.002 g FeSO4 .7H2 O, 0.002 g ZnSO4 .7H2 O, 0.002 g MnSO4 , 0.002 g CuSO4 . The dissolved solution obtained through stirring was further autoclaved in Market Forge Sterilmatic Sterilizer at a temperature of 121 °C with 15 psi pressure for 20 min. All the chemicals mentioned above were purchased from Sigma-Aldrich.

Morphology and Growth Curve of the Isolated Bacteria Gram staining method was used for morphological characterization of the isolated bacteria. The morphological structure of the cells was observed under an optical microscope (AmScope). For the growth curve, first, the single colony was inoculated into 5 ml of autoclaved broth and incubated overnight in a shaking incubator at 37 ºC, then 1 ml of overnight grown culture was inoculated in 100 ml broth medium and incubated at 37 ºC. The optical density at 595 nm (OD595 ) was measured initially and at an interval of half an hour using the UV/Vis Spectrophotometer (Beckman Coulter DU 730). After completion of the experiment, a graph of OD595 on the y-axis versus time in hours on the x-axis was plotted to obtain a growth curve of bacteria.

Biodegradation Treatment Process The initial weight of the sterilized PP plastic pellets was measured before the experiment. The biodegradation experiment was conducted using two types of culture suspension: (1) Cells without centrifugation and (2) Cells with centrifugation. The grown culture that had attained log phase growth was utilized so that the actively growing cells could be utilized for inoculation. Incubated cultures were

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centrifuged at 3000 g RCF for 10 min using the Allegra 64R Benchtop Centrifuge machine. The cells were washed by centrifugation, and resuspended in minimal media to eliminate any traces of broth. The combination of bacterial culture suspension (washed/not washed) and minimal media in the ratio of 1:10 by volume was added in 250 ml Erlenmeyer flasks with initial pH of 6.58. 1% PP pellets were supplemented in the flasks as the carbon source. The experimental flasks were incubated in New Brunswick Gyratory Water Bath Shaker at 37 ºC with a shaking speed of ~120 rpm under aerobic conditions. The preliminary experiment of 7 days was carried out, which also included the addition of Glycerol as a carbon source to monitor the growth of bacteria and compare with plastic as a carbon source. Considering the preliminary results, the PP degradation experiment was set up using the culture that reached log phase growth (absorbance of 0.4 at 595 nm) for the duration of 4 weeks for both cases, i.e., cells not washed and cells washed. The control samples (inoculation in minimal media without PP pellets) were also maintained under similar conditions.

Determination of Cell Growth, pH, and Weight Loss The experiment samples were withdrawn every 3 days and Bacillus sp. cell growth was monitored based on absorbance at 595 nm (OD595 ) using minimal media as blank. The pH was measured at the end of the experiment using a pH Meter (Fisherbrand accumet AE150 Benchtop) and compared with the initial pH. Afterward, the samples were streaked over the agar plate and incubated at 37 ºC for 24 h to examine whether the bacteria cells were alive or not. The bacterial film that colonized on the surface of the PP pellets was removed through washing with 70% ethanol, followed by a sonication process for 30 min using Branson 1510 Ultrasonic Cleaner. For weight loss measurement, washed samples were rinsed using Milli-Q water, placed on filter paper, and then air-dried. The weight of cleaned PP plastics after the biodegradation process was measured using the analytical balance (Voyage Pro Analytical VP214C-OHAUS) with a readability of 0.1 mg. To assess the degradation, the weight loss percentage of PP plastics was calculated against the initial weight before the biodegradation process using the below formula: W eight loss per centage =

(W f − W i) × 100 Wi

where, Wi and Wf are the weight of the PP plastic pellets before and after biodegradation process, respectively.

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Results and Discussion Bacillus sp. was selected among all the microorganisms studied so far for plastic biodegradation because of its promising bioactivity and capablity of degrading most petroleum-based plastics (PE, PVC, PP, PS, and PU) except PET. The pure culture isolation of the Bacillus sp. from a mixed population was accomplished using the streak plate method. This results in forming discrete, isolated colonies of light yellow color with a circular shape that can be observed in Fig. 1a. Gram staining of isolated bacteria was performed to investigate the morphological features under a microscope. This investigation confirmed the presence of Bacillus sp. due to its purple colour, as shown in Fig. 1b. Morphological characterization of Bacillus sp. isolates also revealed the gram-positive group bacteria with ~10 µm long rod-shape [27–28]. A growth curve study of Bacillus sp. was performed using a UV/Vis Spectrophotometer. The cloudiness of the culture medium is assessed by measuring the OD595nm at half -hour intervals, and a graph of growth vs. time was drawn to obtain the growth curve as, shown in Fig. 2. Different phases like lag phase, logarithmic phase, stationary phase, and death phase are visible in the growth curve (Fig. 2). It was found that the log phase continued up to the 15th h, after that stationary phase starts and ends around the 28th h, and later the death phase remains. Maximum growth was observed at the stationary phase in the time duration of 15–28 h. The preliminary experiments were performed using Glycerol and PP as a carbon source to monitor the growth of the bacteria culture in minimal media. Figure 3 clearly shows that Glycerol and PP both act as a carbon source in case of cells not washed and continuously increasing the bacterial growth. Bacillus sp. cell growth was monitored by measuring absorbance at 595 nm (OD595) every 3 days as shown in Fig. 4. The media having cells not washed with PP showed significantly faster growth, while others remained almost constant. However,

Fig. 1 a Pure bacterial isolates grown on agar medium b Microscopic view after Gram staining of Bacillus sp.

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Fig. 2 Growth curve of Bacillus sp.

Fig. 3 Bacterial growth of Bacillus sp. on PP over 7 days of preliminary experiments

a slight increment can be seen in media having washed cells with PP. The optimum growth of Bacillus sp. with OD595 of 1.462 was attained on the 12th day. After incubation of 4 weeks, culture media of all samples were streaked on an agar plate and incubated for 24 h to examine the cell viability, as shown in Fig. 5a. It was found that cells grew in both cases (cells washed/not washed) of PP plastic experiments but did not observe any cell growth in the control experiments because PP plastic was not available to play a role as the carbon source. The pH is an essential factor in the case of biodegradation to detect plastic degradation. The variation in pH indicates the bacteria’s metabolic activities in the growth medium. The pH of the minimal media initially was 6.58, but after incubation of 4 weeks, that increased to 8.64, in which the PP was added in the bacterial culture (cells not washed). Whereas

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Fig. 4 Bacterial growth of Bacillus sp. in PP culture media over 4 weeks of incubation time

in the control setup where the PP was not added in the bacterial culture (cells not washed), pH was not increased, as demonstrated in Fig. 5b. The significant variation in pH is indicative of bacterial culture metabolic activity. This can also be correlated to the yellowish color and turbidness of the culture in this experiment (Cell not washed + MM + PP). However, in the case of cells washed, a very slight increment was observed from 6.58 to 6.66 with PP, and no change in the control setup. As a result of de-polymerization, the polymer composites are broken into monomers by the influence of exo-enzymes that penetrate through the cell wall to be digested as sole carbon and the sources of energy. The main cause of the pH change is a metabolic enzyme that the microbial strain secretes after consuming the sole carbon atoms found in the polymer sheets submerged in the solution. Being a neutrophile bacteria, Bacillus species efficiently produces extracellular alkaline protease from media sugar usage. The metabolized enzyme, which is undoubtedly

Fig. 5 a Incubated streaked plates to examine cell viability b pH comparison of PP culture medium inoculated with Bacillus sp. after 4 weeks of incubation

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basic in nature, accumulated, and this caused the pH of the media to rise with time. Additionally, using the tryptone in the media, the bacterial strain causes the release of ammonia, which raises the pH [29]. As a result, it is confirmed that PP plastics are degraded by unwashed Bacillus sp. cells, which is why the pH in this sample increased more than in others. A weight loss of 0.34% was observed in the case of cells not washed with PP plastic in media. However, similar to pH variation, no significant weight change was observed in other cases. The slow degradation could be due to the hydrophobic backbone of the long carbon chain, high molecular weight, and added antioxidants & stabilizers. The pretreatment techniques, such as UV irradiation and thermal treatment, can reduce hydrophobicity [17, 26]. However, even without any pretreatment, a more in-depth investigation with a longer incubation duration and modification in other parameters might offer significant degradation of PP plastics. The morphological and structural changes in the significantly degraded plastics with microbial treatment can be demonstrated using various other characterization methods such as scanning electron microscopy (SEM) and Fourier-transform infrared spectroscopy (FTIR).

Conclusion The exponential rise in the usage of petroleum-derived plastics causes severe environmental concern and requires immediate attention. In the last few decades, microorganism-assisted plastic degradation has emerged as one of the promising methods to mitigate the accumulation of plastics in the environment. Bacillus sp. was chosen in this study because of its potential bioactivity and ability to break down all petroleum-based polymers (PE, PVC, PP, PS, and PU) except PET. The morphological characterization of Bacillus sp. isolates identified gram-positive bacteria with rod shapes. Growth curve analysis found that lag, log, stationary, and death phases occur in 0–5, 5–15, 15–28, and after 28 h respectively. The grown culture (washed/not washed) that attained log phase growth was utilized for degradation of untreated PP plastic pellets using minimal media in an incubator at 37 ºC with a shaking condition of 120 rpm for 4 weeks. Bacillus sp. grew better in the case of cells not washed with PP plastic in media than cell washed, and it and degraded the weight of PP by 0.34%. The degradation of the PP plastics significantly enhanced the pH of the media in the direction of alkalinity and indicated the PP plastic degradation. The results of this investigation imply that Bacillus sp. has the potential to be used in the biodegradation of PP. Acknowledgements This piece of research work is based upon a project supported by University of Utah-College of Mines and Earth Science Seed grant program.

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References 1. Geyer R, Jambeck JR, Law KL (2017) Production, use, and fate of all plastics ever made. Sci Adv 3(7):e1700782. https://doi.org/10.1126/sciadv.1700782 2. Jambeck JR, Geyer R, Wilcox C, Siegler TR, Perryman M, Andrady A, Narayan R, Law KL (2015) Plastic waste inputs from land into the ocean. Science 347(6223):768–771. https://doi. org/10.1126/science.1260352 3. Zheng Y, Yanful EK, Bassi AS (2005) A review of plastic waste biodegradation. Crit Rev Biotechnol 25(4):243–250. https://doi.org/10.1080/07388550500346359 4. Qin ZH, Mou JH, Chao CYH, Chopra SS, Daoud W, Leu SY, Ning Z, Tso CY, Chan CK, Tang S, Hathi ZJ, Haque MA, Wang X, Lin CSK (2021) Biotechnology of plastic waste degradation, recycling, and valorization: current advances and future perspectives. Chemsuschem 14(19):4103–4114. https://doi.org/10.1002/cssc.202100752 5. Chamas A, Moon H, Zheng J, Qiu Y, Tabassum T, Jang JH, Abu-Omar M, Scott SL, Suh S (2020) Degradation rates of plastics in the environment. ACS Sustain Chem Eng 8(9):3494–3511. https://doi.org/10.1021/acssuschemeng.9b06635 6. Mourshed M, Masud MH, Rashid F, Joardder MUH (2017) Towards the effective plastic waste management in Bangladesh: a review. Environ Sci Pollut Res 24(35):27021–27046. https:// doi.org/10.1007/s11356-017-0429-9 7. Venkatesh S, Mahboob S, Govindarajan M, Al-Ghanim KA, Ahmed Z, Al-Mulhm N, Gayathri R, Vijayalakshmi S (2021) Microbial degradation of plastics: Sustainable approach to tackling environmental threats facing big cities of the future. J King Saud Univ Sci 33(3):101362. https:// doi.org/10.1016/j.jksus.2021.101362 8. Dang TCH, Thai H, DTN, Nguyen TC, Tran TTH, Le VH, Nguyen VH, Tran XB, Pham TPT, Nguyen TG, Nguyen QT (2018) Plastic degradation by thermophilic Bacillus sp. BCBT21 isolated from composting agricultural residual in Vietnam. In: Advances in natural sciences: nanoscience and nanotechnology. https://doi.org/10.1088/2043-6254 9. Ali S, Koutra E, Kornaros M, El-Sheekh M, Abdelkarim E, Zhu D, Sun J, Elsamahy T (2021) Degradation of conventional plastic wastes in the environment: A review on current status of knowledge and future perspectives of disposal. Sci Total Environ 771:144719 10. Gambarini V, Pantos O, Kingsbury JM, Weaver L, Handley KM, Lear G (2021) Phylogenetic distribution of plastic-degrading microorganisms. mSystems 6(1):e01112-e1120. https://doi. org/10.1128/mSystems.01112-20 11. Moshood TD, Nawanir G, Mahmud F, Mohamad F, Ahmad MH, AbdulGhani A (2022) Sustainability of biodegradable plastics: new problem or solution to solve the global plastic pollution? Curr Res Green and Sustain Chem 5:100273. https://doi.org/10.1016/j.crgsc.2022.100273 12. Mamun SSAAA (2020) An inclusive review on recent status of plastic biodegradation. Int J Adv Res (IJAR) 13. Mohanan N, Montazer Z, Sharma PK, Levin DB (2020) Microbial and enzymatic degradation of synthetic plastics. Front Microbiol 11. https://doi.org/10.3389/fmicb.2020.580709 14. Ru J, Huo Y, Yang Y (2020) Microbial degradation and valorization of plastic wastes. Front Microbiol 11. https://doi.org/10.3389/fmicb.2020.00442 15. Abhay Raj MMKR, Ram C, Purohit HJ, Kapley A (2007) Biodegradation of kraft-lignin by Bacillus sp. isolated from sludge of pulp and paper mill. Biodegradation 16. Akhavan SA, Isar G, Emami M, Nakhoda A (2008) Isolation and Characterization of Crude Oil Degrading Bacillus spp. Iran J Environ Health Sci Eng 5(3) 17. Jain K, Bhunia H, Sudhakara Reddy M (2018) Degradation of polypropylene–poly-L-lactide blend by bacteria isolated from compost. Bioremediat J 22(3–4):73–90. https://doi.org/10. 1080/10889868.2018.1516620 18. Auta HS, Emenike CU, Jayanthi B, Fauziah SH (2018) Growth kinetics and biodeterioration of polypropylene microplastics by Bacillus sp. and Rhodococcus sp. isolated from mangrove sediment. Marine Pollut Bullet 127:15–21. https://doi.org/10.1016/j.marpolbul.2017.11.036

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19. Yang J, Yang Y, Wu W-M, Zhao J, Jiang L (2014) Evidence of polyethylene biodegradation by bacterial strains from the guts of plastic-eating waxworms. Environ Sci Technol 48(23):13776– 13784. https://doi.org/10.1021/es504038a 20. Yin C-F, Xu Y, Zhou N-Y (2020) Biodegradation of polyethylene mulching films by a co-culture of Acinetobacter sp. strain NyZ450 and Bacillus sp. strain NyZ451 isolated from Tenebrio molitor larvae. Int Biodeterior Biodegrad 155:105089. https://doi.org/10.1016/j.ibiod.2020. 105089 21. Ibiene AA, Stanley HO, Immanuel OM (2013) Biodegradation of polyethylene by Bacillussp. Indigenous to the Niger Delta Mangrove Swamp. Niger J Biotechnol. https://doi.org/10.4314/ njb.v26i1 22. Kumari A, Chaudhary DR, Jha B (2019) Destabilization of polyethylene and polyvinylchloride structure by marine bacterial strain. Environ Sci Pollut Res 26(2):1507–1516. https://doi.org/ 10.1007/s11356-018-3465-1 ˇ Fojtík J, Mucha M, Malachová K (2022) Biodeterioration of compost-pretreated 23. Novotný C, polyvinyl chloride films by microorganisms isolated from weathered plastics. Front Bioeng Biotechnol 10:832413. https://doi.org/10.3389/fbioe.2022.832413 24. Oikawa, E, Linn KT, Endo T, Oikawa T, Ishibashi Y (2003) Isolation and characterization of polystyrene degrading microorganisms for zero emission treatment of expanded polystyrene. Environ Eng Res 40:373–379 25. Shah AA, Hasan F, Akhter JI, Hameed A, Ahmed S (2008) Degradation of polyurethane by novel bacterial consortium isolated from soil. Ann Microbiol 58(3):381–386. https://doi.org/ 10.1007/bf03175532 26. Jeyakumar D, Chirsteen J, Doble M (2013) Synergistic effects of pretreatment and blending on fungi mediated biodegradation of polypropylenes. Bioresour Technol 148:78–85. https:// doi.org/10.1016/j.biortech.2013.08.074 27. Suharti S, Riesmi MT, Hidayati A, Zuhriyah UF, Wonorahardjo S, Susanti E (2018) Enzymatic Dehairing of goat skin using Keratinase from Bacillus sp. MD24, A newly isolated soil bacterium. Pertanika J Tropical Agric Sci 41(3) 28. Al-Saraireh H, Al-Zereini WA, Tarawneh, K. A. Antimicrobial activity of secondary metabolites from a soil Bacillus sp. 7B1 isolated from south Al-Karak, Jordan. Jordan J Biol Sci 147(3427):1–6 29. Samanta S, Datta D, Halder G (2020) Biodegradation efficacy of soil inherent novel sp. Bacillus tropicus (MK318648) onto low density polyethylene matrix. J Polym Res 27(10):1–16

Comparative Characterization and Assay of Cow Horn Waste and Fish Feed as Biomaterials for Reinforcement in Aquaculture Feeds Ita E. Uwidia, Onyeka K. Chisom, and Osalodion E. Uwidia

Abstract In this study, some biomaterials (cow horn waste and some fish feed) were characterized and compared. The aim was to investigate the possibility of using cow horn wastes as reinforcement feed in aquacultures thereby minimizing the indiscriminate or unsightly disposal as wastes in some abattoirs. The assay revealed that the cow horn contained very high carbohydrate, fat, magnesium and iron contents whereas, ash, moisture and protein were low compared with the fish feed. Results obtained suggests that cow horn wastes compared with fish feed will serve as useful reinforcement in the diet of fishes with low protein and high carbohydrate requirement. The use of cow horn wastes as feed reinforcement will also help to reduce the unsightly nuisance created by its disposal in some abattoirs and create a more friendly and healthy environment. Keywords Biomaterials · Cow-horn · Fish-feed · Reinforcement · Aquaculture · Management

Introduction Improper management of discarded horns still remains a problem in the slaughter sector [1]. Their decay in landfills generates methane gas, hence, a contribution to climate change. According to Justin Worland from Time (2015) “The Environmental Protection Agency (EPA) estimates that landfills are the third-leading cause of methane emissions” [2]. The waste in landfill sites decompose, percolate to the soil and eventually get to water bodies; as a result, they leach out important minerals for plant growth from the soil and cause water pollution. Most times, discarded horns I. E. Uwidia (B) · O. K. Chisom Department of Chemistry, University of Benin, P.M.B.1154, Benin City, Edo State, Nigeria e-mail: [email protected] O. E. Uwidia Faculty of Basic Medical Sciences, Department of Medical Biochemistry, University of Benin, P.M.B.1154, Benin City, Edo State, Nigeria © The Minerals, Metals & Materials Society 2023 The Minerals, Metals & Materials Society, TMS 2023 152nd Annual Meeting & Exhibition Supplemental Proceedings, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-031-22524-6_61

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are incinerated. The ash produced contains toxins and heavy metals while the gases emitted are major contributors to acid rain and air pollution. Various applications of cow horn have been discovered over the years with the aim of minimizing this problem. The aqua feed industry faces problems such as scarcity of feed ingredients and high cost of these materials which affects aquaculture management [3]. This causes fish farmers to look for less expensive supplements or alternatives due to high cost of imported fish feed. Fish nutrition is not only important to the aquaculture industry, because it represents approximately 50% of production cost, but it is also a major factor for optimal fish growth. Non-availability of protein in fish diet due to expensive protein ingredients for the aqua feed industry hinders the healthy growth of fishes and makes less fish available to people for consumption. This also results in disorders (due to proteindeficient diets) such as: Kwashiorkor, Marasmus, impaired mental health, oedema, organ failure, wasting and shrinkage of muscle tissues weak immune system and risk of cardiovascular diseases [4, 5]. Therefore, it is necessary to devise cheaper, yet nutrient-rich alternatives as fish feed supplements. Cow horns are composed of bone (which has minerals) and a protein (keratin) layer, hence, the possibility for horns to be used as a cheaper and local alternative supplement in aquaculture. Therefore, the aim of this research focuses on evaluating the nutrient composition of cow horn powder such that it may be possibly used as reinforcement feed in aquaculture. The process will also help to minimize wastes due to cow horn in the environment.

Materials and Methods Sample Collection, Processing and Grinding Fresh cow horns were collected from a local slaughter house in Ikpoba Okha Local Government Area Unit, Benin City, Edo State. The cow horns were stored in polyethylene bags and transported to the laboratory for processing. The horns were thoroughly scrapped using a knife, washed with water, boiled for one hour, and dried in an oven to remove fat and dismantle the bone marrow for effective release of nutrients. This was done to destroy any pathogen present and to make the grinding process easier. After drying, they were crushed to smaller fragments using a hammer and finally ground to powdered form using a milling machine at the local market. Two different fish feed, were also purchased from a local shop in Benin City, Edo State and used for analysis.

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Proximate Analysis Proximate compositions of the biomaterials (cow horn and fish feed samples) were determined in accordance with the standard methods described by Association of Official Analytical Chemists (AOAC). Crude protein determination was according to AOAC, 2003 [6] while moisture, ash, crude fat, crude fiber, and nitrogen free extract were determined according to AOAC, 2005 [7].

Moisture Content Determination A clean crucible was washed with distilled water, dried to a constant weight in an oven at 120 °C for 1 h was cooled in a desiccator. The weight was recorded as (W1). Three grams (3 g) of the sample was weighed into the crucible as (W2) and dried at 120 °C for 3 h. The dish was removed and cooled in a desiccator for 30 min and weighed again (W3). This was done with a minimum exposure to atmosphere. Finally, the percentage moisture content was calculated [7] as: Percentage moisture content =

W2 − W3 × 100 W2 − W1

where W1 = Initial weight of dish W2 = Weight of dish + sample before heating. W3 = weight of dish + sample after cooling.

Total Ash Determination A crucible was dried in an oven for 24 h, cooled and weighed (W1). 2 g of the dried sample, (W2) was placed in a crucible and subjected to ashing in a muffle furnace at constant temperature of 550 °C until a constant weight for the ash was obtained. Thereafter, the ash was covered with a lid and placed in a desiccator and weighed (W3) [7]. Total ash was calculated as follows: W3 − W1 × 100 W2 − W1 where W1 = Weight of dry crucible W2 = Weight of dry crucible + weight of sample before ashing. W3 = Weight of dry crucible + weight of sample after ashing.

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Lipid Content Determination Three grams (3 g) of sample was weighed and folded in a filter paper. It was then placed in an extraction thimble and cotton wools were placed on top. The whole apparatus was then connected after the addition of about 300 ml of petroleum ether into the weighed extraction flask. The extraction was then carried out for 3 h using a heating mantle and making sure there was continuous flow of water in the condenser. After extraction, the sample was then removed, air-dried and placed in an oven at 80 °C until a constant weight was obtained. The extractible lipid was then calculated as percentage lipid (%) [7]: weight o f ether extract × 100 initial weight o f sample

Crude Protein Protein in the sample was determined by Kjeldahl method [6]. 1.0 g of dried sample was put in a digestion flask, 10 ml of concentrated H2 SO4 and 8 g of digestion mixture (K2 SO4 and CuSO4 at 8: 1) were added. The flask was swirled in order to mix the contents thoroughly then placed on heater to start digestion till the mixture became clear (blue green in color). The digestion required 2 h to complete. The digest was then cooled and transferred to a 100 ml volumetric flask and the volume was made up to the mark by the addition of distilled water. Distillation of the digest was performed in distillation apparatus. 50 ml of digest was introduced in the distillation tube then 50 ml of 50% NaOH was gradually added through the same way. Distillation was continued for 10 min. NH3 was produced and collected as NH4 OH in a conical flask containing 100 ml of 4% boric acid solution with few drops of Tashiro’s indicator. During distillation a brownish color appeared due to NH4 OH. The distillate was then titrated against standard 0.1 M HCl solution till the appearance of a pink color. A blank was also run through all steps as above. Percent crude protein content of the sample was calculated by using the following formula: % Crude Protein = 6.25 x %N %N =

(S − B) × N × 0.014 × D × 100 W ×V

where: S = Sample titration reading. B = Blank titration reading. N = Normality of HCl.

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V = Volume taken for distillation. D = Dilution of sample after digestion. 0.014 = Milli equivalent of Nitrogen. W = Initial weight of the sample.

Crude Fibre 2g of the residual powdered sample from moisture analysis and ether extraction were subjected to successive treatments with boiling 200 ml of 0.1275 M H2 SO4 acid under reflux for 30 min and washed several times with hot water until it was acid free. The residue was again subjected to the same treatment using 200 ml of 0.313 M NaOH solution, washed thoroughly with hot water until it was base free. It was then dried to a constant weight in an oven at 100 °C and cooled in a desiccator. This was then weighed and incinerated in a muffle furnace at 550 °C for 2 h until a constant weight was obtained. The crude fiber was calculated as the loss in weight on washing [7]: weight o f sample a f ter ignition × 100 weight o f sample taken

Carbohydrate Content or Nitrogen Free Extract (NFE) The available carbohydrate content (NFE) was calculated by difference after the other items in the proximate composition was obtained. This was specifically done by subtracting the sum of all the percentages of moisture, fat, crude protein, ash, and crude fiber from 100%. NFE = (100 – % moisture + % crude protein + % crude fat + % crude fiber + % ash).

Mineral Determination Mineral content of cow horn and fish feed were determined by atomic absorption spectrometry.

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Wet Digestion of Sample Exactly 1g of the powdered sample was put in a digesting glass tube. Twelve milliliters (12 ml) of nitric acid, HNO3 was added to the samples and the mixture was kept overnight at room temperature. Then 4.0 ml perchloric acid (HClO4 ) was added to this mixture and was kept in a fumes block for digestion. The temperature was increased gradually, starting from 50 ºC and increasing up to 250-300 ºC. The appearance of white fumes after 70–85 min indicated the completion of the digestion process. The fixture was left to cool down, the contents of the tubes were transferred to a 100 ml volumetric flask and the volumes of the contents were made to 100 ml with distilled water. The wet digested solution was transferred to plastic bottles labeled accurately. The digest was stored and used for mineral determination by Atomic Absorption Spectroscopy [8].

Packed “Tapped” Bulk Density Bulk density of the samples were determined according to Akpapunam and Markakis [9]. In each case, two grams (2g) of sample was taken into a weighed measuring cylinder (W1 ) and the weight of the cylinder and sample (W2 ) was noted. The sample in the cylinder was tapped gently to eliminate air spaces between the particles of the sample. This was repeated until a constant volume was obtained. The new volume (V1 ) of the sample was noted. The packed bulk density was calculated as: PBD =

w2 − w1 v2

Therefore, the bulk density was calculated as the mass of the sample divided by its volume [10–12]. bulk densit y =

weight o f sample (g/ml) volume a f ter tapping

Result and Discussion The proximate composition of the biomaterials (cow horn powder and fish feed) determined are as presented in Table 1. The table showed that cow horn contained ash (3.00%), crude protein (9.10%), crude fat (10.00%), crude fiber (1.94%); moisture (6.00%) and nitrogen free extract i.e. carbohydrate (69.96%). Cow horn has the highest carbohydrate and fat content compared with that of both fish feed. It also has fiber content quite close to that of feed A. Cow horn powder has the lowest moisture content value. Its protein content is by far much lower than the expected

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Table 1 Proximate composition of cow horn powder and fish feed Parameters (%)

Fish feed A

Fish feed B

Cow horn powder

Moisture content

9.00

7.33

6.00

Total ash

5.00

9.50

3.00

Crude fat

9.33

5.33

10.00

Crude fibre

8.00

1.50

1.94

Crude protein

23.06

29.09

9.10

Nitrogen-free extract/total carbohydrate

45.61

47.25

69.96

Table 2 Mineral content of cow horn powder and fish feed

Mineral (mg/g) Fish feed A Fish feed B Cow horn powder Calcium

0.49

1.09

0.30

Phosphorous

0.22

0.23

0.07

Iron

0.23

0.22

0.59

Potassium

5.76

5.23

0.71

Magnesium

0.02

0.02

0.03

protein requirement in fish feed. The variation in results may be due to the oven drying before horns were crushed and ground. The heat applied during drying may have denatured some proteins present.

Mineral Content Using standard procedures, the mineral contents of the cow horn powder and fish feed were determined and presented in Table 2. The Table shows that the tested cow horn contained Calcium (0.30 mg/g), Phosphorous (0.07 mg/g), Iron (0.59 mg/g), Potassium (0.71 mg/g) and Magnesium (0.03 mg/g). Among the minerals, cow horn powder contained the highest amount of Iron and Magnesium. No other study has been found regarding the mineral composition of cow horn.

Bulk Density The bulk densities of cow horn powder, fish feed A and fish feed B are represented in Table 3 below. Although the densities were within a close range of about ± 0.1, cow horn powder had the lowest bulk density (0.53 g/ml).

Comparative Characterization and Assay of Cow Horn Waste and Fish … Table 3 Bulk density of cow horn powder and fish feed

Table 4 Caloric values of cow horn powder and fish feed

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Sample

Bulk density (g/ml)

Cow horn powder

0.67

Fish feed A

0.57

Fish feed B

0.53

Energy kcal/g Protein Carbohydrate Fat

Cow horn powder

Fish feed A

Fish feed B

36.40

116.36

92.04

279.84

242.56

182.44

90.00

83.97

47.97

Energy (Caloric Value) Table 4 shows the percent caloric value in cow horn powder and fish feed. The values were obtained by multiplying the percentage of crude protein and carbohydrate with 4 and crude fat with 9. Cow horn powder contained 36.4 kcal/g metabolizable energy from protein, 279.84 kcal/g energy from carbohydrate and 90 kcal/g energy from fat. The high energy from carbohydrate is a result of the high carbohydrate content in cow horn.

Conclusion The analysis revealed that compared with commercial feed for fishes, cow horn powder has higher carbohydrate and lipid content but lower moisture and protein content. The high mineral content (i.e. Magnesium and Iron) observed is significant for body metabolism. Therefore, cow horn wastes may be useful as feed reinforcement for fishes with low protein and high carbohydrate requirement in their feed. Also, the use of cow horn waste as feed reinforcement in aquaculture will minimize accumulation of discarded horns in landfill sites and abattoirs. This will reduce aesthetic pollution due to cow horns in such environment.

References 1. Bello YO, Oyedemi DT (2009) The impact of abattoir activities and management in residential neighbourhoods: a case study of Ogbomoso, Nigeria. J Soc Sci 19(2):121–127 2. Worland J (2015) Time. How your trash is contributing to climate change. https://www.goo gle.com/amp/s/time.com/4042559/trash-climate-change-landfill. Accessed 14 Nov 2019 3. Udo IU, Dickson BF (2017) The Nigerian aqua-feed industry: potentials for commercial feed production. Niger J Fish Aquac 5(2):86–95

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4. Vis HL (1969) Protein deficiency disorders. Postgrad Med J 45(2):86–95 5. Khan A, Khan S, Jan AA, Khan M (2017) Health complication caused by protein deficiency. J Food Sci Nutr 1(1):1–2 6. Association of Official Analytical Chemists (AOAC) (2003) Official methods of analysis, 17th edn. Arlington, Virginia, USA 7. Association of Official Analytical Chemists (AOAC) (2005) Official methods of analysis, 18th edn. Washington D.C., USA 8. Shumaila G, Mahpara S (2009) Proximate composition and mineral analysis of cinnamon. J Nutr 8(9):1456–1460 9. Akpunam MA, Markakis P (1981) Physicochemical and nutritional aspects of cowpea flour. J Food Sci 46:972–973 10. Shad MA, Nawaz H, Hussain M, Yousuf B (2011) Proximate composition and functional properties of rhizomes of lotus (Nelumbo nucifera) from Punjab, Pakistan. Pak J Bot 43(2):895– 904 11. Ogunyinka BI, Oyinloye BE, Osunsanmi FO, Kappo AP, Opoku AR (2016) Comparative study on proximate, functional, mineral, and antinutrient composition of fermented, defatted, and protein isolate of Parkia biglobosa seed. Food Sci Nutr 5(1):3 12. Owuamanam CI, Ogueke CC, Achinewhu SC, Barimala SI (2011) Quality characteristics of gari as affected by preferment liquor, temperature and duration of fermentation. Am J Food Technol 6:374–384

Effect of Some Bio-Stimulants in the Degradation of Petroleum Hydrocarbons in Crude Oil Contaminated Soil Ita E. Uwidia, Uzuazor O. Eyibara, and Osalodion E. Uwidia

Abstract The aim of this study was to use some bio-materials (pig waste mixed with cassava peel bio-char) as stimulants for enhanced remediation of crude oil contaminated soil. Following standard procedures the raw materials (soil, crude oil, pig waste and cassava peel bio-char) were characterized. The experiment was designed using Response surface methodology (RSM) for two variables at five levels. Higher values of properties in the raw materials: pH, nitrogen, phosphorous and sodium, potassium and magnesium compared with the contaminated soil suggested their potential to facilitate contaminant degradation. Result of the amended soil showed that the composition of bio-stimulants used for remediation had significant effects on the total petroleum hydrocarbon content of the soil. The analysis revealed that the bio-stimulants used for this study have the potential to effect remediation of the hydrocarbon contaminated soils. Keywords Bio-stimulants · Remediation · Crude oil · Pollution · Hydrocarbons

Introduction Soil contamination resulting from petroleum leakage from diverse sources is hazardous to water and soil ecosystems and is expensive to remediate [5]. In developed countries, many oil spill events are reported and prompt actions are taken to remedy the affected ecosystem. However, developing countries do not report many of their oil spills and many times concise efforts are not made to restore the ecosystem to its previous state even when the oil spills are accounted for [2, 10]. Crude oil is composed of complex hydrocarbons with low bioavailability and is persistent in soil I. E. Uwidia (B) · U. O. Eyibara Department of Chemistry, University of Benin, P.M.B.1154, Benin City, Edo State, Nigeria e-mail: [email protected] O. E. Uwidia Faculty of Basic Medical Sciences, Department of Medical Biochemistry, University of Benin, P.M.B.1154, Benin City, Edo State, Nigeria © The Minerals, Metals & Materials Society 2023 The Minerals, Metals & Materials Society, TMS 2023 152nd Annual Meeting & Exhibition Supplemental Proceedings, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-031-22524-6_62

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[17]. Crude oil spill is a regular occurrence in the Niger Delta region of Nigeria. According to National Oil Spill Detection and Response Agency (NOSDRA), the average volume of oil spill annually in Nigeria is about 115,000 barrels, which is worth about $39.84 per barrel [10]. Oftentimes, instead of proper treatment of oil spills by the oil companies, the spills are allowed to attenuate naturally; this can take years and the damage to the environment is incalculable. Soil chemical fertility and quality are significantly impacted by oil spill contamination rendering soil unsuitable for plant growth. The involvement of microorganisms in the degradation of petroleum hydrocarbons in the environment has been established as an efficient, and environmentally friendly treatment method [8, 18]. Studies have shown that chemical fertilizers are useful to augment for mineral nutrients such as nitrogen and phosphorus limitations in the soil during biodegradation. The effectiveness of this treatment method has, however, been conflicting [4, 11]. Nonetheless, in developing countries, fertilizers are not sufficient for agriculture, let alone for cleaning oil spills. It therefore necessitates the search for cheaper and environmentally friendly options of enhancing petroleum hydrocarbon degradation. One such option is the use of poultry and piggery manure as bio-stimulating agents. There are no adequate literatures on the potential use of these animal manures as bio-stimulating agents. However, a few workers including [11, 18] have investigated the potential of these two different manures in the cleanup of soil contaminated with petroleum hydrocarbons and were found to enhance petroleum hydrocarbon biodegradation in a polluted environment. Piggery manure serves as biostimulating agents that can improve the physical characteristics and conditions of the soil and improves the nutrient uptake and crop productivity [13]. Biochar has a large surface area, and high capacity to adsorb heavy metals and organic pollutants. Biochar can potentially be used to reduce the bioavailability and leachability of heavy metals and organic pollutants in soils through adsorption and other physicochemical reactions [20]. The combination of biochar and animal waste manure as contaminated soil amendment material is currently attracting the interest of researchers. Combination of biochar and pig waste (animal manure) will reduce the nutrient limitations of biochar since pig waste contains high percentage of nitrogen, phosphorus, potassium and other nutrients readily available for plant uptake. Response surface methodology (RSM) a statistical technique of experimentation answers the question of how to select the levels for the applied factors to obtain the desirable, smallest or largest, value of the response function in a reduced number of experiments [6]. RSM has been applied in several remediation studies to investigate the efficiency of amendment materials on treating contaminated soils by considering the effects of several variables [7]. The aim of this study is to carry out remediation of crude oil contaminated soil using a blend of biostimulants prepared from cassava peel biochar and pig waste as remediation or amendment material.

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Materials and Methods Material Collection and Preparation Surface soil samples were randomly collected from different locations of a farmland in Benin City, Edo State, Nigeria. The samples were mixed to obtain 10 kg of soil, sieved to 2 mm particle size and the air-dried. Crude oil was obtained from Clough Creek facility in Warri, Delta State and weathered. Pig waste (PW) was collected from a Pig Farm in Ozoro, Delta State and air-dried for 12 days, ground to fine particles and sieved to 3 mm particle size. Cassava peel waste was obtained from a Farm in Ozoro, Delta State, Nigeria. The peels were air-dried for 2 weeks and carbonised using the method by Yuliusman et al. [19]. The resulting charcoal was mashed with mortar and pestle and sieved with a 100-mesh filter to a uniform charcoal size to obtain cassava peel biochar (CPBC) which was again air-dried and milled into fine powder.

Physico-Chemical Analysis of Amendment Materials and Soil The physico-chemical properties of the surface soil, pig waste and biochar were determined using standard methods. Total organic carbon (TOC) content of the samples was carried out using the method of Schumacher [16]. Total nitrogen (N) content was determined using the Kjeldahl digestion, distillation and titration method as described by Association of Analytical Chemists, AOAC [3]. The amount of phosphorus in samples was determined using method of Oludele et al. [12]. The soil phosphate was determined using the ascorbic acid method as described by [1]. Cation exchange capacity (CEC) was estimated titrimetrically by distillation using the ammonium method. The concentrations of K+ and Na+ in the extract were determined by Flame photometer while Mg2+ and Ca2+ were determined using Atomic Absorption Spectrophotometer. Summation of the various cations was reported as cation exchange capacity. Nitrate in the soil was determined following method by Mussa et al. [9]. Sulphate was determined using the soil extract–turbimetric method described by Amponsah et al. [1] while the water holding capacity was determined using the method by Reynold et al. [15].

Determination of Total Petroleum Hydrocarbon (TPH) Soil sample extraction method used to obtain the extract was as described by Oludele et al. [12]. TPH was determined using Gas Chromatography Flame-ionization detectors (GC-FID) [14].

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Contamination of Soil and Mixing of Remediation Material The remediation material was obtained by mixing the biostimulants, pig waste and CPBC to give a composition of 100 g or 100%. The mixture was allowed to stabilize for 2 weeks before it was added to the contaminated soil. Simulation of the soil samples was done by contaminating the soil in the ratio 1 kg of soil to 15 g of crude oil. A total of 10 kg of soil was contaminated and mixed evenly. Initial TPH was determined.

Experimental Design of Remediation of the Contaminated Soil Remediation of soil with the mixture of remediation materials was designed using a two-variable-five-level central composite design of response surface methodology. The remediation (or amendment) variables considered are duration of remediation and composition of biostimulants.

Results and Discussion Characterization of Soil, Crude Oil and Remediation Materials Table 2 gives the physico-chemical properties of the soil, crude oil, cassava peel biochar (CPBC) and pig waste (PW). From Table 1, the pH of the soil under study was determined to be acidic, which shows the soil is unhealthy for vegetative purposes. A pH range of 6–7 is generally most favorable for plant growth because most plant nutrients are readily available. The pH values of CPBC and PW are 6.59 and 6.48 respectively, which indicate that the biostimulants used for remediation are only slightly acidic and can allow for availability of plant nutrients and are favourable for microbial activities, pesticide interaction, mobility of heavy metals and inhibition of soil corrosivity when applied to the contaminated soil. Total organic carbon, total organic matter, total nitrogen, phosphorus, phosphate, sulphate and sulphur were higher in crude oil than in the soil and biostimulants which may be as a result of the huge presence of hydrocarbons and dissolved matter in the crude oil. The presence of these compounds and elements in crude oils outside phosphorus, nitrogen and phosphates impact on soil quality and makes soil unhealthy for agricultural purposes. As expected, the water holding capacity of the soil is very high in comparison with the biostimulants (CPBC and PW). The soil and crude oil were found to have low CEC and low concentrations of exchangeable metals; sodium, potassium and magnesium needed for plant growth. The higher CEC and higher concentrations of these metals in CPBC and PW indicate

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their effectiveness in restoring the quality and health of contaminated soils when used as biostimulants in soil remediation. Table 1 Physico-chemical properties of soil, crude oil and biostimulants (CPBC; PW) used Parameters

Soil

pH

Crude oil 3.60

Total organic carbon (g/kg)

7000.00

Total organic matter (g/kg)

12,075.00

CPBC

–

6.59

6.48

800.00

3100.00

1380.00

8280.00

9605.00 16,560.00

PW

Total nitrogen (g/kg)

696.01

703.63

275.57

463.48

Nitrate (g/kg)

306.04

223.38

210.04

308.71

1.59

1.63

Phosphorus (g/kg)

0.31

32.00

Phosphate (g/kg)

0.71

73.28

3.65

3.73

CEC (meq/100 g)

8.83

–

25.74

13.20

Sulphate (g/kg)

2.31

436.24

59.91

138.11

Sulphur (g/kg)

0.01

Water holding capacity (mi/L)

14,160.00

3.06

0.21

1.12

3000.00

4560.00

0.30

0.40

2.40

–

Sodium (g/kg)

0.30

Potassium (g/kg)

1.80

0.90

69.20

34.40

Magnesium (g/kg)

0.32

0.21

0.45

1.23

Table 2 Experimental design matrix of contaminated soil amendment Run

Coded factor

Actual factor

A

Duration of remediation (weeks)

Composition of biostimulants (% CPBC)

Actual

Predicted

B

TPH degraded (m g/kg)

1

0

0

4.50

30.00

3447.94

3405.932

2

0

0

4.50

30.00

3521.45

3405.932

3

0

0

4.50

30.00

3227.72

3405.932

4

0

0

4.50

30.00

3368.44

3405.932 3006.759

5

1

1

7.00

50.00

3038.51

6

−1

1

2.00

50.00

4002.13

3917.005

7

−1.41

0

0.96

30.00

3003.38

3119.239

8

−1

−1

2.00

10.00

3826.63

3702.146

9

1.41

0

8.04

30.00

3050.84

3091.216

10

0

0

4.50

30.00

3464.11

3405.932

11

0

1.41

4.50

58.28

3966.12

4016.407

12

1

−1

7.00

10.00

4643.87

4572.76

13

0

−1.41

4.50

1.72

4865.86

4971.808

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Fig. 1 Predicted versus actual plot of TPH degradation of contaminated

Analysis of TPH Degradation of Contaminated Soil Table 2 gives the experimental design matrix of the remediation process of the crude oil contaminated and the predicted and actual values of the TPH degraded. Quadratic model summary statistics was used to predict the remediation process. The R2 value was 0.9741. A close correlation was observed in the adjusted and predicted R2 values which were 0.9556 and 0.8838. This shows a close correlation (with difference less than 0.2). The correlation indicates reasonable agreement between the experimental and predicted values of TPH degraded. This close correlation is seen by the linear and positive relationship in the predicted versus actual plot of the soil amendment process as shown by Fig. 1.

Statistical Analysis Analysis of variance (ANOVA) was used to determine the level of significance (pvalue) of all coefficients at a significant level of 5% (probability of error value of 0.05). The goodness and best fit of the model was evaluated by the regression coefficient, R2 . P-values less than 0.05 indicate model terms/factors are significant. The ANOVA for the remediation process is given by Table 3. From the Table 3, the large model Fvalue of 52.69 and a very small p-value of < 0.0001 imply that the model is significant meaning that there is only a 0.01% chance that an F-value this large could occur due to noise. From this study, the composition of biostimulants (B), interaction factor (AB) and quadratic factors (A2 and B2 ) have significant effects on the TPH degraded from the crude oil contaminated soil with p-values less than 0.05 while the effects of other factors were found to be insignificant. The lack of fit F-value of 1.49 implies that the lack of fit is not significant relative to the pure error. There is a 34.57% chance that a lack of fit F-value this large could occur due to noise. This implies that

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Table 3 Analysis of variance of regression model Source

Sum of squares

Model

4.11E + 06

df 5

Mean square

F-value

p-value

8.23E + 05

52.69

pure PC + recycled GF > recycled PC + pure GF > recycled PC + recycled GF > recycled PC [2]. M.C.S. Ribeiro et al. applied GFRP recyclates as an aggregate replacement for polymer mortars and found that GFRP waste-filled polymer mortars show improved mechanical behaviour over unmodified polyester-based mortars, thus indicating the feasibility of GFRP waste reuse as raw material in concrete-polymer composites [3]. P.O. Awoyera and A. Adesina explored various approaches to recycling plastic wastes into construction products and concluded that the possible use of plastic waste as binder, aggregates, and fibres makes it a viable replacement for all components in cementitious composites, with somewhat acceptable detrimental effects on the performance of the resulting composite [4]. D. Jubinville et al. applied reprocessed (simulating recycled) polypropylene (PP) as a matrix for highly loaded wood plastic composites (WPC). Their study has shown that although recycled PP introduced viscosity loss due to chain scission, recycled PP allowed higher wood fiber to be loaded into the system. As a result, the WPC with higher wood fiber content will exhibit an increase in density and material hardness but reduced elongation [5]. Since historical, experience-based service life models for composite building applications are not available, the solution to propel those innovative materials is to build predictable computational mechanics models. Such models can be validated by static and fatigue tests, investigated by uncertainty quantification studies, and reach reasonable confidence intervals to predict composite service life performance on a semi-centennial or centennial time scale. As shown in Fig. 1, Z. Li et al. have developed a Computational and data-driven methodology to model long-term performance of GFRP under environmental aging and fatigue [1, 6, 7]. This effort extends the literature on composite aging from short-time accelerated experiment record

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Fig. 1 A computational and data-driven framework for modeling long-term performance of GFRP under environmental aging and fatigue

to multi-physics-based models that are able to predict long-term service life. This method can help material engineers to assess the viability of new polymer composite building materials by leveraging both experimental, computational, and data-driven approaches. Life cycle assessment (LCA) studies, which consider life prediction given by the multi-physics-based model, found that there is a potential for polymeric composite buildings and building elements to be a significant part of the sustainable built environment. Polymer-based construction material is an emerging area, in which all kinds of innovation have the opportunity to gain investigation and investment. Competitive materials of recycled plastics, such as bio-based and biodegradable composites are also in the early stage of customer discovery. So there is a great opportunity to establish a new circular economy and complete standard without facing challenges such as trying to cope with, yield to or modify a long-history, solidly established supply chain of conventional material.

Multiphysics Modelling of Fiber-Reinforced Composites at Structural Level Mass Transport and Diffusion The model for moisture degradation and polymer deterioration is based on the conservation of mass, provided in Eq. 1.

Fiber-Reinforced Polymeric Composites for Low-Carbon Construction …

    ∂c − ∇ · Dd ∇c = R or c − Didj c, j ,i = R ∂t

743

(1)

where the scalar variable c is the material concentration, c,i : = ∂c/∂x i is the partial derivative notation, and Dd is the diffusion coefficient matrix. Dij = 0 if i/ = j. To simplify the notations, this paper uses D11 = D1 , D22 = D2 , and D33 = D3 in the following results and discussion. The diffusion coefficients of polymer and moisture in Cartesian coordinates directly come from the micromechanics model and its homogenization in [6], which has been validated against accelerated environmental exposure experiments conducted in the laboratory by [8]. The strain can be calculated as in Eq. 2.   = Wm c − cr e f βi j εi(hs) j

(2)

where β ij is the scalar coefficient of swelling/shrinking with β 11 / = 0, β 22 / = 0, β 33 / = 0, and other components equal to zero, W m is the molar mass of the transported matter, c is the material concentration, and cref is the strain-free reference material concentration. To be consistent with the exposure experiment, the composite is strainfree at the beginning of the simulation. δ ij is the Kronecker delta function.

Heat Conduction and the Energy Equation The equation that governs heat transfer in solids is given by Eq. 3. ρC p

∂T + ρC p v · ∇T + ∇ · q = Q with q = −k∇T ∂t

(3)

where Q contains additional heat sources. Internal polymer degradation, such as bonding breaking due to ultraviolet radiation exposure, is caused by radiative heating and the associated internal strains caused by this internal heating. In Eq. 4, the net inward radiative heat flux, qrad , is calculated as the difference between the irradiation, G, and the radiosity, J. [6] q rad = G − J

(4)

Continuum Damage Model (CDM) for High Cycle Fatigue The constitutive model is implemented for finite element analysis using the external material library in the COMSOL. In Continuum damage model (CDM) of orthotropic material, Young’s modulus and shear moduli are functions of the damage variable D (D ∈ [0,1]).

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E i (D) = (1 − D)2 E i0 , i = 1, 2, 3

(5a)

G i j (D) = (1 − D)2 G i0j i j = 23, 31, 12

(5b)

The continuum damage mechanics or CDM-based variable D provides a measure of the reduction of the stiffness tensor with increasing damage. Brittle damage has been established to be a nonlinear function of the equivalent strain rates and expressed as  ∗ (ε/ε0 ) S ε˙ when ε ≥ ε D and ε˙ > 0 ˙ (6) D= 0 when ε < ε D and ε˙ ≤ 0 The damage accumulation law of CDM for fatigue used in this investigation is given in Eqs. (7a) and (7b), which is derived in [9] within the framework of irreversible process in thermodynamics by assuming a brittle damage mechanism and elastic strain domination.  v 2q  B¯ dD v 2q σmax + σmin ,R 1000 °C), all the structural materials and elements, like cement, steel, bricks, and even natural stones, are significantly damaged or totally destroyed. From a structural point of view, the most critical damage occurred due to thermal loads in a fire is the spalling of concrete. Spalling is described as the breaking of layers or pieces of concrete from the surface of a structural element when it is exposed to the high and rapidly rising temperatures experienced in the fire. It is caused particularly by the spontaneous great amounts of heat release and the aggressive fire gases generated and should lead to the loss of the building’s structural reliability and the failure of its operation. In general, spalling phenomena in concrete are expected at several temperatures, depending on the strength and the densification of the concrete; in dense concrete, explosive spalling has been observed at temperatures between 300 and 450 °C [1]. At temperatures higher than 300 °C, the mechanical strength of concrete is considered to be significantly reduced [1] and as it is generally accepted, concrete loses its carrying capacity when exposed to temperatures higher than 380 °C [2]. This temperature is close to the dehydration temperature of portlandite (400 °C) that comprises a basic constituent of cement and the exposure of concrete at this temperature, leads to structural disordering, thus causing a significant reduction of the concrete mechanical strength [3]. Therefore, to maintain the stability of structures in case of a fire and to avoid spalling of concrete, it is crucial to limit the spread of fire and at the same time, to protect the concrete structure against high thermal loads developed during this situation. This could be achieved through active and passive fire protection systems, which are currently used in buildings and structures. Active fire protection is referred to automatic fire detection and fire suppression systems, while passive fire protection is related to the so-called fire-resistant materials. These materials seek to limit a fire in the building location where it occurred for a crucial period and to maintain the temperature of important building components (steel rebars, electric installations, etc.) below a critical value, thus allowing for the building to withstand the anticipated temperatures of a fire without losing its structural stability. The fire-resistant materials currently used in structures include spray fireproofing inorganic plasters, boards, and sheets made of gypsum, calcium silicate, and expanded aggregates and cementitious plasters reinforced with cellulose fibers treated with ammonium sulfate or borate and mineral wool [4]. Among them, the cladding of structures with calcium silicate

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(CaSi) is the most successful, traditional, and popular fireproofing method applied in Europe. Although fire-resistant materials seem to be a preferable solution for the fire protection of buildings and constructions, and a variety of such commercial products already exists, the high cost substantiates a crucial drawback that limits their implementation. Therefore, it is imperative to develop new, cost-effective materials with improved thermal and mechanical properties for the passive fire protection of buildings and constructions. From this point of view, geopolymers or inorganic polymers, based on alkali-activated binders, seem to be advantageous materials. These materials are produced according to the geopolymerization process that involves a chemical reaction between materials rich in silicon and aluminum amorphous phases and alkali silicate solutions, under highly alkaline conditions. The geopolymerization reaction takes place at atmospheric pressure and temperatures below 100 °C and yields amorphous to semi-crystalline solid materials characterized by a specific three-dimensional polymeric structure, consisting of Si–O–T–O bonds, where T denotes principally, Al or Si and secondarily, other metals such as Fe [5, 6]. Geopolymers possess excellent physical, chemical, thermal, and mechanical properties, based on which they should be viewed as alternative materials for certain industrial and construction applications. Except for that, these materials have a very low embodied energy and CO2 footprint, compared to conventional building materials, and exhibit rapid mechanical strength development, as well as durability in corrosive environments. However, their greatest advantage is that, based on the choice of raw materials and the design of the processing, geopolymers can meet a variety of requirements. This flexibility of geopolymer synthesis is of great importance when products with specific properties are required [7–9]. The solid alumino-silicate materials used for the production of geopolymers include natural minerals and rocks, such as clays and industrial minerals, as well as a wide range of industrial and urban waste available in large to enormous quantities, such as fly ash, metallurgical slags, mining overburdens and tailings waste glass, and construction and demolition wastes [5, 6, 8, 10]. Specifically, the latter group of raw materials is extremely attractive for the technological development of construction materials due to their low cost, as well as for environmental reasons. In this paper, two specific streams of Construction and Demolition Waste (CDW), namely waste bricks (WB) and waste ceramic tiles (WCT) have been studied for the development of inorganic polymeric materials to be used for the passive fire protection of structures. CDW results from the construction, renovation, and demolition of buildings, roads, bridges, and other structures. This waste group comprises a wide range of waste materials, including concrete, bricks, tiles, gypsum, wood, glass, metals, plastic, organic materials, and excavated soil. In European Union (EU), the construction sector generated 531 million tons of CDWs in 2014, representing nearly, one quarter of the waste materials generated globally [11]. Accordingly, in USA, 534 million tons of CDW were generated in 2014, of which 28.9 million tons were during construction and 505.1 million tones during demolition activities [12]. Although the efforts to reuse and recycle CDW are constantly increasing worldwide, it is estimated that globally, about 35% of the quantities of CDWs produced are directed to

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landfills. In EU, the management of CDW is steered by the EU Waste Framework Directive 2008/98/EC, which sets a target for the recycling of non-hazardous CDW at a minimum of 70% of its weight by 2020 [13]. Despite its potential, the level of recycling and material recovery of CDW varies greatly (between 0% and over 90%) across the Union [11]. In this paper, the geopolymerization of the selected waste streams to produce fire-resistant construction materials is investigated and the resulting materials are evaluated in terms of mechanical strength and thermal stability, after exposure at high temperatures ranging from 600 to 1050 °C.

Materials and Methods Materials In this study, two specific streams of recycled CDW were used as raw materials for the development of fire-resistant inorganic polymers: waste bricks (WB) and waste ceramic tiles (WCT). Both recycled waste materials were supplied from a recycling plant of CDW in Cyprus (Resource Recovery Cyprus). After manual sorting, representative samples of WB and WCT were crushed and milled to achieve homogeneity of the initial solid raw materials. Table 1 presents the chemical analysis of WB and WCT, as it resulted from the analysis of four representative samples for each material. As shown in Table 1, both WB and WCT are rich in silicon oxide, the content of which is higher in WCT (~64%wt) than in WB (~54%wt). These materials have also increased and quite similar content of aluminum oxide (~14%wt.), as well as of iron oxides (18 to 21%wt), potassium oxide (~3.75%wt), magnesium oxide (~4%wt), and traces of sodium and titanium oxides. According to Table 1, the two solid raw materials differ mainly in the calcium oxide content, which reaches ~8 %wt. in the WB and ~1.5%wt. in the WCT. Table 1 Chemical analysis of WB and WCT raw materials

Oxide

WB

WCT

Mass, %wt SiO2

53.57

62.40

Al2 O3

14.33

14.68

CaO

7.71

1.48

FeO

10.19

8.58

K2 O

3.74

3.76

MgO

4.07

3.68

Na2 O

0.66

0.98

TiO2

1.46

Fe2 O3

11.32

9.54

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The WB is principally crystalline, consisting mainly of quartz (SiO2 ) and feldspars (albite-NaAlSi3 O8 ); hematite (Fe3 O3 ) and mullite (3Al2 O3 ·2SiO2 ) were also detected as secondary phases and calcite (CaCO3 ), as minor. In contrast, the WCT comprises of an amorphous aluminosilicate phase, with quartz (SiO2 ) to be identified as the major crystalline constituent and feldspars (albite-NaAlSi3 O8 ) as secondary. Hematite (Fe3 O3 ), mullite (3Al2 O3 ·2SiO2 ), and spinel (MgAl2 O4 ) also occurred in WCT, as minor phases. Regarding the particle size of the two solid raw materials, WB has a particle size lower than 250 µm with a mean value (d50 ) equal to 35.35 µm, while all particles of the WTL are minus 300 µm, with a mean diameter (d50 ) equal to 48.34 µm. Except of the two solid raw materials, i.e. WB and WCT, a strong alkaline aqueous silicate solution was used as an alkali activator in this study, for the preparation of the fire-resistant geopolymers. The activator consisted of a sodium silicate solution (Merck, Na2 O = 8%, SiO2 = 27%, and d = 1.346 g/mL) and an aqueous solution of 8M KOH (potassium hydroxide) prepared by dissolving solid KOH in the form of pellets (Merck, 99.5% purity) in deionized water. The ratio of Na2 SiO3 xH2 O to KOH solutions in the alkali activator was equal to 1.6.

Experimental Procedure The inorganic fire-resistant polymers were prepared by mixing the pre-defined quantities of the alkali activator with the corresponding solid raw material in a mechanical mixer. A constant solid to liquid ratio (S/L) equal to 2.5 g/mL and 3.4 g/mL was designed for the inorganic polymers based on WB and WCT, respectively. The mixing time was determined at 5 min, after which a homogeneous paste was obtained. The paste was casted in cubic molds of two different dimensions, i.e. 50 x 50 x 50 mm and 100 x 100 x 100 mm, and left for curing in an oven for 7 days, at 50 °C. After oven curing, the cubic specimens were demolded and left for further hardening at ambient temperature and dry conditions for 7 days and 28 days, before measuring physical and mechanical properties and testing thermal stability at high temperatures. The thermal stability of the developed fire-resistant materials was tested at 600, 800, and 1050 °C, using a muffle furnace with a maximum temperature capacity of 1200 °C. The specimens were placed in the furnace at room temperature and the furnace was heated at a rate of 4.4 °C/min until reaching the desired temperature, at which the specimens were left for 2 h. Then, the furnace was turned off and the specimens were allowed to cool down in open air conditions, to room temperature. After thermal treatment, weight loss, compressive strength, and density of materials were measured. Moreover, the surface of specimens was visually inspected and any observed micro-cracks or other surface defects were evaluated. For each studied temperature, 3 specimens were tested and used for the physical and mechanical properties measurements and visual observation.

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Fig. 1 WB-based a and WCT-based b inorganic polymers after exposure at 600, 800, and 1050 °C

Results and Discussion Visual Observation After Exposure at High Temperature Alterations that take place during the heating of materials include moisture loss, evaporation, and transformation of mineralogical phases. Such alterations result in the disruption of the coherence of the materials’ structure and thus, the development of deformation and also of creeping phenomena (Fig. 1). As shown in Fig. 1, up to 600 °C, the specimens of both materials did not develop any kind of surface discontinuity or cracking. After exposure at 800 °C, faint cracks appeared to form on the surface of specimens, mainly of the WCT-based materials; this cracking became more intense after exposure at 1050 °C, without, however, being considered as an indication of failure of specimens.

Physical Properties Table 2 presents the Density and Mass loss of the WB- and WCT- based fire-resistant geopolymers, after their thermal exposure at different temperatures. The treated materials were cured at 50 °C for 7 days and left for hardening for another 7 days or 28 days. The mass loss reported in Table 2 is stated relative to the mass of materials after curing. As seen in Table 2, the density of both WB- and WCT- based fire-resistant inorganic polymers was remarkably decreased after their thermal testing at 600 °C, and then, it remained almost unchanged regardless of the temperature of materials exposure. In general, the WB-based geopolymer was less dense than the WCT-based one, at every tested temperature. Moreover, the hardening time of both materials seems to not affect their density (Table 2). The changes noted for materials density can be attributed to the removal of water from the geopolymeric binder that takes place in three different temperature ranges [14]. At approximately 100–115 °C, the water molecules absorbed on the surface of geopolymers start to evaporate. At higher temperatures, from 150 up to 600 °C, the dihydroxylation process occurs and the hydroxyl groups of the physically bound water molecules (-OH) are removed [14], resulting in the development of a capillary pore structure in geopolymers, which affects the weight of the materials specimens. At temperatures above 600 °C, the

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Table 2 Physical properties of the developed fire-resistant materials Temperature (°C) WB-based fire-resistant geopolymer

WCT-based fire-resistant geopolymer

Density (kg/m3 )

Mass loss (%)

7 days

28 days

7 days

28 days

50

1554

1556

600

1475

1475

6.21

1.60

800

1430

1430

7.53

1.91

7.97

2.13

1050

1434

1436

50

1736

1586

600

1583

1579

9.60

3.47

800

1569

1579

9.55

3.59

1050

1577

1566

9.91

3.64

dihydroxylation of silanol groups (>Si–OH) takes place [14] and the aluminosilicate gel of geopolymers starts to densify into a glass or ceramic state, resulting in contraction of the materials [15, 16]. Regarding the mass loss of the developed materials (Table 2), it was observed to be higher for the WCT-based geopolymer than for the WB-based one, in both cases of hardening time. Moreover, the mass loss was higher in the case of 7d hardening time than of 28d, for both materials developed. After 7d of hardening time, both materials exhibited high mass loss when exposed at 600 °C; this mass loss was increased slightly or kept almost constant, after materials testing at 800 and 1050 °C. More precisely, the mass loss of the WB-based geopolymer was 6.2% after its thermal testing at 600 °C and increased slightly ( 8 was that the increase in hydroxide concentration led to an increase in electronegativity on the surface of MIL-101(Fe), while Cr(VI) ions were also negatively charged, so the electrostatic repulsion between Cr(VI) ions and MIL-101(Fe) was enhanced and the adsorption effect between Cr(VI) ions and MIL-101(Fe) was reduced. When pH ≤ 1 or pH ≥ 12, the adsorption rate was extremely low because the strong acid or strong alkaline element decomposed MIL-101(Fe). Fig. 1 The existence of the Cr(VI) ions form in an aqueous solution at different pH values

100

Count (%)

80

CrO42-

60

Cr2O72HCrO4-

40

20

0 0

2

4

6

8

pH

10

12

14

798 70

Cr(Ⅵ) 60

Adsorption rate (%)

Fig. 2 Effect of pH on Cr(VI) ion adsorption (adsorbent dosage: 0.4 g/L; Cr(VI) ion concentration: 10 mg/L; pH: 1.0 –12.0; adsorption time: 2 h)

J. Zuo et al.

50 40 30 20 10 0 0

2

4

6

8

10

12

pH

Effect of Adsorbent Dosage During the whole adsorption process, the amount of adsorbent is an important parameter affecting the adsorption equilibrium of the system. The effect of adsorbent addition on the adsorption of Cr(VI) ions by MIL-101(Fe) is shown in Fig. 3. As displayed in Fig. 3, when the adsorbent dosage of MIL-101(Fe) was increased from 0.1 g/L to 0.8 g/L, the adsorption capacity of Cr(VI) ions by MIL-101(Fe) decreased from 23.50 mg/g to 11.38 mg/g, while the adsorption rate of Cr(VI) ions increased from 23.61% to 79.85%. The reason was that with the increase in adsorbent dosage, the adsorbent provided more active sites per unit volume of solution, and

Y q

60

20

15 40 10 20 5 0

0 0.0

0.2

0.4

0.6

Adsorbent dosaeg (g/L)

0.8

Adsorption capacity (mg/g)

25

80

Adsorption rate (%)

Fig. 3 Effect of adsorbent dosage on Cr(VI) ion adsorption (adsorbent dosage: 0.1–0.8 g/L; Cr(VI) ion concentration: 10 mg/L; pH: 4.0; adsorption time: 2 h)

Study on the Removal of Cr(VI) Ions by Fe-MOF from Simulated …

55 35 30

45

Y q

25

40 35

20

30 15 25 10

Adsorption capacity (mg/g)

50

Adsorption rate (%)

Fig. 4 Effect of initial concentration on Cr(VI) ion adsorption (adsorbent dosage: 0.4 g/L; Cr(VI) ion concentration: 20–220 mg/L; pH: 4.0; adsorption time: 2 h)

799

20

5 0

50

100

150

200

15 250

C0 (mg/L)

Cr(VI) ions had more opportunities to contact the adsorption active sites of the adsorbent, thus increasing the adsorption rate. However, the constant initial concentration of Cr(VI) ions would make a large number of active sites not fully utilized, resulting in the reduction of Cr(VI) ions mass adsorption per unit mass of MIL-101(Fe), which the reduction of the adsorption capacity was observed. At the same time, the environmental protection factor of saving adsorbent dosage was considered. Therefore, the dosage of MIL-101(Fe) was selected as 0.4 g/L for the following experiments under comprehensive consideration.

Effect of the Initial Cr(VI) Ion Concentration The initial concentration is also an important parameter in heavy metal wastewater treatment processes. The effect of the initial Cr(VI) ion concentration on the adsorption of Cr(VI) ions by MIL-101(Fe) is shown in Fig. 4. As shown in Fig. 4, the adsorption capacity of MIL-101(Fe) for Cr(VI) ions increased with the increase of the initial concentration of Cr(VI) ions, while the adsorption rate decreased with the increase of the initial concentration of Cr(VI) ions. This was mainly due to the gradual saturation of the Fe-MOF adsorption sites with increasing initial ion concentration.

Effect of the Contact Time Equilibrium time is another important parameter in heavy metal wastewater treatment processes. The effect of contact time on the adsorption of Cr(VI) ions by MIL-101(Fe) is shown in Fig. 5.

800

Cr(Ⅵ)

70 60

Adsorption rate (%)

Fig. 5 Effect of contact time on Cr(VI) ion adsorption (adsorbent dosage: 0.4 g/L; Cr(VI) ion: 10 mg/L; pH: 4.0; contact time: 0–180 min)

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50 40 30 20 10 0 0

20

40

60

80

100

120

140

160

180

200

Time (min)

As presented in Fig. 5, the adsorption rate of Cr(VI) ions by MIL-101(Fe) was divided into two distinct phases: (1) rapid adsorption of Cr(VI) ions by MIL-101(Fe) during the first 30 min and (2) gradual slowing of the adsorption increment after 30 min until the adsorption equilibrium was reached at 50 min. The reason was that at the beginning of the contact time, the adsorption efficiency of Cr(VI) ions increased rapidly because the adsorbent had many adsorption sites. When the adsorption sites of the adsorbent were gradually filled with Cr(VI) ions, the adsorption reaction slowly reached equilibrium. Therefore, we believed that the adsorption reaction could reach equilibrium within 60 min.

Conclusions In this work, we prepared MIL-101(Fe) by a solvothermal method and successfully applied it to the adsorption of Cr(VI) ions. The experimental results show that MIL101(Fe) has a good adsorption effect on Cr(VI) ions in hydrometallurgy wastewater. The adsorption process was influenced by the solution pH, adsorbent dosage, contact time, and other factors. The adsorption effect of MIL-101(Fe) on Cr(VI) ions was good at an initial concentration of Cr(VI) ions of 0–200 mg/L, and the adsorption rate was 63.47% under optimal conditions. Therefore, MIL-101(Fe) has a high adsorption capacity for Cr(VI) ions in wastewater and is a promising environmentally friendly adsorbent. Acknowledgements This work was financially supported by the Natural Science Foundation of China (U2004215, No. 51974280 and No. 51774252) and the Educational Commission Fund of Henan Province of China (No. 20HASTIT012 No. 18A450001 and No. 17A450001).

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References 1. Nitin K, Nisha S, Ekta T, Gopala D (2019) Novel synthesis of a clay supported amorphous aluminum nanocomposite and its application in removal of hexavalent chromium from aqueous solutions. RSC Adv 9(20):11160–11169 2. Wu M, Zhang L, Wang C, Ding C, Zheng X (2018) Regulation of interfacial properties of polybenzoxazine for effective removal of Cr(VI) from aqueous solution. Desalin Water Treat 109:279–290 3. Li HY, Yang Y, Zhang M, Wei WL, Xie B (2019) A novel anion exchange method based on in situ selectively reductive desorption of Cr(VI) for its separation from V(V): toward the comprehensive use of hazardous wastewater. J Hazard Mater 368:670–679 4. Rebhi AEM, Lounici H, Lahrech MB, Morel JL (2019) Response of Artemisia herba alba to hexavalent chromium pollution under arid and semi-arid conditions. Int J Phytorem 21(3):224– 229 5. Shi K, Bai YJ, Guo YR, Cheng YW, Hua YY, Yu XL (2020) Assessment of regional water resource security: a case study from hebei province, china. Teh Vjesn 27(6):1781–1790 6. Benalia MC, Youcef LB, Mohamed G, Achour S, Menasra H (2022) Removal of heavy metals from industrial wastewater by chemical precipitation: mechanisms and sludge characterization. Arabian J Sci Eng 47(5):5587–5599 7. Stoller M, Sacco O, Vilardi G, Pulido J, Di Palma L (2018) Technical-economic evaluation on chromium recovery from tannery wastewater streams by means of membrane processes. Desalin Water Treat 127:57–63 8. Hayashi N, Matsumura D, Hoshina H, Ueki Y, Tsuji T, Chen J, Seko N (2021) Chromium(VI) adsorption–reduction using a fibrous amidoxime-grafted adsorbent. Sep Purif Technol 277(119536):1–8 9. Peng H, Guo J (2020) Removal of chromium from wastewater by membrane filtration, chemical precipitation, ion exchange, adsorption electrocoagulation, electrochemical reduction, electrodialysis, electrodeionization, photocatalysis and nanotechnology: a review. Environ Chem Lett 18(6):2055–2068 10. Sun XZ, Guo P, Sun YY, Cui YQ (2021) Adsorption of hexavalent chromium by sodium alginate fiber biochar loaded with lanthanum. Materials 14(9):2224 11. Noraee Z, Jafari A, Ghaderpoori M, Kamarehie B, Ghaderpoury A (2019) Use of metalorganic framework to remove chromium (VI) from aqueous solutions. J Environ Health Sci Eng 17(2):701–709 12. Zhang W, Li N, Xiao T, Tang WT, Xiu GL (2019) Removal of antimonite and antimonate from water using Fe-based metal-organic frameworks: the relationship between framework structure and adsorption performance. J Environ Sci 86(12):213–224 13. Rebeca M, Manuel A, Pilar L, Victor S, Joaquin C (2018) Reactive gas atmospheres as a tool for the synthesis of MOFs: the creation of a metal hybrid fumarate with a controlled Fe/Al composition profile. J Mater Chem A 6(29):14352–14358 14. Dan L, Ovidiu A, Gabriela B, Gheorghe B, Mihaela DL, Alexandur RB, Coldea I, Maria M, Ioan M, Gabriel P (2011) Synthesis and hydrogen adsorption properties of a new iron based porous metal-organic framework. Int J Hydrogen Energy 36:3586–3592

Weather Aged Fique Fabric Reinforced Epoxy Composite: Impact Property Analysis Michelle Souza Oliveira, Fernanda Santos da Luz, Artur Camposo Pereira, Noan Tonini Simonassi, Lucio Fabio Cassiano Nascimento, and Sergio Neves Monteiro Abstract Natural materials have become quite common in recent years and are a major concern of the scientific community. In this sense, lignocellulosic fibers as substitutes for various synthetic reinforcements in polymer composites have shown great potential for technological applications. In particular, the applicability designated in this work are ballistic panels in which reliability, weight reduction, cost reduction and material sources are critical points for evaluation. Thus, the present work aimed to evaluate the impact energy of the epoxy matrix composite reinforced with 40 vol% of fique fabric in the natural aging condition for 2,160 h (90 days). As main results, a significant degradation by photo-oxidative process was observed, as well as the appearance of micropores with concave shape and epoxy matrix microcracks. The energy absorption decreased by ~53% for Charpy test and ~12% for Izod test after the weathering of 2,160 h compared to the non-aged composite. Weibull shape parameter increased after the aging, indication of a premature failure issue. Different behavior between the Charpy and Izod tests was pointed out by ANOVA, suggesting the interference of sample size and exposure area. Keywords Mechanical performance · Epoxy resin · Fique fabric · Aging materials

Introduction The use of natural materials, in order to minimize environmental problems, has become quite common in recent years and is a major concern of the scientific community [1]. The new economic development model aims to improve the quality of life of future generations, incorporating fewer polluting modes of production in its conception [2]. The use of lignocellulosic fibers as substitutes for various synthetic reinforcements in polymer composites has shown great potential for technological M. S. Oliveira (B) · F. S. da Luz · A. C. Pereira · N. T. Simonassi · L. F. C. Nascimento · S. N. Monteiro Military Institute of Engineering—IME, Rio de Janeiro, Brazil e-mail: [email protected]; [email protected] © The Minerals, Metals & Materials Society 2023 The Minerals, Metals & Materials Society, TMS 2023 152nd Annual Meeting & Exhibition Supplemental Proceedings, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-031-22524-6_74

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application, based on significant advantages, such as: low cost and density [3, 4]. In addition, natural fibers are biodegradable, non-toxic and non-polluting, come from renewable sources and are available all over the world, reducing environmental problems. The search for new materials has led researchers to develop composites using these types of fibers as reinforcement. The applicability and potential of composites made with natural fiber reinforcement are related to the country’s economy [1, 5]. In particular, the applicability designated in this work are ballistic panels in which reliability, weight reduction, cost reduction and material sources are critical points for evaluation. Woven-reinforced composites are being investigated as possible advanced engineering materials. The impact properties on epoxy resin matrix reinforced with fique fabric were analyzed by Rua et al. [5] and Oliveira et al. [6]. The last one, compared the Charpy and Izod impact energies and observed a proportional increase with the volume fraction of the fique fabric layers incorporated into the epoxy matrix of up to 50 volume percent. It is understood that these composite materials will be exposed to various natural weather conditions; for example, heat, humidity, rain, air pollutants, wind gusts, sand abrasion, and sunlight, among other things [7]. Natural weathering encompasses the effects of most types of degradation phenomena and generally involves the combined effect of the mechanical properties of the polymer, leading to embrittlement or catastrophic failure, i.e., cracking, of the material [7, 8]. It is of fundamental importance to study the behavior in relation to the composite degradation mechanisms, considering possible applications in external environments, under the action of solar radiation, rain, temperature and humidity. Therefore, the present work aimed to evaluate the impact properties of the epoxy matrix composite reinforced with 40 volume percent of fique fabric in the natural aging condition for 2,160 h.

Materials and Methods Samples Preparation The fique fabric, commercially available in Antioquia, Colombia, was purchased from a local retailer. The polymer used as the matrix material was a commercial epoxy diglycidyl ether bisphenol A type (DGEBA) cured with triethylenetetramine (TETA) using the 100:13 parts resin: hardener. The resin, still liquid, was spread over the fabric and placed inside the metal mold alternately with the layers of fabric. The composite formed was allowed to cure for 24 h under a pressure of 5 tons and at room temperature.

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Exposure to Natural Aging The main weather parameters observed during the exposure period are shown in Fig. 1. The natural aging experiment was carried out in Rio de Janeiro, Brazil. The samples were placed on the roof of a building in the center of the city for 2,160 h (90 days: 10/05/2020 01-05/2021). The sample apparatus presented in previous work [9] was placed on the roof in a southerly direction at an angle of 45°. The geographic coordinate of the aging test apparatus was 22°54 13S and 43°12 W. Location close to Guanabara Bay on the Atlantic Sea, at a distance of 570 m, with an altitude of 20 m above sea level.

Impact Properties Standard specimens measuring 127 × 12.7 × 10 mm were fabricated for the Charpy impact test in accordance with ASTM D6110 and the results were presented in previous work [9], and 63.5 × 12.7 × 10 mm were produced for the Izod impact test in accordance with ASTM D256 (2018). With the number of 5 samples, both were impact tested with a PANTEC hammer pendulum.

Microscopy Analyses After the impact tests, fragments of the broken specimens were collected and microscopic images were taken. Microstructural characterization was performed using an Olympus BX53M optical microscope.

Statistical Analyses The values obtained for the impact energy for both types of impact tests were interpreted using Weibull statistics computer program. Weibull statistical analysis is based on a cumulative distribution function.     F(x) = 1 − ex p − x θ ∧ β]

(1)

Where θ and β are mathematically known as shape and scale parameters. Equation 1 can be conveniently modified into a linear expression by double application of the logarithm:

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Fig. 1 Weather graphics: a Air temperature [the green line indicates an average temperature of 24.65], b Atmosphere pressure, c Global radiation, d Total precipitation, e Wind maximum gust, f Maximum relative humidity [the green line indicates an average temperature of 87.15], g Wind speed in 2020 [the daily range of recorded wind speeds (gray bars), with maximum gust speeds (red dashes)]

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ln(ln1/(1 − F(x))) = βlnx − βlnθ

(2)

Using data from experimental specimens, the computer program constructed the linear graph of Eq. 2 and calculated the Weibull parameters. Analysis of variance (ANOVA) was also applied to verify, with a 95% confidence level, any significant differences between the averages. In positive cases, the mean values of the results were then compared using Tukey’s test, also called honestly significant difference (HSD). The EMS is the error mean square and r is the repetitions number for each condition is calculated by Eq. 3, and q is a tabulated constant. H SD = q



  EMS r

(3)

Results and Discussion Meteorological parameters are the main factors in our study. The climate of Guanabara Bay as a whole is tropical and humid, with a rainy season in summer, from December to April, and a dry season, between June and August [10]. Figure 2 shows the normalized rate of precipitation from October 2020 to January 2021, the period of exposure of the material. From the figure it can be observed that the normalized index of precipitation, called SPI, varies between −0.18 to −1.84, with climate varying from very dry to close to normal. These data are specific to the region where the samples were exposed, and a slight difference can be observed with the general data presented in Fig. 1. Already the accumulated precipitation is 67.3 to 209.6. Data such as precipitation and temperature are extremely important in this study. The climate of Guanabara Bay is influenced by several atmospheric factors, whether dynamic, such as air masses; or static, such as topography, geographic position, maritime nature, continentality, among others. The test duration used in the composites was 2,160 h.

Visual Effects on Samples The common degradation mechanisms in composites due to natural aging are thermooxidative, photo-oxidative and hydrolytic degradations [11]. A particularly significant degradation was observed through this study, demonstrating that the dominant degradation mechanism of the fique fabric composite is the photo-oxidative effect. Figure 3 shows the visual appearance before and after exposure of the Charpy and Izod test samples. Literature has reported that the fading of fabric color after aging is caused by the oxidation of lignin [11]. Micro-cracks that develop on a surface irradiated with

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Fig. 2 Normalized precipitation index curves and temperature variation graph for the period between October 2020 and January 2021

ultraviolet rays provide pathways for the rapid entry of moisture and chemical agents. Water, especially in the form of condensation, can also remove soluble products of photo-oxidation reactions from a UV-irradiated surface and thus expose susceptible surfaces to further degradation by UV radiation [7, 11]. Air pollutants can still cause degradation, but in combination with solar radiation and other climatic factors can be responsible for serious damage to composite structures [7]. Figure 4, obtained from an optical microscope varying the magnification from 5 to 20×, made it possible to observe the micropores formed on the surface of the sample, as well as its concave shape. Also, the presence of microcracks in the epoxy matrix is noted.

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(a)

(b)

Fig. 3 Comparison in the visual changes on the samples a no aged and b natural aged at 2,160 h

Fig. 4 Optical microscopic observation of epoxy matrix damages

Impact Properties Although the heaviest hammer available (22 J) was used in the impact test, samples of composites reinforced with 40 vol% in both conditions, aged and un-aged, did not completely break, as shown in Fig. 3. The fact that the impact energy samples of the fabric composite do not completely break is indicative of the high tenacity of this composite provided by the reinforcement. In fact, if there was a complete breakdown of this composite, the energy absorbed would be even greater. Furthermore, the increase in tenacity is due to the low interfacial shear stress of natural fiber/polymer

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Table 1 Weibull parameters for energy absorbed after Charpy and Izod impact of aged and nonnaturally aged samples Material

β

θ

R2

Average

Standard deviation

Charpy impact Composite no aged Composite natural aged

2.61

551.20

0.94

489.60

201.80

13.04

243.40

0.83

233.90

21.88

Izod impact Composite no aged

4.22

244.10

0.97

222.00

59.24

Composite natural aged

5.79

209.7

0.97

194.5

38.84

matrix. This phenomenon results in a greater absorbed energy due to the longitudinal propagation of the cracks along the interface, which generates larger areas of rupture than a transverse fracture [6]. The decrease in Emax values reached ~53% for Charpy samples and ~12% for Izod samples, after the weathering of 2,160 h compared to the non-aged composite. Table 1 and Fig. 5 present the Weibull parameters. In this statistical method, the shape parameter (β) is one of the most widely examined parameters because it helps to indicate the types of faults that occur based on the slope or value of β. If the value of β > 1, the failure rate will generally increase over time, this could be an indication of premature failure problems or even dictate the life of the material. Table 2 presents the ANOVA results for both impact tests. The Tukey test honest significant difference (HSD) for the composite no aged and aged result present a significant difference. HSD is 314.46 for Charpy test. The hypothesis that the means are equal with a confidence level of 5% is rejected. However, the same behavior was not observed for the Izod samples, with the Fcalculated well below the Fcritical . This may be indicative of the interference of sample size and sample exposure area.

Summary and Conclusions The epoxy composite reinforced with silk fabric, natural fiber, was exposed to weathering. As main results, it was observed by visual analysis that a significant degradation, demonstrating the dominating degradation mechanism of fique’ composite to be photo-oxidative, and, in the surface the appearance of micropores formed with concave shape. As well as microcracks in the epoxy matrix. The decrease in energy absorption values reached ~53% for Charpy test and ~12% for Izod test after the weathering of 2,160 h compared to the non-aged composite. The Weibull shape parameter increased after the aging, this could be an indication of premature failure issues, and lack of proper maintenance of the fique-epoxy composite. Different behavior between the Charpy and Izod test was pointed out by ANOVA, possibly indicating the interference of sample size and exposure area.

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Fig. 5 Weibull parameters of Charpy impact test [9] on composite a no aged and b natural aged and Izod impact test on composite c no aged and d natural aged

Table 2 Optical microscopic observation of epoxy matrix damages Source of variation

Degree of freedom

Sum of square

Mean squares (MS)

Fcalculated

Fcritical

Within

1

156,264.81

Between

8

147,982.48

156,264.81

8.45

5.32

18,497.81

–

–

Total

9

Source of variation

Degree of freedom

304,247.28

–

–

–

Sum of square

Mean squares (MS)

Fcalculated

Fcritical

Within

1

1,873.89

1,873.89

0.99

5.32

Between Total

8

15,106.25

1,888.28

–

–

9

16,980.14

–

–

–

Acknowledgements The authors thank the Brazilian agencies CAPES, FAPERJ and CNPq for the financial support. The authors are also grateful to the institutions that contributed to this research: IME and UENF.

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References 1. Monteiro SN, Drelich JW, Lopera HAC, Nascimento LFC, Luz FS, da Silva LC, Pereira AC (2019) Natural fibers reinforced polymer composites applied in ballistic multilayered armor for personal protection—an overview. In: Ikhmayies S, Li J, Vieira C, Margem (Deceased) J, de Oliveira Braga F (eds) Green materials engineering. The minerals, metals & materials series. Springer, Cham, pp 33–47 2. Hassan KMF, Horvath PG, e Alpár T (2020) Potential natural fiber polymeric nanobiocomposites: a review. Polymers 12(5):1072 3. Mohammed L, Ansari MNM, Pua G, Jawaid M, Islam MS (2015) A review on natural fiber reinforced polymer composite and its applications. Int J Polym Sci 4. Faruk O, Bledzki AK, Fink HP, Sain M (2014) Progress report on natural fiber reinforced composites. Macromol Mater Eng 299:9–26 5. Rua J, Buchely MF, Monteiro SN, Echeverri GI, Colorado HA (2021) Impact behavior of laminated composites built with fique fibers and epoxy resin: a mechanical analysis using impact and flexural behavior. J Market Res 14:428–438. https://doi.org/10.1016/j.jmrt.2021. 06.068 6. Oliveira MS, F da Filho CG, da Luz FS, Pereira AC, da C Demosthenes LC, Nascimento LFC, Monteiro SN (2019) Statistical analysis of notch toughness of epoxy matrix composites reinforced with fique fabric. J Mater Res Technol 8(6):6051–6057. https://doi.org/10.1016/j. jmrt.2019.09.079 7. Sınmazçelik T (2006) Natural weathering effects on the mechanical and surface properties of polyphenylene sulphide (PPS) composites. Mater Des 27(4):270–277. https://doi.org/10.1016/ j.matdes.2004.10.022 8. Oliveira MS, da Luz FS, Monteiro SN (2021) Research progress of aging effects on fiberreinforced polymer composites: a brief review. In: Characterization of minerals, metals, and materials 2021. https://doi.org/10.1007/978-3-030-65493-1_51 9. Oliveira MS, Luz FS, Pereira AC, Garcia Filho FC, Simonassi NT, Nascimento LFC, Monteiro SN (2022) Effects of natural aging on fique fabric-reinforced epoxy composites: an analysis by Charpy impact energy. In: Characterization of minerals, metals, and materials 2022. The minerals, metals & materials series. Springer, Cham. https://doi.org/10.1007/978-3-030-923730_32 10. Silva, Newton Thiago de Castro. Climate diagnosis guanabara ecological station management plan. https://www.icmbio.gov.br 11. da Silva CB, Martins AB, Catto AL, Santana RMC (2017) Effect of natural ageing on the properties of recycled polypropylene/ethylene vinyl acetate/wood flour composites. Matéria (Rio de Janeiro) 22(2). https://doi.org/10.1590/s1517-707620170002.0168

Part XXIII

Computational Thermodynamics and Kinetics

Effect of Different Desulfurizers on Hot Metal Pretreatment Liang Tian, Wufeng Jiang, Suju Hao, and Yuzhu Zhang

Abstract In the process of hot metal pretreatment desulfurization, it is necessary to add desulfurizer to promote the desulfurization reaction. At the same time, as the key to measure the sulfur content in molten iron, the study of sulfur distribution ratio is of great significance to achieve the goal of desulfurization. Based on the theoretical sulfur ratio of desulfurization molecules and the theoretical sulfur ratio of ions, this paper analyzes the factors affecting the sulfur content in molten iron, and concludes that the suitable desulfurization conditions are high temperature, low oxygen level and high oxygen anion concentration. Thermodynamic analysis and related introduction of common desulfurization are carried out, and it is pointed out that CaO–Mg composite desulfurizer has the highest removal efficiency and the lowest consumption, which is conducive to desulfurization. Keywords Desulfurizer · Desulfurization mechanism · Ion theoretical sulfur ratio

Introduction In the desulfurization process of hot metal pretreatment, a certain amount of desulfurizer is often added to promote the desulfurization reaction. The desulfurization reaction is the interface reaction between desulfurizer and hot metal. When the two phases are in contact, the desulfurization reaction occurs [1–5]. In the process of using desulfurizer, it is necessary to overcome the surface tension, resistance and buoyancy of molten iron. In order to meet the requirements of steel-making for sulfur content, desulfurizer suitable for steel smelting and desulfurization objectives should be selected. This can only improve the desulfurization efficiency, and the use of cost-effective desulfurizer can also reduce the consumption of desulfurization L. Tian · W. Jiang (B) · S. Hao · Y. Zhang College of Metallurgy and Energy, North China University of Science and Technology, Ministry of Education Key Laboratory of Modern Metallurgy Technology, Tangshan 063210, Hebei, China e-mail: [email protected] © The Minerals, Metals & Materials Society 2023 The Minerals, Metals & Materials Society, TMS 2023 152nd Annual Meeting & Exhibition Supplemental Proceedings, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-031-22524-6_75

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powder and save production costs. There are many kinds of desulfurizers, four of which are widely used, namely CaC2 , CaO Na2 CO3 and Mg.

Thermodynamic Analysis of Desulfurization Sulfur partition ratio is one of the key factors to determine the sulfur content in molten iron. The molecular reaction process of desulfurization is as follows [6]: [FeS] + (Ca O) = (CaS) + [FeO]

(1)

The equilibrium constant of the reaction is: w(S) · γ S · a[FeO] aCao · a S

(2)

w(S) fS aCa O × = KS × w[S] γS a[FeO]

(3)

KS = The sulfur distribution ratio is: LS =

According to the molecular reaction process of desulfurization, the low FeO reaction proceeds to the right. At the same time, increasing the alkalinity in the slag and reducing the oxygen potential in the slag are conducive to the desulfurization reaction, resulting in the increase of sulfur distribution ratio ls. According to the ionic structure theory of molten slag, the desulfurization reaction between desulfurized slag and molten iron is carried out by diffusion on the slag iron interface. O2− in the desulfurized slag combines with s in molten iron to form S2− and enters the slag to maintain balance with cations such as Ca+ , Mg+ in molten iron [7]. The schematic diagram and process of desulfurization ion reaction are as follows (Fig. 1). The reaction process is:     [S] + O 2− = S 2− + [O]

(4)

G θ = 12455 − 50.26T

(5)

Its equilibrium constant is: KS = The sulfur distribution ratio is:

a S 2− · a O a O 2− · a S

(6)

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Fig. 1 Schematic diagram of desulfurization reaction

LS =

w(S) a O 2− fS × = KS × w[S] γ S 2− w[O]

(7)

KS , γ S are the equilibrium constant and sulfur partition coefficient of the reaction in turn; w(S), w[S], w[O] are sulfur content in slag, sulfur content in molten iron, and oxygen content in molten iron; acao , a[FeO] are the activity of CaO in slag and the activity of ferrous oxide in molten iron respectively; a O 2− , a S 2− are the activity of O2− and S2− in the slag in turn; fS is the activity coefficient of sulfur in molten iron; LS is the ratio of sulfur content in slag to that in molten iron, i.e. sulfur distribution ratio. According to the viewpoint of chemical equilibrium, when sulfur in slag is quantitative, its oxygen potential is inversely proportional to the concentration of oxygen anion, that is, the lower the oxygen potential, the higher the concentration of oxygen anion in slag. Therefore, the lower activity of sulfur ions in the slag, so as to promote the chemical reaction, resulting in the easier s in molten iron to enter the slag, and the increase of S. Because FeO appears in the numerator denominator, FeO decreases and the numerator denominator decreases, which has a greater impact on the denominator, so the desulfurization effect can be improved. At the same time, the ion reaction is an endothermic reaction. The increase of temperature and the addition of a certain amount of FeO are conducive to the dissolution of CaO, improve the fluidity of slag, and promote the desulfurization reaction. Therefore, high temperature, low oxygen level, high oxygen anion concentration and low FeO content are required in hot metal pretreatment desulfurization.

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Desulfurization Mechanism and Development of Desulfurizer CaO CaO is a desulfurizer commonly used in hot metal pretreatment. It is rich in resources, cheap, safe and pollution-free [8]. When using CaO for desulfurization, in order to ensure the desulfurization efficiency, it needs to be ground into powder, but the ground lime powder is prone to moisture deterioration, so it needs moisture-proof storage. Its mechanism is to use CaO and sulfur in molten iron to form CaS. The relevant chemical reaction formula is: Ca O(s) + [S] = CaS(s) + [O]

(8)

G θ = 110000 − 31.1T

(9)

K = a[O] /a[S]

(10)

Formula 10 shows that the increase of temperature leads to the decrease of G θ , and high temperature is conducive to lime desulfurization. The oxygen content in molten iron plays a vital role in the desulfurization reaction. Low oxygen concentration makes the desulfurization reaction more thorough. When molten iron contains Al, the reaction formula of Al participating in desulfurization reaction is: CaO(s) + [S] + 2/3[Al] = CaS(s) + 1/3Al2 O3 (s)

(11)

G θ = −292936 + 100.76T

(12)

There is free oxygen in the product after the Cao desulfurization reaction, which reacts with Si in molten iron to produce CaO·SiO2 with higher melting point, forming a dense film on the surface of CaO to prevent the desulfurization reaction from continuing. The relevant chemical reaction formula is: 2Ca O(s) + [S] + 1/2[Si] = CaS(s) + 1/2Ca2 Si O4 (s)

(13)

G θ = −251930 + 83.36T

(14)

At 1350 °C, the equilibrium constant of molten iron treated with CaO is 6.489, and the end-point sulfur content can reach 0.00037%. Moreover, the desulfurization efficiency of CaO is lower than that of Mg, and the melting point of CaO is high.

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When it is used as desulfurizer in molten iron, it is solid. Flux CaF2 is often added to reduce the melting point and become liquid. After adding flux, the sulfur content in molten iron will decrease significantly. Because CaO is easily hygroscopic and converted into Ca(OH)2 , Ca(OH)2 will cause hot metal splashing during hot metal pretreatment and desulfurization, resulting in the reduction of hot metal temperature.

Na2 CO3 As the first desulfurizer used outside the furnace, Na2 CO3 has strong desulfurization ability and low melting point. At 1350 °C, soda is used in high-carbon molten iron for desulfurization outside the furnace, and the equilibrium constant of desulfurization reaction is 5 × 104 , the sulfur content in molten iron is 4.7 × 10–7 , the desulfurization capacity is stronger than CaO. Only a part of sodium vapor participates in the desulfurization reaction, and most of the remaining sodium vapor will be oxidized in the air, releasing a large amount of smoke, causing serious environmental pollution, which is inconsistent with the current carbon neutral carbon reduction emissions, and is rarely used alone now. The chemical equation is: N a2 C O3 (s) + [S] + 2[C] = N a2 S(s) + 3C O(g)

(15)

G θ = 440979 − 366.54T

(16)

Due to high temperature, Na2 CO3 is rapidly decomposed into Na2 O and CO2 after entering the molten iron, and then Na2 O will continue to react with carbon and silicon in the molten iron to generate CO and Na2 SiO3 respectively. N a2 O(l) + [S] + [C] = N a2 S(l) + C O(g)

(17)

G θ = −34828 + 68.52T

(18)

2/3N a2 O(l) + [S] + 1/2[C] = N a2 S(l) + 1/2N a2 SiO3 (l)

(19)

G θ = −399804 + 64.17T

(20)

The sodium oxide generated by the decomposition of soda at high temperature is liquid, and its content in the slag is high. The fluidity of the slag is good, it is difficult to scrape the slag mechanically, and it erodes the refractory materials in the hot metal tank seriously. Above 1250 °C, Na2 S will be oxidized by air, and the generated Na2 O may continue to be reduced to gas sodium. Sodium vapor and carbon monoxide burn in the air, producing a large amount of smoke, causing great damage

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to the environment, which is inconsistent with the current carbon neutralization, so its use should be reduced.

Mg The melting point of magnesium is low, only 650 °C, and the boiling point is 1107 °C. The desulfurization reaction is mainly the homogeneous reaction of molten iron. For low-temperature molten iron, magnesium is one of the strongest desulfurizers. After reacting with molten iron, the amount of desulfurization slag is small and the iron loss is small [9]. Most of magnesium exists as magnesium vapor in molten iron, and the other part of magnesium dissolves in molten iron and reacts with sulfur. The final products of both are magnesium sulfide. Mg(g) + [S] → MgS(s)

(21)

G θ = −404700 + 169.6T

(22)

At 1350 °C and PMg = 0.1 MPa, apply G θ = −RT ln K to calculate the sulfur activity when the reaction reaches equilibrium as: K 1 = 1/a[s] = 1.46 × 104

(23)

a[s] = 6.82 × 10−5

(24)

[Mg] + [S] → MgS(s)

(25)

G θ = −544767 + 212.0T

(26)

According to the calculation of sulfur activity, it is very low, indicating that magnesium has strong desulfurization ability, and the end-point sulfur content after desulfurization with magnesium is 1.6 × 10–5 , its desulfurization capacity is far greater than CaO. Magnesium metal has high activity and is easily oxidized. It is flammable and explosive. Magnesium particles can be safely transported and used only after surface passivation treatment. After passivation treatment, a protective film is formed on the surface of magnesium particles, which limits the activity of magnesium and enables magnesium to smoothly participate in desulfurization reaction in molten iron. At the same time, the price of magnesium is expensive, and the desulfurized slag after desulfurization is thin, which brings the problem of slag removal and causes the pressure of sulfur recovery to the converter. Therefore, when using magnesium desulfurizer,

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Table 1 Desulfurization effect of composite magnesium desulfurizer Number

Desulfurizer composition

Primary sulfur/%S

Final sulfur/%S

Unit consumption of Mg/(kg/t iron)

Desulfurization rate/%

1

100% Mg

0.0287

0.0067

0.710

77

2

50% Mg + 50% Al2 O3

0.0259

0.0063

0.430

76

3

50% Mg + 50% Ca(OH)2 0.0204

0.0061

0.397

70

4

50% Mg + 40% CaO + 10% CaF2

0.0262

0.0046

0.556

82

5

50% Mg + 50% CaO

0.0269

0.0045

0.550

83

some non-metallic magnesium mixed desulfurizer is often added. Table 1 shows the desulfurization effect ratio of composite magnesium desulfurizer [10, 11]. It can be seen from Table 1 that the desulfurization effect of using metal magnesium desulfurizer alone is not the best, and the desulfurization rate of No. 4 and No. 5 metal magnesium mixed desulfurization slag is the highest. However, No. 4 contains CaF2 , which seriously erodes the ladle lining, so the use of CaO–Mg composite desulfurizer is a more reasonable desulfurizer.

CaC2 The main component of calcium carbide powder is CaC2 . Industrial CaC2 is actually used (containing about 80% of CaC2 , 16% of CaO, and the rest is carbon), and the price is relatively expensive. Its desulfurization rate can reach 90%, and the reaction speed is fast. The reaction formula is: CaC2 + [S] = CaS + 2[C]

(27)

G θ = −359245 + 109.5T

(28)

The equilibrium constant of calcium carbide desulfurization reaction is usually 6.90 × 105 . When the reaction reaches equilibrium, the sulfur content in molten iron can reach 4.9 × 10–7 . The desulfurization reaction with calcium carbide is exothermic, which is conducive to reducing the temperature loss of molten iron. The melting point of desulfurization product CaS is 2450 °C, so after desulfurization, loose solid slag will be formed on the surface of molten iron to reduce the sulfur recovery of molten iron. And the corrosion to the lining of molten iron tank is light, which is convenient for slag removal. However, it is very easy to deliquesce and deteriorate, and the following reactions are produced rapidly when it contacts with water in the atmosphere:

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CaC2 + H2 O = Ca O + C2 H2 ↑

(29)

CaC2 + 2H2 O = Ca(O H )2 + C2 H2 ↑

(30)

Acetylene (C2 H2 ) gas produced by this reaction is very explosive, so it needs to be sealed during transportation and storage to prevent the occurrence of the above reaction. In addition, when calcium carbide is mixed with other desulfurizers, it will also absorb the above water and react, so calcium carbide should be mixed before injection.

Conclusions (1) Through thermodynamic analysis of desulfurization, it is found that hot metal pretreatment desulfurization requires high temperature, low oxygen level, high oxygen anion concentration and low FeO content. (2) By analyzing and comparing the desulfurization efficiency of the three desulfurizers, it is concluded that mg has the highest desulfurization efficiency, followed by CaC2 , and CaO has the lowest efficiency. At the same time, it is pointed out that CaO–Mg composite desulfurizer is an ideal desulfurizer. Acknowledgements National Natural Science Foundation of China (No. 51274084), Hebei Natural Science Foundation (E2018209323), and Project of North China University of Science and Technology GP201507. National Natural Science Foundation of China (No. 51274084), Hebei Natural Science Foundation (E2018209323), and Project of North China University of Science and Technology GP201507.

References 1. Vuolio T, Visuri V, Sorsa A, et al. (2019) Genetic algorithm-based variable selection in prediction of hot metal desulfurization kinetics. Steel Res Int 90(8) 2. Fei X (2013) Metallurgical physicochemical study on magnesite based desulfurizer. Liaoning University of science and technology 3. Dong C (2021) Analysis and reduction measures of steel material consumption in converter. Metall Manag (11):1–2 4. Wei W, Li H (2019) Production practice of ultra-low sulphur pipeline steel L245NCS without pretreatment of hot metal 5. Yunzong G, Benliang Z, Hui W (2015) Research on hot metal desulfurization technology by Kr method. Wide Thick Plate (03):36–40 6. Jialong Q (2016) CaO-Al2 O3 -SiO2 -MgO-TiO2 -Na2 O Study on Desulfurization Kinetics of Six Element Slag System and Thermodynamic Properties of CaO in Slag. Jiangxi University of science and technology 7. Zhiming Y (2019) Basic research on the Structure and Properties of Aluminosilicate Based Blast Furnace Slag. Chongqing University

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8. Xiulan P, Yanhong W, Huizhi L, et al. (2010) Development status and prospect of hot metal pretreatment technology. World Steel (06):29–36 9. Hanjie G (2007) Dynamics of hot metal desulfurization process with magnesium particles. Steel 42(005): 7–41 10. Xianhui W, Siming Z, Xiaofen M (2013) Analysis of factors affecting desulfurization efficiency of Cao based composite desulfurizer. Steelmaking 029(002):30–33 11. Wu W, Han Z, Hu Y (2008) Desulfurizer desulphurization kinetics by the injection method. J Beijing Univ Sci Technol (Engl Ed)

Modeling of Slag Modification on Inclusions in 54SiCr6 Spring Steel Xuefeng Bai, Yanhui Sun, and Huibin Wu

Abstract A kinetic model of slag-steel-inclusion reactions in 54SiCr6 spring steel was established to predict the effect of slag composition on inclusions using FactSage Macro Processing. The dissolved aluminum, total oxygen, and inclusion composition with varying slags were calculated using the current model. The predicted results are in accordance with the lab-scale experimental ones. Furthermore, the effect of refining slag on the transformation of inclusions was predicted. For 54SiCr6 spring steel during slag modification process, the composition of CaO–SiO2 –Al2 O3 – 5%CaF2 slag was optimized to suppress the Al pick-up from top slag and promote the formation of low-melting-point inclusions in molten steel. Keywords Modeling and simulation · Spring steel · Slag modification · Inclusion

Introduction Non-metallic inclusions with high hardness and low deformability are one of the main reasons for fatigue fracture in spring steel. Under harsh service conditions, the uniform continuity of steel is destroyed by these harmful inclusions, where microcracks can be easily generated and grow with periodic stress, thereby accelerating fatigue failure and leading to spring fracture [1, 2]. Modification of refining slag has a significant effect on inclusion chemistry. Lowing their melting point by adjusting inclusion composition through Si–Mn deoxidation and slag-steel reactions can significantly improve their plastic deformation performance and reduce stress concentration [3]. To suppress the Al2 O3 -rich inclusion, Park et al. [4] proposed an operating window of ladle slag to obtain the inclusions with a low liquidus temperature. Ren et al. [5] established a thermodynamic model for slag-steel-inclusion reactions in stainless steel. Zhang et al. [6] investigated the X. Bai · Y. Sun (B) · H. Wu Collaborative Innovation Center of Steel Technology, University of Science and Technology Beijing, 30 Xueyuan Road, Haidian District, Beijing 100083, China e-mail: [email protected] © The Minerals, Metals & Materials Society 2023 The Minerals, Metals & Materials Society, TMS 2023 152nd Annual Meeting & Exhibition Supplemental Proceedings, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-031-22524-6_76

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effects of basicity and the CaO/Al2 O3 ratio in CaO–SiO2 –Al2 O3 –MgO on inclusion transformation by considering the inclusion behavior at the liquid “steel-slag” interface. In practical refining, the equilibrium between slag and steel is hardly reached due to the limitation of kinetic conditions and refining time [7]. In the current work, a kinetic model of slag-steel-inclusion reactions in 54SiCr6 spring steel was developed based FactSage through a Macro-processing feature. The basicity and Al2 O3 content in slag were varied to assess the effect of slag modification on evolutions of steel chemistry and inclusion type. Moreover, the optimum range of slag composition was proposed to suppress the Al pick-up from slag and promote low-melting-point inclusion.

Model Description An “Effective Equilibrium Reaction Zone” (EERZ) theory was adopted to examine the influence of slag modification on inclusions. This theory has already been employed for process simulations, including lab-scale experiments and industrial production such as RH refining [8], LF refining [9, 10], and continuous casting [11]. It is assumed that all phases within the reaction zone at the interface have reached an equilibrium. The reacting volume of slag and steel per unit step can be calculated by Eq. (1): Wi =m i Aρi t

(1)

where m i is the mass transfer coefficient in phase i; ρi is the density of phase i; A is the interfacial area between slag and steel; and t is the time step. After each time step, the reacted phase is mixed back into the bulk region, homogenized and re-equilibrated. Thus, the amount and type of inclusion and steel composition are calculated through the equilibrium of the new bulk steel chemistry. The current kinetic model was performed by using the macro-processing feature [9] of FactSage 8.0 with databases of FactPS, FToxid, FTmisc, and FSstel, wherein FTmisc and FSstel databases were available for calculation at 1600 °C and 1400 °C, respectively. The input conditions, all process schedules, and calculation results were externally stored in Microsoft Excel™ worksheets. The constant mass transfer coefficients were applied, wherein the slag mass transfer coefficient was fixed at one-tenth of the steel mass transfer coefficient. Considering the low Al concentration in steel, the steel mass transfer coefficient was estimated by Al pick-up, which was 8.00 × 10–6 m/s [12]. Alloy additions or reoxidation was not included. The inclusion population removed by floating was assumed at the same rate as slag-metal reactions. A constant dissolution rate of MgO refractory to the molten slag was embedded based on plant or laboratory examinations. Considering the low dissolution rate, refractory interaction with steel was not considered in the present model.

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The initial spring steel composition was Fe-0.75Mn-1.6Si-0.6C-0.004O (wt%). CaO–Al2 O3 –SiO2 –5CaF2 slags (wt%) with various basicities and Al2 O3 contents were investigated.

Verification of Kinetic Model The validity of the current model was referenced by comparing it with calculated results and experimental date reported in previous studies [4, 13]. Results of lab-scale experiments by Shin et al. [14] revealed that the slag composition had no significant changes with initial O content less than 0.01%. Therefore, the accuracy of the model was verified by steel composition. As shown in Fig. 1, the precited results of measured Al agree well with the measured results in the range of 0.001% to 0.01%, showing for Al pick-up during slag modification. The predicted results of other main elements further confirm the validity of the present model.

Fig. 1 Comparison of predicted and measured data of Si–Mn steels

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Results and Discussion Effect of Slag Basicity on Inclusions During Slag Modification Figure 2 presents the calculated activity of the component of the initial slag with the aid of the thermodynamic software FactSage 8.0. With increasing slag basicity, the activity of SiO2 shows a significant decrease especially when slag basicity is controlled below 1.0. There is a steady increase in the activities of Al2 O3 and CaO with increasing slag basicity. Figure 3 shows the change in steel composition as a function of reaction time. It can be seen that the dissolved Al contents both increase with reaction time. The Al pick-up in molten steel, which mainly originates from the reaction shown in Eq. (2), can be accelerated by increasing slag basicity. This is due to the increase of Al2 O3 activity and the decrease of SiO2 activity. Even so, the Al contents at the end of six heats are controlled below 0.0006%: 4[Al] + 3(SiO2 )slag = 3[Si] + 2(Al2 O3 )slag

(2)

The transfer of Ca from slag to molten steel occurs by Eq. (3), which can be enhanced by increasing basicity. This is owing to the increase in CaO activity as shown in Fig. 2: 2[Al] + 3(CaO)slag = 3[Ca] + (Al2 O3 )slag

(3)

The effect of slag basicity on the relative fraction of each type of inclusion after slag addition is shown in Fig. 4. The main inclusion type produced from the initial steel composition is SiO2 . Due to the slag modification in steel chemistry, the existing SiO2 in steel transformed into liquid inclusion gradually. The results of six sets of numerical simulation tests show that the transformation from SiO2 to liquid inclusion at 1600 °C can be completed within 30 min. Furthermore, the transformation process can be considerably accelerated by increasing slag basicity. When the initial slag basicity is 1.5, the 100% liquid inclusion can be obtained within 10 min. Fig. 2 The calculated activity of component of initial slag with varied slag basicity

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Fig. 3 Evolution of steel composition with various slag basicities: a Al; b Si; c Ca; d Total O

The prime target of plasticization control for automotive suspension spring steel is to optimize the composition of inclusions so that the melting temperature can be controlled below 1400 °C liquidus. In this study, the fraction of each inclusion type at 1400 °C was calculated through the steel composition at 1600 °C. The precited results are presented in Fig. 5. Due to the temperature drop, the transformation from liquid to other phases in inclusions may occur depending on the melt chemistry. When slag basicity is 0.4, the dominant inclusion at 1400 °C is SiO2 , showing a weak influence of low-basicity slag modification on plasticization control. As the slag basicity increases, the fraction of liquid inclusion increases with reaction time. When slag basicity increases to 1.5, low-melting-point inclusions can be formed within the current reaction time.

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Fig. 4 Effect of slag basicity on inclusion fraction at 1600 °C: a CaO/SiO2 = 0.4; b CaO/SiO2 = 0.8; c CaO/SiO2 = 1.0; d CaO/SiO2 = 1.2; e CaO/SiO2 = 1.4; f CaO/SiO2 = 1.5

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Fig. 5 Effect of slag basicity on inclusion fraction at 1400 °C: a CaO/SiO2 = 0.4; b CaO/SiO2 = 0.8; c CaO/SiO2 = 1.0; d CaO/SiO2 = 1.2; e CaO/SiO2 = 1.4; f CaO/SiO2 = 1.5

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Fig. 6 The calculated activity of component of initial slag with varied Al2 O3 content

Effect of Al2 O3 Content in Slag on Inclusions During Slag Modification Figure 6 shows the effect of Al2 O3 content on the activity of the component of initial slag. With increasing Al2 O3 content, the activity of SiO2 presents a significant downward trend. Compared with the marked increase in Al2 O3 activity, a slight increase in CaO activity can be obtained from the calculated results. Evolution of steel composition with reaction time is shown in Fig. 7. Compared with the results in Fig. 3, the increase of the Al2 O3 content has a considerable influence on Al pick-up. According to the current study, the initial Al2 O3 content should be controlled below 3% to weaken the Al pick-up from slag to molten steel. The total O contents in steel present a downward trend with reaction time and have no significant difference between different Al2 O3 contents. In this work, inclusions entering the reaction zone were assumed to be totally absorbed by slag. The change of total O content reveals a major role of inclusion floating on inclusion removal. The effect of Al2 O3 content on the relative fraction of each type of inclusion after slag addition is shown in Fig. 8. Compared with predicted results shown in Fig. 4, the change of Al2 O3 content shows a significant effect on inclusion evolution. With increasing Al2 O3 content in slag, the formation of the liquid phase can be facilitated at the cost of Al pick-up in molten steel.

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Fig. 7 Evolution of steel composition with various Al2 O3 contents: a Al; b Si; c Ca; d Total O

Figure 9 predicts the effect of Al2 O3 content on non-metallic inclusion plastic control. When initial Al2 O3 content is 3%, low-melting-point inclusions can be obtained within the current studied time range. As Al2 O3 content increases to 6%, mullite forms at 1400 °C, whose content increases with steel-slag reaction time. When Al2 O3 content changes to 15%, the 100% liquid inclusion at 1400 °C can be obtained when the reaction time is less than 10 min. Long steel-slag reaction time will lead to the formation of mullite and even Al2 O3 at 1400 °C.

Conclusions In this study, a kinetic model is developed to investigate the influence of slag on inclusions in 54SiCr6 spring steel. The main conclusions are drawn as follows: (1) The current kinetic model established using FactSage Macro Processing precited well the evolutions of slag, steel, and inclusions during slag modification in spring steel.

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Fig. 8 Effect of Al2 O3 content on inclusion fraction at 1600 °C: a Al2 O3 = 3%; b Al2 O3 = 6%; c Al2 O3 = 15%

(2) The plasticization control for inclusions by slag modification can be more easily achieved under the following conditions: higher basicity, appropriate and sufficient reaction time, and appropriate Al2 O3 content in slag. (3) The increasing Al2 O3 content in slag played a more considerable role than basicity in Al pick-up in molten steel. (4) It is recommended that the basicity be controlled at 1.5 with Al2 O3 content less than 3% to retard the increase in Al content and improve the formation of low-melting-point inclusion when CaO–SiO2 –Al2 O3 –5%CaF2 slag is adopted.

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Fig. 9 Effect of Al2 O3 content on inclusion fraction at 1400 °C: a Al2 O3 = 3%; b Al2 O3 = 6%; c Al2 O3 = 15%

References 1. Liu Y, Zhang W, Tong Q, Wang L (2014) Effects of temperature and oxygen concentration on the characteristics of decarburization of 55SiCr spring steel. ISIJ Int 54(8):1920–1926 2. Podgornik B, Torkar M, Burja J, Godec M, Senˇciˇc B (2015) Improving properties of spring steel through nano-particles alloying. Mater. Sci. Eng., A 638:183–189 3. Xue ZL, Li ZB, Zhang JW, Yang W, Gan CF, Wang Y (2003) Theory and practice of oxide inclusion composition and morphology control in spring steel production. J Iron Steel Res Int 10(2):38–44 4. Park JS, Park JH (2014) Effect of slag composition on the concentration of Al2 O3 in the inclusions in Si-Mn-killed steel. Metall Mater Trans B 45(3):953–960 5. Ren Y, Zhang L (2017) Thermodynamic model for prediction of slag-steel-inclusion reactions of 304 stainless steels. ISIJ Int 57(1):68–75 6. Zhang H, Peng Y, Zhang S, Liu C, Cheng R, Ni H (2022) Effects of refining slag on transformation and removal of inclusions in type 430 stainless steel. Metall Mater Trans B 53:702–715 7. Yuan T, Zhang L, Ren Y, Zhao Q, Liu C (2021) Effect of slag modification on inclusions in Si–Mn-killed 304 stainless steels. Steel Res Int 92(4):2000506 8. Van Ende MA, Kim YM, Cho MK, Choi J, Jung IH (2011) A kinetic model for the ruhrstahl heraeus (RH) degassing process. Metall Mater Trans B 42(3):477–489

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9. Van Ende MA, Jung IH (2017) A kinetic ladle furnace process simulation model: Effective equilibrium reaction zone model using factsage macro processing. Metall Mater Trans B 48(1):28–36 10. You D, Michelic SK, Bernhard C (2020) Modeling of ladle refining process considering mixing and chemical reaction. Steel Res Int 91(11):2000045 11. Van Ende MA, Jung IH (2014) Development of a thermodynamic database for mold flux and application to the continuous casting process. ISIJ Int 54(3):489–495 12. Piva SPT, Kumar D, Pistorius PC (2017) Modeling manganese silicate inclusion composition changes during ladle treatment using factsage macros. Metall Mater Trans B 48(1):37–45 13. Papadopoli Tone S (2018) Non-metallic inclusion changes in Si-Mn killed steels. PhD thesis, Carnegie Mellon University 14. Shin JH, Chung Y, Park JH (2017) Refractory–slag–metal–inclusion multiphase reactions modeling using computational thermodynamics: kinetic model for prediction of inclusion evolution in molten steel. Metall Mater Trans B 48(1):46–59

Part XXIV

Deformation-Induced Microstructural Evolution During Solid Phase Processing: Experimental and Computational Studies

Analysis of Coarse Crystal Defect During Rolling of 3J1A Alloy Jing Jianfa, Wang Shuai, Chen Feng, Yang Lingzhi, and Fu Baoquan

Abstract In this study, the stainless steel 3J1A alloy bar rolling coarse grain defects were analyzed. The coarse grain defect area is located in the edge of the alloy bar position; coarse grain level of 3.5 does not meet the production contract requirements of grade 6 above uniform organization requirements. The original billet composition, rolling temperature, rolling deformation, heat treatment temperature, and other factors on the microstructure were analyzed. The causes of coarse grain defects in the rolling process were identified and corresponding improvement measures were proposed. The results show that the cause of coarse grain defects is the lack of deformation of coarse grains during the rolling process, and the chance of coarse grain defects is reduced by subsequent heat treatment rolling or heat treatment before rolling. Suitable hot working process parameters for 3J1A are recommended, i.e., a suitable rolling temperature of 1050 °C and a suitable solid solution treatment temperature of 950 °C for the rolled bar under fixed deformation conditions, which provides a technical reference for the rolling of subsequent alloy bars. Keywords 3J1A stainless steel · Rolling temperature · Rolling deformation · Coarse crystal defect

Introduction 3J1A alloy is an Fe–Ni–Cr austenitic precipitation-reinforced high elasticity alloy with high strength and modulus of elasticity, small elastic after-effects and hysteresis, good corrosion resistance, and thermal stability. The alloy is widely used in machinery, aviation, electronics, and instrumentation, and its main products are J. Jianfa (B) · W. Shuai · C. Feng · Y. Lingzhi School of Minerals Processing and Bioengineering, Central South University, Changsha 410083, China e-mail: [email protected] F. Baoquan Western Superconducting Technology Co., Ltd, Xi’an 710018, Shaanxi, China © The Minerals, Metals & Materials Society 2023 The Minerals, Metals & Materials Society, TMS 2023 152nd Annual Meeting & Exhibition Supplemental Proceedings, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-031-22524-6_77

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diaphragms, membrane boxes, bellows, etc. [1]. The 3J1A alloy has high temperature strength and low processing challenges; such alloys can be processed for cold-work deformation as well as hot-work deformation [2, 3]. In the presence of coarse grain flaws, 3J1A alloy’s initial usage of 3 + 1 cross-row rolling processing production was found from the diameter of F53 mm rolled to a F24 mm alloy bar. The fault is organized in the F24 mm bar edge position, with a grain level of 3.5. This research uses a F53 mm diameter 3J1A bar as raw material to examine and evaluate aspects such as elemental segregation, thermal processing method, and coarse grain genetics of the original billet to detect coarse grain defects originating during the rolling and processing of 3J1A alloy. On this basis, the sources of coarse grain flaws in 3J1A bar rolling were discovered, and corresponding solutions were given, providing a technical reference for the later hot working of such elastic alloys.

Experimental Procedures The Fe–Ni–Cr austenitic elastic alloy of 3J1A with F53 mm diameter was used as raw material in this study. The 3JIA alloy had the following chemical composition (wt%): Fe-1.22Al-12.18Cr-0.86Mn-35.38Ni-0.49Si-2.94Ti-0.01C-0.0049P. The 3 + 1 cross-row rolling mill was used to roll the 3J1A elastic alloy bar of F53 mm at different temperatures and deformations, and the R-state of the rolled bar was analyzed for a metallurgical organization. The raw billets and rolled bars are heat-treated using a box-type resistance furnace for holding and heating, and the metallographic organization is analyzed after the heat treatment. The microstructures of the 3J1A alloy and the heat-treated samples were characterized by optical microscopy (OM) and SEM. The samples for the SEM investigation were prepared by mirror-polishing and followed by electro-polishing in 10 g (NH4 )2 SO4 , 20 g tartaric acid, and 1 L at 10 V for 5–10 s at room temperature. The grain structures were photographed with a Leica DMR (Wetzlar, Germany). Further details were analyzed by a combination of selected area electron diffraction, EDX analysis, and comparative analysis with the Powder Diffraction File database of the Joint Committee on Powder Diffraction Standards (JCPDS). The final product is required to have a uniform grain structure of more than 6 grades, and the extreme difference in grain cannot exceed 2 grades, bar diameter F22 mm.

Results and Discussion Analysis of the Original Billet Structure Figure 1 shows the microstructures of the 3J1A alloy with F53 mm diameter. The bar has a very poor grain size of 2.5 and is coarse-grained at the borders, with separate

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Fig. 1 Microstructure of 53 billet; a edge position; b 1/2R position

coarse grains at the dichotomous radius and uniform grains at the heart. Figure 2 is the microstructures analyzed by SEM-EDS, and the results show that the elemental content of the normal area and the defective area are comparable, which indicates that there is no elemental segregation in the coarse crystal defective area [4]. Coarse grain defects in the original billet are somewhat hereditary and are the main cause of coarse grain defects in subsequently rolled bars.

Effect of Hot Rolling Process on Coarse Grain Defects Temperature Figure 3 shows the microstructure results of bars rolled and deformed from 53 mm to 24 mm at heating temperatures of 1070 °C, 1050 °C, 1000 °C, and 900 °C. The microstructure of the bars rolled under heating conditions at 1070 °C corresponds to a grain level of 8–9, but there are individual coarse grains and an uneven grain structure (as shown in Fig. 3a). Rolled at 1050 °C under heating conditions, the bars have a grain size of 9–9.5. Compared with 1070 °C, the number of coarse crystals decreases and the difference between the coarse and fine grain size classes decreases (as shown in Fig. 3b). Figure 3c indicated that the fine grain size is further reduced to a grain level of 10, but the coarse grain size remains unchanged, resulting in a larger difference between the coarse grain level and the fine grain level when the rolling temperature is 1000 °C. From Fig. 3d, we found that the fine grain grade is up to 11, but the coarse grain size is unchanged when the rolling temperature is 900 °C. According to the aforementioned data, adjusting the rolling heating temperature can regulate the organization of the bar alloy but cannot erase coarse grain flaws, so the rolling temperature is not believed to impact the main cause of coarse grain defects.

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Fig. 2 SEM-EDS analysis of 53 billet

Rolling Deformation To further identify coarse grain defects, the effect of different rolling deformations on the microstructure of the bars was studied at a rolling heating temperature of 1050 °C. When the deformation is 54%, the grain structure of the rolled bar is 9–9.5 grade, with individual coarse grains present (as shown in Fig. 4a). As can be seen from Fig. 4b, when the deformation is further increased to 75%, the grain organization of the rolled bar is 11 grades, and the coarse grain is refined, which indicates that increasing the processing deformation helps to refine the grains. When the deformation is increased to 81%, the grain of the rolled bar is fully refined, the grain level is 11.5, the bar is uniformly organized, and no coarse grains exist (as shown in Fig. 4c). Therefore, the reason for the existence of coarse crystals in the rolling process is that the original large grain rolling process deformation is insufficient, that is, at a fixed amount of rolling deformation, and a fixed rolling heating temperature condition, the cause of coarse crystal defects for the original billet coarse crystal heredity.

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Fig. 3 Microstructure of rolled bars at different heating temperatures; a 1070 °C; b 1050 °C; c 1000 °C; d 900 °C

Fig. 4 Microstructure of bars at different rolling deformations; a 54%; b 75%; c 81%

Improvement of the Coarse Crystal Defects By Heat Treatment of the Rolled Bars Improving coarse grain defects by changing the organisation of the product bar through heat treatment is a common method. Figure 5 shows the microstructure of the rolled bar after solid solution treatment at 900 °C, 930 °C, 950 °C, 980 °C, and

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1000 °C for 1 h. Comparing Fig. 5a–c, it can be seen that the organization of the alloy bar does not change, with individual coarse grains present in the edge organization, consistent with the original grain results. Figure 5d–f shows that when the solid solution treatment temperature is 930 °C, the grain size of the edge tissue starts to grow, and the grain size is 7–7.5 after the growth, and there is an obvious grain growth transition zone at 1/2R, and the grain size of the heart position is consistent with the R-state grain size, which indicates that the grain size starts to grow when the temperature is higher than 930 °C. When the solid solution treatment temperature is 950 °C, the grain size of the alloy bar grows uniformly at the edge position, at 1/2R and at the heart position; the grain size level after treatment is 6–6.5, the organization is uniform, and the grown grain size is comparable to the original R-state coarse grain size (as shown in Fig. 5g, h, and i). As is shown in Fig. 5j–l, when the solid solution treatment temperature is 980 °C, the grain size further grows rapidly, the grain size level of the alloy bar is 5–5.5 at the edge position, 1/2R and at the heart position; this size has exceeded the grain level required by the product. When the solid solution treatment temperature is 1000 °C, the grain size of the bar grows all over (as is shown in Fig. 5m–o). Therefore, the suitable temperature for heat treatment of the rolling bars is 950 °C.

By Heat Treatment of Raw Blanks Figure 6 shows the original billet F53 mm bar before rolling the heat treatment results. The original billet F53 mm bar is held at 1050 °C for 4 h, and the bar is uniformly organised at the edges, 1/2R, and at the heart position, with a grain level of 1–1.5. In the subsequent conditions of the same temperature and the same amount of deformation, rolling to fine grain requires equal energy, and its corresponding grain refinement requires equal deformation, so as to obtain the final homogeneous organization (as shown in Fig. 6a–c). Figure 7a–c shows that the improved treated alloy bars are rolled with a homogeneous organization at the corresponding edge, at 1/2R, and at the heart position, with a grain organization level of 8–9. The bars processed in this way have a more homogeneous structure and finer grain size than the bars heat-treated at 950 °C after rolling. The recommended method for improving coarse grain is therefore heat treatment prior to rolling.

Conclusions The original billet organization, the effect of rolling temperature and rolling deformation on the organization were analyzed. The causes of coarse grain defects were identified. By analyzing the heat treatment of the bar before and after rolling, a method to solve the coarse grain defects is proposed. The main findings of the study are as follows.

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Fig. 5 Microstructure of rolled bars at 1050 °C after solid solution treatment at different temperatures: a 900 °C edge position, b 900 °C 1/2R position, c 900 °C core position; d 930 °C edge position, e 930 °C 1/2R position, f 930 °C core position; g 950 °C edge position, h 950 °C 1/2R position, i 950 °C core position; j 980 °C edge position, k 980 °C 1/2R position, l 980 °C heart position; m 1000 °C edge position, n 1000 °C 1/2R position, o 1000 °C heart position

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Fig. 6 Original billet F53 mm bar before rolling the heat treatment microstructure, a edge position, b 1/2R position, c core position

Fig. 7 Microstructure of rolled alloy bar after improved treatment a edge position, b 1/2R position, c core position

(1) The appropriate rolling temperature for alloy 3J1A is 1050 °C and the appropriate heat treatment temperature for the rolled alloy bar is 950 °C. (2) Coarse grain defects are inherited from the original billet and can be improved using heat treatment of the bar before and after rolling. (3) The alloy bar grain refinement of the energy necessary for equal, at 1050 °C rolling, in the rolling deformation of 54% of the premise can be obtained under the organization of uniform 9–9.5 grade grain size bars.

References 1. Guo XD, Zhou JL (2014) Manufacturing technology and properties analysis of precision Alloy 3J1 bar. Heilongjiang Metall 4:20–21 2. Guo XD, Zhou JL, Bi FC, Shi JX (2015) Effect of aging temperature on mechanical properties of cold-rolled 3J1 alloy. Heilongjiang Metall 35:11–12 3. Zhang X, Yi DF (2006) A research on thermal treatment of elastic alloy Ni36CrTiAl(3J1). J Zhejiang Industry&Trade Polytech 6:69–72 4. Wenjia X, Yuxiang X, Hui X (2021) Investigation of the Nb element segregation for laser additive manufacturing of nickel-based superalloys. Int J Heat Mass Transf, 121800

Part XXV

Electrical Steels

Constitutive Modelling of High-Temperature Flow Behavior of a Non-oriented Electrical Steel with 3.2 wt% Si Gyanaranjan Mishra, Kanwal Chadha, Youliang He, and Clodualdo Aranas

Abstract Hot rolling is an indispensable thermomechanical processing step in the manufacturing of electrical steel sheets, which plays a vital role in forming the final microstructure and texture of the steel, hence affecting the final magnetic properties. The strain rate, the amount of strain, and the deformation temperature are important operational parameters that not only influence the hot rolling microstructure and texture, but also affect the rolling operations since the material behaves differently under different deformation conditions and requires appropriate control of the rolling forces and speeds. These are important operational parameters to be determined during electrical steel production but have not yet been paid close attention to in electrical steel research. In this study, hot compression tests were performed on a non-oriented electrical steel containing 3.2 wt% Si. The samples were deformed up to a true strain of 0.7 at strain rates varying between 0.01 and 1 s−1 , and temperatures ranging from 850 to 1050 °C. The experimental stress–strain data was fitted using a number of constitutive models, e.g., Zener–Hollomon, Johnson–Cook, and Hensel– Spittel. The accuracy of each model with varying strain, strain rates, and temperatures was evaluated using the correlation coefficient (R) and average absolute relative error (AARE). The Hensel–Spittel model is found to give the best fitting for most of the conditions. The results may be used to determine the hot rolling operation parameters during the production of non-oriented electrical steel with 3.2 wt% Si. Keywords Non-oriented electrical steel · Flow stress · Constitutive modelling · Recrystallization

G. Mishra · K. Chadha · C. Aranas (B) Department of Mechanical Engineering, University of New Brunswick, Fredericton, NB, Canada e-mail: [email protected] G. Mishra · Y. He (B) CanmetMATERIALS, Natural Resources Canada, Hamilton, ON, Canada e-mail: [email protected] © The Minerals, Metals & Materials Society 2023 The Minerals, Metals & Materials Society, TMS 2023 152nd Annual Meeting & Exhibition Supplemental Proceedings, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-031-22524-6_78

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Introduction Electrical steels as an energy conversion material play a pivotal role in the manufacturing of electrical machineries, transformers, and electric vehicles [1–4]. The final application of the product in either unidirectional or constantly changing flux paths dictates the nature of the processing procedures, which in turn controls the final microstructure and texture of the material [1, 2]. In the case of non-oriented electrical steel sheets, a ||ND (normal direction) texture is desirable as the grains are aligned with the easy magnetization direction in the sheet plane, coinciding with the rotating magnetization directions [5–7]. However, achieving such texture has been difficult since all the processing steps involved in the steel sheet production will affect the formation of the final microstructure and texture. The unfavorable ||ND fiber is normally the major texture after cold rolling, which may be further strengthened in the subsequent processing stages, e.g., annealing [8–11]. Effective texture control may only start from the first stage of thermomechanical processing, i.e., hot rolling, as the solidification texture after casting is normally very similar (with the in the cooling direction), and essentially no parameters can be adjusted to control the texture. Thus, controlling the hot rolling process plays a significant role in achieving the appropriate final microstructure and texture. The recrystallization and recovery associated with the hot rolling process can result in substructured grains or coarse recrystallized grains [12, 13], which affect the deformation and microstructure formation during cold rolling. A coarse microstructure after hot rolling usually leads to better microstructure and texture after cold rolling and recrystallization annealing, and results in superior final magnetic properties [14–17]. For non-oriented electrical steels with phase transformations, hot rolling is especially an effective process to control the microstructure and texture, as phase transformation may induce significantly different textures and microstructures if the deformation is performed in different regions. The evolution of texture and microstructure of non-oriented electrical steels during thermomechanical processing has been extensively studied. However, the flow behavior of electrical steels at high temperatures, which is critical information for proper hot rolling operation and control in steel production, has not been investigated in detail. This paper investigates the flow behavior of a 3.2 wt% Si non-oriented electrical steel at a common hot rolling temperature range of 850–1050 °C. By varying the strain rates during hot compression tests, constitutive models are used to simulate the stress–strain response. The results may provide guidance for hot rolling operations in non-oriented electrical steel production.

Experimental The material investigated in this study is a 3.2 wt% Si non-oriented electrical steel. Its chemical composition is listed in Table 1. The steel was melted in a vacuum induction furnace and cast into an ingot with a cross section of 200 × 200 mm2 .

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Table 1 Composition of the investigated non-oriented electrical steel (in wt %) C

Mn

Si

Al

S

P

Fe

0.0029

0.4

3.2

0.58

0.0095

0.0024

Bal.

Fig. 1 Thermomechanical processing scheme used for the hot compression experiments

The cast ingot was homogenized at 1200 °C for 2 h and rough-rolled to a thickness of 25 mm in a 2-high reversing rolling mill at a temperature range of 900–1050 °C. Cylindrical samples (diameter and height of 6 mm and 9 mm, respectively) for hot compression experiments were machined from the hot-rolled plate such that the axis of the cylinder is along the rolling direction of the plate. The hot compression tests were carried out in a Gleeble 563 thermomechanical simulator with varying strain rates of 0.01, 0.1, and 1 s−1 , and a total strain of 0.7. The testing temperatures vary between 850 and 1050 °C. The thermomechanical processing scheme used for the compression tests is shown in Fig. 1. The stress–strain response obtained from the compression experiments was fitted using the Zener–Hollomon [18], Johnson–Cook [19], and Hensel–Spittel [20] constitutive models. The suitability of each model was discussed based on the computed correlation coefficient (R) and the average absolute relative error (AARE).

Results and Discussion The stress–strain curves of the 3.2 wt% non-oriented electrical steel deformed at strain rates of 0.01, 0.1, and 1 s−1 are shown in Fig. 2. As expected, the flow stress increases with increasing strain rate and decreasing deformation temperature. The difference in the flow behavior can be attributed to the varying work hardening behavior along with the dynamic recrystallization and recovery during deformation at high temperatures. The samples deformed at a small strain rate of 0.01 s−1 tend

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b)

c)

Fig. 2 Stress–strain behavior of the 3.2 wt% non-oriented electrical steel deformed up to a 0.7 true strain at a strain rate of a 0.01 s−1 , b 0.1 s−1 , and c 1.0 s−1

to show a flow softening behavior for the entire temperature range, which could be due to dynamic recrystallization which annihilates the deformation microstructure and essentially avoids the work hardening. Only when the deformation temperature is 850 °C is there slight work hardening. The samples deformed at 0.1 and 1 s−1 strain rates have a flow-softening tendency only at higher temperatures. At lower temperatures, there are signs of strain hardening and an increase in flow stress. In the present work, three constitutive models, namely (a) Johnson–Cook (JC) [19], (b) Zener–Hollomon (ZH) [18], and (c) Hensel–Spittel (HS) [20], are used to simulate the experimental results, based on their applicability at different strain rates and temperatures. The detailed methodology to determine the associated constants has been presented elsewhere [21, 22]. The Johnson–Cook model considers the independent effects of strain hardening, strain rate hardening, and thermal softening on the flow stress, as formulated in the individual terms (parentheses) in order in the following equation:    m    T − Tref ε˙ 1− σ = A + Bεn 1 + Cln ε˙ 0 Tmelt − Tref

(1)

where σ is the von Mises flow stress, A is the yield stress at the reference temperature and strain rate, B is the coefficient of strain hardening, n is the strain hardening exponent, ε is the plastic strain, T is the absolute temperature, ε˙ is the strain rate, ε˙ 0

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is the reference strain rate, and C and m are the material constants representing the strain rate hardening and thermal softening exponent, respectively. T ref = 1173 K and T melt = 1700 K are used in the current study for the determination of the constants. The constants obtained from the measured stress–strain curves are listed in Table 2. The re-calculated stress–strain curves using the constants listed in Table 2 are shown in Fig. 3. The deformation behavior at different strain rates and temperatures may be expressed by the Zener–Hollomon (ZH) model using a parameter (Z) and an exponential relationship as  Z = ˙ exp

Q RT

 (2)

where R is the universal gas constant, Q is the activation energy, and T is the temperature of deformation. The flow stress is expressed as an Arrhenius-type equation related to the parameter Z as Table 2 Calculated coefficients for the JC model A

B

C

n

m

70

62.57

−0.12

0.23

0.47

a)

b)

c)

Fig. 3 JC model predictions compared to experimental data at a strain rate of a 0.01 s−1 , b 0.1 s−1 , and c 1 s−1 . The symbols represent the model predictions, and the lines are the experimental data

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Z = Asinh(ασ )n

(3)

Combining Eqs. 2 and 3, the flow stress is related to the strain rate and temperature as  ˙ exp

Q RT

 = Asinh(ασ )n

(4)

Unlike the Johnson–Cook model, the stress–strain behavior cannot be modelled for the entire range of stress–strain for the Arrhenius-type model, as there is no strain term correlating with the stress as shown in Eq. 4. The material constants for the Zener–Hollomon model are calculated by fitting the strain-dependent polynomial expressions at discrete true strain values [23]. The stress–strain behavior in the current study has been modelled to a strain of 0.7 with an interval of 0.05. The detailed methodology for the determination of the constants α, A, Q, and n can be found elsewhere [23, 24]. The modelled stress–strain curves using the ZH model are shown in Fig. 4. The Hensel–Spittel (HS) model is rather unpopular when compared to other models, which correlates the flow stress to strain and strain rates using a series of coefficients as

a)

b)

c)

Fig. 4 ZH model predictions compared to experimental data at a strain rate of a 0.01 s−1 , b 0.1 s−1 , and c 1 s−1 . The symbols represent the model predictions, and the solid lines are the experimental data

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Table 3 Calculated constants for the HS model A

m1

m2

m3

m4

m5

m6

m7

m8

12,821.7

−0.00412

0.47406

−0.13463

0.0195

−0.00212

0.9766

12,821.71

0

m4

σ = Aem1 T εm2 ε˙ m3 e ε (1 + ε)m5 T em6 ε ε˙ m7 T T m8

(5)

where A and m1 –m8 are material constants, σ is the flow stress, ε˙ is the strain rate, ε is the strain, and T is temperature in K. The experimental data was used to determine the coefficients, which are listed in Table 3. The comparison of the experimental flow stress and the flow stress determined by using the constants in Table 3 is shown in Fig. 5. The Hensel–Spittel model does not explain the strain rate sensitivity variation for a given temperature and strain. The applied stress has a power law dependence on the strain rate in the original HS equation, but in the hot working region, the strain rate sensitivity of the flow stress is inversely proportional to the increasing strain rate. This behavior can be explained using the Garofalo equation [25], which is not detailed in this work. The performance of each model was compared by determining the average absolute relative error (AARE) and the correlation coefficient (R) using Eqs. 6 and 7, respectively:

a)

b)

c)

Fig. 5 HS model predictions as compared to experimental data at a strain rate of a 0.01 s−1 , b 0.1 s−1 , and c 1 s−1 . The symbols represent the model predictions, and the solid lines are the experimental data

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Table 4 Accuracy comparison of the discussed constitutive models in terms of AARE and R2

Models

JC

ZH

HS

R2

0.93309

0.96863

0.97397

AARE

9.2834

7.4959

5.9809

 N  1  σEi − σCi  AARE(%) = × 100 N i  σEi 

  σEi − σ E σCi − σ C R =   2 N  i 2 N i i σE − σ E i σC − σ C

(6)

N  i

(7)

where σ C and σ E are the calculated and experimental flow stresses, respectively. The Johnson–Cook model (as shown in Fig. 3) matches closely with the experimental flow stresses at high temperatures, but the accuracy declines at higher strain rates, and even more at lower temperatures than higher temperatures. The Zener–Hollomon model also shows a similar tendency, in which the calculated flow stress matches the experimentally determined flow stress at lower strain rates. At higher strain rates, the accuracy decreases for all the test temperatures. The Hensel–Spittel model gives the best accuracy at higher strain rates, but the accuracy is low at high strain rates with intermediate temperatures. The calculated AARE and R2 values for the discussed models are shown in Table 4. The distribution of the AARE with respect to the temperature and strain rate for the models discussed above is shown in Fig. 6. The higher accuracy of the HS model may be attributed to the higher number of fitting parameters as compared to the other models.

Conclusions 1. The experimental stress–strain behavior of the 3.2 wt% Si non-oriented steel showed essentially no strain hardening at the lowest strain rate of 0.01 s−1 for most of the deformation temperatures (except 850 °C), which was due to the dynamic recrystallization which destroyed the deformation microstructure and work hardening. 2. The stress–strain behavior of the 3.2 wt% Si non-oriented electrical steel was simulated using Johnson–Cook, Zener–Hollomon, and Hensel–Spittel models. All the models predicted the stress–strain behavior in a reasonably accurate manner with an AARE of less than 10% and R2 higher than 93%. 3. In the investigated ranges of strain rate, temperature, and strain, the Hensel– Spittel model (AARE = 5.98% and R2 = 97.397%) predicted the stress–strain behavior most accurately. However, due to the larger number of materials constants involved, the computation time was significantly higher.

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b)

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b)

c)

Fig. 6 The distribution of the AARE with temperature and strain rate for: a Johnson–Cook, b Zener–Hollomon, and c Hensel–Spittel models

References 1. Verlinden B, Driver J, Samajdar I, Doherty R (2007) Thermo-mechanical processing of metallic materials, 1st edn. Elsevier, Oxford(UK) 2. Moses AJ (1990) Electrical steels. Past, present and future developments. IEE Proc A: Phys Science Meas Instrumentation Manag Education Rev 137(5):233–245. 3. Brissonneau P (1984) Non-oriented electrical sheets. J Magn Magn Mater 41(1–3):38–46 4. Matsumura K, Fukuda R (1984) Recent developments of non-oriented electrical steel sheets; Recent developments of non-oriented electrical steel sheets. IEEE Trans Magn 20(5) 5. da Cunha MA, da C Paolinelli S (2008) Low core loss non-oriented silicon steels. J Magn Magn Mater 320(20):2485–2489. 6. Mehdi M (2019) Texture evolution of non-oriented electrical steels during thermomechanical processing. PhD Thesis, University of Windsor, Canada 7. Shimanaka H, Irie T, Matsumura K, Nakamura H (1980) A new non-oriented Si-steel with texture of {100} . J Magn Magn Mater 19(1–3):63–64. 8. Raabe D (2003) Overview on basic types of hot rolling textures of steels. Steel Res Int 74(5):327–337 9. Liu HT, Li HL, Wang H, Liu Y, Gao F, An LZ, Zhao SQ, Liu ZY, Wang GD (2016) Effects of initial microstructure and texture on microstructure, texture evolution and magnetic properties of non-oriented electrical steel. J Magn Magn Mater 406:149–158 10. Liu H-T, Wang Y-P, An L-Z, Wang Z-J, Hou D-Y, Chen J-M, Wang G-D (2016) Effects of hot rolled microstructure after twin-roll casting on microstructure, texture and magnetic properties of low silicon non-oriented electrical steel. J Magn Magn Mater 420:192–203

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11. Hawezy D, Birosca S (2021) Disparity in recrystallization of α- & γ-fibers and its impact on cube texture formation in non-oriented electrical steel. Acta Mater 216:117141 12. Hutchinson WB (2007) Deformation substructures and recrystallisation. Mater Sci Forum 558–559:13–22 13. Sahoo G, Singh CD, Deepa M, Dhua SK, Saxena A (2018) Recrystallization behaviour and texture of non-oriented electrical steels. Mater Sci Eng, A 734:229–243 14. Herzer G (1990) Grain size dependence of coercivity and permeability in nanocrystalline ferromagnets. IEEE Trans Magn 26(5):1397–1402 15. Bertotti G, di Schino G, Ferro Milone A, Fiorillo F (1985) On the effect of grain size on magnetic losses of 3% non-oriented SiFe. J Phys Colloq 46(C6):385–388 16. Yashiki H, Okamoto A (1987) Effect of hot-band grain size on magnetic, properties of nonoriented electrical steels. IEEE Trans Magn 23(5):3086–3088 17. An L-Z, Wang Y, Song H-Y, Wang G-D, Liu H-T (2019) Improving magnetic properties of non-oriented electrical steels by controlling grain size prior to cold rolling. J Magn Magn Mater 491:165636 18. Sellars CM, McTegart WJ (1966) On the mechanism of hot deformation. Acta Metallurgica 14(9):1136–1138 19. Johnson GR, Cook WH (1985) Fracture characteristics of three metals subjected to various strains, strain rates, temperatures and pressures. Eng Fract Mech 21(I):31–48 20. Hensel A, Spittel T (1978) Kraft- und Arbeitsbedarf bildsamer Formgebungsverfahren, 1st edn. VEB Deutscher Verlag für Grundstoffindustrie, Leipzig 21. Brown C, McCarthy T, Chadha K, Rodrigues S, Aranas C, Saha GC (2021) Constitutive modeling of the hot deformation behavior of CoCrFeMnNi high-entropy alloy. Mater Sci Eng, A 826:141940 22. Lewis J, Pasco J, McCarthy T, Chadha K, Harding M, Aranas C (2022) High strain rate and high temperature mechanical response of additively manufactured alloy 625. J Manuf Process 81:922–944 23. Ge G, Zhang L, Xin J, Lin J, Aindow M, Zhang L (2018) Constitutive modeling of high temperature flow behavior in a Ti-45Al-8Nb-2Cr-2Mn-0.2Y alloy. Sci Rep 8(1):1–9 24. Samantaray D, Mandal S, Bhaduri AK (2009) A comparative study on Johnson Cook, modified Zerilli–Armstrong and Arrhenius-type constitutive models to predict elevated temperature flow behaviour in modified 9Cr–1Mo steel. Comput Mater Sci 47(2):568–576 25. Spigarelli S, el Mehtedi M (2014) A new constitutive model for the plastic flow of metals at elevated temperatures. J Mater Eng Perform 23(2):658–665

Effect of Melt Superheat on Microstructure and Texture of Non-oriented Electrical Steel Sheet Produced by the Ultra-Thin Strip Casting Lulu Song, Wanlin Wang, Peisheng Lyu, Huhu Wang, Xueying Lyu, and Yunli Zhang Abstract As a new technology, the ultra-thin strip casting technology has inherent advantages in the production of non-oriented silicon steel, with excellent initial texture and a short process. The effect of melt superheat on the texture and microstructure of 3.5% non-oriented silicon steel produced by the ultra-thin strip was studied. The experimental results showed that the superheat significantly affects the microstructure and texture. With the increase of superheat, the equiaxed crystal microstructure decreased and the columnar crystal microstructure increased. And a strong {100} uvw texture was formed. The results were of great significance for controlling the texture and microstructure by adjusting the melt superheat in the process of non-oriented silicon steel strip casting. Keywords Ultra-thin strip casting · Melt superheat · Texture · Microstructure

Introduction New policies to improve the energy efficiency of household appliances and motor industries have been introduced, so the market demand for high-grade non-oriented electrical steel increased. Advanced non-oriented silicon steel requires both low iron loss and high magnetic permeability. As a new technology, the ultra-thin strip casting technology has an inherent advantage in the production of non-oriented silicon steel [1]. The initial cast strip has excellent microstructure and texture, and the overall process is shorter than conventional processes. Due to the genetic nature of the microstructure, the initial microstructure not only affects the properties of the casting belt but also affects the quality of the final product [2]. It is feasible and important to control the solidification structure and texture by changing technological parameters. Pouring temperature is a key factor that affects the forming and initial solidification structure of thin strips. The purpose of this paper aims to study the influence of L. Song · W. Wang (B) · P. Lyu · H. Wang · X. Lyu · Y. Zhang School of Metallurgy and Environment, Central South University, Changsha 410083, China e-mail: [email protected] © The Minerals, Metals & Materials Society 2023 The Minerals, Metals & Materials Society, TMS 2023 152nd Annual Meeting & Exhibition Supplemental Proceedings, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-031-22524-6_79

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different casting superheating on the microstructure and texture of Fe-3.5% Si nonoriented silicon steel casting strips.

Experimental According to the calculated phase diagram of Fe–Si alloy, the solidified structure of non-oriented 3.5% silicon steel is a single-phase α-ferrite structure. The experiments were carried out on a Schematic representation of a strip-casting simulator developed by CSU, and the schematic diagram of the equipment is shown in Fig. 1. The melt superheat was controlled at 67 °C, 104 °C, and 120 °C to obtain cast strips with different superheat. The thickness and width of the cast strips were 2 mm and 40 mm, respectively. The experimentally obtained samples were wire-cut to obtain metallographic samples of suitable size. The samples were then etched using a 4% nitric acid alcohol solution to show their microstructure morphology. The orientation distribution function (ODF) of the as-cast strips was determined by the Electron Backscatter Diffraction (EBSD) system.

Fig. 1 Schematic representation of strip-casting simulator [3]

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Results and Discussion Microstructure of Cast Strip When the heat transfer conditions at the front of the solid–liquid interface were altered, the solidification microstructure of the cast strip changed. The factors that affect the heat transfer conditions are casting roll speed, roll opening, casting overheating, casting and rolling force, heat transfer capacity of crystallization roll, and roll surface roughness [4, 5]. In the current experimental conditions, the cast strip came from the same experimental equipment. Therefore, the superheat was the main factor that affects the solidification structure of the thin strip. The metallographic microstructure of the longitudinal section of the cast strip obtained at different melt superheats is shown in Fig. 2. Under different superheat conditions, the initial solidification microstructure of the cast strips differed significantly. With the increase in superheat degree, the fine equiaxial crystal microstructure gradually decreased, and the coarse columnar crystal microstructure increased and dominated. However, for columnar crystals and equiaxed crystals, the grain boundaries between the grains are relatively simple. When the superheat was 67 °C, the cast strip was a mixture of columnar crystal and equiaxed crystal microstructure. At the initial stage of solidification, a certain temperature gradient occurs at the front of the solid phase. The temperature gradient provides conditions for the selective growth of columnar crystals. At the end of solidification, the temperature gradient decreases, which leads to the loss of the advantage of selective growth of columnar crystals. As a result, an equiaxed microstructure was formed in the central region (Fig. 2a). When the overheating is 104 °C to 120 °C (Fig. 2b, c), almost all of the casting zone is columnar microstructure. With the proceeding of the solidification process, it can be observed that the large-size columnar grains

Fig. 2 The IPF map of cast steel strip at different melt superheat a 67 °C; b 104 °C; c 120 °C

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grow along the heat transfer direction, which is perpendicular to the substrate surface [6]. The higher superheat made the heat conduction coefficient between the steel and the copper mold surface smaller while the steel solidifies with more heat exported during the solidification process [7]. This provided favorable conditions for the selective growth of columnar crystals. In addition, the direction perpendicular to the cast strip surface was the fastest in the solidification process. During the whole solidification stage of the casting belt, the temperature gradient at the front of the solid phase satisfies the growth conditions of columnar crystals with {100} orientation, thus forming a complete columnar crystal microstructure. And the microstructure was symmetrical on both sides along the thickness direction [8]. Figure 3 shows the influence mode of overheating on the grain size of the cast strip. With the increase of superheat, the proportion of equiaxed crystals decreases, and the formation of coarse columnar crystals made the average grain size gradually increase. The grain size of the cast strip increased from 228 μm to 284 μm. Because of the initial coarse columnar crystal microstructure, it is beneficial to obtain a larger finished grain size and reduce the later iron loss. The magnetic properties of the final finished plate of the columnar crystal cast strip were higher compared to the final finished plate of the equiaxed crystal cast strip. Therefore, from the point of view of improving the magnetic induction intensity of the finished plate, the goal of controlling the microstructure of the non-oriented silicon steel casting belt is to obtain the solidification microstructure of columnar crystals as thick as possible. Therefore, the superheat should be increased appropriately during the casting process to ensure adequate columnar crystal growth. 290

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Fig. 3 Grain size at different melt superheats: a Grain size distribution of cast strip; b Average grain size distribution of cast strip

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Texture of Cast Strip Crystal orientation is a decisive factor for the magnetic induction strength of nonoriented silicon steel, and the magnetization behavior of bcc crystals varies along different orientations. The magnetization ability of Fe single crystals varies considerably in the three main crystallographic orientations , , and . The orientation is the easiest to magnetize, followed by , while is the most difficult to magnetize. The {001} face has two easy magnetization directions, so as many {001 faces as possible should be formed to increase the magnetic susceptibility and reduce the magnetic anisotropy [9]. According to the EBSD grain orientation distribution diagram in Fig. 2, the area fractions of the main surface textures {100}, {110}, and {111} in the sample were calculated, and the results are shown in Fig. 4. The results showed that the texture fractions of the cast strips obtained by casting at different superheating temperatures are obviously different. It can be seen from the figures in Table 1 that at 67 °C, the area fractions of {100} and {111} are 26% and 27.5%, respectively. With the increase of superheat to 104 °C, the {100} texture area fraction increased significantly, up to 78.2%. Overall, the texture of the {100} plane is dominant. The {111} texture is obviously decreased, accounting for only 1.14%. When the superheat increases to 120 °C, the area fraction of the {100} texture decreases, and the area fraction of the {111} texture increases. The reason for the above phenomenon may be that the superheat was too high. At the end of solidification, due to the remelting and the collision of columnar crystals, new nucleation points were formed. Therefore, some equiaxed grains were produced at the tips of the dendrites. This slightly weakens the {100} texture of the cast band [10]. However, the favorable {100} texture at the temperature of 120 °C still accounts for a higher percentage than that at the temperature of 67 °C, and the unfavorable 90

Fig. 4 Area fraction of specific texture in cast strips at different superheats

{100}texture {110}texture {111}texture

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{100}

{110}

{111}

67 104

26

12.2

27.5

78.2

10.9

120

34.3

6.2

1.14 11.3

{111} texture accounts for a smaller percentage. Therefore, the casting temperature should be appropriately high to obtain a complete columnar crystal structure and reduce the proportion of equiaxed crystals. Then, the genetic effect of solidified columnar crystals can be used to increase the volume fraction of favorable textures in non-oriented silicon steel products. The initial microstructure of the cast strip determines the different crystal textures. The macroscopic texture diagrams of the casting belt at different superheats are shown in Fig. 5. With the increase of superheat, the weak and random texture gradually evolved into a well-developed ND texture. When overheated by about 15 °C, the texture of the cast strip is very weak, and the texture features are not obvious (Fig. 5a). This texture feature was consistent with the equiaxed crystal microstructure. The cast band texture was significantly enhanced at a superheat of 104 °C, and the formation of the developed columnar crystal microstructure showed a strong ND texture. This {100} texture was typical of the solidification texture of ferritic steels [11, 12]. When the superheating is 120 °C, a small amount of equiaxed crystals appear in the center of the cast strip at the end of solidification, which reduces the strength of the {100} texture. Therefore, the initial microstructure can be controlled by adjusting the superheat degree of the molten steel in the molten pool, so as to obtain the required type of microstructure.

Fig. 5 ODF2 = 45° section of cast strip at different superheat: a 67 °C; b 104 °C; c 120 °C

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Conclusions In this paper, the influence of superheat of steel on the initial microstructure and texture of Fe-3.5% Si cast strip was studied, and the following conclusions were obtained: (1) The solidification structure of Fe-3.5% Si can be controlled by changing the superheating temperature. When the superheat of the melt increased from 67 to 120 °C, the fine equiaxed crystal structure decreases gradually, while the coarse ferrite columnar crystal structure increased and dominates. However, whether it is a columnar crystal or equiaxed crystal, the grain boundaries among grains are relatively straight. (2) With the increase of melt overheating from 67 °C to 120 °C, the time required to complete solidification increases, the grain growth became more sufficient, and the grain size of the cast strip increases from 228 μm to 284 μm. (3) When the overheating of the melt increases from 67 °C to 104 °C, the weak and random texture gradually evolved into a well-developed ND texture. At a superheat of 104 °C, the favorable {100} texture accounted for as much as 78.2%, and the harmful {111} texture accounted for only 1.14%. At the superheating temperature of 120 °C, the favorable {100} texture percentage decreases, which may be due to a small amount of equiaxed crystals produced in the central layer of the cast strip.

References 1. Ge S, Isac M, Guthrie R (2012) Progress of strip casting technology for steel; historical developments. ISIJ Int 52(12):2109–2122 2. Yuanxiang Z, Yunbo X, Haitao L, et al. (2012) Microstructure, texture and magnetic properties of strip-cast 1.3% Si non-oriented electrical steels. J Magn Magn Mater 324(20) 3. Peisheng L, Wanlin W, Hairui Q, et al. (2020) Formation of naturally deposited film and its effect on interfacial heat transfer during strip casting of martensitic steel. JOM 72(5) 4. Mizoguchi T, Miyazawa K (1995) Formation of solidification structure in twin roll casting process of 18Cr-8Ni stainless steel. ISIJ Int 35(6):771–777 5. Tavares RP, Isac M, Guthrie RIL (1998) Roll-strip interfacial heat fluxes in twin-roll casting of low-carbon steels and their effects on strip microstructure. ISIJ Int 38(12):1353–1361 6. Wanlin W, Hairui Q, Dawei C, et al. (2021) Microstructure and magnetic properties of 6.5 Wt Pct Si steel strip produced by simulated strip casting process. Metall Mater Trans A 7. Shimanaka H, Ito Y, Matsumara K, et al. (1982) Recent development of non-oriented electrical steel sheets. J Magn Magn Mater 26(1–3) 8. Spinelli JE, Tosetti JP, Santos CA et al. (2004) Microstructure and solidification thermal parameters in thin strip continuous casting of a stainless steel. J Mater Process Tech 150(3)

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9. Miao X, Zhang WK, Wang YD (2011) Effect of Al content on the microstructure, texture and magnetic properties of 2.2% Si non-oriented silicon steel. Spec Steel 32(6):59 10. Gandin C-A, Rappaz M (1994) A coupled finite element-cellular automaton model for the prediction of dendritic grain structures in solidification processes. Acta Metall Mater 42:2233– 2246 11. Park JY, Oh KH, Ra HY (1999) Microstructure and crystallographic texture of strip-cast 3wt%Si steel sheet. ScrA Mater 40(8):881–885 12. Rodriguez-Calvillo P, Houbaert Y, Petrov R, et al. (2012) High temperature deformation of silicon steel. Mater Chem Phys 136:710–719

Effect of Natural Deposited Films on Interfacial Heat Transfer During Sub-rapid Solidification of Non-oriented Electrical Steel Yunli Zhang, Wanlin Wang, Peisheng Lyu, Huihui Wang, Xueying Lyu, and Lulu Song Abstract As an emerging process, the ultra-thin strip casting has significant advantages in the production of non-oriented electrical steel with a short process flow. However, there is a problem of poor surface quality of casting product, which is significantly influenced by the interfacial heat transfer. During the production of ultra-thin strip casting, natural deposition films are deposited while the steel solidifies on the water-cooled copper rolls. The deposited films between the molten steel and the rolls significantly affect the interfacial heat transfer. In this study, the copper substrate used for the experiments was modified to make them more accurate. The effect of naturally deposited films on the interfacial heat transfer during sub-rapid solidification of non-oriented electrical steel was investigated by means of the droplet solidification technique. On the basis of the temperature data collected by one pair of high-sensitivity thermocouples, the heat flux are calculated by the Inverse Heat Conduction Program (IHCP). It was found that the heat flux of the high-silicon steel showed a trend of decreasing, then increasing and finally decreasing as the number of depositions increased. Keywords Non-oriented electrical steel · Naturally deposited film · Interfacial heat transfer · Ultra-thin strip casting

Introduction Castrip technology, also known as ultra-thin strip technology, is a near-final forming process that directly casts and rolls to meet the dimensional and physical properties of the final product. The process is also called twin-roll casting (TRC) because it uses a pair of copper casting rolls to replace the crystallizer of traditional continuous casting to complete the solidification process of the steel and to produce the cast billets. Compared with the conventional hot rolling process, the double-roll casting Y. Zhang · W. Wang (B) · P. Lyu · H. Wang · X. Lyu · L. Song School of Metallurgy and Environment, Central South University, Changsha 410083, China e-mail: [email protected] © The Minerals, Metals & Materials Society 2023 The Minerals, Metals & Materials Society, TMS 2023 152nd Annual Meeting & Exhibition Supplemental Proceedings, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-031-22524-6_80

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and rolling process for ultra-thin strips omits the billet process, and the steel can be solidified directly into strips as thin as 1.4 mm thick, making it possible to manufacture thinner gauge products. The high speed cooling water passing through the copper rolls can take away a lot of heat in an instant, and the cooling rate is more than 1000 °C/s [1], the steel can complete the transformation from liquid to solid in 1 s, and the rapid solidification can make the steel strip almost free of elemental segregation. The risk of slagging is avoided and the steel cleanliness is better. At present, Castrip LLC is the important company in the world that achieves the commercialization of TRC technology [2]. However, the major products obtained through Castrip technology are limited to low-carbon steel strips [3–7]. The droplet solidification technique is a new sub-rapid solidification interface heat transfer test technique that shoots molten metal droplets directly onto a watercooled substrate and enables the calculation of the interfacial heat flow during the initial solidification process and the high-speed in situ observation of the droplet solidification process [8–10]. This technique was first established by the center of iron and steel research (CISR) at Carnegie Mellon University (USA). In recent years, the melt-drop solidification technique has been further developed at the Center of iron and steel research (CISR) at CSU. The melt-drop solidification technique can simulate the conditions of direct contact between metal and crystallization rolls in thin strip continuous casting, and can realize the sub-fast cooling conditions (102 –103 K/s) in thin strip continuous casting, which is very effective for the basic research related to thin strip continuous casting. The use of this technique to study the solidification heat transfer in thin strip continuous casting has the advantages of small scale, low consumption and high accuracy, and can provide a new scientific tool for the study of thin strip continuous casting. A large number of scholars have done research on heat transfer in thin strip casting, but few scholars have considered the effect of deposited films on heat transfer. It is noted that the naturally deposited film formed during the strip-casting process and the wetting behavior between the melt and water-cooled substrate have a significant influence on the interfacial heat transfer behavior [11]. Therefore, in this study, a novel droplet solidification technique was used to investigate the natural deposition of films in 3.5 wt pct Si high-silicon steel produced by ultra-thin strip casting and its effect on interfacial heat transfer.

Experimental Apparatus and Procedure Sample Preparation Before the experiments, the electrical steel was cut into individual rectangles with a mass of 3.5 g (±0.1 g) and the surface was polished with sandpaper to remove the oxide layer, then placed in anhydrous ethanol solution and cleaned using ultrasonic waves. The copper substrate was polished with a stainless steel brush to remove the

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interference of the deposited film with the experiment. The melt drop solidification technique device was used to perform 9 repeated melt drop experiments on both materials, and only the solidified drops were removed after each drop and before the next experiment. The deposited film was kept intact and contamination of the deposited film was avoided as much as possible in order to study the effect of the natural deposition phenomenon of the film on heat transfer. During the melt drop solidification experiments, the oxygen partial pressure was controlled to be below 10–3 atm.

Experimental Apparatus and Procedure In recent years, Central South University has established a new melt-drop solidification technique based on the droplet solidification technique of Carnegie Mellon University. The droplet solidification technique of Central South University adopts a new atmosphere control and temperature control system, which leads to a substantial increase in experimental accuracy. For the atmosphere control, a large-range glass rotameter was selected and a mixed atmosphere device was designed to facilitate the control of the ratio of the incoming mixed atmosphere. In addition, the partial pressure of oxygen was monitored in real time at the inlet and outlet positions of the furnace chamber respectively. In terms of temperature control, a Raytek far-infrared pyrometer with a high-resolution proportional-integral-derivative (PID) controller was used to enable the molten drop temperature to be accurately and constantly set at the target temperature (fluctuations of no more than plus or minus 5 °C when tested constant at 1600 °C). In order to improve the accuracy of the equipment, the copper mold and the circulating water tank are improved. Copper mold improvement: the original copper mold is only designed to enter and exit 2 water pipes, the cooling water flow area is limited, which will cause uneven cooling. In order to make the experimental data more accurate and close to the factory, now design 6 water channels, water channel distance from the roll surface 7 mm, water channel spacing 2 mm, the water flow direction is crossed, cooling water flow more uniform wide surface; design 4 thermocouple holes, the upper and lower distance from the roll surface 1, 3 mm, 2 thermocouples are located in the most central position of the wide surface, the other 2 thermocouples are located at a distance of 3 mm from the center position, heat flow measurement range becomes larger. Circulating water tank improvement: change to temperature-controlled circulating water tank, remove the interference caused by the different temperature of cooling water to the experimental heat flow data, and facilitate the control of a single variable. A schematic figure is shown in Fig. 1. Before the experiment, boron-free medium and high-silicon steel were cut into rectangular samples with a size of 6 × 6 × 13 mm using metal wires, and the surface of the samples was subsequently polished with sandpaper to remove the surface oxide layer. The polished samples were placed in alcohol solution and cleaned 3–4 times repeatedly with ultrasonic waves to ensure the cleanliness of the samples, and then the cleaned samples were air-dried. The copper substrate was polished with a

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Fig. 1 Schematic of the droplet solidification technique

stainless steel brush to maintain the same roughness. The melt-drop solidification technique device was used to conduct 9 repeated melt-drop experiments for both materials, and the copper substrate was polished with a steel brush to maintain the same roughness after each melt-drop low and before the next experiment to avoid the influence of the deposited film on heat transfer. A heat transfer system was used to record the temperature data during the experiment. After the experiment, the natural film deposited on the substrate was sanded clean using sandpaper.

Experimental Result and Discussion One-Dimensional Inverse Problem (IHCP) In the melt-drop solidification technique device, temperature was collected by embedding thermocouples at different depths from the surface within the copper substrate, and further later on, the instantaneous interfacial heat flow density during solidification was verified by the inverse heat conduction problem (IHCP), [12–15] which evolves the instantaneous interfacial heat flow density during the solidification process. In the IHCP method, the heat transfer is calculated by solving Fourier’s law for:   ∂ ∂T ∂T = k , xi ∈ , t ≥ 0 (1) cρ ∂t ∂ xi ∂ xi k

∂T |x =0 = q ∂ xi i

(2)

T |xi =d = TS

(3)

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where c denotes the specific heat capacity (J/K·kg); ρ denotes the density (kg/m3 ); T denotes the temperature (K); t denotes the time (s); k denotes the thermal conductivity in W/m·K; xi denotes the location (m);  denotes the one-dimensional computational domain in the range of 1–3 mm; q is the heat flux across the substrate surface; d is the location and is equal to 3 mm; TS is the thermocouple measurement of the substrate temperature, which is 3 mm from the substrate surface. In this paper, Beck’s nonlinear estimation method [12, 13] is used to solve the IHCP model, which can improve the convergence speed and reduce the computational effort of the IHCP solution. With the help of high-sensitivity thermocouples and the IHCP model, the real-time interfacial heat transfer behavior during the solidification of the molten drop could be measured and calculated and plotted against time as shown in Fig. 2. Figure 2a shows that the responding temperature 1 mm from the hot surface increases rapidly during the first 0.2 s, as the substrate is heated when the steel starts contact with the copper mold. The copper mold reaches its maximum temperature at 0.2 s and then starts to decrease. This could be explained by the fact that when the molten steel drops on the copper mold, there is a huge temperature gap between the steel with high temperature and the copper mold. At the same time, the steel was well wettable and had a small air gap with the substrate, thus their thermal resistance is also small. These two aspects lead to the rapid increase of heat flux. Then the steel solidified and shrunk, the air gap between the steel and the copper mold became larger, resulting in an increase in thermal resistance, and coupled with the cooling effect of the cooling water, their temperature gap decreased, so the temperature dropped. After 0.6 s, the heat flux became stable because the air gap no longer changed and a thermal equilibrium was reached between the copper mold and the steel. Figure 2b shows the interfacial heat flux between the melt and the substrate and the total heat removed by the copper mold during the test. This shows that the variation of the interfacial heat flux follows a similar pattern to the variation of the reaction temperature, with the heat flux reaching a maximum value of 7.5 MW/m2 at 0.2 s and then decaying rapidly to 2.2 MW/m2 during cooling. The total amount of heat removed during the first 1.0 s of solidification time is about 3.5 MJ/m2 . In the ultra-thin strip casting, the interfacial heat flux can reach 6–15 MW/m2 . The variation and magnitude of the interfacial heat flux in the droplet solidification experiment (Fig. 2) is similar to what happens in industrial strip casting, which proves the method is reliable.

Effect of Deposited Film on Interfacial Heat Transfer Between Droplet and Substrate Figure 3a showed the variation of the heat flux at the interface between the droplet and the matrix in the same series of droplet solidification tests for the high-silicon steel. It was clear that the heat flux profiles show a similar trend in all solidification tests. Initially, the interfacial heat flux increases rapidly within the first 0.2 s because the temperature gradient at the interface between the droplet and the substrate was very high when the high-temperature molten droplet started to come into contact with the cooled substrate. Then, the interfacial heat flux rapidly decreases due to a sharp

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Fig. 2 a The responding temperatures measured by thermocouples and b the interfacial heat transfer between molten steel and substrate

increase in the interfacial thermal resistance between the droplet and the substrate, which was caused by the shrinkage of the solidified molten steel forming an air gap between the molten steel and the substrate. The decrease in the bottom temperature of the steel melt might be another reason for the decrease in the interfacial heat flux due to the decay of the temperature gradient. After about 0.75 s, the thermal resistance between the steel drop and the substrate becomes stable, so the heat flux between the steel and the substrate stabilized and decreased slightly during the subsequent cooling of the steel drop. By integrating the heat flux versus time, the removed heat flux per unit area of the matrix versus time during the same series of droplet solidification tests for high silicon steel could be obtained, as shown in Fig. 3b, which could reflect the quality of interfacial heat transfer between the steel and the copper mold. The peak heat flux is generated at the impact point, the impact area is small and the impact position cannot be fully controlled, resulting in a difference with drop location and thermocouple temperature measurement location, and the peak heat flux fluctuates greatly, which cannot accurately reflect the thermal conduction capacity [16]. As shown in Fig. 4, the pattern of peak heat flux and the number of the experiment is not obvious, and the amount of removed heat flux in the first 2 s is used to more accurately reflect the thermal conduction capacity. Further, two indicators, peak heat flux density and total heat conductivity in the first 2 s, were used to quantitatively describe the heat transfer in the molten drop solidification experiments, as shown in Fig. 4, where the peak heat flux density and total heat conductivity in the first 2 s were compared with the number of high-silicon steel drop solidification tests. It can be seen that both the peak heat flux and the total heat conductivity show a similar pattern of decreasing then increasing and finally decreasing with the number of drop solidification experiments, which indicates that the heat transfer rate between the steel and the substrate decreases then increases and finally decreases with the deposition of the oxide film on the copper substrate. In the first five drop tests, the total heat transfer in the first 2 s tended to decrease from 5.653 to 3.55299 MJ/m2 . In the eighth drop test, the total heat transfer in the first 2 s increased to 5.01311 MJ/m2 for the high-silicon steel. It is mainly the low thermal conductivity of a solid deposited film formed on the surface of the substrate that

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Fig. 3 a Variation of interfacial heat flux vs time and b variation of amount of removed heat versus time

Fig. 4 Variation of peak heat flux and amount of removed heat at first 2 s with the number of the experiment

increases the interfacial thermal resistance between the steel and the substrate, thus decreasing the interfacial heat flux. However, as the number of experiments increases, the deposited film starts to melt and the melting area gradually increases, and the liquid deposited film with good flow ability can fill the cavities or air pockets between the substrate and the steel to increase the effectiveness of the initial melt/substrate contact, thus increasing the interfacial heat flux. This conclusion is consistent with that obtained by Lyu using a strip-casting simulator of their own design [17].

Conclusion In this study, the droplet solidification apparatus was modified and was successfully used to simulate the initial melt/roll contact during the strip casting process. In order to

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investigate the effect of deposited oxide films on the interfacial heat transfer behavior during sub-rapid solidification of electrical steel droplets, a series of continuous drop experiments were performed. The main findings are summarized as follows. As the oxide film was deposited, both the peak heat flux and the total thermal conductivity within the first 2 s decrease, then increase and finally decrease with the increase in the number of droplet solidification tests. This is because as the number of tests increases, a solid deposition film formed on the surface of the substrate increases the interfacial thermal resistance between the steel and the substrate, thus decreasing the interfacial heat flow density. However, as the number of experiment increases, the deposition film starts to melt and the melting area gradually increases, increasing the interfacial heat flow density, and finally decreasing the heat flux due to the increase in interfacial thermal resistance caused by the gradual increase in the thickness of the deposition film.

References 1. Wang W, Mao S, Zhang H, Lu C, Lyu P (2021) Simulation of subrapid solidification and secondary cooling for the strip casting of IF steel. Materials 14:5274 2. Campbell P, Blejde W, Mahapatra R, Wechsler R, Gillen G (2005) Iron Steel Technol 2:56 3. Wang Z, Carpenter K, Chen Z, Killmore C (2017) Mater Sci Eng A 700:234 4. Xie K, Yao L, Zhu C, Cairney J, Killmore C, Barbaro F, Williams J, Ringer S (2011) Metall Mater Trans A 42:2199 5. Felfer P, Killmore C, Williams J, Carpenter K, Ringer S, Cairney J (2012) Acta Mater 60:5049 6. C. Killmore H, Kaul J, Burg K, Carpenter J, Williams D, Edelman PC, Blejde W (2008) In: The 3rd international conference on thermo-mechanical processing of steels. Padova, Italy, p 16. 7. Xie K, Shrestha S, Felfer P, Cairney J, Killmore C, Carpenter K, Ringer S (2013) Mater Trans A 44:848 8. Nolli P, Cramb AW (2007) Interaction between Iron Droplets and H2S during solidification: effects on heat transfer, surface tension and composition [J]. ISIJ Int 47(9):1284–1293 9. Nolli P (2007) Initial solidification phenomena: factors affecting heat transfer in strip casting [D]. Carnegie Mellon University, Pittsburgh 10. Nolli P, Cramb AW (2008) Naturally deposited oxide films in near-net-shape casting: importance, mechanisms of formation, and prediction of their composition [J]. Metall and Mater Trans B 39(1):56–65 11. Wang W, Cai D, Lu C et al (2022) Formation of deposited oxide film during the sub-rapid solidification of silicon steel droplet and its effect on interfacial heat transfer behavior. Metall Mater Trans B 53:198–207 12. Beck JV, Woodbury KA (1998) Meas Sci Technol 9:839–847 13. Beck JV, Litkouhi B, St. Clair CS (1982) Heat Transfer A 5:275–286 14. Zhang HH, Wang WL, Zhou D, Ma FJ, Lu BX, Zhou LJ (2014) Metall Mater Trans B 45B:1038– 1047 15. Zhou D, Wang WL, Zhang HH, Ma FJ, Chen K, Zhou LJ (2014) Metall Mater Trans B 45B:1048–56 16. Nolli P, Cramb A (2007) Interaction between iron droplets and H2S during solidification: effects on heat transfer, Surface Tension and Composition. ISIJ Int 47:1284–1293 17. Lyu P, Wang W, Qian H et al (2020) Formation of naturally deposited film and its effect on interfacial heat transfer during strip casting of martensitic steel. JOM 72:1910–1919

Effects of Normalization Process on Microstructure and Texture of Non-oriented Electrical Steel Produced by Ultra-Thin Strip Casting Huihui Wang, Wanlin Wang, Hualong Li, Peisheng Lyu, Shengjie Wu, Xueying Lyu, Lulu Song, and Yunli Zhang Abstract The ultra-thin strip casting technology has unique advantages in the production of non-oriented electrical steels with strong λ-fiber texture. This article looked at non-oriented electrical steel with 2.43 wt% Si. Dip tester was used to simulate strip casting process for obtaining the electrical steel cast strip, and then hot rolling was performed at various reduction rates. Effects of normalizing temperatures and soaking time on microstructure and texture of electrical steel samples were investigated across full thickness. The results show that the grain size changes little, and the normalized texture is similar to that of the hot-rolled sheet, but the normalization can enhance the strength of the λ-fiber texture and improves magnetic properties. In addition, increasing soaking time is beneficial to the nucleation and growth of recrystallized grains, which has less effect compared to normalization temperature. Keywords Normalizing · Ultra-thin strip casting · Texture · Recrystallization

Introduction Currently, studies on the ultra-thin strip casting and rolling process for the preparation of electrical steels are on the rise, and its sub-rapid solidification and near-net shape provide unique advantages for the preparation of electrical steels [1–3]. In both conventional and thin slab processes, normalization process is often employed in order to improve the microstructure, texture and its magnetic properties [4–7], and plays an important role.

H. Wang · W. Wang (B) · P. Lyu · S. Wu · X. Lyu · L. Song · Y. Zhang School of Metallurgy and Environment, Central South University, Changsha 410083, China e-mail: [email protected] H. Li Institute of Research of Iron & Steel, Jiangsu Province/SHA-STEEL Co., Ltd., Suzhou 215625, China © The Minerals, Metals & Materials Society 2023 The Minerals, Metals & Materials Society, TMS 2023 152nd Annual Meeting & Exhibition Supplemental Proceedings, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-031-22524-6_81

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The ultra-thin strip process is a cutting-edge technology in the field of near-net shape process, in which thin strips of 1–5 mm thickness are formed directly by subrapid solidification [8]. In order to optimize grain size and texture of as-cast strip and properties of final product, dramatically increased research has been carried out. It has been pointed out that the ultra-thin strip process cast strip tissue has been dominated by coarse columnar crystals, similar in size to conventional hot-rolled sheets after normalization, which can be directly cold-rolled and annealed to prepare electrical steel products [1]. Zhang et al. [9] investigated the effect of grain size on the fiber texture and magnetic properties of non-oriented electrical steel after normalization of hot-rolled sheet. They concluded that the grain size of hot-rolled sheet has a significant effect on texture and properties, and normalization increases the grain size to reduce the strength of γ-fiber texture and increases the magnetic properties. Zu et al. [10] conducted a study on the normalization of non-oriented electrical steel cast strip prepared by twin roll casting. The study showed that the 4.5% non-oriented electrical steel casting strip was dominated by columnar crystals at an angle of 5–20° to the normal direction, with a small amount of precipitated phase, and the fiber texture was dominated by λ-fiber texture, and the surface layer fiber texture was diffuse, and after normalization, the size of the precipitated phase increased and the surface layer λ-fiber texture was enhanced. However, there are few studies related to the effect of normalization process on the microstructure and fiber texture of ultra-thin strip hot-rolled sheets, and the emerging technology requires a detailed study of the effect of each process. Therefore, in this paper, we focused on the characteristics of hot-rolled sheet and the effect of normalization process on microstructure and texture.

Experimental Procedures The experimental material was 1.8 mm thick Fe-2.43 wt% Si non-oriented electrical steel prepared by ultra-thin strip process, the main chemical composition is: C, Si, Al, the balance is Fe. The cast strip was hot rolled on a medium rolling mill, the sample was put into the reheating furnace and heated to 1100 °C, holding for 3 min, the start rolling temperature was 750 °C, the reduction rate was 50–70%, and thickness after hot rolling was 0.54–0.9 mm. Samples from the hot-rolled plate were placed in the SK-G06163-750F controlled atmosphere tube furnace for normalization, the protective atmosphere was argon, the samples were air cooled. Normalization parameters are shown in Table 1. Specimens after normalization were subjected to metallographic preparation, and 4% sodium nitrate alcohol solution was selected for etching and microstructure observation. The EBSD samples were prepared by mechanical polishing, and light corrosion stress relief was performed after the completion of grinding and polishing to characterize the fiber texture changes of the hot-rolled and normalized plates. HKLChannel 5 was used to process the data and analyze the effects of different processes on microstructure and texture.

Effects of Normalization Process on Microstructure and Texture … Table 1 Normalization parameters of non-oriented electrical steel

Sample

Normalizing temperature/°C

877 Time/min

1

850

5

2

950

5

3

1050

5

4

950

3

Results and Discussion Analysis of Hot-Rolled Plate Microstructure The microstructure of the as-cast strip of non-oriented electrical steel under subrapid solidification is well-developed columnar crystals and {100} oriented grains are predominant. Figure 1 shows the microstructure of the hot-rolled plate of nonoriented electrical steel. From Fig. 1a, it can be seen that the grain size of the hot-rolled plate is not uniform after the rolling process, and sub-grain boundaries are found inside the larger size grains, and fine recrystallized grains are generated along the grain boundaries, and had the striation formed dislocations. According to Fe-Si phase diagram, the rolling process of this experimental steel occurs in the ferrite singlephase region, and no phase transformation occurs during the hot rolling process. Ferrite is a high dislocation energy phase, and dislocations tend to develop upward cross-slip and climb [11], arranged into a large number of small angular sub-grain boundaries, which indicates that dynamic recovery occurs during this process. This indicates that dynamic recovery occurs during this process and also provides help to reduce the energy required for recrystallization. With increasing type variation, dislocations and short cyclic dislocation groups intertwine, including many deformed substructures, which are considered to be oriented grains that are easy to nucleate. Figure 1b shows the EBSD micrograph of the hot-rolled plate, where the generation of stripes can be clearly observed. The stripes exist between the two deformation strips with an angle of 20° to the rolling direction, and the stripes consist of fine newly

Fig. 1 Microstructures of hot-rolled sheets of ultra-thin strip non-oriented electrical steel. a Optical microscopy images b BC map of EBSD

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nucleated grains. In addition, it can be observed that some {100} oriented grains are retained in the cast strip.

Analysis of Normalized Plate Microstructure The normalization process plays an important role in traditional production. After hot rolling, the normalization process is required to improve the tissue uniformity, increase the size of the second phase and enhance the favorable fiber texture, which has become an important part in improving the magnetic properties of the finished products. The ultra-thin strip process can control the formation and size of precipitates well and form a strong {100} fiber texture structure by virtue of the sub-fast solidification characteristics. Therefore, it is necessary to study the effect of normalization process on the hot-rolled sheet of non-oriented electrical steel with ultra-thin strip. The hot-rolled plates were normalized at different temperatures and times. Figure 2a–c shows that, with the increase of normalization temperature, the recrystallization of hot-rolled plate along the thickness direction of each region gradually increased, the grain size increased. Under 850 °C normalization, the grain internal dynamic recovery is dominated, while there are small recrystallized grains generated. When comparing to the hot-rolled plate structure, it is found that the normalized microstructure and its basic consistency inherited the hot-rolled microstructure inhomogeneity. When normalized at 950 °C, recrystallization and grain growth occur, grain growth near the surface region is obvious, those grains in the center region are equiaxed, the grain size is still uneven. When 1050 °C normalization, the grain growth is not obvious, the growth rate slows down, but the grains in center region start to grow, and the overall grain size is relatively uniform.

Fig. 2 Microstructures along the thickness direction under 5 min. normalization: Reduction rate50% a 850 °C, b 950 °C, c 1050 °C; Reduction rate: 70% d 850 °C, e 950 °C, f 1050 °C

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In summary, when normalized at 850 °C, the normalized plate still retains the microstructure characteristics of hot-rolled plate. Including microstructural heterogeneity, deformation bands and slip bands in the grains. With the increase of normalization temperature, the recrystallized grains in each region start to nucleate and grow, and the grain size is gradually uniform. However, the normalization temperature should not be too high, too high temperature will lead to the abnormal growth of some grains and destroy the uniformity of the structure, which will lead to higher iron loss and worse magnetic properties of the finished plate. From Fig. 2d–f, it can be seen that the microstructures are almost equiaxed when the reduction rate is 70%, and the large equiaxed crystal shows a tendency to be flattened. After normalization, new fine recrystallized grains appear in the structure, and the original large-sized grains remain in their original state. With the increase of normalization temperature, the grain size gradually increases, some of the grains grow abnormally, and some of the grains are small. This leads to uneven microstructure and too fine grains, which is not favorable in terms of improving the magnetic properties. Therefore, it is necessary to properly control the hot rolling reduction rate to avoid inhomogeneity of the microstructure after normalization.

Texture Analysis of Hot Rolled and Normalized Plates The easy magnetization orientation of electrical steel is crystallographic orientation, followed by crystallographic orientation, and crystallographic orientation is the most difficult magnetization orientation. There are two easy magnetization orientations on the {100} surface, one easy magnetization orientation on the {110} surface, while there is no easy magnetization orientation on the {111} surface and one difficult magnetization orientation on the {112} surface [9]. For non-oriented electrical steels, the more texture on the {100} and {110} faces in the finished plate is more favorable to the magnetic properties, while γ-fiber texture is an unfavorable orientation for magnetic properties, so increasing the strength of the texture on the {100} and {110} faces and reducing the γ-fiber texture are favorable means to improve the magnetic properties. It can be seen from Fig. 3a that the microstructure is heterogeneous along the plate thickness direction and can be divided into three regions. It can be seen that the surface grain size along the thickness direction is smaller and accompanied by equiaxed crystals, which is the result of being subjected to shear deformation with recrystallization. This is followed by the elongated grains in the core, mainly in red, and purple, //ND and //ND, respectively. Shear deformation also occurred in this region, but the energy reserved by the small degree of deformation is not sufficient for its hair-on recrystallization. The ODF shown in Fig. 3b indicates the fiber texture, with the main orientation −

−

−

−−

being {114}, {332},{001}, and the weak {001} fetching −

orientation, where {114} belongs to α* fiber texture, which is easily formed

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Fig. 3 Cross-section (RD-ND) microstructure and microtexture of the hot-rolled 2.43 wt% Si electrical steel: a EBSD inverse pole figure (IPF) map, b ϕ2 = 45°section of the ODF showing the major texture components

in rotation and recrystallization. α* fiber texture is a kind of unstable deformation structure, and the existence of α* will weaken the existence of γ fiber texture, which is beneficial to enhance the magnetic properties [12]. Figure 4 shows the IPF diagram of the normalized plates with different normalization temperatures, time and the ODF cross section with ϕ2 = 45°. Figure 4a, b shows the normalization at 850 °C for 5 min., where the IPF diagram shows that the recrystallization of {110} orientation starts to occur in the surface layer, which is caused by the different energy storage in each orientation. The energy storage sequence is {110}>{111}>{112}>{100}, and the higher energy storage is preferentially for the nucleation of recrystallized grains, In addition to the grain growth in the core, it can be seen from the ODF diagram (Fig. 4b) that the fiber texture is basically −

the same as that of the hot-rolled plate that is rotating Cube texture ({001} −−

−

and {001} ) and stronger {112} as well as the {332} fiber texture. As the temperature increases, it is found (Fig. 4c, d) that the recrystallized grains are increasingly //RD oriented, with predominantly equiaxed grains and slightly larger core grain size, and the fiber texture type still does not differ much from that of the hot-rolled plate. When the normalization temperature rises to 1050 °C, it can be seen in Fig. 4e, f that some grains grow abnormally and swallow the surrounding equiaxed grains of smaller size, and at this time the fiber texture is dominated by −

the rotated Cube fiber texture, and the {112} gradually weakens. Figure 4g, h shows that the microstructure, of recrystallization at 950 °C for 3 min, is similar to that of 850 °C for 5 min, and the fiber texture is similar to that of the hot-rolled −

plate with {114} , but the strength is less than that of the hot-rolled plate. In summary, it can be seen that the normalization temperature and time have significant effects on the evolution of microstructure and texture. When normalized at 850 °C for 5 min, the nucleation and recrystallization started from the surface layer, the core grains grew, and the microstructure and fiber texture were similar to that of 3 min of normalization at 950 °C. When the normalization time is set at

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Fig. 4 Cross-section (RD-ND) microstructure and microtexture of the normalised 2.43 wt% Si electrical steel: a IPF map and b ODF diagram of ϕ2 = 45° section of hot-rolled strip normalized at 850 °C for 5 min; c IPF map and d ODF diagram of ϕ2 = 45°section of hot-rolled strip normalized at 950 °C for 5 min; e IPF map and f ODF diagram of ϕ2 = 45° section of hot-rolled strip normalized at 1050 °C for 5 min; g IPF map; and h ODF diagram of ϕ2 = 45° section of hot-rolled strip normalized at 950 °C for 3 min

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5 min, with the increase of normalization temperature, α fiber texture, λ fiber texture strength increases. The grain size and fiber texture of the initial hot-rolled plate greatly affect the grain size and fiber texture after normalization, which in turn affects the magnetic properties of the finished plate. In the traditional non-oriented electrical steel process, normalization plays a role in homogenizing the grains and reducing the strength of the γ-fiber texture. In the casting and rolling process of ultra-thin strip with subfast solidification characteristics, its unique advantages make it have super strong λ-fiber texture in the as-cast strip, while there is no γ-fiber texture after hot rolling. Due to the well-developed columnar grains of the cast strip, slip and deformation bands will be formed during hot rolling, which provides a good environment for subsequent recrystallization. Therefore, considering the above, the normalization process step can be considered to be omitted when processing electrical steel made by strip casting, and the regulation of grain and fiber texture structure can be carried out directly by designing the hot rolling process.

Conclusions In this paper, the effects of the normalization process on cross-thickness microstructure and microtexture evolution of non-oriented electrical steel with 2.43 wt% Si are investigated by means different hot rolling reduction rates, and the following conclusions are obtained. (1) The hot rolling reduction rate directly affects the magnitude of tissue recrystallization, and the heavier the reduction rate, the more intensive the recrystallization. (2) Normalized plate fiber texture and hot-rolled plate fiber texture have a certain degree of similarity. With the increase of normalization temperature, the recrystallization of each region in the direction of plate thickness is increasing; 950 °C normalization for 5 min., along the full thickness of the direction of recrystallization is more complete, the grain size distribution is relatively uniform; 1050 °C normalization for 5 min., some grains exhibit abnormal growth, and destroy the overall uniformity. (3) In addition, with the increase of normalization temperature, the overall trend of normalized plate is weaker in α-fiber texture and stronger in λ-fiber texture, and there is no γ-fiber texture; at 950 °C for 5 min of normalization, the grains are more uniform and similar to the fiber texture of hot-rolled plate; at 1050 °C for 5 min of normalization, the λ-fiber texture is the strongest, but some grains grow abnormally. Therefore, for the high-grade non-oriented electrical steel studied in this experiment, the optimal normalization temperature should be located between 950 and 1050 °C, and the time to avoid abnormal grain growth should be less than 5 min.

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(4) Combining the process, energy consumption, and the characteristics of subrapid solidification of ultra-thin strip process, the preparation of high-Si content non-oriented electrical steel by ultra-thin strip process can be considered to omit the normalization process and directly control the tissue morphology and fiber texture structure by designing the hot rolling process.

References 1. Zhang Y, Xu Y, Liu H, Li C, Cao G, Liu Z, Wang G (2012) Microstructure, texture and magnetic properties of strip-cast 1.3% Si non-oriented electrical steels. J Magn Magn Mater 324(20):3328–3333 2. Zhu C, Wang W, Lu C (2019) Sub-rapid Solidification and its related interfacial heat-transfer behaviors in strip casting process. J Sustain Metall 5(3):378–390 3. Zhu C, Wang W, Lu C (2019) Characterization of cermet coatings and its effect on the responding heat transfer performance in strip casting process. J Alloy Compd 770:631–639 4. Wu S, Chen A, Liu H, Liu Z, Li H, Wang G (2014) Microstructure and texture evolution of strip-cast and hot-rolled Fe-3 %Si steel sheet. Metallogr Microstruct Anal 3(5):390–396 5. Li H, Feng Y-l, Song M, Liang J-L, Cang D-Q (2014) Effect of normalizing cooling process on microstructure and precipitates in low-temperature silicon steel. Trans Nonferrous Metals Soc China 24(3):770–776 6. Li C, Hua Y, Wang Y, Yu Y (2011) Texture of Cold rolled strip of Fe-3Si steel produced by thin slab casting and rolling. J Iron Steel Res Int 18(3):40–46 7. Cheng L, Yang P, Fang Y, Mao W (2012) Preparation of non-oriented silicon steel with high magnetic induction using columnar grains. J Magn Magn Mater 324(23):4068–4072 8. Zu G, Zhang X, Cui Y (2014) Study on state of initial and normalized non- oriented silicon steel by twin-roll strip casting[J]. J Mater Metall 13(3):201–205 9. Zhang W, Mao W, Wang Y, Li H, Bai Z (2007) Influence of grain size in normalized hot band on texture and magnetic properties of non-oriented silicon steel sheet. Iron Steel 42(2):64–67 10. Zu G, Zhang X, Zhao J, Cui Y, Wang Y, Jiang Z (2015) Analysis of the microstructure, texture and magnetic properties of strip casting 4.5wt.% Si non-oriented electrical steel. Mate Des 85:455–460 11. Wang F, Zhu Z, Guo H, Li H (2017) Effects of normalizing microstructure and texture of high grade non-oriented silicon steel. Steel Roll 34(6):28–32 12. Liu Y, Cheng L, Cao R, Liu G, Li X, An D, Li Z (2021) Microstructure and texture changes of non-oriented silicon steel in the whole process. Part A Phys Test 54(4):29–33

The Role of Temper Rolling and Annealing on the Magnetic Property Improvement of a Low Si Non-oriented Electrical Steel Youliang He, Tihe Zhou, Haden Lee, Chad Cathcart, and Peter Badgley

Abstract Temper rolling is a light cold deformation process (normally under tension, with less than ~10% thickness reduction) applied to annealed non-oriented electrical steel sheets to improve the surface quality of the final product. The small plastic deformation and the subsequent annealing, however, have a considerable effect on the magnetic properties of electrical steels. This is because the strain introduced in the temper rolling process changes the distribution of the stored energy in grains with different orientations, which significantly affects the grain growth and texture development during final annealing, thus influencing the magnetic properties. In this study, a low Si non-oriented electrical steel was hot rolled, cold rolled, and batch annealed to produce 0.5-mm-thick sheets. Temper rolling (~6% reduction) was then applied to the annealed sheets and annealed again at different temperatures (500– 900 °C) for a fixed time (2 h) or at a fixed temperature (800 °C) for different times (0.5–24 h). It was found that temper rolling and annealing could significantly improve the magnetic properties, i.e., decreasing the core loss by up to ~22% and increasing the relative peak permeability by up to 68% at 1.5 T and 60 Hz, as compared to that without temper rolling. The improvement of magnetic properties was correlated to the changes in microstructure and texture induced during the temper rolling and final annealing processes. Keywords Non-oriented electrical steel · Temper rolling · Annealing · Magnetic properties · Texture · Recrystallization

Y. He (B) CanmetMATERIALS, Natural Resources Canada, Hamilton, ON, Canada e-mail: [email protected] T. Zhou · H. Lee · C. Cathcart · P. Badgley Stelco Inc, Hamilton, ON, Canada © His Majesty the King in Right of Canada, as represented by the Minister of Natural Resources 2023 The Minerals, Metals & Materials Society, TMS 2023 152nd Annual Meeting & Exhibition Supplemental Proceedings, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-031-22524-6_82

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Introduction When producing semi-processed non-oriented electrical steel sheets, temper rolling, also known as skin pass rolling, is a common step applied to cold rolled and annealed steel sheets to improve the surface quality of the product. This process is usually performed at room temperature, with thickness reductions up to ~10% [1] and extensions up to about 20% [2]. The temper rolled sheets have to be annealed again, known as quality development annealing (QDA) [2], to obtain optimal final magnetic properties, usually after the lamination process has been completed. It has been shown that the slight cold reduction on the steel sheets introduces a critical strain [2] in the annealed material, which enables the development of a coarse-grained microstructure with specific textures after final annealing, thus leading to improved magnetic properties. The annealing before temper rolling may produce a completely or partially recrystallized microstructure, which results in different stored energy distributions in the temper rolled material, leading to different final annealing textures. The study of Takashima et al. [3] has shown that intermediate annealing (before temper rolling) that produces a microstructure consisting of both recrystallized //ND (normal direction) and deformed //RD (rolling direction) grains plays a critical role to forming a strong {001} texture after final annealing. The effects of the temper mill extension and the roll roughness on the magnetic properties have been studied by Cheong et al. [2]. It was shown that high temper mill extension and smooth work rolls lead to a sharp texture and large magnetic anisotropy between the rolling and transverse directions. The amount of thickness reduction during temper rolling may also play a role in determining the final microstructure and texture of the material, which has been investigated by Barros et al. [1] and Mehdi et al. [4]. It was shown [1] that 1–4% temper rolling reduction produces a very inhomogeneous microstructure with both fine and coarse grains. The crystallographic textures in the fine and coarse grains are significantly different. A 6% reduction during temper rolling would result in the optimal magnetic properties [1]. Mehdi et al. [4] intentionally increased the temper rolling reduction to up to 20% and studied the effect of temper rolling reduction on the //ND texture originally dominating the microstructure. It was shown that temper rolling weakened the cube texture and promotes the {112} texture (like in cold rolling), while the //ND and //RD textures were essentially not changed. After final annealing, the cube texture was weakened, while the rotated cube texture was strengthened if the temper rolling reduction was controlled below 10%. The above-mentioned studies, however, did not investigate the effect of the final annealing condition on the magnetic properties of temper rolled electrical steel in detail, which also has considerable effects on the final microstructure and texture of the steel. This paper examines the effect of annealing condition on the microstructure, texture as well as magnetic properties of temper-rolled non-oriented electrical steel sheets. The purpose is to optimize the processing parameters to produce

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semi-processed electrical steel sheets that will lead to excellent final magnetic properties.

Material and Experimental Procedure The material investigated in this study was a low silicon non-oriented electrical steel containing about 0.50% Si and 0.30% Al (weight percentage). The carbon content was controlled below ~0.003%. The steel was melted, hot rolled, and cold rolled in a commercial production line to a thickness of 0.5 mm. The coil was then batch annealed and temper rolled to 0.47 mm (with ~6% thickness reduction). Epstein frame strips (280 × 30 mm) were cut from the coil before temper rolling (after batch annealing) and after temper rolling along both rolling and transverse directions (RD and TD). The strips were then annealed at various temperatures (500–900 °C) for a fixed time, or at a fixed temperature (800 °C) for various times (0.5–24 h). Two types of annealing experiments were performed: (i) batch annealing in a production line in which the coil is gradually heated to the designated temperature and held for a specific time (hydrogen as the protective gas); (ii) laboratory annealing in which small samples were inserted into a pre-heated furnace at the designated temperature and held for a specific time (argon as the protective gas). Each set of Epstein frame samples consists of 8 strips along the RD and 8 strips along the TD. Magnetic properties were measured using a Magnetic Instrumentation SMT-700 Epstein frame system at 60 Hz. The core loss and relative peak permeability at a magnetic induction of 1.5 T, magnetic flux densities at 800 (B8) and 5000 A/m (B50), and saturation magnetic flux density (Bm) are reported. Selected samples were characterized by electron backscatter diffraction (EBSD) using an EDAX OIM system (V8.1) in a Nova NanoSEM (FEI) scanning electron microscope to evaluate the microstructure and microtexture. Since the materials were all annealed and had relatively large grain sizes, the EBSD characterization was conducted on the RDTD plane (mid-thickness plane) to cover a large number of grains. The textures are represented by orientation distribution functions (ODFs) calculated from the EBSD orientation data. A harmonic series expansion method with a series rank of 22 and a Gaussian half width of 5° was used to perform the calculations. The results are plotted on the ϕ2 = 45° section of the orientation space (Bunge notation).

Results and Discussion Figure 1 illustrates the magnetic properties of the steel sheets after batch annealing, temper rolling and final annealing at various temperatures for a fixed time of 2 h. It is seen (Fig. 1a) that temper rolling (even with only 6% reduction) significantly increases the core loss from about 7.0 to 9.6 W/kg (~40% increase), and decreases the relative peak permeability from ~1600 to ~800 (~50% drop). The deterioration of

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(a)

Annealing Time: 2 h

(b)

Annealing Time: 2 h

Fig. 1 Variation of the magnetic properties with respect to the annealing temperature for a fixed time of 2 h: a core loss and relative peak permeability, b magnetic flux densities at B8 and B50 and saturation flux density Bm

the magnetic properties is due to the changes in the stress state, microstructure, and domain structure of the material induced by the plastic deformation during temper rolling. The plastic deformation changes the interplanar spacing of the lattice through the generation of dislocations, which creates strain fields and alters the volume of magnetic domains and domain configurations. The dislocations serve as pinning sites to impede domain wall motion. The deformation also produces internal stresses and increases the number of 180° domain wall movements. All these deteriorate the magnetic properties, i.e., increasing the core loss and decreasing the magnetic permeability [5]. It should be noted that the classical eddy current loss is unrelated to the stress, while the hysteresis loss component is largely influenced by the stress [6]. From Fig. 1b, it is seen that temper rolling also significantly decreases the lowfield magnetic flux density at 800 A/m (B8), but only slightly changes the high-field magnetic flux density B50 (at 5000 A/m) and saturation magnetization flux density Bm. Since B8 is associated with a small excitation field, the magnetization is mainly realized through the motions of the 180 and 90° domain walls, which are hindered by the dislocations within the material [6]. As a result, B8 behaves similarly to the relative peak permeability. The B8 measured after batch annealing is about 1.45 T, which is largely decreased to about 1.15 T after temper rolling. This decrease is mainly due to the internal strains and dislocations induced in the material by temper rolling, which makes the material magnetically harder [5]. To a lesser extent, the decrease of the B8 may be due to the change of the crystal orientation, which also makes the material harder to magnetize. The B50 and Bm, on the other hand, are only slightly decreased because of the temper rolling. This is because only when the material has a phase transformation or a change of the state of atomic ordering does the plastic deformation affect the saturation induction [5]. Annealing the temper rolled steel sheets at increasing temperatures from 500 to 800 °C gradually decreases the core loss from 9.59 to 5.45 W/kg (a 43% reduction). In the meantime, the relative peak permeability increases from 800 to 2683 (more than 3 times). Further increase of the annealing temperature to 900 °C slightly increases the

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core loss to 5.98 W/kg (a 10% increase), while the relative peak permeability slightly decreases to 2458 (an 8.4% decrease). The change of the low-field magnetic induction B8 is similar to that of the relative peak permeability, i.e., it gradually increases with the annealing temperature until about 700 °C at which a maximum B8 is achieved, which is due to the gradual release of the internal stress as well as the elimination of the pinning dislocations in the material. After that, the B8 only slightly fluctuates with the annealing temperature. The B50 and Bm, on the other hand, maintain stable values when the annealing temperature is increased. It is thus seen that the core loss, permeability, and B8 are very sensitive to the annealing temperature, while the magnetic inductions B50 and Bm only vary slightly with the annealing temperature. Compared to the values after batch annealing (before temper rolling), a drop of ~22% in core loss and an increase of ~68% in relative peak permeability can be achieved by temper rolling and final annealing. The magnetic properties of the temper rolled steel after annealing at a fixed temperature of 800 °C for different times are shown in Fig. 2. The core loss is significantly reduced and the permeability largely increased after annealing for 0.5 h. Increasing the annealing time to 2 h further decreases the core loss to the lowest value of 5.45 W/kg (with very high permeability), while increasing the annealing time to 4 h results in the highest permeability of 2740 (with a very low core loss). Further increasing the annealing time gradually increases the core loss and slightly decreases the permeability. The B8 shows a large increase after annealing for 0.5 h, but essentially does not change when the annealing time is further increased. Similar to the variation with respect to the annealing temperature, extending annealing time only slightly affects the B50 or Bm. Figure 3 illustrates the microstructures and grain orientations of the samples after temper rolling and annealing at 800 °C for different times. Temper rolling essentially does not induce apparent morphology change of the recrystallized grains as the microstructure is still composed of equiaxed grains (Fig. 3a). It is also noted that the grains after temper rolling show very large differences in size (which was caused (a)

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Fig. 2 Variation of the magnetic properties with respect to the annealing time at a fixed temperature of 800 °C: a core loss and relative peak permeability, b magnetic flux densities at B8 and B50 and saturation flux density Bm

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by the batch annealing before temper rolling), i.e., the grain sizes vary from ~3 to ~250 μm, with an average of ~59 μm. Annealing the temper rolled steel at 800 °C for 2 h in laboratory (small samples) significantly increases the average grain size to ~165 μm (Fig. 3b), which also largely reduces the number of small grains as compared to the temper rolled steel (Fig. 3a). It is thus seen that the small temper rolling reduction has a significant effect on the grain growth of the steel during final annealing. The colors of the grains are changed from mostly red ({001}) and blue ({111}) to a variety of different colors. Batch annealing (large steel coil) for 24 h at the same temperature only slightly increases the average grain size to ~195 μm. However, if the steel is first batch annealed for 24 h and then laboratory annealed again for 2 h, the average grain size is significantly increased to 270 μm. Although the small plastic deformation during temper rolling does not cause significant morphological changes, it does change the stored energy distribution in the microstructure since the Taylor factors (and the resistances to plastic deformation) of grains with different orientations are different. The result is that the deformation is not uniform among grains. Figure 4 shows the grain orientation spread (GOS) of the samples after temper rolling and after annealing. GOS is the mean value of the misorientations between all the pixels of a grain and the mean orientation of this grain, which is very sensitive to the deformation state of the material [7]. From Fig. 4a it is seen that the small temper rolling reduction results in apparently different GOS among grains, indicating different deformation (and stored energy) in these ND

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Fig. 3 EBSD inverse pole figure maps showing the microstructures and orientations after temper rolling and annealing: a after temper rolling, b after laboratory annealing at 800 °C for 2 h, c after batch annealing at 800 °C for 24 h, d after batch annealing at 800 °C for 24 h plus laboratory annealing at 800 °C for 2 h

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Fig. 4 Grain orientation spread (GOS) maps showing the variation in deformation of grains with different orientations: a after temper rolling, b after laboratory annealing at 800 °C for 2 h, c after batch annealing at 800 °C for 24 h, d after batch annealing at 800 °C for 24 h plus laboratory annealing at 800 °C for 2 h

grains. After annealing (Fig. 4b–d), the GOS is considerably reduced; extending the annealing time gradually decreases the GOS. The crystallographic textures of the temper rolled and annealed samples are shown in Fig. 5. Temper rolling leads to a relatively strong texture that is composed of a //ND fiber and a rotated cube. However, the common α-fiber (//RD) after plane-strain compression in bcc metals is not seen. Annealing for 2 and 24 h both produce rather random textures, but there are some differences: after 2 h, there is a Goss ({011 ) and a {001} component, while after 24 h, the Goss texture is weak and a //ND fiber appears. In both cases, there is no γ-fiber. Batch annealing plus laboratory annealing not only significantly increases the grain size, but also largely strengthens the texture (maximum intensity increases to 6.72). The strongest component is now the {111} on the γ-fiber (//ND), although there is also a rotated cube and an α-fiber. It has been reported in numerous studies [8–12] that the magnetic properties of non-oriented electrical steels are largely dependent on the grain size and texture of the final annealed material. It is generally accepted that the core losses (hysteresis, eddy current, and excess) are significantly affected by the grain size [8, 9, 11], while the magnetic flux density and magnetic permeability are closely correlated to the crystallographic texture [10, 11]. However, the effects of the grain size on the three types of losses are different: the hysteresis loss decreases with the increase of the grain size, while the eddy current and excess losses increase with the grain size [9, 11]. As a result, there is an optimum grain size for the best magnetic properties. Matsumura and Fukuda [8] reported an optimum grain diameter of 150 μm for highgrade electrical steel sheets. It was also shown that when the grain size is smaller than about 100 μm, the dominant core loss is the hysteresis loss [8, 11], which is mainly controlled by the grain size. It has been shown in this study that the average grain size of the temper rolled steel annealed at 800 °C for 2 h is ~165 μm, and this sample shows the lowest core loss among all the samples. The texture of this

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Fig. 5 Textures of the steel after temper rolling and annealing: a after temper rolling, b after laboratory annealing at 800 °C for 2 h, c after batch annealing at 800 °C for 24 h, d after batch annealing at 800 °C for 24 h plus laboratory annealing at 800 °C for 2 h, e texture key showing the common fibers and components of bcc metals on the ϕ2 = 45° ODF section (Bunge notation)

sample, on the other hand, is quite random, thus the magnetic flux density did not show much difference from other samples. The relative peak permeability does show a local maximum, which may be attributed to the Goss texture (with easy axes in the rolling direction) and the near cube texture (with easy axes close to the RD and TD). The sample after batch annealing at 800 °C for 24 h has a grain size of about 200 μm, which is considerably larger than the optimal grain size, thus the core loss is larger than that of the sample after annealing for 2 h. The relatively strong //ND texture of this sample results in a slightly higher B50 and Bm than the sample after annealing for 2 h. To evaluate the effect of texture on magnetic properties, magnetocrystalline anisotropy energy (MAE) can be calculated from the crystallographic texture. MAE is a measure of the energy required to magnetize in the directions away from an easy axis ( for Fe-Si) [11], which is proportional to the anisotropy parameter, i.e. MAE ≈ K1 (α12 α22 + α22 α32 + α32 α12 )

(1)

where α1 , α2 , and α3 are the direction cosines of the crystal axes [100], [010], and [001] with respect to the magnetization direction, respectively, and K1 is the firstorder magnetocrystalline anisotropy constant. From the ODF data, the anisotropy parameter A = α 21 α22 + α22 α32 + α32 α12 can be calculated [11], which is used to evaluate the magnetic quality of the texture. A texture that maximizes the density of the directions in the magnetization direction would minimize the anisotropy parameter and optimize the magnetic properties of the steel. The //ND texture minimizes the anisotropy parameter in two directions in the sheet plane, since when one of the

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Fig. 6 Comparison of the anisotropy parameter to magnetic properties: a anisotropy parameter vs. core loss, permeability, and B8, b anisotropy parameter vs. B50 and Bm. The anisotropy parameters are the average of RD and TD

two directions is along the magnetization direction, the direction cosines of the other two directions are zero, which leads to a zero anisotropy parameter [13]. Figure 6 illustrates the calculated anisotropy parameters for the 4 samples characterized in Figs. 3 and 5 in this study. The average anisotropy parameter values calculated from the RD and TD directions are compared to the measured magnetic properties (which are also measured in both the RD and TD). From Fig. 6a it is seen that the core loss essentially follows the same trend as the anisotropy parameter, i.e., a higher anisotropy parameter leads to a higher core loss, while the magnetic permeability and the B8 inversely follow the anisotropy parameter. A smaller anisotropy parameter means a “closer” direction of the “average” easy axis of the steel to the magnetization direction [13, 14], thus it is easier to magnetize. As a result, the core loss (mainly the hysteresis loss) is smaller and the magnetic permeability is higher. Figure 6b shows comparison of the anisotropy parameter to the magnetic flux densities B50 and Bm. It is seen that these two densities do not show apparent correlations to the anisotropy parameter. This is somewhat different from the findings of Lee et al. [11], where it was shown that the B50 linearly decreases with the anisotropy parameter. One possible reason is that the number of data points in this study (thus the range of variations of the anisotropy parameter and B50 values) is much smaller than that of Lee et al. [11], which does not show a clear statistic correlation.

Summary and Conclusions In this study, a low silicon non-oriented electrical steel was temper rolled and annealed at different temperatures for a fixed time and at a fixed temperature for different times. The magnetic properties were measured by Epstein frame method in both RD and TD, and the microstructure and microtexture were characterized by EBSD. The findings can be summarized as follows.

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Even a small temper rolling reduction of 6% results in significant deterioration of the magnetic properties. However, the plastic deformation leads to uneven distribution of stored energy in the microstructure, which provided driving force for the grain growth during the subsequent annealing. As a result, a large grain size can be obtained in the final annealed steel sheet. Annealing at 800 °C for 2 h after temper rolling gives rise to an average grain diameter of ~165 μm and the lowest anisotropy parameter (a quite random texture), which lead to the lowest core loss, the largest relative peak permeability, and the largest B8 magnetic induction. Compared to the steel after cold rolling and batch annealing (without temper rolling), the core loss is reduced by 22% and the relative peak permeability is increased by ~68%. The core loss generally follows the same trend as the anisotropy parameter, i.e., the smaller the anisotropy parameter, the lower the core loss, while the magnetic permeability and the magnetic flux density B8 follow an inverse relation with the anisotropy parameter. The B50 and saturation flux density do not seem to correlate closely to the anisotropy parameter. Acknowledgements Funding for this research is provided by the Program of Energy Research and Development (PERD), Natural Resources Canada, and Stelco Inc. Mehdi Mehdi is gratefully acknowledged for magnetic property measurements and sample preparation. Michael Attard is thanked for laboratory annealing of the steels. The authors are indebted to Renata Zavadil and Jian Li for EBSD measurements.

References 1. Barros J, Targhetta A, León O, Ros T, Schneider J, Houbaert Y (2007) Effect of temper rolling on the texture formation and magnetic properties of non-oriented semi-processed electrical steel. J MagnMagn Mater 316 (2): e865–e867 2. Cheong SW, Hilinski EJ, Rollett AD (2003) Effect of temper rolling on texture formation in a low loss cold-rolled magnetic lamination steel. Metall Mater Trans A 34(6):1311–1319 3. Takashima M, Komatsubara M, Morito N (1997) {001} Texture development by twostage cold rolling method in non-oriented electrical steel. ISIJ Int 37(2):1263–1268 4. Mehdi M, He Y, Hilinski EJ, Edrisy A (2017) Effect of skin pass rolling reduction rate on the texture evolution of a non-oriented electrical steel after inclined cold rolling. J Magn Magn Mater 429:148–160 5. Bozorth RM (1951) Ferromagnetism. D. Van Nostrand Company Inc., Toronto 6. Daem A (2021) Manufacturing effects on electromagnetic properties of ferromagnetic cores in electrical machines. PhD thesis, Ghent University, Belgium 7. Ayad A, Allain-Bonasso N, Rouag N, Wagner F (2011) Grain orientation spread values in if steels after plastic deformation and recrystallization. Mater Sci Forum 702–703:269–272 8. Matsumura K, Fukuda B (1984) Recent development of non-oriented electrical steel sheets. IEEE Trans Magn 20(5):1533–1538 9. Bertotti G, Di Schino G, Ferro Milone A, Fiorillo F(1985) On the Effect of grain size on magnetic losses of 3% non-oriented SiFe. J De Physique, Colloque C6, Suppllement No. 9, Tome 46:385–388 10. Kang HG, Lee KM, Huh MY, Kim JS, Park JT, Engler O (2011) Quantification of magnetic flux density in non-oriented electrical steel sheets by analysis of texture components. J Magn Magn Mater 323:2248–2253

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11. Lee KM, Park SY, Huh MY, Kim JS, Engler O (2014) Effect of texture and grain size on magnetic flux density and core loss in non-oriented electrical steel containing 3.15% Si. J Magn Magn Mater 354:324–332 12. Leuning N, Steentjes S, Hameyer K (2019) Effect of grain size and magnetic texture on iron-loss components in NO electrical steel at different frequencies. J Magn Magn Mater 469:373–382 13. He Y, Mehdi M, Liu H, Hilinski EJ, Edrisy A (2021) Angular magnetic Barkhausen noise of incline- and cross-rolled non-oriented electrical steel sheets. Mater Char 177:111200 14. He Y, Mehdi M, Hilinski EJ, Edrisy A (2018) Through-process characterization of local anisotropy of Non-oriented electrical steel using magnetic Barkhausen noise. J Magn Magn Mater 453:149–162

Part XXVI

Electronic Packaging and Interconnection

Dynamic Material Characterization Through In-Situ Electrical Resistivity Measurements of High Temperature Transient Liquid Phase Sinter Alloys G. Nave and P. McCluskey

Abstract As part of the effort to implement additive manufacturing techniques into the world of power electronics devices and materials that can operate at harsh environments, researchers and industry must mitigate multi-level challenges that span processing techniques, manufacturing scaling, manufacturing mobility, cost reduction, optimal material properties, and reliable material performance. This study presents a new method to dynamically test the electrical properties of a given solder alloy. The method is capable of testing the electrical properties from the moment in which the solder is pasty and mixed with multiple organics, to the point where the organics are evaporated and reacted, and the remaining material is only diffused metal powder. This new testing method allows to quantify multiple effects such as organic–metallic interactions, chemical effects, metallurgical effects, and in the context of additive manufacturing, this testing method provides a new design tool for faster processing, temperature profiles designs, and paste formulation design. Keywords Additive manufacturing · Electronic materials · Dynamic resistivity test

Introduction In the metallurgy and material development fields, experiments are expensive, timeconsuming, and lacking natural source of large data output. Furthermore, when a given material is still in the development phase, and before any failure analysis can be performed, immense amount of material combinations and interactions are present, and must be considered [1]. Thus, it is crucial to develop efficient tools and frameworks that allow for: spatial mapping of material combinations and interactions, quantitative assessments of indirect and direct measurements, and the ability G. Nave · P. McCluskey (B) Department of Mechanical Engineering, University of Maryland, College Park, MD, USA e-mail: [email protected] G. Nave e-mail: [email protected] © The Minerals, Metals & Materials Society 2023 The Minerals, Metals & Materials Society, TMS 2023 152nd Annual Meeting & Exhibition Supplemental Proceedings, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-031-22524-6_83

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to perform deep analysis with multi-level and multidisciplinary insights. Such tools and frameworks can accelerate the development phase, improve the material quality at early stages, reduce the development costs, extend the capabilities of current measuring instruments, and help designing more rigorous and meaningful statisticalbased experiments. Among these frameworks and development tools, numerical simulations are often suggested as a fast and cheap solution compared to real and expensive physical experiments [2]. However, to make such simulations fast and cheap, relatively large number of assumption and simplifications must be made. Without these assumptions and simplifications, the numerical models will become numerically expensive, time-consuming, and somewhat impractical at the early development phase, or when multi and complex phenomena take place simultaneously. Another growing approach to deal with the vast amount of material combinations and interactions is the use of diversified machine learning (ML) algorithms [3]. These ML algorithms offer new ways to uncover material combinations [4], predict material properties [5], and help design better-informed and more efficient experiments [6]. In addition, in growing number of instances, combinations of the numerical simulations and the ML algorithms can be applied [7]. Nevertheless, many or most ML algorithms require relatively large and sufficient amount of training, validating, and testing datasets. Without relatively large datasets of ground-truth physical experiments, these algorithms are only good as theoretical methods. To fully take advantage of the numerical simulations and the ML algorithms, it is necessary and important to keep develop, implement, and execute real physical experiments that can be integrated-with and feed these tools, and also provide stand-alone empirical data and insights towards an improved process of material development. In academia, research in different disciplines tends to focus on specific and somewhat narrow subjects. While the specific and deep research has its own merits, holistic approaches are often neglected, or overseen. This paper is motivated by the fact that bridging through multiple academic and research disciplines has the potential to deepen a given research, while maintaining its holistic nature. This type of research approach, especially in the long run, can also benefit multiple researchers, and multiple new set of experiments and hypotheses. In this paper, we present an original testing method to dynamically (in-situ) measure and characterize the formation and development of the electrical conductivity property of a given material. The test is done while and during the material is heated up, but not limited to 300 °C. The name of the new test is Dynamic Resistivity Test (DRT). The use of the DRT was demonstrated with an electrically-conductive solder material. The test was performed from the moment in which the solder is pasty and mixed with multiple organics, to the point where the organics are evaporated and reacted, and the remaining material is only diffused metal powder. The DRT was designed to quantitatively measure the formation of the material property, during the initial formation stage in which multi-phenomena takes place simultaneously. More specifically, the test was done on Transient Liquid Phase Sinter (TLPS) hightemperature solder paste material. The results are presented for three different paste formulations. Each paste formulation exhibits different organic flux. This paper also discusses the design considerations of the testing apparatus. In addition, we use a

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dynamic one-sample Kolmogorov–Smirnov (KS) test for each of the paste formulations, to inform on the dynamic nature of the time-dependent statistical distributions of the measurements. The one-sample KS tests were designed to improve the statistical robustness and reliability of future analysis.

Background With the recent advancements of Wide Band Gap semiconductors [8, 9], and with the increased demand for more efficient, reliable, faster, and smaller power electronics, new line of packaging materials is required. These packaging materials must support harsh environment operation-compatibility, high mechanical reliability, high electrical and thermal conductivities, attach versatility, Restriction of Hazardous Substances (RoHS) legislation, and industrial-scale production feasibility [10]. In addition, with the recent advancements in additive manufacturing (AM) and its premises [11], the new line of packaging materials must exhibit AM compatibility. TLPS joints are strong candidates to replace the current die attach materials due to their mechanical, thermal, and electrical properties [12]. While exhibiting exaptational and reliable thermal properties, to become a fully-reliably, competitive, and a consistent holistic-technology, TLPS materials still require additional research and development. The processing of TLPS often results with high porosity levels. In comparison to bulk metals, the porosity often leads to inferior mechanical, and electrical properties. Moreover, porous materials are more susceptible to experience destructive and damaging oxidation [13]. In addition, the TLPS’ thermallystable IMC phases and alloys, often prove to be brittle, which limit their ability to sustain stresses and strains. Thus, fully controlling or eliminating the porosity levels, or the brittle inter-metallic phases, are the current research motivations and challenges associated with the TLPS materials. While many researchers tried to approach the TLPS research from the metallurgical point of view, few are the researchers that put an emphasis on the effects caused by the organic flux binders. The lack of holistic approach towards researching TLPS materials serves as an additional research motivation. In terms of Additive Manufacturing, for a given successful AM technique, its processing speed and general throughput capabilities are crucial. Among different AM processing methods of metallic powders, printing metal in a paste-form offers multiple processing advantages such as dynamic, robust, low-budget, fast, scalable, and user-friendly processing. However, printing metal powders in a paste-form means that the metal powder is mixed with organic materials, i.e. flux. In a perfect and ideal metallic crystal, there is no space for these organic molecules. Thus, these ideal crystals often exhibit optimal mechanical, electrical, and other material properties. In the paste-form, however, the presence of organic molecules between the metal powder causes micro and macro-scale imperfections in the resultant material. Thus, enhanced research that addresses the efficient removal of the flux content from the final metallic high-temperature solder structure is required.

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Figure 1a shows a generic temperature profile of a TLPS material that requires to reach to a specific thermally-stable IMC phase. As seen in Fig. 1a, the first stage of the TLPS processing is marked with yellow, and it only lasts for few minutes. In this stage, three main events take place: flux-related activity, phase transition, and transient liquid-state diffusion. This stage usually happens during the initial ramping of the temperature, and the beginning of the isothermal holding. In comparison to the isothermal holding and homogenization stages of the TLPS processing, the liquidstate diffusion is dominant in the first stage, and it is at least one order of magnitude shorter than the following two steps individually [14]. During the transient process of liquid-state diffusion, flux evaporates, the structure solidifies, and gains enough structural stability. From AM point of view, the fast solidification is crucial for the successful printing of additional layers in the vertical direction. Because short solidification times aren’t enough for a full metallurgical thermally-stable phase completion, once the 3D structure has reached its final shape, the far-from-equilibrium structure can go through the full and final sintering process. As seen in Fig. 1a, the isothermal holding stage should continue until full consumption of In-rich phases, and the formation of the thermodynamically, and thermally-stable alloy. Among multiple TLPS metal systems, this study investigates the Ag-In TLPS system due to its known electrical conductivity property [14]. Figure 1b features a Scanning Electron Microscope (SEM) image of the top surface of a stencil-printed Ag-In TLPS sample that was heated at 160 °C for 10 s. The figure shows that indium-melt liquid bridges (yellow) between the solid-state Ag particles (purple) are present in multiple locations despite the short heating time, the relatively low heating temperature, and the presence of the metal particles within the flux binder. The figure also shows that while some indium melted and wetted the Ag particles, some indium particles were still present in their sphere configuration. It is highly important to monitor the wetting angle of the indium on the Ag spheres, as well as

Fig. 1 Shows a generic temperature profile for TLPS materials. The profile features an initial stage of ramping up the temperature, followed by isothermal holding, and cooling to room temperature (a). EDX monitoring of the top surface of a printed surface that was subject to 160 °C heating for 10 s (b-top). On the bottom row (b), from left to right, the EDS dot map shows: Silver (white), Indium (yellow), Carbon (white)

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Fig. 2 Flow diagram that represents the two main categories that need to be considered during the early-stage processing of the TLPS material

the amount of liquid-melt present at a given time, as it determines the ability of the structure to further densify after the flux removal [15]. In addition, the individual X-ray dot maps in Fig. 1b show that the liquid metal bridge is forming when carbon is still covering the liquid metal. This suggests that the formation of a structure is initiating prior to sufficient removal of the flux binder, which may hinder the ability of the liquid-base capillary force to attract the silver particles towards each other. This qualitative observation is only one in many phenomena that takes place during the formation of the conductive TLPS traces. As shown in Fig. 2, the need to focus on the early stage of the TLPS processing can be separated into two main categories, which are linked with each other but can be analyzed separately: the flux performance, and the performance of the melted low-temperature filler metal (e.g. indium, tin). For the flux, there are several parameters that are critical for successful TLPS paste-based processing such as (1) the initial drying and thinning behavior at low temperatures below the melting point of the indium, (2) the flux activation temperature and efficiency, and (3) the flux evaporation kinetics, routes, and residues, each of which has major impact on the sample’s final structure and performance. With respect to the melt phase of the low melting temperature particles, the ability to have good wetting on the high melting temperature particles, the amount of liquid present at a given time, and the diffusion mechanisms of the liquid-state diffusion process, determine if the structure will undergo densification, swelling, or if it will remain mostly “frozen” until the end of the sintering process.

Dynamic Resistivity Test Development and Experiment Procedure Based on the qualitative observations that were presented above, it was theorized that the electrical resistivity of the TLPS also evolves at the initial stages of the

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material formation and solidification. Thus, the Dynamic Resistivity Test was theorized and developed. In the DRT experiment, there are three main components: the heating medium, the tested samples, and the probing set-up. The combination of these three components determines the feasibility, and the quality of the experiment, the measurements, and the final results. The full DRT apparatus and its components are shown in Fig. 3a. The apparatus is divided into three main levels. The lower level features the heavy equipment such as the diaphragm pump and the water reservoir. The middle level holds the testing, and other electrical equipment such as the main PC, the data loggers, and a Wi-Fi receiver. The top level is the main experiment zone, and it features the hot plate, the testing probes, the probe’s lifting crane, the main electrical board, an adjustable light and a magnifier, an adjustable monitor, and an adjustable lifter keyboard mount. The experiment utilizes the four-point probe methods to dynamically measure the electrical resistivity of the TLPS paste, from the point of an electrical overload state to the milliohm-level resistance. The use of the four-point probe method eliminates multiple parasitic resistances and allows for more accurate measurement of resistance. In addition, due to the nature of the test, the DRT probes were logarithmically calibrated from one megaohm, down to single-digit milliohms. Closer look at the DRT probe design is shown in Fig. 3b. As shown in the figure, the proposed probe configuration is based on using two-part Pogo-Pin probes. As shown in Fig. 3b, the first part of the probes is a stationary socket. The stationary sockets are soldered to the electrical wires to provide reliable, and consistent electrical contacts. The soldered connections were covered, insulated, and protected with pieces of heat-shrink plastic. The socket part of the probe is held stationary by the top and bottom, thermally and electrically insulating, fiber glass board plates. The second part of the Pogo-Pins features an internal spring, and pressfitted into the stationary socket part of the Pogo-Pin. The spring-based probe’s head is usually made of stainless steel coated with some electrical element (e.g. Sn, Ni, etc.). The stainless steel core provides a reliable structural design that effectively increases the probe’s life. For the dynamic resistivity measurements, the choice to use a hot plate as a heating medium originated from the probe’s requirement for an easy, and a feasible access. Making the dynamic resistivity testing outside of an oven or a muffle furnace allows to keep multiple types of materials independent of the experiment’s heating. The flexibility in material choice is crucial to a successful, and rapid design of the probe’s fixture. The test includes stencil printing solder paste onto a ceramic non-conductive substrate. The stencil and the stencil design are important elements in the successful, and efficient execution of the dynamic resistivity experiments. First, the stencil determines the length and the cross-sectional area of the sample for the resistivity calculations. The length of the paste traces must be long enough to improve the accuracy of the measurements and reduce the magnitude of the noise with respect to the actual measurement. Second, the quality of the stencil is important for a successful, and consistent deposition of the paste onto the ceramic boards. Third, the design of the stencil must match and optimize the “real-estate” area available during the experiment. This real-estate area is usually determined by the size of the ceramic board, and

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Fig. 3 DRT apparatus and its components (a). DRT experiment’s environment (b). One set of samples that belong to one of the test fluxes (c). The set includes stencil-printed TLPS traces on bare alumina substrates

the maximum size of uniform heating area. The need to use as much of the available heating area is important for the efficiency of the experiment. More paste traces per ceramic board leads to faster acquisition of statistical data. Fourth, the design of the stencil must feature a large enough pad area that can easily fit the size of the testing probes. Custom-made hot plate was designed and manufactured for the DRT. The first step in designing the custom-made hot plate was to define a list of requirements and objectives. There were three main requirements: first, the hot plate must have reliable temperature feedbacks. These temperature feedbacks are crucial to the hot plate’s control performance, the synchronization of the test signals, and the quality of the experiment itself. Second, the hot plate dimensions must match, or at least fit the dimensions of the testing ceramic boards. This requirement is crucial when trying to heat and cool the ceramic boards without generating any uneven stresses in these boards. These uneven stresses can crack the boards and terminate the experiment unsuccessfully. Third, the heating and the cooling system of the samples must be uniform. Non-uniform heating and cooling can lead to diffusion variabilities that can affect the results of the measurement. In addition, similar to the ceramic boards, when non-uniformly cooled, the TLPS samples can also be cracked and snapped, which can lead to a falsified cross-sectional analysis.

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In this work, three sets of organic flux binders were tested (A, B, and C). Each of the organic binders was mixed with a constant formulation and ratio of Ag-In powders. Each set of organic flux contained 24 samples. Figure 3c. shows one set of the tested TLPS paste samples. Each individual sample is a stencil-printed Ag-In TLPS solder on an Alumina non-conductive substrate. In this work, the temperature profile of the DRT experiment included a heating ramping rate of 82 °C/min., followed by isothermal holding for five minutes at 300 °C, and cooling back to room temperature.

Experiment Results To achieve accurate representation of the DRT experiment, the data must be preprocessed before plotting. The data processing includes removal of data point before the start time of the experiment, removal of negative values, removal of numericallyregistered overload values, time synchronization, linear interpolation, and another removal of forward, and backward-interpolated data points. The start time of the DRT experiment is determined by an arbitrary (but calculated) temperature selection. The first temperature reading of the DRT experiment that crosses the selected temperature marks the start time of the experiment. This method allows to calibrate the start time of the experiments, and reduce the results’ noise in the time domain. The temperature selection for the start time must be higher than the room temperature, but below a temperature that initiates or significantly accelerates chemical or metallurgical interactions. The removal of negative values is due to the fact that they have no real physical meaning in resistance measurements. Negative values in the dataset originate from poor probe contact, instrument noise, and other instrumentation dependencies. If the negative values are consistent across a sample and over time, the specific reading is discarded, and labeled as an unqualified DRT measurement. Unlike the removal of the negative data points, the removal of the numericallyregistered overload values is due to their nature to skew statistical analysis. The numerically-registered overload values are registered as 9.9E33 . While these data points are important, their statistical analysis requires further data manipulation, and other special statistical attention. Future analysis will look into analyzing the overload signals. In this paper, the overload signals are completely omitted. The time synchronization and the data interpolations are necessary data-processing steps to allow for time-based statistical analysis. For example, a statistical t-test that is used to differentiate between populations of signals must be performed at the exact same time value. In a continuous test such as the DRT, the statistical time-based t-test can be as a function of time as well, and thus show a time-based statistical significance of the measured resistance data. Due to the monotonic nature of the DRT data, and the data does not feature sinusoidal behavior, and the data was allowed to be interpolated linearly. However, as for now, the forward and backward interpolations were removed from the final plots and analysis. Figure 4a shows the resultant plot that was generated by the DRT. In the figure, the logarithmic left axis represents resistivity in units of −cm. The linear right axis

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represents the temperature in units of degrees Celsius. The horizontal axis represents time in units of seconds, minutes, or hours. The three RGB dynamic box plots represent the resistivity measurements of the TLPS paste, mixed with different fluxes: FluxA, FluxB, and FluxC. The flux box plots feature the corresponding median values for each flux with corresponding colors. In Fig. 4a, b, the orange curves represent the measured temperature. With respect to the same temperature axis, the plot also features the melting point of indium. Figure 4a shows the regions that are affected by the different flux formulations. For example, the drastic drop in resistivity after the melting of indium is significantly delayed in the blue curve, but not in the red or the green curves. This is due to some flux formulations that delays the formation of percolation channels, and the

Fig. 4 Shows the resultant plot that was generated by the DRT (a). Zoom-in on the section where FluxC experiences a secondary significant drop in resistivity starting at 150 s (b)

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Fig. 5 Dynamic One-Sample KS test (above). Values below 0.05 correspond to rejection of the null hypothesis that the data is normally distributed. Sum of rejected null hypothesis done in the KS tests (bottom)

development of a conductive TLPS matrix and microstructure. Another major flux effect can be seen in the minimum resistivity values that each of the flux categories achieves. The green flux results in approximately one order of magnitude higher resistivity than the red and blue curves. Figure 4b shows a time-based zoom-in on the plot from Fig. 4a. In Fig. 4b, by looking only at the green curve, at approximately 150 s, the resistivity of FluxC experiences and secondary relatively significant drop in its resistivity value. This drop triggers when the temperature is around 270 °C. Figure 5 shows a complementary non-parametric dynamic one-sample KS test that provides information on the distribution’s nature of the TLPS samples during the DRT experiment. The null hypothesis for each of the flux groups, at each point in time, is that the samples are normally distributed. The alternative hypothesis is that the samples are not normally distributed. A significance level of 95% percent was chosen for the test. In Fig. 5, when the p-value of a given flux at each time point is smaller than 0.05, the null hypothesis can be rejected. The bottom plot of Fig. 5 shows the sum of rejected null hypothesis at every given time.

Discussion The DRT proves to be a significant test that must be implemented in the development of any conductive material. The experiment sheds light on multiple phenomena that takes place both simultaneously, and sequentially. The experiment proves to provide chemical, metallurgical, and mechanical information and evidence. For example, Fig. 4a shows that after the melting of indium, the resistivity of the samples drops by a few orders of magnitude. Thus, the DRT proves to be a tool that can qualitatively, and quantitatively quantify metallurgical phase-transitions. Currently obtained resistivity results of the DRT must not be considered as absolute values. At the relatively high temperature that the samples experience during the experiment, the effects of

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temperature coefficient of resistance (TCR) might be significant. Thus, using the same DRT apparatus, future studies should attempt to measure the TCR, and be able to perform live-baseline subtraction of the resistivity values at each temperature level during the DRT. For the sake of metallurgical analysis completeness, with each set of DRT experiments, it is necessary to perform optical and other means of microscopy such as SEM cross-sectional analysis of the test samples. Due to the open nature of the DRT experiment, future experiments can also implement live imaging of the samples for further analysis of the sintering process. The merit and the importance of performing the dynamic one-sample KS test is the idea that many parametric, more powerful statistical tests require set of assumptions about the data’s distribution. Since the DRT measurement is time dependent, and since the sample’s distributions are also time dependent, any type of future statistical analysis that considers a simple one-assumption fits-all might not work properly, be unreliable, and should be avoided. The one-sample KS test that was performed in this study increases the robustness of future statistical analysis of the DRT. In addition, upon further and future investigation of the TLPS material, the ability to link different sample’s distributions to different physical observed or measured phenomena can assists in obtaining deeper insights towards the complex nature of the process.

Conclusion This paper presents the development and execution of a new and original DRT that was yet to be performed in the literature. The paper discusses the motivation and background that led to the development of the DRT. In addition, the paper discusses the important elements that must be considered when performing the DRT. In the paper, we applied the new developed test on TLPS materials. We showed and discussed how the electrical conductivity of the TLPS solder paste develops over time and temperature. We concluded the paper with a statistical analysis of the DRT experiment that supports future statistical tests, provides statistical-physical insights, and improves the overall reliability of the new DRT.

References 1. Schmidt J, Marques MRG, Botti S, Marques MAL (2019) Recent advances and applications of machine learning in solid-state materials science. NPJ Comput Mater 5(83) 2. Shymchenko A, Tereshchenko V, Ryabov Y, Salkutsan S, Borovkov A (2017) Review of the computational approaches to advanved materials simulation in accordance with modern advanced manufacturing trends. Mater Phys Mech 32:328–352 3. Fu Z, Liu W, Huang C, Mei T (2022) A review of performance prediction based on machine learning in materials science. Nanomaterials 12(17)

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4. Morgan D, Jacobs R (2020) Opportunities and Challenges for Machine Learning in Materials Science. Annu Rev Mater Res 50 5. Jha D, Gupta V, Liao W-k, Choudhary A, Agrawal A (2022) Moving closer to experimental level materials property prediction using AI. Sci Rep 12(11953) 6. Aggarwal R, Demkowicz MJ, Marzouk YM (2015) Information-driven experimental design in materials science. In: Information science for materials discovery and design. Cham, Springer, pp 13–44 7. Ren L, Geng S, Jiang P, Gao S, Han C (2022) Numerical simulation of dendritic growth during solidification process using multiphase-field model aided with machine learning method. Calphad 78(102450) 8. Sadhana Singh TCGK (2021) Recent advancements in wide band semiconductors (SiC and GaN) technology for future devices. Silicon 9. Armstrong KO, Das S, Cresko J (2016) Wide bandgap semiconductor opportunities in power electronics. In: IEEE 4th workshop on wide bandgap power devices and applications (WiPDA). Fayetteville, AR 10. Zhang H, Minter J, Lee N (2019) A brief review on high-temperature, Pb-free Die-attach materials. J Electron Mater 48:201–210 11. Horst DJ, Duvoisin CA, Vieira RdA (2018) additive manufacturing at industry 4.0: a review. Int J Eng Tech Res (IJETR) 8(8) 12. Tatsumi H, Lis A, Monodane T, Yamaguchi H, Kashiba Y, Hirose A (2018) Transient liquid phase sintering using copper-solder-resin composite for high-temperature power modules. In: IEEE 68th electronic components and technology. San Diego, CA, USA 13. Zhao S-Y, Li X, Mei Y-H, Lu G-Q (2015) Study on high temperature bonding reliability of sintered nano-silver joint on base copper plate. Microelectron Reliab 55(12):2524–2531 14. Quintero PO (2008) Development of a shifting melting point Ag In paste via transient liquid phase sintering for high temperature environments. University of Maryland, Colelge Park 15. German RM, Suri P, Park SJ (2009) Review: liquid phase sintering. J Mater Sci 44:1–39

Effects of Diameter on Copper Pillar with Solder Cap Interconnections During Reflow Soldering Process Jing Rou Lee, Mohd Sharizal Abdul Aziz, Mohd Arif Anuar Mohd Salleh, Chu Yee Khor, and Mohammad Hafifi Hafiz Ishak

Abstract Recently, the copper pillar with solder cap interconnection has been introduced as an alternative for the solder bump interconnection to tackle the limitations, such as the collapsing nature of the solder bump and larger pitch size. This paper presents an effective simulation tool to evaluate the effects of different diameters of the copper pillar with solder cap during the reflow soldering process. A threedimensional numerical approach is used to investigate the thermal behavior of the copper pillar with solder caps with different diameters. The interconnection bump diameters are 150, 200, 250, 300, and 350 µm. The model is developed and meshed using the Computational Fluid Dynamics (CFD) software. The temperature distributions of the copper pillar with solder caps with different diameters during the reflow soldering process are predicted. The paper aims to provide an understanding of the effect of diameters on the temperature distribution of copper pillars with solder caps during reflow soldering. Keywords Copper pillar with solder cap · Infrared-convection reflow oven · Computational fluid dynamics · Surface mount technology

J. R. Lee · M. S. Abdul Aziz (B) School of Mechanical Engineering, Engineering Campus, Universiti Sains Malaysia, Nibong Tebal, Seberang Perai Selatan, 14300 Penang, Malaysia e-mail: [email protected] M. A. A. Mohd Salleh Faculty of Chemical Engineering and Technology, Universiti Malaysia Perlis, Perlis, Malaysia C. Y. Khor Faculty of Mechanical Engineering and Technology, Universiti Malaysia Perlis, Perlis, Malaysia M. H. H. Ishak School of Aerospace Engineering, Engineering Campus, Universiti Sains Malaysia, Nibong Tebal, Seberang Perai Selatan, 14300 Penang, Malaysia © The Minerals, Metals & Materials Society 2023 The Minerals, Metals & Materials Society, TMS 2023 152nd Annual Meeting & Exhibition Supplemental Proceedings, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-031-22524-6_84

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Introduction The growing demand for smart gadgets like smart home appliances and smartphones which can provide users with a range of features and applications, has accelerated the development of modern technology. Along with the increase in the demand for high-performance smart gadgets, the demand for semiconductor packaging solutions is also rising. However, due to the size reduction of the gadgets, the solder bump has reached its limitation. Therefore, the copper (Cu) pillar technology with a range size of 50–100 µm [1] is introduced due to its excellent thermal and electrical performances, and it can provide a greater standoff height during the reflow soldering process [2, 3]. Several numerical approaches can be used to simulate the reflow soldering process, and the most common approaches are the Finite Element Method (FEM), Finite Volume Method (FVM) [4–8], and Fluid–Structure Interaction (FSI) [9–15]. Most researchers performed the FEM approach for the structural analysis while utilizing the FVM approach for the fluid analysis [16]. By adopting FVM to simulate a reflow process, Son and Shin [7] found that the conveyor speed affected the heat transfer rate in the reflow oven. Najib et al. [8] studied the effect of fan speed on the temperature distribution in the solder bumps. Ahmad et al. [17] summarized that the fan speed, board position, and board thickness impact the temperature distribution of solder bumps. Through FVM, Abdul Aziz et al. [17] and Abdul Aziz et al. [18, 19] modeled the flow of molten solder during the soldering process. For the Cu pillar bump, it was found that FEM analysis was mainly applied to predict the crack formation in the bumps. The effect of various interconnection bump structures on stress distribution is more focused. Lau et al. [20] studied the influence of various solder bump structures on the stress distribution, while Che et al. [21], Shih and Hong [22], Long et al. [23], and Sun et al. [24] investigated the effect of different copper pillar bump structures on the stress distribution. The solder paste will be printed on the substrate using a stencil before sending the substrate into the reflow oven. The control of the reflow temperature profile is extremely important because an appropriate temperature control can avoid solder defects like solder bridges. In this paper, the solder and copper pillar bumps are compared in terms of reflow peak temperature and time. The temperature profiles for copper pillar bumps with different diameters are also predicted.

Experimental Procedures An experiment is performed to validate the simulation model. An infrared-convection reflow oven with infrared heating lamps and a circulating fan was used to reflow the solder bumps. Figure 1a and b display the workstation and the schematic diagram of the experimental setup.

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Fig. 1 a Workstation, b Schematic diagram

Numerical Methods ANSYS 2021 R2 is used to model the 3D assembly of the fluid domain, as shown in Fig. 2a. The 3D model consists of a reflow oven, a ball grid array (BGA) chip, a substrate, and solder bumps or copper pillar bumps. The basic idea is to set up a numerical method by using ANSYS Fluent, an FVM-based software, to simulate a virtual reflow oven environment and analyze the temperature profiles of the solder bumps and copper pillar bumps. The surface and volume meshes are generated through ANSYS Fluent Meshing, as shown in Fig. 2b. The RNG k-epsilon with swirl factor is applied to the turbulent flow in the reflow oven [17]. The DO radiation model is used to model the heating behavior of a localized heating source [25]. Also, the walls in the model are set based on their materials and functions, which are fan, heater, BGA chip, substrate, copper pillar, solder, and wall oven.

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Fig. 2 a Reflow oven geometry, b Computational meshes

Result and Discussion Grid Independence Test A grid independence test is conducted for the fluid domain. There are five numbers of mesh for the mesh independence test, and they are 377,707, 416,251, 458,961, 500,365, and 539,649 numbers of cells. Based on Table 1, the fluid mesh is declared independent as the temperature at 60 and 100 s did not change by more than + −5% between the successive mesh. Also, it is observed that Case 4 (500,365 meshes) gives the least deviation compared to others. Hence, Case 4 is chosen as the optimal case in terms of accuracy and computational costs.

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Table 1 Grid independence test Case

1

2

3

4

5

Cell number

377,707

416,251

458,961

500,365

539,649

Temperature at 60 s (K)

320

321

321

319

318

Deviation from case 5 (%)

0.625

0.935

0.935

0.313

0.000

Temperature at 100 s (K)

383

385

390

376

373

Deviation from case 5 (%)

2.611

3.117

4.359

0.798

0.000

Fig. 3 Comparison between experimental and simulation results

Experimental Validation Figure 3 presents the validation study for the fluid domain in the preheating and soaking stages (0 ~ 200 s). In this study, the solder bumps during the reflow soldering process are simulated, and the experiment using solder bumps is conducted. The result shows that the discrepancy between experimental and simulation values was below 10%, which shows that the simulation results are in a good agreement with the experimental data.

Comparison on Solder Bump and Cu Pillar Bump with Solder Cap Every bump has its reflow temperature profile. In this paper, points 1 and 2 with coordinates (2.0x, 5.5y, 2.0z) and (−0.005x, 5.5y, 1.0z) respectively have been selected for comparing the temperature of solder bump and Cu pillar bump with solder cap (Fig. 4). Based on the graph in Fig. 5, it can be observed that the Cu pillar bumps require higher temperatures at the preheating and soaking stages (0 ~ 200 s) regardless of

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Fig. 4 Point 1 and 2

Fig. 5 Graph of temperature against time for solder bump and Cu pillar bump with solder cap

the location of the bumps. However, when it comes to the reflow stage (200 ~ 320 s), the reflow temperatures of solder are higher than that of Cu pillar bumps. This may be due to the material properties of Cu, such as the thermal expansion coefficient (CTE) mismatch, where Cu requires a higher temperature at the initial stages. When bumps’ location is considered, solder and Cu pillar bump show that the inner location (P2) requires longer reflow time but lower reflow temperature to reach peak temperature. In other words, the bumps in the outer location heat up more quickly during the reflow process, and they lose heat more quickly when they cool down from the peak [26]. Besides, Table 2 compares the solder and Cu pillar bump with the solder cap at the peak temperature and peak reflow time. For the Cu pillar bump with the solder cap, the deviation of points 1 and 2 is smaller in terms of peak temperature and time, whereas for the solder bump, the deviation is slightly larger. This shows that every Cu pillar bump with a solder cap can absorb the heat evenly during the reflow soldering process, whereas for the solder bumps, the temperature is not equally distributed [27, 28].

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Table 2 Comparison on peak temperature and peak reflow time Time (s)

P1 (K)

P2 (K)

Peak temperature (K)

Peak reflow time (s)

Peak temperature (K)

Peak reflow time (s)

Solder bump

513.08

Cu pillar bump

501.77

250

503.29

270

260

497.56

270

Effect of Diameter on Reflow Temperature Profile Figures 6 and 7 show the effect of various diameters of Cu pillar bump with solder cap on the reflow temperature profile. Fig. 6 Graph of temperature against time at P1

Fig. 7 Graph of temperature against time at P2

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Point 1 (outer location): At the preheating stage, all the bumps with different diameters have a similar heat rate. During the soaking stage, bumps with smaller diameters need more heat and higher temperature when compared to bumps with larger diameters (i.e., 0.30 and 0.35 mm). When it comes to the reflow stage, the bumps with larger diameters have higher reflow temperatures but lower cooling rates. Point 2 (inner location): The bumps with smaller diameters located at the inner part have a higher heating rate initially but a slower reflow rate during the reflow soldering process, whereas for the bumps with a larger diameter, vice versa.

Effect of Diameter on Temperature Contour The temperature contours of copper pillar bumps with different diameters from 240 to 290 s are shown in Table 3. It can be seen that all the copper pillar bumps present a similar pattern though they are different in size. From 240 s, the bumps begin to absorb the heat energy from the surroundings, and the heat is also transferred from the Cu substrate and chip to the bumps, and the bumps will slowly absorb the heat and begin to melt [9]. Then, the heat equilibrium is gradually achieved. Also, similar patterns and reflow temperatures show that all the copper pillar bumps absorb the heat energy at the same rate during the reflow soldering process. The consistency of temperature distribution of copper pillar bumps indicates that it is heated up uniformly, lowering the risk of joint failure.

Conclusion In this paper, the numerical approach has been applied as a prediction tool to study the effect of various diameters of Cu pillar bump with solder cap on the temperature distribution during the reflow soldering process. ANSYS 2021 R2 is used to simulate the bumps in the virtual reflow oven environment. The simulation findings are in good agreement with the experimental data, with a percentage of error less than 10%. The Cu pillar bump with solder cap with a diameter of 0.25 mm is compared with the solder bump in the same diameter, and it is found that the Cu pillar bump with solder cap requires a higher heat rate at the beginning of the reflow process. It is also observed that the Cu pillar bump with the solder cap gives a more consistent peak temperature and peak reflow time when compared to solder bumps. The simulations with various Cu pillar diameters show that the bumps with smaller diameters need higher preheat and soaking heat rates but slower reflow and cooling rates. Lastly, the temperature contours clearly indicate that the bumps gradually absorb the heat from the surroundings and the substrate and chip, then reach an equilibrium. This virtualizes the reflow soldering process and allows the monitoring of different parameters.

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Diameter = 0.15 mm

240 s (513.8 K)

250 s (514.4 K)

260 s (508.8 K)

270 s (500.5 K)

280 s (488.3 K)

290 s (483.5 K)

Diameter = 0.25 mm

240 s (516.9 K)

250 s (517.0 K)

260 s (513.4 K)

270 s (505.2 K)

280 s (493.5 K)

290 s (485.6 K)

Diameter = 0.35 mm (continued)

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J. R. Lee et al. Diameter = 0.15 mm

240 s (517.6 K)

250 s (519.3 K)

260 s (512.0 K)

270 s (502.2 K)

280 s (489.0 K)

290 s (484.1 K)

Acknowledgements The work is financially supported by the Ministry of Higher Education under Fundamental Research Grant Scheme, (Grant number FRGS/1/2020/TK0/USM/03/6). The authors would also like to thank Universiti Sains Malaysia for providing technical support.

References 1. Gregorich T, Gu A (2019) Accelerate the development of advanced IC packages using 3D X-ray microscopes to measure and characterize buried features 2. Lau JH (2016) Recent advances and new trends in flip chip technology. J Electron Packag Trans ASME 138(3):16–22 3. Lau JH (2018) Chapter 2 flip chip technology versus FOWLP. In: Fan-out wafer-level packaging. Singapore, Springer, pp 21–68 4. Rusdi MS, Abdullah MZ, Chellvarajoo S, Abdul Aziz MS, Abdullah MK, Rethinasamy P et al (2019) Stencil printing process performance on various aperture size and optimization for lead-free solder paste. Int J Adv Manuf Technol 102:3369–3379 5. Asghar R, Rehman F, Aman A, Iqbal K, Nawaz AA (2020) Defect minimization and process improvement in SMT lead-free solder paste printing: a comparative study. Solder Surf Mt Technol 32(1):1–9 6. Whalley DC (2004) A simplified reflow soldering process model. J Mater Process Technol 150(1–2):134–144 7. Son YS, Shin JY (2005) Thermal response of electronic assemblies during forced convectioninfrared reflow soldering in an oven with air injection. JSME Int J Ser B Fluids Therm Eng 48(4):865–873

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8. Najib AM, Abdullah MZ, Khor CY, Saad AA (2015) Experimental and numerical investigation of 3D gas flow temperature field in infrared heating reflow oven with circulating fan. Int J Heat Mass Transf [Internet] 87:49–58. http://dx.doi.org/https://doi.org/10.1016/j.ijheatmasstransfer. 2015.03.075. 9. Srivalli C, Abdullah MZ, Khor CY (2015) Numerical investigations on the effects of different cooling periods in reflow-soldering process. Heat Mass Transf 51(10):1413–1423 10. Abdul Aziz MS, Abdullah MZ, Khor CY (2015) Thermal fluid-structure interaction of PCB configurations during the wave soldering process. Solder Surf Mt Technol 27(1):31–44 11. Abdul Aziz MS, Abdullah MZ, Khor CY (2014) Influence of PTH offset angle in wave soldering with thermal-coupling method. Solder Surf Mt Technol. 26(3):97–109 12. Abdul Aziz MS, Abdullah MZ, Khor CY, Jalar A, Che Ani F, Yan N et al (2016) Finite volume-based simulation of the wave soldering process: influence of the conveyor angle on pin-through-hole capillary flow. Numer Heat Transf Part A Appl. 69(3):295–310 13. Aziz MSA, Abdullah MZ, Khorc CY, Azid IA, Jalar A, Che Ani FC (2017) Influence of printed circuit board thickness in wave soldering. Sci Iran. 24(6):2963–2976 14. Khor CY, Abdullah MZ, Lau CS, Leong WC, Abdul Aziz MS (2014) Influence of solder bump arrangements on molded IC encapsulation. Microelectron Reliab [Internet] 54(4):796–807. http://dx.doi.org/https://doi.org/10.1016/j.microrel.2013.12.010. 15. Khor CY, Abdullah MZ, Leong WC (2012) Fluid/structure interaction analysis of the effects of solder bump shapes and input/output counts on moulded packaging. IEEE Trans Compon Packag Manuf Technol 2(4):604–616 16. Lee JR, Abdul Aziz MS, Ishak MHH, Khor CY (2022) a review on numerical approach of reflow soldering process for copper pillar technology. Int J Adv Manuf Technol (7–8) 17. Ahmad MI, Abdul Aziz MS, Abdullah MZ, Salleh M, Anuar MA, Ishak MHH et al (2021) Investigations of infrared desktop reflow oven with FPCB substrate during reflow soldering process. Metals (Basel). 11(8):1155 18. Abdul Aziz MS, Abdullah MZ, Khor CY, Jalar A, Che Ani F (2014) CFD modeling of pin shape effects on capillary flow during wave soldering. Int J Heat Mass Transf [Internet] 72:400–10. http://dx.doi.org/https://doi.org/10.1016/j.ijheatmasstransfer.2014.01.037. 19. Abdul Aziz MS, Abdullah MZ, Khor CY, Che Ani F, Adam NH (2016) Effects of temperature on the wave soldering of printed circuit boards: CFD modeling approach. J Appl Fluid Mech 9(4):2053–2062 20. Lau CS, Abdullah MZ, Abdul Mujeebu M, Md Yusop N (2014) Finite element analysis on the effect of solder joint geometry or the reliability of ball grid array assembly with flexible and rigid PCBS. J Eng Sci Technol 9(1):47–63 21. Che FX, Wai LC, Zhang X, Chai TC (2015) Characterization and modeling of fine-pitch copper ball bonding on a Cu/low-k chip. J Electron Mater 44(2):688–698 22. Shih MK, Hong PC (2016) Structural design guideline for Cu pillar bump reliability in system in packages module. In: 2015 IEEE 17th electronics packaging and technology conference (EPTC). Singapore, pp 1–4 23. Long XJ, Shang JT, Zhang L (2020) Design optimization of pillar bump structure for minimizing the stress in brittle low K dielectric material layer. Acta Metall Sin 33(4):583–594 24. Sun H, Gao B, Zhao J (2020) Thermal-mechanical reliability analysis of WLP with fine-pitch copper post bumps. Solder Surf Mt Technol 33(3):178–186 25. Chhanwal N, Anishaparvin A, Indrani D, Raghavarao KSMS, Anandharamakrishnan C (2010) Computational fluid dynamics (CFD) modeling of an electrical heating oven for bread-baking process. J Food Eng [Internet] 100(3):452–60. http://dx.doi.org/https://doi.org/10.1016/j.jfo odeng.2010.04.030 26. Xia W, Xiao M, Chen Y, Wu F, Liu Z, Fu H (2014) Thermal warpage analysis of PBGA mounted on PCB during reflow process by FEM and experimental measurement. Solder Surf Mt Technol 26(3):162–171

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The Effect of Grain Boundary Type on Void Formation in a Through Silicon Via (TSV) Armin Shashaani and Panthea Sepehrband

Abstract In the 3D IC packaging technology, to achieve mechanical and electrical interconnection in the vertical direction, the chips are stacked by Through Silicon Vias (TSV). As the fundamental structure of 3D IC packaging, TSV reliability plays a critical role in the service life of the chip. Void nucleation is considered the initial phase of various failure mechanisms in TSVs. Void nucleation is a complex process to study experimentally and there are conflicting views on the impact of crystallographic textures and type of grain boundary on void formation. Through Molecular Dynamics (MD) simulations, in-situ analysis of atomic arrangement in specially designed bicrystals of copper (the main material of TSV) is carried out for various misorientation tilt angles with crystallographic misorientation axes of in the tilt grain boundary in order to systematically study the effect of texture and detect the initial phase of void nucleation. The effect of grain orientations and grain boundary characteristics on vacancy diffusion, which leads to void nucleation, is investigated, and it is concluded that the tilt angles of 11.421 and 36.87° have the lowest and highest resistance toward void nucleation, respectively. Keywords TSV · Tilt grain boundary · MD · Void nucleation · Copper

Introduction The three-dimensional (3D) integrated circuits (IC) packaging technology is the most promising approach for breaking the limits of Moore’s Law and complying with the requirements of lower power consumption, smaller size, and higher performance. In the 3D IC packaging technology, to achieve mechanical and electrical interconnection A. Shashaani (B) · P. Sepehrband Mechanical Engineering Department, Santa Clara University, 500 El Camino Real, Santa Clara, CA 95053, USA e-mail: [email protected] P. Sepehrband e-mail: [email protected] © The Minerals, Metals & Materials Society 2023 The Minerals, Metals & Materials Society, TMS 2023 152nd Annual Meeting & Exhibition Supplemental Proceedings, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-031-22524-6_85

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in the vertical direction, the chips are stacked by Through Silicon Vias (TSV) [1]. A sharp increase in the use of the TSV method is seen in recent years, because of its several advantages, including increased bandwidth, decreased signal delay, enhanced power management, and smaller form factor [2]. Due to its lower electrical resistance and improved resistance to electromigration, copper has recently replaced aluminum as the connecting metal for integrated circuits [3]. One of the biggest concerns with TSVs is reliability, including Cu pumping, electromigration, and stress voiding which might shorten the lifetime of the Cu TSV. The thermal expansion mismatch between Cu TSV and Silicon wafer is the main source of the reliability issues [4]. This mismatch can lead to localized stress concentrations at the Cu TSV’s weak points, and voids can form as a result [5]. Texture and Grain structure can cause stress concentration at some locations inside the Cu TSV which can be a possible location for void formation [6]. Numerous researches have examined the impact of microstructure on the void tendency in the Cu TSV [3, 7–10]. However, the effect of crystallographic texture on void formation is not clearly understood and there are conflicting views on which texture provides the highest resistance to void nucleation. It has been proposed that the (111) texture exhibits superior resistance to void formation. It was noted, for instance, that Cu TSVs often have a texture of {111}, resulting in specific [322] twin boundaries that are especially prone to void formation [7]. Contrary to this observation, (100) texture was suggested as a strong texture for void formation, whereas (111) texture is reported as a weak texture for void likelihood [8]. Such observation is justified considering that the elastic modulus in the [111] direction is three times more than the [100] direction, which is claimed to cause a stress gradient at grain boundaries. In addition (111) texture films exhibit higher thermal stress than (100) texture films [3]. On the other hand, other researchers claim that different orientations and textures exhibit greater resistance than the {111} texture. According to Feng et al. study [9], a larger ratio of (111)/(200) can lead to a decrease in the void defects. They claimed that the closed-packed planes of the (111) texture in the Fcc structure make it more chemically resistant than the (200) texture after Cu Chemical Mechanical Polishing (CMP) [9]. Another reason in favor of that finding came from a different study where it was said that the lifetime of the strong (111) texture was four times longer than the strong (200) texture [10]. The goal of this paper is to conduct an atomic-scale computational analysis to analyze the effect of crystallographic orientation on void nucleation and identify the crystallographic configuration that provides the highest resistance to void formation. For this purpose, the dislocation evolution during tensile loading and the nucleation and growth of the voids in bicrystals with various tilt grain boundaries are examined using Molecular Dynamics (MD) simulations. Investigations are conducted into how misorientation affects void formation.

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Methodology The LAMMPS package, which stands for Large-scale Atomic/Molecular Massively Parallel Simulator, is used to run MD simulations [11] and Embedded Atom Method (EAM) interatomic potential for copper [12]. Crystallographic misorientation axes of is employed in molecular dynamics simulations to model tilt grain boundaries at varying angles using a dataset of tilt grain boundaries defined in [13]. All of the simulations have two-grain boundaries, one of which is on the y-axis edge of the simulation box, as schematically shown in Fig. 1. Periodic boundary conditions are considered in all directions. After generating the lowest energy for each grain boundary, the simulation box is sufficiently replicated in the x and z directions to prevent any image effects. For all cases, the total number of atoms is maintained between 400,000 and 800,000. The simulation box is thermalized under the isobaric and isothermal conditions at zero pressure and 300 K temperature, respectively, after attaining the minimal level of energy. At a strain rate of 109 s−1 and a temperature of 300 K, the uniaxial tension is applied in the y direction and the zero-strain condition is considered in other directions. The tensile test is carried out continuously until a fracture occurs and void nucleation and propagation are observed. For the purpose of calculating the mechanical properties, stress is calculated for all atoms in each timestep in the direction of the force being applied (y-direction). The open visualization program OVITO is utilized for post-processing [14]. For observation of defect and stacking fault, the Common Neighbor Analysis (CNA) [15] is considered. For analyzing the dislocations and calculating the Burgers vectors and Fig. 1 Schematic of the Simulation Box, there are two-grain boundaries, one in the middle and another one at the edge of the simulation box

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the dislocation type, the Dislocation Extraction Algorithm (DXA) [16] from OVITO is used.

Results and Discussion MD simulations are run for tilt grain boundaries with misorientations ranging from 0 to 90°. For each case, void nucleation is determined using the “construct surface mesh” method explained in [17]. To correctly identify void nucleation and overcome the inherent limitation of this OVITO visualization option that only considers the surface, the simulation box is sliced at each timestep. Figure 2 illustrates the various stages of void nucleation for the grain boundary with an 11.421° tilt angle. FCC, HCP, and other atoms are shown in Fig. 2 in green, red, and white, respectively. As seen in Fig. 2a, b no voids or vacancies is visible till 8% strain. However, as the timestep extends, vacancies start to group together and form a void, as shown in Figs. 2c, d. Figures 2e, f show the start of fracture where additional voids are noticed in other places. The level of stress and strain associated with void nucleation for each case is calculated as shown in Fig. 3. As a reference, the level of yield strength for each condition is also provided in the figure. All three properties (i.e., yield strength, void nucleation stress, and void nucleation strain) show a similar trend as a function of tilt

Fig. 2 Void nucleation formation for the case of the 11.421° tilt angle, a and b FCC atoms and Surface Mesh at 8% strain before void nucleation, c and d FCC atoms and Surface Mesh at 8.5% strain when void nucleates at the grain boundary, e and f FCC atoms and Surface Mesh at 9% strain when void starts to grow

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Fig. 3 Yield strength, void nucleation stress, and void nucleation strain versus tilt angle

angle. Dividing the grain boundaries into a high angle and low angle at a threshold limit between 10 and 15° [18], it can be seen that for low angle grain boundaries, as the tilt angle increases, the grain boundary becomes weaker and as a result, the yield strength, void nucleation stress, and void nucleation strain decreases. On the other hand, for high-angle grain boundaries, as the tilt angle increases, there is an initial increase in the grain boundary strength which follows by a sharp decrease. At the tilt angle of 37 degrees, the yield strength and void nucleation stress curves rise to their maximum values at 13.3 GPa and 14.6 GPa, respectively. Precisely, the maximum is observed at the tilt angle of 36.87° which corresponds to a special grain boundary 5(310)/[001] in copper [19]. The range of void nucleation strain for all cases is between 8 and 11%, as can be shown in Fig. 3. This range of strain is particularly noteworthy, as a recent study found that the thermal mismatch between the Si and Cu TSV causes the maximum strain in the TSV trench to typically be around 10% [20]. Therefore, this result shows that the level of the strain caused by thermal mismatch between Cu and Si is enough to initiate void nucleation; specifically in grain boundaries that are more susceptible to void nucleation, such as the grain boundary with a tilt angle of 11° for which void nucleation happens at the lowest detected value of strain. It is interesting to note that this grain boundary has a higher level of yield strength compared to the grain boundaries with tilt angles of 80 degrees and more, but shows a weaker behavior against the void formation. It is also surprising that the grain boundary with such a low misorientation angle shows the least tolerance towards the void formation. From Fig. 3, it is clear that the stress needed for void nucleation is more than the yield strength, which shows that for all cases, plastic deformation precedes void

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nucleation. To quantitively track plastic deformation, the evolution of dislocation density is analyzed. In Fig. 4, dislocation density is plotted as a function of strain for the weakest and strongest tilt angles, i.e. 11° and 37°, respectively. As can be seen from Fig. 4, for both cases, the level of dislocation density remains unchanged during the initial stage of deformation. For 11°, after 6% of straining, dislocation density gradually increases which follows by a sharp peak at about 10% of strain. In the case of 37°, initially, no dislocation is observed till the appearance of the peak at about 12% of strain. The fact that no dislocation exists at the beginning of deformation suggests that the atomic structure at the grain boundary with 37° has a very good match with the bulk. To further analyze this case, the evolution of dislocation density for the grain boundaries with tilt angles of 34° and 40° are also plotted in Fig. 4 for comparison. Although the tilt angles in these two cases are very close to 37°, the evolution of dislocation density during deformation is significantly different. For both cases, there are dislocations at the initial stages of deformation which suggest a mismatch between the atomic structure at the grain boundaries and the bulk. This mismatch is accommodated by the formation of dislocations in the system, the so-called geometrically necessary dislocations. For all four cases, the level of strain at which void nucleation occurs is marked on the graph. It is seen that void nucleation happens before the sudden increase in the dislocation density. The lack of dislocations at the grain boundary which is an indication of high matching at the grain boundary with a 37° tilt angle explains the highest level of resistance for void nucleation that is observed for this condition. Figure 5 shows a selection of OVITO images taken at various strains and timesteps for the bicrystals with tilt angles of 37° and 11°. Figure 5 confirms that with a 37°

Fig. 4 Dislocation density versus timestep for various cases

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Fig. 5 OVITO images of different strains for the angles of 11.421 and 36.87°

misorientation angle, no dislocations are generated until the void begins to nucleate. It can be inferred that this angle has excellent grain boundary compatibility between two grains. Dislocations are one of the easiest paths for vacancy diffusion which is a well-established mechanism for void nucleation. The structure exhibits greater resistance to void nucleation since no dislocation can be seen in the structure until a considerable level of strain has been applied. In contrast, the grain boundary for the situation of an 11° tilt angle has a significant number of dislocations. As the strain increases, so does the dislocation density, and as a result, vacancies can pass through the dislocations and create a void. This behavior is in agreement with what is observed in Fig. 2 where the void formation occurs at the grain boundary.

Conclusion The effect of grain boundary characteristics on void nucleation is investigated by MD simulation of bicrystals of Cu with various tilt angles. It is shown that the bicrystals with tilt angles of 11.421 and 36.87° have the lowest and highest resistance toward void nucleation, respectively. Based on the dislocation analysis, it is suggested that the delayed void nucleation in the 36.87° can be linked to the absence of dislocations that could act as easy diffusion paths for vacancies. The level of strain that leads to void nucleation is found to be close to the amount of strain that is experienced by TSV as a result of the thermal mismatch between Si and Cu. Therefore, the precise

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control of texture and grain boundary type is proposed as a viable technique for delaying failure in TSVs. Acknowledgements This work was supported by the National Science Foundation (CMMI1728652) and the School of Engineering at Santa Clara University. Computing resources for running the LAMMPS simulations were provided by the Wiegand Advanced Visualization Environment (WAVE) at Santa Clara University.

References 1. Kaushik BK, Ramesh Kumar V, Majumder MK, Alam A (2016) Through silicon vias: materials, models, design, and performance. Through Silicon Vias. https://doi.org/10.1201/978131536 8825 2. Burkett SL, Jordan MB, Schmitt RP, Menk LA, Hollowell AE (2020) Tutorial on forming through-silicon vias. J Vac Sci Technol A: Vac, Surf Films 38(3):031202. https://doi.org/10. 1116/6.0000026 3. Basavalingappa A, Shen MY, Lloyd JR (2017) Effect of texture and elastic anisotropy of copper microstructure on reliability. In: Proceedings of the 2016 IEEE international integrated reliability workshop, IIRW 2016. pp. 57–60. https://doi.org/10.1109/IIRW.2016.7904901 4. Frank T et al (2013) Reliability of TSV interconnects: electromigration, thermal cycling, and impact on above metal level dielectric. Microelectron Reliab 53(1):17–29. https://doi.org/10. 1016/J.MICROREL.2012.06.021 5. Gambino JP, Adderly SA, Knickerbocker JU (2015) An overview of through-silicon-via technology and manufacturing challenges. Microelectron Eng 135:73–106. https://doi.org/10.1016/ J.MEE.2014.10.019 6. Kumar P, Lee TK, Dutta I, Huang Z, Conway P (2021) Microstructure and mechanical reliability issues of TSV. Springer Ser Adv Microelectron 64:71–105. https://doi.org/10.1007/978-98115-7090-2_4/COVER 7. Sekiguchi A, Koike J, Kamiya S, Saka M, Maruyama K (2001) Void formation by thermal stress concentration at twin interfaces in Cu thin films. Appl Phys Lett 79(9):1264–1266. https://doi. org/10.1063/1.1399021 8. Koike J, Wada M, Sanada M, Maruyama K (2002) Effects of crystallographic texture on stressmigration resistance in copper thin films. Appl Phys Lett 81(6):1017. https://doi.org/10.1063/ 1.1498495 9. Feng HP, Cheng MY, Wang YL, Chang SC, Wang YY, Wan CC (2006) Mechanism for Cu void defect on various electroplated film conditions. Thin Solid Films 498(1–2):56–59. https://doi. org/10.1016/J.TSF.2005.07.062 10. Ryu C et al (1999) Microstructure and reliability of copper interconnects. IEEE Trans Electron Devices 46(6):1113–1120. https://doi.org/10.1109/16.766872 11. Plimpton S (1995) Fast Parallel Algorithms for Short-Range Molecular Dynamics. J Comput Phys 117(1):1–19. https://doi.org/10.1006/JCPH.1995.1039 12. Mishin Y, Mehl MJ, Papaconstantopoulos DA, Voter AF, Kress JD (2001) Structural stability and lattice defects in copper: Ab initio, tight-binding, and embedded-atom calculations. Phys Rev B 63(22):224106. https://doi.org/10.1103/PhysRevB.63.224106 13. Tschopp MA, Coleman SP, McDowell DL (2015) Symmetric and asymmetric tilt grain boundary structure and energy in Cu and Al (and transferability to other fcc metals). Integr Mater Manuf Innov 4(1):176–189. https://doi.org/10.1186/S40192-015-0040-1/FIGURES/6 14. Yang L, Ma Z, Stukowski A (2009) Visualization and analysis of atomistic simulation data with OVITO–the open visualization tool. Model Simul Mat Sci Eng 18(1):015012. https://doi. org/10.1088/0965-0393/18/1/015012

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15. Faken D, Jónsson H (1994) Systematic analysis of local atomic structure combined with 3D computer graphics. Comput Mater Sci 2(2):279–286. https://doi.org/10.1016/0927-025 6(94)90109-0 16. Stukowski A, Albe K (2010) Extracting dislocations and non-dislocation crystal defects from atomistic simulation data. Model Simul Mat Sci Eng 18(8):085001. https://doi.org/10.1088/ 0965-0393/18/8/085001 17. Stukowski A (2014) Computational analysis methods in atomistic modeling of crystals. JOM 66(3):399–407. https://doi.org/10.1007/s11837-013-0827-55 18. Faraji G, Kim HS, Kashi HT (2018) Effective parameters for the success of severe plastic deformation methods. Sev Plast Deform 187–222. https://doi.org/10.1016/B978-0-12-8135181.00006-0 19. Chung H, Cho M (2018) A molecular dynamics study on the biased propagation of intergranular fracture found in copper STGB. J Mech Sci Technol 32(11):5351–5361. https://doi.org/10. 1007/S12206-018-1034-7 20. Sukharev V et al (2010) 3D IC TSV-based technology: stress assessment for chip performance. AIP Conf Proc 1300(1):202. https://doi.org/10.1063/1.3527127

Part XXVII

Environmental Degradation of Multiple Principal Component Materials

High Temperature Oxidation of CoNiFeMnCr High Entropy Alloys Reinforced by MC-Carbides Patrice Berthod

Abstract CoNiFeMnCr alloys are possibly alternative solutions to cast cobalt or nickel superalloys able for service at 1000 °C and beyond. The partial substitution of Co and Ni by Fe and Mn allow lower dependence on the Co and Ni critical elements. In this work, equimolar CoNiFeMnCr alloys without or with added carbon and tantalum or hafnium were elaborated by high-frequency induction melting under inert atmosphere. Script-like eutectic TaC or HfC were obtained in the grain boundaries of the concerned alloys, forming a strengthening carbides network. Oxidation tests were carried out in air at 1000 °C and at 1100 °C for 50 h. The oxide scale externally formed is made of (Cr,Mn)2 O3 . Internal oxidation led to CrTaO4 or HfO2 oxides. Numerous deep oxidation penetrations were noted for the CoNiFeMnCr alloy while the MC-containing alloys were not affected by this phenomenon, evidencing a possible beneficial influence of the presence of the carbides on the oxidation behavior. Keywords High entropy alloys · MC carbides · High temperature · Hot oxidation

Introduction Service conditions involving exposure to high temperature, applied mechanical stresses and hot corrosion by gases and melts, lead to superalloys for the concerned components (e.g. combustion cans, blades and disks in turbines, spinners in the glass industries). These high-performance metallic alloys are generally based on nickel, nickel–iron or cobalt [1]. Nickel and cobalt were recently introduced in the lists of the critical elements [2, 3], and there is a tendency to decrease their contents in alloys when possible. In the recently emerged HEA family [4] (High Entropy Alloys), there are also alloys containing cobalt or nickel but in lower quantities since their molar P. Berthod (B) Institut Jean Lamour, 2 Allée André Guinier, Campus ARTEM, 54000 Nancy, France e-mail: [email protected] Université de Lorraine, Campus Victor Grignard, 54500 Vandoeuvre-Lès-Nancy, France © The Minerals, Metals & Materials Society 2023 The Minerals, Metals & Materials Society, TMS 2023 152nd Annual Meeting & Exhibition Supplemental Proceedings, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-031-22524-6_86

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fractions are maintained at the level of the ones of many of the other metals simultaneously present in the composition. Some of these HEA alloys can be used at high temperature [5–7] for applications involving mechanical stresses. These HEAs well behave thanks to the good intrinsic mechanical properties of their matrixes. However, the polycrystalline HEAs—the ones elaborated by classical foundry, for instance— must be strengthened in their grain boundaries to make difficult the intergranular decohesion. Intergranular reinforcement can be achieved by precipitated hard particles. Efficient strengthening can be achieved by choosing particles presenting favorable shape and volume fraction as well as a good stability at elevated temperature. MC carbides, notably TaC and HfC, well correspond to these specifications. First HEA designed to contain script–shaped TaC carbides or HfC carbides were successfully obtained by casting with such microstructures [8, 9]. Their chemical compositions are reminded in Table 1 (“HfC alloy”) and Table 2 (“TaC alloy”). Both alloys result from the association of an equimolar CoNiFeMnCr HEA and of addition of carbon and of a MC–former element (Hf or Ta). The chosen C and Hf (or Ta) contents were the ones which are known to allow developing carbide network dense enough to strengthen efficiently the alloy and staying not interconnect to avoid low ductility and toughness. For both alloys, a carbide network of script-like eutectic TaC of HfC carbides located in the interdendritic spaces was successfully obtained. The as-cast microstructures of these alloys are reminded in Fig. 1-left (for the “HfC alloy”) and in Fig. 1-right (for the “TaC alloy”). The presented micrographs were taken at two magnifications during the observation—using a Scanning Electron Microscope JEOL 6010-LA—of metallographic samples prepared with a mirror-like surface. Table 1 Chemical compositions of the HfC alloy and of its matrix (average and standard deviation calculated from five full frame EDS results obtained on five ×250 randomly chosen areas, or from five EDS spot analyses in the dendrites cores) Hfc-alloy

Co

Ni

Fe

Mn

Cr

Hf

General

19.9

20.2

18.4

18.2

19.3

4

0.3

0.6

0.4

0.2

0.5

1.9

21.1

19.7

21.6

16.8

20.7

0.1

1.2

0.7

1.6

1.8

0.3

0.1

Wt% Matrix Wt%

Table 2 Chemical compositions of the TaC alloy and of its matrix (average and standard deviation calculated from five full frame EDS results obtained on five ×250 randomly chosen areas, or from five EDS spot analyses in the dendrites cores) TaC alloy

Co

Ni

Fe

Mn

Cr

Ta

General

19.3

20.1

18.6

18.3

19.2

4.5

Wt% Matrix Wt%

0.2

0.5

0.5

0.3

0.3

0.4

20.5

20.5

20.9

16.7

19.4

2.1

0.4

0.3

0.9

1.4

0.6

0.1

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Fig. 1 Micrographs illustrating the as-cast microstructure of the “HfC alloy” and of the “TaC alloy” (right) taken with a Scanning Electron Microscope (JEOL JSM 6010-LA) in Back Scattered Electron Mode for an acceleration voltage equal to 15 kV

The high temperature behaviors (mechanical, resistance to oxidation resistance by hot gases and to corrosion by hot melts) of these alloys were not yet investigated. This study aims to test them in oxidation by air at high temperature (1000 and 1100 °C) and to characterize the produced deterioration of the alloys by metallographic characterization of the oxides formed externally and internally and of the general chemical and microstructure modifications induced in the subsurface by oxidation.

Experimental First, it can be reminded that the alloys were produced by a high frequency induction furnace (from CELES, France) under pure Argon with as main parameters a 110 kHZ frequency and a 5 kV maximum voltage. The mixtures of pure elements (Co, Ni, Fe, Mn, Cr, C Ta, or Hf) were melted and then chemically homogenized for 10 minutes at the maximum voltage. The obtained 40 g-weighing ingots were cut into parts for the microstructure and chemical composition controls [8, 9] (results reminded or illustrated in Tables 1, 2 and Fig. 1). For the present study (high temperature oxidation-tests), additional parts were cut to obtain 10 mm × 10 mm × 3 mm samples (approximative dimensions). They were ground with #1200-grit SiC papers on their six faces. Edges and corners were also ground with the same papers to smooth them for avoiding local overoxidation. A “HfC alloy” sample and a “TaC alloy” sample were placed in the hot part of a muffle resistive furnace in which they were heated at +20 °C min−1 up to 1000 °C, isothermal temperature at which they were maintained for 50 h. Another “HfC alloy” sample and another “TaC alloy” sample were subject to a similar test but at 1100 °C. After return to room temperature, the samples were subjected to X-ray diffraction (D8 Advance diffractometer from Bruker, wavelength: 1.5408 Angström), before being embedded in resin (stiffening at room temperature). The obtained diffractograms were unfortunately not exploitable due to fluorescence effects and they

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cannot allow contributing to the identification of the oxides (diffractograms much too noised). This problem unfortunately affected both alloys oxidized at each of the two temperatures. The embedded samples were then cut using a metallographic saw, to obtain two halves which were thereafter ground (SiC papers from #240 to #1200) and polished (1 µm hard particles on textile disks) to obtain cross-sectional metallographic samples with a mirror state. The external oxides and subsurfaces deteriorated by oxidation were observed with a Scanning Electron Microscope (JEOL JSM6010-LA, Japan) in Back Scattered Electrons mode (SEM/BSE), acceleration voltage rated to 15 kV. Spot analysis was performed using the Energy Dispersive Spectrometer equipping the SEM, to identify the different oxides formed externally and internally. It also contributed for specifying the changes in chemical composition in part of the subsurface which was affected by oxidation.

Results and Discussion Oxidation of the HfC Alloy at 1000 and 1100 °C In both cases, a large part of the external oxide unfortunately detached from the samples during the cooling. Nevertheless, there were some locations where the external oxides were still attached to the substrate. The surface states of the HfC alloy after 50 h of oxidation by air are illustrated by SEM/BSE micrographs in Fig. 2 (1000 °C) and Fig. 3 (1100 °C). For the two temperatures of oxidation, the same qualitative remarks can be done. First the external oxide scales (the rare parts which stayed on surface) are composed essentially of Mx Oy oxides (close to the M2 O3 stoichiometry) with variable parts of Cr and Mn in the “M” sites. Pale oxides, mainly in the outer part of the scale, contain more Mn than Cr (shown by “Mn(Cr)2 O3 ” in the micrographs)

Fig. 2 Surface and subsurface states of the “HfC alloy” after 50 h of oxidation at 1000 °C (SEM/BSE micrographs; left: ×250, right: ×1000)

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Fig. 3 Surface and subsurface states of the “HfC alloy” after 50 h of oxidation at 1100 °C (SEM/BSE micrographs; left: ×250, right: ×1000)

while dark oxides (shown by “Cr(Mn)2 O3 ” in the micrographs)—mainly in the inner part of the scale, in contact with the substrate—are richer in Cr than in Mn. One can also notice the presence of some HfO2 oxides, featuring as compact white islands. The subsurface was modified, over a depth which is greater for 1100 °C than for 1000 °C, essentially by the presence of bright internal HfO2 oxides seemingly resulting from the in situ oxidation of the HfC carbides (same interdendritic location). Some Cr(Mn)2 O3 oxides also formed internally. Quantitatively, the subsurface depth affected by oxidation is about 70 µm for 50 h at 1000 °C and about 100 µm for 50 h at 1100 °C.

Oxidation of the TaC Alloy at 1000 and 1100 °C For the “TaC alloy” too, cooling induced a significant loss of the external scale of oxides. The surface and subsurface states of the samples after 50 h of oxidation by air are illustrated by SEM/BSE micrographs in Fig. 4 (1000 °C) and Fig. 5 (1100 °C). Here too, for these two temperatures of oxidation there are common observations. As for the previous alloy, the external oxide scales are mainly made of Mx Oy oxides (close to the M2 O3 stoichiometry, again). The parts of Cr and of Mn are also varying, leading to variations of gray level in the parts of external oxide scale remaining on the substrate (pale oxides: Mn(Cr)2 O3 , dark oxides: “Cr(Mn)2 O3 ”). Similar to the HfC alloy again, white compact oxides exist at the scale/alloy interface. They are CrTaO4 . The depth of subsurface affected by oxidation (disappearance of the TaC carbides, presence of internal CrTaO4 and Cr(Mn)2 O3 or Cr2 O3 oxides, porosity formation) seems to be lower for this TaC alloy than for the HfC alloy for a given temperature (about 30 µm against 70 µm for the HfC alloy at 1000 °C, and about 120 µm against 100 µm for the HfC alloy at 1100 °C).

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Fig. 4 Surface and subsurface states of the “TaC alloy” after 50 h of oxidation at 1000 °C (SEM/BSE micrographs; left: ×250, right: ×1000)

Chemical Changes in the Subsurfaces Except either Hf or Ta, the elements involved in the oxidation phenomena are mainly chromium and manganese. Obviously, neither cobalt nor nickel took part in oxidation. Globally Cr and Mn diffused outwards, to the oxidation front. The presence of internal oxides suggests that oxygen atoms diffused inwards. To characterize Cr and Mn diffusion, EDS profiles were acquired from the scale/alloy interfaces in the direction of the bulk. There are shown in Fig. 6 (HfC alloy oxidized at 1000 °C and at 1100 °C) and in Fig. 7 (TaC alloy). The profiles confirm, for both alloys and for both temperatures of oxidation, the impoverishment in chromium and in manganese. For the HfC alloy, the contents in Cr and Mn in extreme surface (i.e. in the alloy very close to the scale/alloy interface) have decreased from about 20 wt% initially to about 5 wt% and 2 wt% respectively during the 50 h at 1000 °C, and to about 12 wt% and 3 wt% respectively during the 50 h at 1100 °C. The Cr-depleted depths

Fig. 5 Surface and subsurface states of the “TaC alloy” after 50 h of oxidation at 1100 °C (SEM/BSE micrographs; left: ×250, right: ×1000)

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Fig. 6 Chemical states of the subsurfaces affected by oxidation at 1000 °C (left) and at 1100 °C (right) in the case of the “HfC alloy” (SEM, EDS profiles, 15 kV)

Fig. 7 Chemical states of the subsurfaces affected by oxidation at 1000 °C (left) and at 1100 °C (right) in the case of the “TaC alloy” (SEM, EDS profiles, 15 kV)

are about 60 µm for 1000 °C and 130 µm for 1100 °C. For Mn, the depletions are significantly deeper: 110 µm for 1000 °C and 250 µm for 1100 °C. For the TaC alloy, the contents in Cr and Mn in extreme surface have also decreased: from about 20 wt% initially to about 10 wt% and 2 wt% respectively (1000 °C), and to about 8 wt% and 2 wt% respectively during the 50 h at 1100 °C. The Cr-depleted depths are about 60 µm for 1000 °C and 120 µm for 1100 °C. For Mn, the depletion is significantly deeper: 100 µm for 1000 °C and 200 µm for 1100 °C. In all cases, whatever the alloy and whatever the temperature, Mn participated thus more than Cr to the external oxide formation. Concerning hafnium and tantalum, they did not diffuse and they reacted with oxygen diffusing inwards.

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Commentaries The oxidation behaviors of the two alloys at 1000 °C and even at 1100 °C are satisfactory. Only Cr and Mn were involved, and they fed the oxidation front for the formation and development of a M2 O3 external scale which possibly acted as a barrier for the species involved by oxidation. However one can fear that this oxide scale, with heterogeneous distribution of Cr and Mn, is not as efficient as chromia, since it is first maybe more permeable to diffusion, and second not resistant against spallation. Cr and Mn were obviously much consumed and the subsurfaces are rather deeply impoverished in these elements. This and the minimal contents in Cr and Mn close to the oxidation front suggest that generalized oxidation will certainly occur soon. Modifications, such as protective coating or alloys enrichment in Cr, appear to be done compulsory.

Conclusions It was recently demonstrated that MC carbides may be obtained with the required morphologies and locations in the microstructures of alloys based on the equimolar CoNiFeMnCr alloy [8, 9]. Here such alloys were subjected to oxidation at high temperature and they showed only acceptable behavior over the first 50 h of exposure to hot air. Taking into account the consumption of Cr and Mn, it appears clear that catastrophic oxidation was not far. Since it is currently observed in parallel work that they are able of promising creep resistance, it is compulsory to improve the oxidation resistance to take real benefit of their interesting mechanical properties at high temperature provided by the HEA matrix and the MC carbides. This can be achieved by the coatings way or by enriching bulk materials with more chromium. In the second way, the elaborations and tests of {CoNiFeMnCr2 + MC} alloys are scheduled for investigations soon. Acknowledgements The author wishes to thank Pierre–Jean Panteix for his help for the oxidation tests in furnace.

References 1. Donachie, MS; Donachie, SJ (2002) Superalloys: a technical guide, 2nd ed. ASM International, Materials Park (USA) 2. Tkaczyk AH, Bartl A, Amato A, Lapkovskis V, Petranikova M (2018) Sustainability evaluation of essential critical raw materials: cobalt, niobium, tungsten and rare earth elements. J Phys D: Appl Phys 51:203001 3. Grandell L, Lehtilä A, Kivinen M, Koljonen T, Kihlman S, Lauri LS (2016) Role of critical metals in the future markets of clean energy technologies. Renew Energy 95:53–62

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4. George EP, Raabe D, Ritchie RO (2019) High-entropy alloys. Nat Rev Mater 4:515–534 5. Olaru MT, Mitrica D, Soare V, Constantin I, Burada M, Dumitrescu D, Caragea A, Carlan BA, Banica CI, Stoiciu F, Badilita V, Geanta V, Stefanoiu R (2019) Synthesis and characterization of high entropy alloys for high temperature applications. Scientific Bulletin - University “Politehnica” of Bucharest. Ser B: Chem Mater Sci 81:249–256 6. Xu ZQ, Ma ZL, Wang M, Chen YW, Tan YD, Cheng XW (2019) Design of novel low-density refractory high entropy alloys for high-temperature applications. Mater Sci Eng, A: Struct Mater: Prop, Microstruct Process 755:318–322 7. Lim KR, Lee KS, Lee JS, Kim JY, Chang HJ, Na YS (2017) J Alloy Compd 728:1235–1238 8. Berthod P (2022) As-cast microstructures of high entropy alloys designed to be TaCstrengthened. J Metallic Mater Res 5:1–10 9. Berthod P. As-cast microstructures of HEA designed to be strengthened by HfC. J Eng Sci Innov 7:305–314

Part XXVIII

Environmentally Assisted Cracking: Theory and Practice

Hydrogen Effects on Mechanical and Toughness Properties of Pipeline Steels Xin Pang and Su Xu

Abstract This paper reviews commonly used hydrogen charging methods and effects of hydrogen on Charpy toughness. Preliminary ex-situ Charpy tests of electrolytically pre-charged specimens of three pipe steels were performed at room temperature. The gaseous hydrogen charging method is directly applicable to hydrogen pipelines but the lack of testing capability has limited its utilizations in R&D and qualification. The electrolytic charging method can be convenient and appropriate for investigating the effects of hydrogen especially if correlations between current density or potential and gaseous pressure are established. Preliminary experimental results have shown that the Charpy absorbed energy (CVN) of the electrolytically pre-charged specimens were lower than those of uncharged specimens by 8–20% for the steels investigated. Based on the load–deflection curves, the effects of hydrogen on Charpy toughness were to facilitate fracture initiation from the notch and accelerate fracture propagation after fracture initiation. In-situ Charpy and fracture toughness testing at slow rates would be more suitable for pipeline applications than impact testing. Keywords Hydrogen embrittlement · Pipeline steel · Electrolytic hydrogen charging · Toughness · Charpy test

Introduction Recently a new wave of interest in hydrogen as a potential clean energy solution has been seen around the world with unprecedented political and business momentum [1, 2]. One of Canada’s advantages is that its extensive network of natural gas transmission and distribution pipelines could be leveraged as large-scale energy storage and distribution networks for hydrogen, carrying either a blend of hydrogen and natural X. Pang (B) · S. Xu CanmetMATERIALS, Natural Resources Canada, 183 Longwood Road South, Hamilton, ON L8P 0A1, Canada e-mail: [email protected] © His Majesty the King in Right of Canada, as represented by the Minister of Natural Resources 2023 The Minerals, Metals & Materials Society, TMS 2023 152nd Annual Meeting & Exhibition Supplemental Proceedings, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-031-22524-6_87

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gas or pure hydrogen. A key issue of transporting hydrogen in natural gas pipelines is the hydrogen compatibility of the materials, especially the issue of hydrogen embrittlement of steels [3]. Pipelines can be exposed to not only the transported hydrogen gas but also the hydrogen generated from aqueous solutions related to cathodic protection. The detrimental effects of hydrogen on the metallic mechanical properties (e.g., the loss in mechanical ductility, reduced fracture toughness, and degradation of fatigue properties) may lead to unexpected failures and considerable design and maintenance issues of pipelines. This paper reviews commonly used hydrogen charging methods and effects of hydrogen on Charpy tests in the discussion. Preliminary ex-situ Charpy tests of electrolytically pre-charged specimens of three pipe steels were conducted, but only main results are reported in this paper because of the space limitation. This work does not represent an attempt of a comprehensive review, acknowledging the existing enormous literature on the topics and many recent publications, but is a focused examination of relevant topics. It can serve as a basis to further investigations useful for design of new pipelines and engineering assessment of existing pipelines for transporting hydrogen or hydrogen blends.

Literature Survey on Hydrogen Charging Methods The publications on hydrogen-metal interactions are extensive, and readers can refer to some comprehensive review papers and books [4–9] for more detailed description of the phenomena and mechanisms. Test methods and technologies that simulate the operational conditions of pipelines are crucially important to quantify the effects of hydrogen [4, 10]. In laboratory experiments, the charging of hydrogen into steels is commonly performed by either gaseous hydrogen charging or electrochemical charging (also called electrolytic charging) methods [11, 12]. Theoretically, the same amount of hydrogen can enter the steel by different methods, if the appropriate parameters are chosen. As soon as the hydrogen is absorbed into the steel lattice structure, the fundamental hydrogen-metal interactions responsible for hydrogen embrittlement for a given microstructure are the same [4]. To quantify the impact of hydrogen degradation, many studies first pre-charged the mechanical test specimens with hydrogen and then tested them in ambient environments, as this involved less complicated testing equipment and procedures compared with in-situ testing, where loading and hydrogen exposure are concurrent. Tables 1 and 2 summarize some typical examples of the gaseous and electrochemical hydrogen charging procedures used in the literature with key parameters, including both the pre-charging and in-situ charging methods. From the practical standpoint, the gaseous charging process must be designed and performed with extra care to control the variables that can affect the hydrogen–steel surface interactions and hydrogen penetration distances (e.g., gas purity, specimen surface cleanliness, and dynamic loading rate, etc.), as they may cause mechanical tests involving different hydrogen charging methods to yield notably different results

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Table 1 Gaseous hydrogen charging method Material

Charging mode

Gas Charging environment temperature

Charging pressure

Pre-charging Refs. time

Austenitic stainless steels

Pre-charging H2

300 °C

Up to 138 MPa

14 days

X70A, X70B steels

Pre-charging H2

300 °C

10.1325 MPa 24 h

[17]

X80 steel

Pre-charging H2 or N2 and in-situ

Ambient

0.1–30 MPa

[13]

X80 base metal and girth weld

Pre-charging H2 or N2 and in-situ

Ambient

Up to 100 bar 17 h

[14]

316 stainless steel

Pre-charging H2

300 °C

138 MPa

10 days

[18]

Martensitic steel

Pre-charging H2

250 °C

138 MPa

21 days

[19]

2–1/4Cr-1Mo Pre-charging H2 steel

450 °C

15, 25 MPa

48 h

[20]

3.5NiCrMoV Pre-charging H2 steel

200 °C for 2, 20, 80, 2 h and then 200 bar cool to ambient

24 h

[21]

30–60 min

[16]

X70 steel

In-situ

CH4 and 1% 25 °C H2

10 MPa

–

[15]

X70 steel

In-situ

H2

Ambient

10 MPa

–

[22]

X80 steel

In-situ

CH4 and H2

Ambient

12 MPa

–

[23]

X52, X65, and X100 pipeline steels

In-situ

H2

Ambient

0.2, 5.5, 13.8, – 27.6, 69.0 MPa

[24]

[4]. For safety considerations, some studies used non-reactive and non-flammable N2 gas in place of air as the gas atmosphere for testing [13, 14]. It was observed that even 1% hydrogen in the hydrogen/natural gas blend could cause a significant reduction in the fracture toughness of pipeline steel base and weld metals [15]. Although the gaseous pre-charging method is directly applicable to pipeline transportation applications, the lack of testing capability has limited its utilization in the R&D and qualification that would be essential to the design of pipelines carrying hydrogen and hydrogen/natural gas blends. This method would be especially useful for high-strength pipe steels, since fracture toughness is critical to high designstress applications. In addition, the effects of surface conditions and impurities may cause difficulties to interpret results and need to be controlled carefully. Impurities such as O2 , CO, SO2 , CS2, and N2 O have been shown to be effectively reduce the severity of hydrogen degradation effect by impeding the metal surface reactions

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Table 2 Electrochemical hydrogen charging method Material

Charging mode

X65 steel

X42, X52, X70, AISI 1020 steels

Charging solution

Charging current density

Pre-charging Charging Refs. time Temperature

Pre-charging 0.2 M H2 SO4 + 3 g/L NH4 SCN

20 mA/cm2

24 h

Ambient

[31]

Pre-charging 0.5 M and in-situ H2 SO4 + 1.85 mM Na4 P2 O7

1 mA/cm2

48 h

Ambient

[32]

Ambient

[33]

X56N, X70M Pre-charging 0.01 M, steels 0.05 M, 0.5 M H2 SO4 + 0.1, 1 g/L CH4 N2 S, or 0.1 M NaOH + 1, 3, 10 g/L NH4 SCN

0.2–10 mA/cm2 Up to 48 h

ASTM A182 F22, X65 steels

Pre-charging 0.4 M 0.5 mA/cm2 CH3 COOH + 0.2 M CH3 COONa + Na2 S

20 h

25 ± 3 °C

[34]

X60, X80, X100 steels

Pre-charging 0.5 M H2 SO4 + 5 g/L KH2 AsO4

Up to 70 mA/cm2

15 min

40 °C

[35]

X80 steel

Pre-charging 0.5 M and in-situ H2 SO4 + 2 g/L CH4 N2 S

2.5, 5 mA/cm2

12 h

25 °C

[36]

X80 steel

Pre-charging 0.1 M and in-situ NaOH

2.53, 4.60, 6.03 mA/cm2

1h

Ambient

[37]

X100 steel

Pre-charging 0.05 M, 0.5 M H2 SO4 + 250 mg/L As2 O3

10, 20, 200 mA/cm2

1, 3, 5 h

~21 °C

[38]

X70, X52 steels

In-situ

1, 5, 10 mA/cm2

–

Ambient

[39]

NS4 solution purged with 5% CO2 + 95% N2 gas

(continued)

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Table 2 (continued) Material

Charging mode

Charging current density

Pre-charging Charging Refs. time Temperature

Low carbon ultra-high strength steel

Pre-charging 3% NaCl + 0.3 g/L NH4 SCN

0.04 to 0.50 mA/cm2

2 to 24 h

25 °C

[40]

3Cr1Mo1/4 V Pre-charging 0.5 M Steel H2 SO4 + 250 mg/L As2 O3

40 mA

1h

Ambient

[41]

316L Pre-charging 3% NaCl + stainless steel 0.3% NH4 SCN

2 mA/cm2

12, 24, 48 h

Ambient

[42]

304L and 310 Pre-charging 0.5 M stainless H2 SO4 + 250 mg/L steels Na2 SO3

25 mA/cm2

1h

100 °C

[43]

Ambient

[44]

AISI 4135 steel

Charging solution

Pre-charging 3% NaCl + 0.03–3 mA/cm2 1–72 h 0.3% NH4 SCN or 0.1 N NaOH

associated with hydrogen uptake [25–28], and addition of H2 S increases the severity of hydrogen embrittlement [29]. O2 and H2 O in H2 gas are impurities of particular importance since they will impact the effectiveness of the testing chamber purging procedures and the quality of the chamber seals [4]. Purging method involving helium purging (21 MPa) alternating with evacuation (~50 mtorr) for three cycles, followed by high purity (99.9999%) hydrogen purging (21 MPa) alternating with evacuation for three cycles has been recommended by Sandia National Laboratories and proven consistently effective to achieve oxygen levels at or below 1 ppm [30]. The electrochemical charging has been widely used in investigations of hydrogen effects due to its relatively simple test setup and convenience. The variable parameters include charging time, temperature, charging mode (galvanostatically or potentiostatically), the applied current density/potential, solution type and concentration, and surface status of the specimen, etc. These parameters influence the saturation level of hydrogen concentration in a charged specimen and how long it takes to reach the saturation. A variety of solutions, both acidic and alkaline, used for hydrogen charging of pipeline steels can be found in literature (Table 2), often with an added poison (e.g., thiourea CH4 N2 S, ammonium thiocyanate NH4 SCN, and As2 O3, etc.) to inhibit the hydrogen recombination and promote hydrogen uptake. In this study, 0.1 M NaOH solution was used for hydrogen charging as it was reported to induce less hydrogen damage than the commonly used 0.5 M H2 SO4 solution [33]. It is of huge practical interest to correlate the electrochemical charging and gaseous charging methods, to identify the equivalent hydrogen gas pressure (fugacity) for various electrochemical charging conditions.

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Correlation of Electrochemical Charging and Gaseous Charging Calculations of effective hydrogen fugacities associated with electrochemical charging techniques can be done by comparison of steady state fluxes from these two hydrogen-charging methods at the same temperature. This approach was first suggested by Oriani [45] and later applied experimentally by Podgurski et al. [46] and Kumnick et al. [47] for calculation of equivalent hydrogen fugacity of electrolytic charging at a constant current density. On the other hand, Atrens et al. [48] established an equivalence between gaseous charging and electrolytic hydrogen charging at a constant potential at the same temperature, through determination of the hydrogen activity (or pressure for gaseous hydrogen charging) during the two charging processes. Based on this concept, his group published a series of papers [21, 49–51] on determination of the equivalent hydrogen fugacity during electrochemical charging for various steel materials. For example, Liu et al. found that for an ultralow carbon steel charged in 0.1 M NaOH, the equivalent gaseous hydrogen pressure or fugacity f H2 (bar) was related to the electrochemical charging overpotential ï by the following equations [49]: 

f H2 f H2

 −ηF = 15.360 exp , for |η| < 0.35 V; and 3.30RT   −ηF = 560 exp , for |η| > 0.35 V 14.35RT

where F is the Faraday constant, R is the gas constant, and T is the absolute temperature. Note that the equivalent hydrogen pressure is influenced by the (i) nature of the material, (ii) type of charging electrolyte, and (iii) applied current density or applied potential [50], and thus a case-by-case analysis must be conducted to establish an appropriate correlation of the two charging methods for a given material and charging process.

Preliminary Experiments and Discussion Spare Charpy samples of three pipe steels with different yield strength (YS) from previous R&D research projects, referred to as X65 (YS 428 MPa), X80 (YS 557 MPa), and X120 (YS 877 MPa), were used to study the effects of hydrogen on Charpy toughness. The main chemical compositions (wt.%) of the steels are 0.072 C1.4 Mn-0.19 Si-0.037 Al-0.049 Nb-0.033 Ti-0.019 Cu-0.023 Cr (X65), 0.024 C-1.7 Mn-0.23 Si-0.03 Al-0.11 Nb-0.013 Ti-0.28 Cu-0.28 Cr (X80), and 0.061 C-1.74 Mn0.11 Si-0.017 Al-0.038 Nb-0.009 Ti-0.23 Cu-0.035 Cr (X120). An aqueous solution containing 0.1 M NaOH and 150 mg/L As2 O3 as hydrogen recombination poisoner

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was used to pre-charge hydrogen into the steel LECO and Charpy specimens at ambient temperature under galvanostatic conditions. During the charging process, the specimens were subjected to a constant cathodic current in the solution for various durations up to 24 h. To prevent the potential hydrogen damage caused by a high charging current [52], the charging current densities of 1.0, 2.5, and 20 mA/cm2 were used. Prior to hydrogen pre-charging, the surface of the steel specimens was hand polished lightly using 600 grit SiC sandpaper to remove the surface oxide layer, degreased in acetone, rinsed in DI water, and blow dry using an air gun. Charpy V-notch testing is widely used to qualify toughness of steels and welds (e.g., [53, 54]). Experimental investigations of the effect of hydrogen on Charpy toughness in the literature have been performed mainly using electrolytic charging and ex-situ testing. This is because in-situ Charpy testing is very difficult. The gaseous charging method requires special apparatus and strict laboratory safety procedures, and suffers from hydrogen outgassing during depressurization and opening-up of apparatus and transportation of specimens from a gaseous hydrogen lab to testing lab. The hydrogen pre-charged into the X65 steel led to obviously reduced Charpy toughness (i.e., CVN) of the steel. The effect aggravated with an increase in charging time and charging current density and saturated at around 5 h of charging time and 2.5 mA/cm2 of charging current density. Hydrogen charging resulted in a decrease in CNV for all three grades of pipeline steels, with approximately 20% reduction for both X65 and X120 steels and 8% reduction for X80 high toughness steel. An example of the load vs. deflection curves for X120 steel is shown in Fig. 1. The tests are considered preliminary because the hydrogen contents in the samples have not been determined and hydrogen egress was not quantified. Nevertheless, the tests showed the effect of hydrogen on upper-shelf Charpy toughness of the pre-charged unstressed steel samples. Because there are some hydrogen egress between the removal of samples from the pre-charging bath and testing (