Sustainable Chemistry Research. Volume 2: Computational and Industrial Aspects [2] 9783111070919

This edited book of proceedings is a collection of seventeen selected and peer-reviewed contributions from the Virtual C

161 20 16MB

English Pages 344 [345] Year 2023

Report DMCA / Copyright

DOWNLOAD FILE

Polecaj historie

Sustainable Chemistry Research. Volume 2: Computational and Industrial Aspects [2]
 9783111070919

Table of contents :
Cover
Half Title
Also of interest
Sustainable Chemistry Research. Volume 2: Computational and Industrial Aspects
Copyright
Preface of the Book of Proceedings of the Virtual Conference on Chemistry and its Applications (VCCA-2022)
Contents
List of contributing authors
1. Computational study of propene selectivity and yield in the dehydrogenation of propane via process simulation approach
Abstract
1.1 Introduction
1.2 Materials and methods
1.2.1 Process modeling and simulations
1.2.1.1 Collections and addition of components
1.2.1.2 Thermodynamic model selections
1.2.1.3 Modeling the propane dehydrogenation process
1.2.2 Process optimization studies
1.2.3 Impact of feed purity on the dehydrogenation process
1.3 Results and discussions
1.3.1 Modeling the propane dehydrogenation process
1.3.2 Process optimization studies
1.3.3 Impact of feed purity on the dehydrogenation process
1.4 Conclusions
References
2. Computational chemistry in the undergraduate inorganic curriculum
2.1 Introduction
2.2 Computational chemistry module
2.2.1 Module Goals/Plan
2.2.2 Instructional Videos and Manual
2.2.2.1 Video 1: GaussView 5.0: introduction
2.2.2.2 Video 2: GaussView 5.0: calculations and IR spectra
2.2.2.3 Video 3: GaussView 5.0: calculations and NMR spectra
2.2.2.4 Video 4: molecular orbital
2.2.3 Assignments
2.2.3.1 Assignment 1
2.2.3.2 Assignment 2
2.3 Results and discussion
2.4 Conclusions
References
3. Computational design of the novel building blocks for the metal-organic frameworks based on the organic ligand protected Cu4 cluster
Abstract
3.1 Introduction
3.2 Computational details
3.3 Results and discussion
3.3.1 Energetics and structural features
3.3.2 Frontier molecular orbitals and NPA charge
3.3.3 Molecular electrostatic potential (MEP) plots
3.3.4 GRP analysis
3.4 Conclusions and perspectives
References
4. Computational investigation of Arbutus serratifolia Salisb molecules as new potential SARS-CoV-2 inhibitors
4.1 Introduction
4.2 Part 1: Materials and methods
4.2.1 Phytochemical study
4.2.1.1 Extraction of secondary metabolites of A. serratifolia Salisb
4.2.1.2 Separation and purification of bioactive molecules
4.2.1.2.1 (1S,5R,6R,8S,9S)-6,8-Dihydroxy-8-methyl-1,5,6,7,8,9-hexahydrocyclopenta [c] pyran-1-yl-β-D-glucopyranoside (Figure 4.1).
4.2.1.2.1.1 Separation
4.2.1.2.1.2 Detection
4.2.1.2.1.3 Spectroscopic data
4.2.1.2.2 (1S,5R,6S,8S,9S)-6,8-Dihydroxy-8-methyl-1,5,6,7,8,9-hexahydrocyclopenta [c] pyran-1-yl-β-D-glucopyranoside (Figure 4.2).
4.2.1.2.2.1 Separation
4.2.1.2.2.2 Detection
4.2.1.2.2.3 Spectroscopic data
4.2.1.2.3 Methyl((1R,5R,6S,8R,9R)-6,8-Dihydroxy-8-methyl-1-(β-D-glucopyranosyloxy)-1,5,6,7,8,9-hexahydrocyclopenta [c] pyran-4-yl) carboxylate (Figure 4.3).
4.2.1.2.3.1 Separation
4.2.1.2.3.2 Detection
4.2.1.2.3.3 Spectroscopic data
4.2.1.2.4 Methyl((1S,5S,8S,9S)-1-(β-D-glucopyranosyloxy)-8-hydroxy-8-(hydroxymethyl)-1,5,8,9-tetrahydrocyclopenta [c] pyran-4-yl) carboxylate (Figure 4.4)
4.2.1.2.4.1 Separation
4.2.1.2.4.2 Detection
4.2.1.2.4.3 Spectroscopic data
4.2.1.2.5 Methyl((1S,5S,8S,9S)-1-(β-D-glucopyranosyloxy)-8-hydroxy-8-(hydroxymethyl)-1,5,6,7,8,9-hexahydrocyclopenta [c] pyran-4-yl) carboxylate (Figure 4.5)
4.2.1.2.6 Pr(6):(1S,5S,6S,8S,9R)-8-Methyl-11-oxo-1,5,6,7,8,9-hexahydro-4H-2,12-dioxacy-clopenta [cd] inden-1-yl-β-D-glucopyranoside (Figure 4.6)
4.2.1.2.7 Pr(7):[(6S,9S,1S,5S)-1-(β-D-Glucopyranosyloxy)−13-oxo-1,5,6,9-tetrahydro-1H-2,14-dioxacyclopenta [cd] inden-8-yl] methyl acetate (Figure 4.7)
4.2.1.2.8 Pr(8):Methyl((1S,5S,9S)-1-(β-D-glucopyranosyloxy)-8-(hydroxymethyl)-1,5,6,9-tetrahydrocyclopenta [c] pyran-4-yl) carboxylate (Figure 4.8)
4.2.1.2.9 Pr(9): (1R,5S,6R,7R,8S,9R)-6-Hydroxy-1-(β-D-glucopyranosyloxy)-1,5,6,7,8,9-hexahydrooxireno [2’’,3’’:4”’,5”’] cyclopenta [c] pyran (Figure 4.9)
4.2.1.2.10 Pr(10):((1S,5S,8R,9S)-1-(β-D-Glucopyranosyloxy)-8-hydroxy-8-(hydroxymethyl)-1,5,8,9-tetrahydrocyclopenta [c] pyran-4-yl) carboxylic acid (Figure 4.10)
4.2.1.2.11 Pr(11):((1S,5S,6R,8R,9S)-6,8-Dihydroxy-8-(hydroxymethyl)-1-(β,D-glucopyranosyloxy)-1,5,6,7,8,9-hexahydrocyclopenta [c] pyran-4-yl) carboxylic acid
4.2.1.2.12 Pr(12):(1R,5R,6S,7R,8S,9S)-5,6-Dihydroxy-1-(β-D-glucopyranosyloxy)-1,5,6,7,8,9-hexahydrooxireno [2’’,3’’:4”’,5”’] cyclopenta [c] pyran
4.2.2 In-silico assessment
4.2.3 Ligand optimization
4.2.3.1 Drug likeness and ADMET calculations
4.2.3.2 Preparation of receptor and its binding site
4.2.3.3 Molecular docking
4.2.3.3.1 Protein preparation
4.2.3.3.2 Determination of active site and grid box
4.2.3.3.3 Ligand-receptor interactions
4.3 Results and discussions
4.3.1 Drug likeness and ADMET calculations
4.3.2 Docking study
4.4 Conclusions
References
5. Formulation of a herbal topical cream against Tinea capitis using flavonoids glycosides from Dicerocaryum senecioides and Diospyros mespiliformis
5.1 Introduction
5.2 Materials and methodology
5.2.1 47 Chemicals and reagents
5.2.2 Plant material
5.2.2.1 Preparation of plant extract
5.2.3 Preparation of cream formulations
5.2.4 Preliminary stability tests
5.2.4.1 Centrifugation test
5.2.4.2 [12] Thermal stress
5.2.4.3 Freeze and thaw cycles
5.2.5 Accelerated stability test
5.2.6 Long term stability tests
5.2.6.1 Spread ability
5.2.6.2 [16] Measurement of pH
5.2.6.3 In vitro occlusivity test
5.2.6.4 Phase separation
5.2.6.5 Particle size and phase inversion
5.2.6.6 Creaming index:
5.2.7 In vitro antifungal assay
5.2.7.1 Determination of MIC
5.2.8 Microbiological assessment
5.2.9 Clinical trials
5.2.10 Data analysis
5.3 Results
5.3.1 Stability tests results
5.3.2 In vitro antifungal assay
5.3.3 Microbial assessment
5.3.4 Clinical trials results
5.4 Discussion
5.5 Conclusion
References
6. Immediate effects of atrazine application on soil organic carbon and selected macronutrients and amelioration by sawdust biochar pretreatment
6.1 Introduction
6.2 Materials and methods
6.2.1 Description of the experimental soil
6.2.2 Biochar preparation
6.2.3 Treatments, design, and experimental set up
6.2.4 Data collection
6.2.5 Data analysis
6.3 Results
6.3.1 The pH and nutrient characteristics of the biochar produced and tested
6.3.2 Immediate effects of atrazine and biochar pretreatment on soil organic carbon
6.3.3 Immediate effects of atrazine and biochar pretreatment on soil pH
6.3.4 Immediate effects of atrazine and biochar pretreatment on soil available P
6.3.5 Immediate effects of atrazine and biochar pretreatment on exchangeable bases in the soil
6.3.6 Immediate effects of atrazine and biochar pretreatment on dry biomass weight of maize srrdlings
6.3.7 Regression analysis indicating contributions of selected biochar nutrient properties to organic carbon and macronutrient contents in the atrazine-treated soil
6.4 Discussion
6.5 Conclusions
References
7. Process configuration of combined ozonolysis and anaerobic digestion for wastewater treatment
Abstract
Abbreviations
7.1 Introduction
7.2 Methodology
7.2.1 Materials
7.2.2 Distillery wastewater and waste activated sludge
7.2.3 Ozonolysis pre-treatment process for WAS and DWW
7.2.4 Anaerobic digestion of WAS and DWW
7.2.5 Ozonolysis post-treatment of anaerobically digested DWW
7.2.6 Physical and chemical analysis
7.3 Results and discussion
7.3.1 Characteristics of WAS and DWW before and after ozonolysis pre-treatment
7.3.2 Effect of ozone pre-treatment on anaerobic digestion of WAS
7.3.3 Effect of ozone pre-treatment on anaerobic digestion of DWW
7.3.4 Ozonolysis of anaerobically digested DWW (post-treatment)
7.3.4.1 Characteristics of the anaerobically digested DWW
7.3.4.2 Optimisation of the ozone transfer during post-treatment
7.3.4.3 Changes in COD, BOD5, and solids concentration
7.4 Conclusions
References
8. Concentration levels and risk assessment of organochlorine and organophosphate pesticide residue in selected cereals and legumes sold in Anambra State, south-eastern Nigeria
Abstract
8.1 Introduction
8.2 Materials and methods
8.2.1 Sample collection and preparation
8.2.2 Chemicals
8.2.3 Extraction of pesticide residues from samples
8.2.4 Clean-up
8.2.5 Analysis of organochlorine and organophosphate pesticides
8.2.6 Quality control
8.2.7 Statistical analysis
8.2.8 Pesticide toxicity Index
8.2.9 Health and exposure risk assessment
8.2.9.1 Non-carcinogenic assessment
8.2.9.2 Carcinogenic assessment
8.3 Results and discussion
8.3.1 Mean Concentration of organochlorine and organophosphate pesticides residues
8.3.2 Pesticide toxicity index
8.3.3 Health risk assessment
8.4 Conclusions
References
9. Adsorption of trichloroacetic acid from drinking water using polyethylene terephthalate waste carbon and periwinkle shells–based chitosan
Abstract
9.1 Introduction
9.2 Material and methods
9.2.1 Collection of materials
9.2.2 Preparation of caustic alkali from cocoa husk ash
9.2.2.1 Preparation of cocoa husk ash
9.2.2.2 Calculation of ash yield from cocoa husk
9.2.3 Chemical activation of the carbon
9.2.3.1 Activation of PET with caustic alkali
9.2.3.2 Carbonization of the activated PET
9.2.3.3 Determination of the pH of the activated (PET) carbon
9.2.3.4 Determination of ash content of activated (PET) carbon
9.2.4 Preparation of chitosan from Periwinkle shell
9.2.4.1 Deproteinization of periwinkle shell
9.2.4.2 Demineralization of deproteinized periwinkle shell
9.2.4.3 Decolorization of demineralized–deproteinized periwinkle shell
9.2.4.4 Deacetylation of chitin
9.2.4.5 Preparation of chitosan gel
9.2.5 Determination of chitosan yield
9.2.6 Determination of moisture content of chitosan
9.2.7 Determination of ash content of chitosan
9.2.8 Modification of PET activated carbon
9.2.9 Characterization of activated PET and chitosan modified activated PET carbon
9.2.9.1 Preparation of samples for SEM/EDX analysis
9.2.9.2 Preparation of samples for FT-IR analysis
9.2.10 Trichloroacetic acid analysis
9.2.11 Batch adsorption experiment
9.2.12 Water sampling for TCA analysis
9.2.13 Recovery experiment for photometry determination of TCA standard
9.2.14 Reusability potential of the adsorbent
9.3 Results and discussion
9.3.1 Cocoa husk ash
9.3.2 Caustic alkali from cocoa husk ash
9.3.3 Physicochemical properties of polyethylene terephthalate activated carbon (PETEC)
9.3.4 Physico-chemical properties of chitosan from periwinkle shell
9.3.5 Characterization of PET activated carbon and chitosan modified activated carbon with SEM-EDX before adsorption
9.3.6 Functional groups of polyethylene terephthalate activated carbon (PETEC) and plyethylene terephthalate modified activated carbon (PETMAC)
9.3.7 Characterization of water samples
9.3.8 Recovery experiment for TCA photometric determination
9.3.9 Parametric studies on the TCA removal from aqueous solution
9.3.10 Adsorption of TCA from raw water and conventionally treated water
9.3.11 Characterization of PETAC and PETMAC with SEM-EDX after adsorption
9.3.12 Adsorption equilibrium isotherm
9.3.13 Reusability potential of the adsorbent
9.4 Conclusions
References
10. Comparative study of the photocatalytic degradation of tetracycline under visible light irradiation using Bi24O31Br11-anchored carbonaceous and silicates catalyst support
Abstract
10.1 Introduction
10.2 Materials and methods
10.2.1 Preparation of activated carbon from zinc chloride, and phosphoric acid (ACZ, and ACH) from carbonized material (CM)
10.2.2 Preparation of MCM-41 and SBA-15
10.2.3 Preparation of BOB photocatalysts
10.2.4 Materials characterization
10.3 Result and discussion
10.3.1 Structural, morphological, and optical characteristics
10.3.2 Charge transfer properties
10.3.3 Photocatalytic activity
10.4 Conclusions
References
11. Synergistic effect in bimetallic gold catalysts: recent trends and prospects
11.1 Introduction
11.2 Synthesis of Au bimetallic catalysts
11.2.1 Controlling the particle size and composition
11.2.2 Controlling the morphology of the catalyst
11.2.3 The role of the support material
11.3 Catalysts characterization
11.4 Applications
11.4.1 Oxidation of hydrocarbons
11.4.2 Fuel cell processes
11.4.3 Oxidation of biomass derived products
11.4.4 Photocatalytic oxidation
11.5 Conclusion and outlook
References
12. Simultaneous removal of methylene blue, copper Cu(II), and cadmium Cd(II) from synthetic wastewater using fennel-based adsorbents
Abstract
12.1 Introduction
12.2 Resources and procedures
12.2.1 Resources
12.2.2 Method used to produce the adsorbents
12.2.2.1 Virgin fennel seed (PFS)
12.2.2.2 Acid treated adsorbent (H3FS)
12.2.2.3 Base treated adsorbent (CaFS) preparation
12.2.3 Methods of adsorption preparation
12.2.4 Point zero charge process
12.2.5 Reusability procedure
12.2.6 Adsorption data management
12.3 Characterization of the adsorbents
12.4 Results and discussion
12.4.1 Ultraviolet–Visible spectroscopy results
12.4.2 X-ray crystallography results
12.4.3 Scanning electron microscope and energy dispersive X-ray analysis results
12.4.4 Fourier transform infrared spectroscopy results
12.4.5 Point zero charge (pH(pzc))
12.4.6 Effect of concentration
12.4.7 Isotherm studies
12.4.8 Effect of time
12.4.9 Kinetic model studies
12.4.10 Effect of temperature
12.4.11 Thermodynamics studies
12.4.12 Effect of pH
12.4.13 Proposed mechanism reaction
12.4.14 Reusability studies
12.4.15 Comparison studies of the qemax of the current adsorbents with previous studies
12.5 Post adsorption results
12.5.1 FTIR results after adsorption
12.6 Conclusions
References
13. The investigation of the physical properties of an electrical porcelain insulator manufactured fromlocally sourcedmaterials
13.1 Introduction
13.2 Materials
13.2.1 Characterization
13.2.2 Method
13.2.3 Water absorption
13.2.4 Linear shrinkage
13.2.5 Apparent porosity
13.2.6 Bulk density
13.3 Result and discussion
13.3.1 X-ray fluorescence and X-ray diffraction analysis of the clay
13.3.2 Apparent porosity
13.3.3 Water absorption
13.3.4 Linear shrinkage
13.3.5 Bulk density
13.4 Conclusion
References
14. A new sphingoid derivative from Acacia hockii De Wild (Fabaceae) with antimicrobial and insecticidal properties
14.1 Introduction
14.2 Material and methods
14.2.1 General experimental procedure
14.2.2 Plant material
14.2.3 Extraction and isolation
14.2.4 Evaluation of the antimicrobial activity
14.2.5 Insecticidal test of the hexane, acetone, methanol extracts and compound 1
14.3 Results and discussion
14.3.1 Identification of compound 1
14.3.2 Result of the insecticide test on C. maculatus
14.3.3 Results of the antimicrobial test
14.4 Conclusion
References
15. Protection of wood against bio-attack and research of new effective and environmental friendly fungicides
Abstract
15.1 Introduction
15.2 Materials and methods
15.3 Preparation of soluble soap
15.4 Production of metallic soap [copper (II) soap]
15.5 Synthesis of urea complexes from metallic soap
15.6 Synthesis of thiourea complexes from metallic soap
15.7 Physicochemical parameters of the synthesized compounds
15.7.1 Melting point
15.7.2 Moisture content
15.7.3 Determination of ash content
15.7.4 Determination of sulphated ash contents
15.7.5 Solubility test
15.7.6 Colour
15.7.7 Absorption spectral analysis
15.7.8 Infrared spectroscopy analysis
15.7.9 Scanning electron microscope coupled with energy dispersive X-ray spectroscopy (SEM/EDS)
15.8 Antifungal assay
15.9 Results and discussions
15.9.1 Physical properties of synthesized compounds
15.10 UV–visible spectra of synthesized compounds
15.11 Infra-red spectra of syntthesized compounds
15.11.1 Energy-dispersive X-ray analysis (EDX) with Scanning electron microscope (SEM)
15.12 Anti fungi assay
15.13 Conclusions
References
16. Exploring the solvation of water molecules around radioactive elements in nuclear waste water treatment
Abstract
16.1 Introduction
16.2 Computational details
16.3 Results and discussion
16.3.1 Coordination environment
16.3.2 Interactions between water molecules and metals/metal oxides
16.3.3 Strength of interactions among water molecules and metal/metal oxides
16.4 Conclusions
References
17. Changing our outlook towards vulnerable women for societal resilience
17.1 Introduction
17.2 Methods
17.2.1 Study design overview
17.2.2 Inclusion and exclusion criteria
17.2.3 Consent and confidentiality
17.2.4 Topic guide for interview
17.2.5 Data analysis
17.3 Results
17.3.1 Theme 1: drug injection scenario
17.3.2 Theme 2: sex work interplay
17.3.3 Theme 3: sexual behaviour screenplay
17.3.4 Additional data
17.4 Discussion
17.4.1 Background and setting
17.4.2 Injection practices of WIDUs
17.4.3 Sexual behaviours of WIDUs
17.4.4 Participants’ insight
17.4.5 Strengths and limitations
17.4.6 Recommendations
17.5 Conclusions
References
Index

Citation preview

Ponnadurai Ramasami (Ed.) Sustainable Chemistry Research

Also of interest Sustainable Chemistry Research Volume : Chemical and Biochemical Aspects Ponnadurai Ramasami (Ed.),  ISBN ----, e-ISBN ----

Sustainable Chemistry Research Volume : Analytical Aspects Ponnadurai Ramasami (Ed.),  ISBN ----, e-ISBN ----

Physical Sciences Reviews. e-ISSN -X

Sustainable Chemistry Research Volume 2: Computational and Industrial Aspects Edited by Ponnadurai Ramasami

Editor Prof. Dr. Ponnadurai Ramasami Computational Chemistry Group, Department of Chemistry, Faculty of Science, University of Mauritius, Réduit 80837, Mauritius E-mail address: [email protected]

ISBN 978-3-11-107091-9 e-ISBN (PDF) 978-3-11-107142-8 e-ISBN (EPUB) 978-3-11-107163-3 Library of Congress Control Number: 2023939130 Bibliographic information published by the Deutsche Nationalbibliothek The Deutsche Nationalbibliothek lists this publication in the Deutsche Nationalbibliografie; detailed bibliographic data are available on the internet at http://dnb.dnb.de. © 2023 Walter de Gruyter GmbH, Berlin/Boston Cover image: Petmal/iStock/Getty Images Plus Typesetting: TNQ Technologies Pvt. Ltd. Printing and binding: CPI books GmbH, Leck www.degruyter.com

Preface of the Book of Proceedings of the Virtual Conference on Chemistry and its Applications (VCCA-2022) A virtual conference on chemistry and its applications (VCCA-2022) was organized online from 8th to 12th August 2022. The theme of the virtual conference was “Chemical Sciences for the New Decade”. There were 210 presentations for the virtual conference with 500 participants from 55 countries. A secured platform was used for virtual interactions of the participants. After the virtual conference, there was a call for full papers to be considered for publication in the conference proceedings. Manuscripts were received and they were processed and reviewed as per the policy of De Gruyter. This book, volume 2, is a collection of the seventeen accepted manuscripts covering computational and industrial aspects. I hope that these chapters of this volume 2 will add to literature and they will be useful references for researchers. To conclude, VCCA-2022 was a successful event and I would like to thank all those who have contributed. I would also like to thank the Organising and International Advisory committee members, the participants and the reviewers. Prof. Ponnadurai Ramasami, UNESCO Chair in Computational Chemistry Computational Chemistry Group, Department of Chemistry, Faculty of Science, University of Mauritius, Réduit 80837, Mauritius E-mail address: [email protected]

https://doi.org/10.1515/9783111071428-201

Contents Preface V List of contributing authors

XVII

Toyese Oyegoke, Fadimatu N. Dabai, Saidu M. Waziri, Adamu Uzairu and Baba Y. Jibril 1 Computational study of propene selectivity and yield in the dehydrogenation of propane via process simulation approach 1 1.1 Introduction 2 1.2 Materials and methods 3 3 1.2.1 Process modeling and simulations 1.2.2 Process optimization studies 4 1.2.3 Impact of feed purity on the dehydrogenation process 5 1.3 Results and discussions 5 1.3.1 Modeling the propane dehydrogenation process 5 1.3.2 Process optimization studies 9 12 1.3.3 Impact of feed purity on the dehydrogenation process 1.4 Conclusions 13 References 14 John P. Canal 2 Computational chemistry in the undergraduate inorganic curriculum 2.1 Introduction 17 2.2 Computational chemistry module 18 2.2.1 Module Goals/Plan 18 2.2.2 Instructional Videos and Manual 19 2.2.3 Assignments 22 2.3 Results and discussion 26 31 2.4 Conclusions References 32 Francisca Claveria-Cádiz and Aleksey E. Kuznetsov 3 Computational design of the novel building blocks for the metal-organic frameworks based on the organic ligand protected Cu4 cluster 35 36 3.1 Introduction 3.2 Computational details 37 3.3 Results and discussion 38 3.3.1 Energetics and structural features 38 42 3.3.2 Frontier molecular orbitals and NPA charge 3.3.3 Molecular electrostatic potential (MEP) plots 45 3.3.4 GRP analysis 46

17

VIII

3.4

Contents

Conclusions and perspectives References 48

47

Nadjah Belattar, Ratiba Mekkiou, Adel Krid and Abdelhamid Djekoun 4 Computational investigation of Arbutus serratifolia Salisb molecules as new potential SARS-CoV-2 inhibitors 51 4.1 Introduction 52 4.2 Part 1: Materials and methods 53 4.2.1 Phytochemical study 53 4.2.2 In-silico assessment 69 4.2.3 Ligand optimization 69 71 4.3 Results and discussions 4.3.1 Drug likeness and ADMET calculations 71 4.3.2 Docking study 71 4.4 Conclusions 79 References 79 Rudo Zhou, Pamhidzai Dzomba and Luke Gwatidzo 5 Formulation of a herbal topical cream against Tinea capitis using flavonoids glycosides from Dicerocaryum senecioides and Diospyros mespiliformis 81 5.1 Introduction 81 5.2 Materials and methodology 82 5.2.1 47 Chemicals and reagents 82 5.2.2 Plant material 83 83 5.2.3 Preparation of cream formulations 5.2.4 Preliminary stability tests 83 5.2.5 Accelerated stability test 84 5.2.6 Long term stability tests 84 5.2.7 In vitro antifungal assay 85 5.2.8 Microbiological assessment 85 85 5.2.9 Clinical trials 5.2.10 Data analysis 86 5.3 Results 86 5.3.1 Stability tests results 86 5.3.2 In vitro antifungal assay 86 86 5.3.3 Microbial assessment 5.3.4 Clinical trials results 86 87 5.4 Discussion 5.5 Conclusion 95 References 95

Contents

IX

Yetunde Bunmi Oyeyiola and Beatrice Olutoyin Opeolu 6 Immediate effects of atrazine application on soil organic carbon and selected macronutrients and amelioration by sawdust biochar pretreatment 99 6.1 Introduction 100 6.2 Materials and methods 102 6.2.1 Description of the experimental soil 102 6.2.2 Biochar preparation 102 6.2.3 Treatments, design, and experimental set up 103 104 6.2.4 Data collection 6.2.5 Data analysis 105 6.3 Results 105 6.3.1 The pH and nutrient characteristics of the biochar produced and tested 105 6.3.2 Immediate effects of atrazine and biochar pretreatment on soil organic carbon 106 6.3.3 Immediate effects of atrazine and biochar pretreatment on soil pH 107 6.3.4 Immediate effects of atrazine and biochar pretreatment on soil 108 available P 6.3.5 Immediate effects of atrazine and biochar pretreatment on exchangeable bases in the soil 109 6.3.6 Immediate effects of atrazine and biochar pretreatment on dry biomass weight of maize seedlings 110 6.3.7 Regression analysis indicating contributions of selected biochar nutrient properties to organic carbon and macronutrient contents in the 110 atrazine-treated soil 6.4 Discussion 115 6.5 Conclusions 117 References 118 Benton Otieno, Mervyn Khune, John Kabuba and Peter Osifo 7 Process configuration of combined ozonolysis and anaerobic digestion for 121 wastewater treatment Abbreviations 122 7.1 Introduction 122 7.2 Methodology 123 123 7.2.1 Materials 7.2.2 Distillery wastewater and waste activated sludge 124 7.2.3 Ozonolysis pre-treatment process for WAS and DWW 124 7.2.4 Anaerobic digestion of WAS and DWW 125 7.2.5 Ozonolysis post-treatment of anaerobically digested DWW 126 126 7.2.6 Physical and chemical analysis 7.3 Results and discussion 126

X

7.3.1 7.3.2 7.3.3 7.3.4 7.4

Contents

Characteristics of WAS and DWW before and after ozonolysis pre-treatment 126 Effect of ozone pre-treatment on anaerobic digestion of WAS Effect of ozone pre-treatment on anaerobic digestion of DWW Ozonolysis of anaerobically digested DWW (post-treatment) 133 Conclusions References 134

127 128 130

Patrick Leonard Omokpariola, Patrice A. C. Okoye, Victor U. Okechukwu and Daniel Omeodisemi Omokpariola 8 Concentration levels and risk assessment of organochlorine and organophosphate pesticide residue in selected cereals and legumes sold in Anambra State, south-eastern Nigeria 137 8.1 Introduction 138 139 8.2 Materials and methods 8.2.1 Sample collection and preparation 139 8.2.2 Chemicals 140 8.2.3 Extraction of pesticide residues from samples 140 8.2.4 Clean-up 140 8.2.5 Analysis of organochlorine and organophosphate pesticides 140 8.2.6 Quality control 141 141 8.2.7 Statistical analysis 8.2.8 Pesticide toxicity Index 141 8.2.9 Health and exposure risk assessment 142 8.3 Results and discussion 143 8.3.1 Mean Concentration of organochlorine and organophosphate pesticides residues 143 8.3.2 Pesticide toxicity index 148 150 8.3.3 Health risk assessment 8.4 Conclusions 153 References 154 Babasanmi Oluwole Abioye, Aderonke Adetutu Okoya and Abimbola Bankole Akinyele 9 Adsorption of trichloroacetic acid from drinking water using polyethylene terephthalate waste carbon and periwinkle shells–based chitosan 159 9.1 Introduction 160 9.2 Material and methods 161 9.2.1 Collection of materials 161 9.2.2 Preparation of caustic alkali from cocoa husk ash 161 162 9.2.3 Chemical activation of the carbon 9.2.4 Preparation of chitosan from Periwinkle shell 163

Contents

9.2.5 9.2.6 9.2.7 9.2.8 9.2.9 9.2.10 9.2.11 9.2.12 9.2.13 9.2.14 9.3 9.3.1 9.3.2 9.3.3 9.3.4 9.3.5 9.3.6 9.3.7 9.3.8 9.3.9 9.3.10 9.3.11 9.3.12 9.3.13 9.4

XI

Determination of chitosan yield 163 Determination of moisture content of chitosan 164 Determination of ash content of chitosan 164 Modification of PET activated carbon 164 Characterization of activated PET and chitosan modified activated PET 165 carbon Trichloroacetic acid analysis 165 Batch adsorption experiment 166 Water sampling for TCA analysis 166 Recovery experiment for photometry determination of TCA standard 166 Reusability potential of the adsorbent 167 Results and discussion 167 167 Cocoa husk ash Caustic alkali from cocoa husk ash 167 Physicochemical properties of polyethylene terephthalate activated carbon (PETAC) 168 Physico-chemical properties of chitosan from periwinkle shell 168 Characterization of PET activated carbon and chitosan modified activated 168 carbon with SEM-EDX before adsorption Functional groups of polyethylene terephthalate activated carbon (PETAC) and polyethylene terephthalate modified activated carbon (PETMAC) 170 Characterization of water samples 170 Recovery experiment for TCA photometric determination 170 Parametric studies on the TCA removal from aqueous solution 170 Adsorption of TCA from raw water and conventionally treated water 174 175 Characterization of PETAC and PETMAC with SEM-EDX after adsorption Adsorption equilibrium isotherm 177 Reusability potential of the adsorbent 177 Conclusions 178 References 178

Saheed O. Sanni, Samson O. Akpotu, Agnes Pholosi and Vusumzi E. Pakade 10 Comparative study of the photocatalytic degradation of tetracycline under visible light irradiation using Bi24O31Br11-anchored carbonaceous and silicates catalyst support 181 10.1 Introduction 182 10.2 Materials and methods 183 10.2.1 Preparation of activated carbon from zinc chloride, and phosphoric acid 183 (ACZ, and ACH) from carbonized material (CM) 10.2.2 Preparation of MCM-41 and SBA-15 183 10.2.3 Preparation of BOB photocatalysts 184

XII

10.2.4 10.3 10.3.1 10.3.2 10.3.3 10.4

Contents

Materials characterization 184 Result and discussion 185 Structural, morphological, and optical characteristics Charge transfer properties 188 Photocatalytic activity 188 189 Conclusions References 190

185

Siphumelele T. Mkhondwane and Viswanadha Srirama Rajasekhar Pullabhotla 11 Synergistic effect in bimetallic gold catalysts: recent trends and prospects 193 193 11.1 Introduction 11.2 Synthesis of Au bimetallic catalysts 194 11.2.1 Controlling the particle size and composition 195 11.2.2 Controlling the morphology of the catalyst 196 11.2.3 The role of the support material 201 11.3 Catalysts characterization 201 11.4 Applications 207 207 11.4.1 Oxidation of hydrocarbons 11.4.2 Fuel cell processes 209 11.4.3 Oxidation of biomass derived products 210 11.4.4 Photocatalytic oxidation 212 11.5 Conclusion and outlook 213 References 214 Ntandokazi Mabungela, Ntaote David Shooto, Fanyana Mtunzi and Eliazer Bobby Naidoo 12 Simultaneous removal of methylene blue, copper Cu(II), and cadmium Cd(II) from synthetic wastewater using fennel-based adsorbents 223 12.1 Introduction 223 12.2 Resources and procedures 224 224 12.2.1 Resources 12.2.2 Method used to produce the adsorbents 225 12.2.3 Methods of adsorption preparation 225 12.2.4 Point zero charge process 226 12.2.5 Reusability procedure 226 226 12.2.6 Adsorption data management 12.3 Characterization of the adsorbents 227 227 12.4 Results and discussion 12.4.1 Ultraviolet–Visible spectroscopy results 227 12.4.2 X-ray crystallography results 228

Contents

12.4.3 12.4.4 12.4.5 12.4.6 12.4.7 12.4.8 12.4.9 12.4.10 12.4.11 12.4.12 12.4.13 12.4.14 12.4.15 12.5 12.5.1 12.6

XIII

Scanning electron microscope and energy dispersive X-ray analysis results 228 Fourier transform infrared spectroscopy results 229 Point zero charge (pH(pzc)) 230 Effect of concentration 231 232 Isotherm studies Effect of time 232 Kinetic model studies 234 Effect of temperature 234 Thermodynamics studies 234 Effect of pH 234 Proposed mechanism reaction 236 236 Reusability studies Comparison studies of the qemax of the current adsorbents with previous studies 238 Post adsorption results 238 FTIR results after adsorption 238 Conclusions 239 240 References

Uche Eunice Ekpunobi, Uzochukwu Abraham Onuigbo, Ifeyinwa Tabugbo, Emma Amalu, Christopher Ihueze, Caius Onu, Philomena Igbokwe, Azubike Ekpunobi, Sunday Agbo and Happiness Obiora-Ilouno 13 The investigation of the physical properties of an electrical porcelain insulator manufactured from locally sourced materials 243 13.1 Introduction 244 13.2 Materials 245 13.2.1 Characterization 245 245 13.2.2 Method 13.2.3 Water absorption 246 13.2.4 Linear shrinkage 247 13.2.5 Apparent porosity 247 13.2.6 Bulk density 247 13.3 Result and discussion 248 248 13.3.1 X-ray fluorescence and X-ray diffraction analysis of the clay 13.3.2 Apparent porosity 249 13.3.3 Water absorption 250 13.3.4 Linear shrinkage 251 13.3.5 Bulk density 251 252 13.4 Conclusion References 253

XIV

Contents

Edwige Anagued Haman, Valéry Paul Moumbon, spce Abdourahman Fadimatou, Jean Momeni and Bathelemy Ngameni 14 A new sphingoid derivative from Acacia hockii De Wild (Fabaceae) with antimicrobial and insecticidal properties 255 256 14.1 Introduction 14.2 Material and methods 257 14.2.1 General experimental procedure 257 14.2.2 Plant material 257 14.2.3 Extraction and isolation 257 14.2.4 Evaluation of the antimicrobial activity 258 14.2.5 Insecticidal test of the hexane, acetone, methanol extracts and 259 compound 1 14.3 Results and discussion 259 14.3.1 Identification of compound 1 259 14.3.2 Result of the insecticide test on C. maculatus 262 14.3.3 Results of the antimicrobial test 263 14.4 Conclusion 267 References 267 Kazeem A. Alabi, Ibrahim O. Abdulsalami, Kazeem O. Ajibola, Nusirat A. Sadiku, Mariam D. Adeoye, Abdul Azeez T. Lawal and Rasheed A. Adigun 15 Protection of wood against bio-attack and research of new effective and environmental friendly fungicides 269 15.1 Introduction 270 15.2 Materials and methods 271 272 15.3 Preparation of soluble soap 15.4 Production of metallic soap [copper (II) soap] 272 15.5 Synthesis of urea complexes from metallic soap 272 15.6 Synthesis of thiourea complexes from metallic soap 273 15.7 Physicochemical parameters of the synthesized compounds 273 15.7.1 Melting point 273 274 15.7.2 Moisture content 15.7.3 Determination of ash content 274 15.7.4 Determination of sulphated ash contents 274 15.7.5 Solubility test 275 15.7.6 Colour 275 15.7.7 Absorption spectral analysis 275 275 15.7.8 Infrared spectroscopy analysis 15.7.9 Scanning electron microscope coupled with energy-dispersive X-ray spectroscopy (SEM/EDS) 275 15.8 Antifungal assay 276

Contents

15.9 15.9.1 15.10 15.11 15.11.1 15.12 15.13

XV

Results and discussions 277 Physical properties of synthesized compounds 277 UV–visible spectra of synthesized compounds 278 Infra-red spectra of syntthesized compounds 278 Energy-dispersive X-ray analysis (EDX) with Scanning electron microscope 279 (SEM) Anti fungi assay 283 Conclusions 283 References 284

Cheriyan Ebenezer and Rajadurai Vijay Solomon 16 Exploring the solvation of water molecules around radioactive elements in nuclear waste water treatment 287 16.1 Introduction 287 16.2 Computational details 289 16.3 Results and discussion 289 16.3.1 Coordination environment 289 16.3.2 Interactions between water molecules and metals/metal oxides 291 16.3.3 Strength of interactions among water molecules and metal/metal 293 oxides 16.4 Conclusions 296 References 297 Nitish Sookool and Marie Chan Sun 17 Changing our outlook towards vulnerable women for societal resilience 301 17.1 Introduction 301 17.2 Methods 303 17.2.1 Study design overview 303 17.2.2 Inclusion and exclusion criteria 303 303 17.2.3 Consent and confidentiality 17.2.4 Topic guide for interview 303 17.2.5 Data analysis 304 17.3 Results 304 17.3.1 Theme 1: drug injection scenario 305 308 17.3.2 Theme 2: sex work interplay 17.3.3 Theme 3: sexual behaviour screenplay 309 310 17.3.4 Additional data 17.4 Discussion 310 17.4.1 Background and setting 310 17.4.2 Injection practices of WIDUs 311

XVI

17.4.3 17.4.4 17.4.5 17.4.6 17.5

Index

Contents

Sexual behaviours of WIDUs 311 Participants’ insight 311 Strengths and limitations 312 Recommendations 312 Conclusions 312 313 References 317

List of contributing authors Ibrahim O. Abdulsalami Department of Chemical Sciences College of Natural and Applied Sciences Fountain University P.M.B 4491 Osogbo Nigeria Babasanmi Oluwole Abioye Institute of Ecology and Environmental Studies Obafemi Awolowo University Ile-Ife Nigeria E-mail: [email protected] Mariam D. Adeoye Department of Chemical Sciences College of Natural and Applied Sciences Fountain University P.M.B 4491 Osogbo Nigeria Rasheed A. Adigun Department of Chemical Sciences College of Natural and Applied Sciences Fountain University P.M.B 4491 Osogbo Nigeria Sunday Agbo Project Development Institute Independent Layout Enugu Nigeria Kazeem O. Ajibola Department of Chemical Sciences College of Natural and Applied Sciences Fountain University P.M.B 4491 Osogbo Nigeria Abimbola Bankole Akinyele Pure and Industrial Chemistry Department Nnamdi Azikwe University Awka Anambra State Nigeria

https://doi.org/10.1515/9783111071428-202

Samson O. Akpotu Biosorption and Water Treatment Research Laboratory Department of Biotechnology and Chemistry Vaal University of Technology Vanderbijlpark 1900 South Africa Kazeem A. Alabi Industrial and Environmental Unit Department of Chemical Sciences College of Natural and Applied Sciences Fountain University P.M.B 4491 Osogbo Nigeria E-mail: [email protected] Emma Amalu Department of Pure and Industrial Chemistry Nnamdi Azikiwe University Awka Anambra State Nigeria Nadjah Belattar Pharmaceutical Sciences Research Center (CRSP) Ali Mendjli Constantine 25000 Algeria; Research Unit of Valorisation of Natural Resources Bioactive Molecules and Physicochemical and Biological Analyses (VARENBIOMOL) Mentouri Brothers University Constantine -1 Algeria E-mail: [email protected] John P. Canal Department of Chemistry Simon Fraser University Burnaby Canada E-mail: [email protected]

XVIII

List of contributing authors

Francisca Claveria-Cádiz Programa de Doctorado Conjunto en Ciencias Mención Química Universidad Técnica Federico Santa María 2390123 Valparaíso Chile; Universidad de Valparaíso 2360102 Valparaíso Chile E-mail: [email protected] Fadimatu N. Dabai Chemical Engineering Department University of Abuja Abuja 234 Nigeria Abdelhamid Djekoun Pharmaceutical Sciences Research Center (CRSP) Ali Mendjli Constantine 25000 Algeria Pamhidzai Dzomba Department of Chemistry Bindura University of Science Education Bindura Zimbabwe E-mail: [email protected] Cheriyan Ebenezer Department of Chemistry Madras Christian College (Autonomous) [Affiliated to the University of Madras] Chennai Tamil Nadu 600 059 India Azubike Ekpunobi Department of Physics and Industrial Physics Nnamdi Azikiwe University Awka Anambra State Nigeria

Uche Eunice Ekpunobi Department of Pure and Industrial Chemistry Nnamdi Azikiwe University Awka Anambra State Nigeria E-mail: [email protected] spce Abdourahman Fadimatou Laboratory of Organic Chemistry and Applications Department of Chemistry Faculty of Science University of Ngaoundere Ngaoundere Cameroon E-mail: [email protected] Luke Gwatidzo Department of Chemistry Bindura University of Science Education Bindura Zimbabwe E-mail: [email protected] Edwige Anagued Haman Laboratory of Organic Chemistry and Applications Department of Chemistry Faculty of Science University of Ngaoundere Ngaoundere Cameroon E-mail: [email protected] Philomena Igbokwe Department of Chemical Engineering Nnamdi Azikiwe University Awka Anambra State Nigeria

List of contributing authors

XIX

Christopher Ihueze Department of Industrial and Production Engineering Nnamdi Azikiwe University Awka Anambra State Nigeria

Ntandokazi Mabungela Department of Chemistry Applied Chemistry and Nano Science Laboratory Vaal University of Technology 1900 Vanderbijlpark South Africa E-mail: [email protected]

Baba Y. Jibril Chemical Engineering Department Ahmadu Bello University Zaria 234 Nigeria

Ratiba Mekkiou Research Unit of Valorisation of Natural Resources Bioactive Molecules and Physicochemical and Biological Analyses (VARENBIOMOL) Mentouri Brothers University Constantine -1 Algeria

John Kabuba Department of Chemical Engineering Vaal University of Technology Vanderbijlpark South Africa Mervyn Khune Department of Chemical Engineering Vaal University of Technology Vanderbijlpark South Africa Adel Krid Pharmaceutical Sciences Research Center (CRSP) Ali Mendjli Constantine 25000 Algeria Aleksey E. Kuznetsov Departamento de Química Universidad Técnica Federico Santa María 7660251 Santiago Chile E-mail: [email protected] Abdul Azeez T. Lawal Department of Chemical Sciences College of Natural and Applied Sciences Fountain University P.M.B 4491 Osogbo Nigeria

Siphumelele T. Mkhondwane Department of Chemistry University of Zululand Kwa-Dlangezwa 3886 South Africa Jean Momeni Laboratory of Organic Chemistry and Applications Department of Chemistry Faculty of Science University of Ngaoundere Ngaoundere Cameroon E-mail: [email protected] Valéry Paul Moumbon National Advanced School of Agro-Industrial Sciences University of Ngaoundere Ngaoundere Cameroon E-mail: [email protected] Fanyana Mtunzi Department of Chemistry Applied Chemistry and Nano Science Laboratory Vaal University of Technology 1900 Vanderbijlpark South Africa

XX

List of contributing authors

Eliazer Bobby Naidoo Department of Chemistry Applied Chemistry and Nano Science Laboratory Vaal University of Technology 1900 Vanderbijlpark South Africa Bathelemy Ngameni Department of Pharmacognosy and Pharmaceutical Chemistry Faculty of Medicine and Biomedical Sciences University of Yaounde I Yaounde Cameroon E-mail: [email protected]

Daniel Omeodisemi Omokpariola Department of Pure and Industrial Chemistry Faculty of Physical Science Nnamdi Azikiwe University Anambra 420261 Nigeria E-mail: [email protected] Caius Onu Department of Chemical Engineering Nnamdi Azikiwe University Awka Anambra State Nigeria

Happiness Obiora-Ilouno Department of Statistics Nnamdi Azikiwe University Awka Anambra State Nigeria

Uzochukwu Abraham Onuigbo Department of Pure and Industrial Chemistry Nnamdi Azikiwe University Awka Anambra State Nigeria E-mail: [email protected]

Victor U. Okechukwu Department of Pure and Industrial Chemistry Nnamdi Azikiwe University Awka Anambra Nigeria

Beatrice Olutoyin Opeolu Faculty of Applied Sciences Cape Peninsula University of Technology Cape Town South Africa E-mail: [email protected]

Aderonke Adetutu Okoya Institute of Ecology and Environmental Studies Obafemi Awolowo University Ile-Ife Nigeria

Peter Osifo Department of Chemical Engineering Vaal University of Technology Vanderbijlpark South Africa

Patrice A. C. Okoye Department of Pure and Industrial Chemistry Nnamdi Azikiwe University Awka Anambra Nigeria

Benton Otieno Research Centre for Renewable Energy and Water Vaal University of Technology Vanderbijlpark South Africa Department of Chemical Engineering Vaal University of Technology Vanderbijlpark South Africa E-mail: [email protected]

Patrick Leonard Omokpariola Chemical Evaluation and Regulation National Agency for Food and Drug Administration and Control Isolo Industrial Estate Oshodi Expressway Isolo Lagos 101263 Nigeria

List of contributing authors

Toyese Oyegoke Chemical Engineering Department Ahmadu Bello University Zaria 234 Nigeria E-mail: [email protected] Yetunde Bunmi Oyeyiola Department of Crop Production and Soil Science Ladoke Akintola University of Technology Ogbomoso Nigeria E-mail: [email protected] Vusumzi E. Pakade Biosorption and Water Treatment Research Laboratory Department of Biotechnology and Chemistry Vaal University of Technology Vanderbijlpark 1900 South Africa Agnes Pholosi Biosorption and Water Treatment Research Laboratory Department of Biotechnology and Chemistry Vaal University of Technology Vanderbijlpark 1900 South Africa Viswanadha Srirama Rajasekhar Pullabhotla Department of Chemistry University of Zululand Kwa-Dlangezwa 3886 South Africa E-mail: [email protected] Nusirat A. Sadiku Department of Forest Resources Management University of Ilorin Ilorin Kwara State Nigeria

XXI

Saheed O. Sanni Biosorption and Water Treatment Research Laboratory Department of Biotechnology and Chemistry Faculty of Applied and Computer Sciences Vaal University of Technology Vanderbijlpark 1900 South Africa E-mail: [email protected] Ntaote David Shooto Department of Chemistry Applied Chemistry and Nano Science Laboratory Vaal University of Technology 1900 Vanderbijlpark South Africa E-mail: [email protected] Rajadurai Vijay Solomon Department of Chemistry Madras Christian College (Autonomous) [Affiliated to the University of Madras] Chennai Tamil Nadu 600 059 India E-mail: [email protected] Nitish Sookool Harm Reduction Unit Ministry of Health and Wellness Port Louis Mauritius Marie Chan Sun Department of Medicine Faculty of Medicine and Health Sciences University of Mauritius Réduit 80837 Mauritius E-mail: [email protected]

XXII

List of contributing authors

Ifeyinwa Tabugbo Department of Pure and Industrial Chemistry Nnamdi Azikiwe University Awka Anambra State Nigeria Adamu Uzairu Chemistry Department Ahmadu Bello University Zaria 234 Nigeria

Saidu M. Waziri Chemical Engineering Department Ahmadu Bello University Zaria 234 Nigeria Rudo Zhou Department of Chemistry Bindura University of Science Education Bindura Zimbabwe E-mail: [email protected]

Toyese Oyegoke*, Fadimatu N. Dabai, Saidu M. Waziri, Adamu Uzairu and Baba Y. Jibril

1 Computational study of propene selectivity and yield in the dehydrogenation of propane via process simulation approach Abstract: Propene is a vital feedstock in the petrochemical industry with a vast range of applications. And there is a continuous rise in propene demand. To gain insight into how the on-purpose method could help meet the demand in the propene market, we investigated the impact of temperature (T ) and pressure (P) on product distribution in terms of product yield and selectivity using the process simulation approach. Existing related studies were deployed to identify possible products that could be evaluated in the simulation. In the study, we used Gibbs minimization (with Gibb’s reactor) to predict the likely products obtained at different T and P. The impact of feed purity on product distribution was also evaluated. The study was aided by using the Aspen HYSYS process simulator, while Design Expert was used to search for the optimum conditions for higher conversion, yield, and selectivity. Results obtained for the modeling and simulation of the process show that operating the production process at a lower pressure would favor higher selectivity within the temperature range of 500–600 °C. In comparison, the one run at a higher pressure was predicted to be only promising, showing better selectivity within the range of 550–650 °C. The feed purity significantly impacts the propene amount, especially for one with sulfur impurity, leading to the formation of smaller olefins and sulfide compounds. Our study reveals the importance of reviewing feed purity before charging them into the dehydrogenation reactor to prevent poisoning, coking, and other activities, which do lead to undesired products like methane and ethylene. A catalyst can also be designed to efficiently dehydrogenate the propane to propene at a lower temperature to prevent side reactions. Keywords: impurity; optimization; propane; propene; selectivity; yield.

*Corresponding author: Toyese Oyegoke, Chemical Engineering Department, Ahmadu Bello University, Zaria 234, Nigeria, E-mail: [email protected]. https://orcid.org/0000-0002-2026-6864 Fadimatu N. Dabai, Chemical Engineering Department, University of Abuja, Abuja 234, Nigeria Saidu M. Waziri and Baba Y. Jibril, Chemical Engineering Department, Ahmadu Bello University, Zaria 234, Nigeria Adamu Uzairu, Chemistry Department, Ahmadu Bello University, Zaria 234, Nigeria As per De Gruyter’s policy this article has previously been published in the journal Physical Sciences Reviews. Please cite as: T. Oyegoke, F. N. Dabai, S. M. Waziri, A. Uzairu and B. Y. Jibril “Computational study of propene selectivity and yield in the dehydrogenation of propane via process simulation approach” Physical Sciences Reviews [Online] 2023. DOI: 10.1515/psr-2022-0242 | https://doi.org/10.1515/9783111071428-001

2

1 Computational study of C3H6 selectivity and yield in C3H8 dehydrogenation

1.1 Introduction The role of olefins in producing petrochemical products and other valuable materials like polymers is highly significant. Most significantly, ethylene and propene have been shown to have a wide range of applications in the production of packaging, automotive, construction & infrastructure, polymers, fibers, consumable goods, chemicals, resins, pharmaceutical products, electronics, and many other areas [1, 2]. The report from the market survey indicates that the demand for propene is widely spread across all the continents due to its wide range of applications earlier stated. Reports show that the market value for polypropene was 75 billion US dollars in 2020 and 109 billion US dollars in 2022 and has been predicted to rise progressively with time [1]. According to a Global Newswire report [2], the propene market value would increase to 135 billion US dollars in 2029. The propene could be produced via different approaches, which include steam cracking, fluid catalytic cracking (FCC), metathesis of olefins (MO), methanol-to-propene (MTP), and propane dehydrogenation (otherwise referred to as the on-purpose method of producing propene) using either coal, petroleum crude, natural gas, or biomass [3]. In meeting the massive demand for propene, research works have been exploring different innovations to improve the propene yield via the promotion of the on-purpose method for propene synthesis, which currently claims a lower share in the market. The bulk of the propene sold in the market is produced via the fluid catalytic and steam cracking (FCSC) unit in the refineries. And the FCSC units are becoming overwhelmed with meeting the market demand due to other materials it produces. The wide availability of shale gas also makes the on-purpose method worth considering for meeting the market demand. As a result, it is, therefore, essential to seek better measures for understanding the on-purpose techniques [4, 5] and ways of improving its productivity. The on-purpose methods would add value to propane, enabling it to take advantage of the available market that propene is offering across the globe. Research works have investigated the way forward for the on-purpose method to take advantage of the market feasibly. Some of the works have attempted to seek better measures of improving the propene yield from the dehydrogenation of propane via the design of a better catalyst for the process. Platinum and chromium oxide–based catalysts are presently used in commercial plant productions. Works like Chen et al. [6] confirm that alloying of Pt with zinc (Zn) is a way to obtain higher propene yield with lesser deactivation. Srisakwattana et al. [7] found that an alloy of Pt with indium (In) could significantly retard the coking of the catalyst. The alloy of Pt with gallium (Ga) has also been established to substantially improve the yield via the drastic reduction in the deactivation rate reported by Wang et al. [8]. Similarly, some other works explore the catalysis of the chromium oxide–based catalysts for the process. The studies include one [9], which unraveled that dormancy of chromium oxide–based catalyst with Cr–Cr site activities significantly promotes the deactivation of the catalyst. In contrast, another study [10] further shows that the Cr–O site favors propene production compared to the

1.2 Materials and methods

3

surface with Cr site dominance (Cr–Cr site). The study suggests the reduction of the Cr sites on the catalyst via the substitution of the site with new metals like Mo [11] to improve the yield and retards the deactivation rate on its surface. The studies of Hu et al. [12] further established measures for enhancing the stability of the CrO catalyst and its selectivity toward propene production with the use of HZSM-5 zeolite as a support in the catalyst synthesis. Moreover, the efficiency of the CrO-based catalyst was further improved by Golubina et al. [13] via the introduction of ZrO2-SiO2 in the catalyst. Many other metals were also established for alloying the Pt and substituting the Cr site on CrO-catalysts in the literature to improve the propene yield from propane dehydrogenation. However, less attention is given to exploring the thermodynamic implication on product selectivity and yield in the literature. The bulk of the studies primarily focuses on exploring the catalysis of the process with less attention to the thermodynamics of the on-purpose method of synthesizing propene from propane. This research, therefore, explored this insight to understand the thermodynamic contribution and implications on propene yield and its selectivity during production as temperature and pressure vary (in the absence of the catalytic effects). In the study, we deployed the process simulation and modeling approach via the Gibbs minimization approach [14, 15] using the Peng Robinson thermodynamic model in the Aspen HYSYS simulation package. In addition, the impact of the feed purity was also evaluated in our study.

1.2 Materials and methods 1.2.1 Process modeling and simulations A process simulation approach was employed using the Aspen HYSYS process simulator to study the influence of temperature and pressure on product yield and selectivity. The study approach is diagrammatically summarized in Figure 1.1, and details on the method employed in the study are as follows in the later subsections. The feed material stream was modeled to be 100% propane. This choice of focusing the analysis on the pure form of the propane in this study was due to the established studies [16, 17], which has successfully designed and analyzed feed preparation units. The designed feed preparation unit is comprised of depropanizer [18, 19], which is vital for extracting pure propane from the feed mixture via specialized design distillation and adsorption columns. Other components include demethanizer, de-ethanizer, and desulfurizer for removing methane, ethane, propane, and sulfur [18]. 1.2.1.1 Collections and addition of components Previous related studies were used to identify possible products (or components) that could be investigated [9, 19, 20]. The components to be collated were limited to all the nonsurface

4

1 Computational study of C3H6 selectivity and yield in C3H8 dehydrogenation

Figure 1.1: Flow chart showing the approach employed in investigating the temperature and pressure on propane dehydrogenation.

species involved in the studies, both in dehydrogenation and cracking mechanisms. These concerned species were all added to the process simulation environment. 1.2.1.2 Thermodynamic model selections The Peng–Robinson model was selected to predict the physical properties of the species involved in this process to be simulated. The choice of Peng–Robinson was made since the species involved in the studies were gases and nonpolar molecules, according to the literature [21]. 1.2.1.3 Modeling the propane dehydrogenation process The propane dehydrogenation process was modeled via the two material streams, one for the feedstock (propane) and the other for the product (propene), using a Gibb reactor model that employs thermodynamic principles to predict the possible products obtainable at different temperatures and pressures. The feedstock material stream was modeled by composition specification (100% propane), while a range of the temperatures (38–1000 °C) and pressures (1 and 10 atm) was explored. One-factor-at-a-time (OFAT) study approach was used to understand the effect of operating conditions on the conversion, yield, and selectivity toward propene.

1.2.2 Process optimization studies A full central composite design of response surface methodology was employed in the experimental design to determine the optimum conditions that would give the highest conversion (C), yield (Y ), and better selectivity (S). The understanding gained from the effect of the operating conditions studied in the preceding section (Section 1.2.1.3) was used

1.3 Results and discussions

5

to make a proper choice of boundary (that is, study range) condition specifications for the temperature and pressure employed in the study. The analysis involved 16 runs, which encompass 11 noncenter and 5 center points. The optimization problem was solved with the aid of a Design Expert and reported appropriately.

1.2.3 Impact of feed purity on the dehydrogenation process Here, we further evaluated the impact of feed purities on the nature of the product distribution obtained from the dehydrogenation of propane into propene. Different cases were investigated computationally. To understand this, we introduce some selected impurities, like methane, ethane, and sulfur, singly into pure propane to formulate different impure propane cases. The cases were compared to the pure case (pure propane) to gain insight into how they affect propane dehydrogenation product distribution using the process simulation. The introduction of the impurity to feed (propane) was set to be in the mole ratio of 2.268/0.5.

1.3 Results and discussions Here, the results are presented for the modeling, simulation, effect (i.e., temperature and pressure) analysis, process optimization study, and impact of the feed purity on the product selectivity and yield.

1.3.1 Modeling the propane dehydrogenation process The model for propene production simulation via propane was successfully built, and the process flow diagram developed for the modeled process is presented in the subsection; other reports concerning the optimization studies and the thermodynamic effect on the process were also reported in the later subsections. 1.3.1.1 Process flow diagram The process model built to produce the propene from propane is pictorially represented in Figure 1.2, where the reactor model (Gibb-reactor model) used, the inlet material streams (i.e., 100% propane and 0% hydrogen) carrying the feed charged into the reactor, and the outlet material streams (i.e., vapor and liquid products), including some control systems, were represented successfully. This includes the compressor modeling for varying the reaction pressure and the heater for changing the reaction temperature were explored in the study.

6

1 Computational study of C3H6 selectivity and yield in C3H8 dehydrogenation

Figure 1.2: Process flow diagram for modeling and simulation of propene production from propane under different thermodynamic conditions.

A Gibbs reactor model was successfully used to simulate the impact of temperature (T ) and pressure (P) on the process absence of the catalyst. 1.3.1.2 Effect of temperature and pressure on product selectivity The effect of temperature (T ) and pressure (P) on the product (propene) selectivity was investigated, revealing that at 550 °C, the propene selectivity was found to be 0.58 (58%) at a lower (1 atm) pressure. In comparison, at 600 °C, the propene selectivity was 0.56 (56%) at the higher (10 atm) pressure. The study unveils that a lower (1 atm) pressure was more favorable compared to a higher (10 atm) pressure when a higher selectivity is desired, which is graphically presented in Figure 1.3 (low P) and Figure 1.4 (high P).

Figure 1.3: Temperature (T ) and pressure (P) effect on selectivity at low P.

1.3 Results and discussions

7

Figure 1.4: Temperature (T ) and pressure (P) effect on selectivity at high P.

Moreover, it was deduced that a lower pressure (1 atm in Figure 1.3) displayed a higher propene selectivity (58%) at a much lower temperature (550 °C) compared to higher pressure (10 atm in Figure 1.4) that displayed the highest selectivity (56%) at 600 °C. Findings from the study reveal that production operated at lower pressure favors higher selectivity within 500–600 °C. In contrast, one operated at a higher pressure promotes better selectivity within a temperature range of 550–650 °C. The findings were found to have shown agreement with the experimental reports in the literature [22–25] that were reported to be favorable at the temperature of 500 °C and above. The propene selectivity (maximum of 58%) obtained from our studies was found to be lower than that reported by Maddah [26] for the existing use of CATOFIN (at 600 °C and 0.3–1.0 bar) and OLEFLEX (at 630–650 °C and 1.2–2.0 bar) technology, which shows a higher selectivity of 80–90% for the propene synthesis from propane in the presence of chromium oxide and platinum-based catalysts, respectively. This implies that the higher selectivity (80–90%) obtained in the literature resulted from the catalytic effect on the process. 1.3.1.3 Effect of temperature and pressure on product yield and feed conversion Further evaluation of the impact of temperature (T ) and pressure (P) on the product (propene) yield reveals that at low (1 atm in Figure 1.5) pressure, the best propene yield was found to be 55.2% at 616.9 °C, while at high (10 atm in Figure 1.6) pressure, the best propene yield was found to be 52.9% at 682.1 °C. Moreover, the study also revealed that dehydrogenation would not hold at room temperature (38 °C) due to endothermic nature

8

1 Computational study of C3H6 selectivity and yield in C3H8 dehydrogenation

(requiring energy to make it feasible), which agrees with existing literature [22–25] that reported it to be an endothermic process. Moreover, a lower pressure (1 atm in Figure 1.5) would be more favorable than higher pressure (10 atm in Figure 1.6) to attain a higher yield. A lower pressure (1 atm) achieved a higher propene yield of 55.2% at a lower temperature of 616.9 °C compared to higher pressure (10 atm), which displayed the highest yield of 52.9% at a higher temperature of 682.1 °C. It was also deduced that the propane conversion (C = ∼87%, R = 13% at 500 °C and C = ∼96%, R = 4% at 600 °C) is relatively higher at low pressure (1 atm) than one carried out at higher pressure (10 atm, which was C = ∼78%, R = 22% at 500 °C and C = ∼90%, R = 10% at 600 °C). This implies that lower pressure favor higher conversion, C, of propane, and lesser recovery, R, of unconverted propane. The result also reveals that lower pressure (within the temperature range of 550– 650 °C) and higher pressure (within the temperature range of 650–750 °C) would favor higher propene yield. However, using propane dehydrogenated at a lower pressure (1 atm) was found to have shown better yield (compared to higher pressure, 10 atm) with the study temperature range, which shows a good agreement with experiment reports by Wang et al. [8] and Chen et al. [6] that obtained better yield at 600 °C and low P.

Figure 1.5: Temperature (T ) and pressure (P) effect on yield and feed conversion at low P. Note that the “recovery, R” axis label is for the feed (propane) unconverted trend, the “conversion, C” is 1 − R, while the “yield, Y” axis label is for the new species (ethane, methane, ethylene, propene, and hydrogen) evolution/ appearance trend across the different temperature range.

1.3 Results and discussions

9

Figure 1.6: Temperature (T ) and pressure (P) effect on yield and feed conversion at high P. Note that the “recovery, R” axis label is for the feed (propane) unconverted trend, the “conversion” is 1 − R, while the “yield, Y” axis label is for the new species (ethane, methane, ethylene, propene, and hydrogen) evolution/ appearance trend across the different temperature range.

1.3.2 Process optimization studies A temperature range of 500–750 °C and a pressure range of 2–10 atm were used in the design of the full central composite response surface experiment for the process optimization studies. The matrix for the design that shows the factor (T and P) and response (C, Y, and S) variables are presented in Table 1.1. It comprises 16 runs (different cases) with five central and 11 noncentral points. R-squared values in Table 1.2 confirm the choice of the cubic model used for the mathematical representation of the propane conversion, C, propene yield, Y, and propene selectivity, S, due to its higher R-squared values obtained from its model analysis, according to the literature reports [27, 28]. The set of the developed prediction models for predicting the propane conversion, C, propene yield, Y, and selectivity, S, are presented as below equations. C(Propene) = 7.63765 − 0.034235 * T − 0.316824 * P + 0.000491 * T * P + 0.000058 * T 2 + 0.024837 * P2 − 1.09E−08 * T 2 * P − 0.000038 * T * P2 − 3.24E−08 * T³ − 0.000055 * P³

10

1 Computational study of C3H6 selectivity and yield in C3H8 dehydrogenation

Table .: A central composite randomized response surface design matrix for the study. T (oC)

P (atm)

C (propane)

Y (propene)

S (propene)

. . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . .

Y (Propene) = 4.95568 − 0.022908 * T − 0.194915 * P + 0.000262 * T * P + 0.000040 * T 2 + 0.016568 * P2 + 6.60E−08 * T 2 * P − 0.000026 * T * P2 − 2.29E−08 * T³ − 8.63E−06 * P³ S(Propene) = 1.21139 − 0.003555 * T − 0.025258 * P + 6.53434E−06 * T * P + 6.89E −06 * T 2 + 0.002869 * P2 + 6.08E−08 * T 2 * P − 5.38E−06 * T * P2 − 4.57E−09 * T ³ +0.000022 * P³ The optimum pressure and temperature for the highest propene selectivity and yield were evaluated via the use of the constraints and objective functions presented in Table 1.3, where the constraints were to limit the search to a range of temperature (501–749 °C) and pressure (1.0–10 atm). Moreover, the objectives were set to target = 1 for yield and Table .: A summary of model statistics for the various response variables in the analysis. Model

C 

R Linear FI Quadratic Cubic

. . . .

Y 

Adj. R

. . . .



R

. . . .

S 

Adj. R

. . . .



Adj. R

. . . .

. . . .

R

1.3 Results and discussions

11

Table .: Optimization solution. Name o

A:T ( C) B:P (atm) C (propene) Y (propene) S (propene)

Goal Minimize Is in range Is in range Is target =  Is target = 

Lower limit

Upper limit

Lower weight

Upper weight

Importance

  . . .

    

    

    

    

selectivity, while the conversion is set to be within a range of 0.5–1. The optimization problem was solved using the simplex optimization method (with a simplex fraction of 0.01 and 99,000 design points). Analysis of the contour plot in Figure 1.7 shows that higher temperatures (570 °C and above) favor high propane conversion and lower temperatures (less than 649 °C) favor higher propene selectivity. In contrast, moderate (or average) temperatures (550–670 °C) favor high propene yield around the low-pressure region. Moreover, the optimization

Figure 1.7: Contour plot used in the search for optimum condition. Note that red is 1 (high), light green is 0.5 (moderate), and blue is 0 (low). The flag shows the predicted optimum.

12

1 Computational study of C3H6 selectivity and yield in C3H8 dehydrogenation

solution presented in Figure 1.7 (in the flag) confirms that low (1 atm) pressure and 590.6 °C was the optimum pressure and temperature when simulated in the process simulator, favored conversion of 95%, a yield of 54%, and selectivity of 57%. The temperature obtained from the optimization implies that any temperature above the optimum would favor more production of undesired products with poorer selectivity for propene. The study, therefore, suggests using an alternative approach (like the use of catalysts) to attain higher yield and selectivity above this instead of raising the temperature of the reaction, which was predicted to yield more small molecules.

1.3.3 Impact of feed purity on the dehydrogenation process The result obtained for the analysis of propane purity on its dehydrogenation process to yield propene is presented in Figure 1.8. The results displayed the mass fractions of components that significantly evolved in the dehydrogenation process across different cases, which includes propane, ethylene, ethane, methane, propene, and hydrogen sulfide. Findings from the results indicated that all the various impurities evaluated influenced the amount of the propene produced, especially for the feeds with sulfur and methane impurities, which show a much lower amount. The feed with sulfur shows the

Figure 1.8: A plot of product distribution for the case of different feed purities. The feed/impurity mole ratio was set to 2.268/0.5 for methane, ethane, and sulfur in the feed. No impurity denotes propane only.

1.4 Conclusions

13

lowest amount of propene produced with the formation of hydrogen sulfide. The sulfurpolluted propane (feed) tends to retard the formation of smaller paraffin (ethane and methane) and favors the formation of smaller olefins like ethylene. Such activities could lead to coke formation, which can deactivate the catalyst, and the presence of hydrogen sulfide could also poison the catalyst [29, 30]. It is, therefore, essential to design an efficient feedstock purification unit to prepare feedstock (propane) for the dehydrogenation process. Literature [31–33] has also established the need to engineer the catalysis of the process to convert the sulfur impurity in the feed or catalyst to become an advantage for the process (as a promoter).

1.4 Conclusions The process for dehydrogenating propane into propene was successfully modeled with the simulation of temperature and pressure effects on propene production, using the key species reported in previous studies for the propane dehydrogenation and cracking mechanism. The analysis reveals that the production process operated at lower pressure would favor higher selectivity within the temperature range of 500–600 °C, while one operated at a higher pressure would only be promising with better selectivity within the range of 550–650 °C. Process optimization studies were further carried out across a range of temperatures and pressures, from which it was confirmed that low (1 atm) pressure and 590 °C were the optimum pressure and temperature that best favored a high conversion (C = 95%), yield (Y = 54%), and selectivity (S = 57%). Some undesired products like ethylene and methane gas were produced in the simulation even in the optimum condition. Moreover, findings from these studies reveal that the production of undesired species during propane synthesis is not only a result of surface catalysis (or activities) but also that of process thermodynamics, which our studies have successfully revealed via our simulation. From this, it was unveiled that the reaction thermodynamics also favors the production of cracking products (methane, ethylene, and ethane) during the propane dehydrogenation process. However, further works can focus their search on the design of catalytic material that would facilitate high yield at a lower temperature and hinder further the undesired activity of ethylene and methane production (i.e., the scission of the propane and propene) found in this simulation. The impact of the purity on the dehydrogenation process was also evaluated. Methane, ethane, and sulfur were the impurities considered. Our evaluation reveals that the sulfur content shows the most significant impact on the process due to the lowest amount of propene predicted for its simulation. It is, therefore, essential to carefully design an efficient purification unit for preparing the feedstock (propane) and to explore the potential of the catalysis taking its advantage to become a promoter other than poison for its surface.

14

1 Computational study of C3H6 selectivity and yield in C3H8 dehydrogenation

References 1. Fortune Business Insights. Polypropylene market size, share & trends | report. Fortune Business Insight report 2022. Available from: https://www.fortunebusinessinsights.com/industry-reports/polypropylenepp-market-101583 [Accessed 29 Oct 2022]. 2. Propylene market destine to reach USD 135.21 billion with. GlobeNewswire report 2022. Available from: https://www.globenewswire.com/news-release/2022/07/18/2481422/0/en/Propylene-Market-Destine-toReach-USD-135-21-Billion-with-Size-Share-Industry-Growth-Rate-Demand-Revenue-Forecast-By-2029. html [Accessed 29 Oct 2022]. 3. Clariant P. Analysis of alternatives and socio-economic analysis: the use of chromium trioxide in a catalyst for the dehydrogenation of propane to propene. Deutschland: Apeiron-Team NV, The Economics Interface Limited Economics for the Environment Consultancy (EFTEC); 2016. 4. Li CF, Guo X, Shang QH, Yan X, Ren C, Lang WZ, et al. Defective TiO2 for propane dehydrogenation. Ind Eng Chem Res 2020;59:4377–87. 5. Jibril BY. Propane oxidative dehydrogenation over chromium oxide-based catalysts. Appl Catal Gen 2004; 264:193–202. 6. Chen S, Zhao ZJ, Mu R, Chang X, Luo J, Purdy SC, et al. Propane dehydrogenation on single-site [PtZn4] intermetallic catalysts. Chem 2021;7:387–405. 7. Srisakwattana T, Watmanee S, Wannakao S, Saiyasombat C, Praserthdam P, Panpranot J. Comparative incorporation of Sn and in in Mg(Al)O for the enhanced stability of Pt/MgAl(X)O catalysts in propane dehydrogenation. Appl Catal Gen 2021;615:118053. 8. Wang Y, Suo Y, Lv X, Wang Z, Yuan ZY. Enhanced performances of bimetallic Ga-Pt nanoclusters confined within silicalite-1 zeolite in propane dehydrogenation. J Colloid Interface Sci 2021;593:304–14. 9. Oyegoke T, Dabai N, Uzairu A, Jibril EY. Density functional theory calculation of propane cracking mechanism over chromium (III) oxide by cluster approach. J Serb Chem Soc 2021;86:44. 10. Oyegoke T, Dabai FN, Uzairu A, Jibril BE-Y. Mechanistic insight into propane dehydrogenation into propylene over chromium (III) oxide by cluster approach and density functional theory calculations. Eur J Chem 2020;11:342–50. 11. Oyegoke T, Dabai FN, Waziri SM, Uzairu A. Jibril BY. Impact of Mo and W on CrXO3 (X = Cr, Mo, W) catalytic performance in a propane non-oxidative dehydrogenation process. Kemija u Industriji 2022;71:583–90. 12. Hu ZP, Wang Y, Yang D, Yuan ZY. CrOx supported on high-silica HZSM-5 for propane dehydrogenation. J Energy Chem 2020;47:225–33. 13. Golubina Ev, Kaplin IY, Gorodnova Av, Lokteva ES, Isaikina OY, Maslakov KI. Non-oxidative propane dehydrogenation on CrOx-ZrO2-SiO2 catalyst prepared by one-pot template-assisted method. Molecules 2022;27:6095. 14. Haydary J. Chemical process design and simulation. Hoboken, New Jersey: John Wiley & Sons, Inc.; 2018. 15. Haydary J. Reactors. In: Chemical process design and simulation. Hoboken, New Jersey: John Wiley & Sons, Inc.; 2019:101–24 pp. 16. Xiang H, Zhang H, Liu P, Yan Y. Preparation of high purity propane from liquefied petroleum gas in a fixed bed by removal of sulfur and butanes. Chem Eng J 2016;284:224–32. 17. Ahmed MJ, Theydan SK. Modeling of propane separation from light hydrocarbons by adsorption on 4A molecular sieve zeolite. J Nat Gas Sci Eng 2014;18:1–6. 18. Agarwal A, Sengupta D, El-Halwagi M. Sustainable process design approach for on-purpose propylene production and intensification. ACS Sustainable Chem Eng 2018;6:2407–21. 19. Walker K. Techno-economic feasibility of propane dehydrogenation in novel membrane reactors. Eindhoven, Netherlands: Eindhoven University of Technology; 2020. 20. Kenneth DK, Gonurubon J, Michael AO, Douglas NH. Simulation of a process unit for the recovery of light ends from natural gas mixture. Bioprocess Eng 2022;6:33.

References

15

21. Oyegoke T, Dabai FN, Uzairu A, Jibril BEY. Mechanistic insight into propane dehydrogenation into propylene over chromium (III) oxide by cluster approach and density functional theory calculations. Eur J Chem 2020; 11:342–50. 22. Liu J, Liu Y, Ni Y, Liu H, Zhu W, Liu Z. Enhanced propane dehydrogenation to propylene over zinc-promoted chromium catalysts. Catal Sci Technol 2020;10:1739–46. 23. Forero GLA, Velásquez JJA. A generalized cubic equation of state for non-polar and polar substances. Fluid Phase Equil 2016;418:74–87. 24. Xu BJ, Zheng B, Hua WM, Yue YH, Gao Z. High Si/Al ratio HZSM-5 supported Ga2O3: a highly stable catalyst for dehydrogenation of propane to propene in the presence of CO2. Stud Surf Sci Catal 2007;170:1072–9. 25. Docherty SR, Rochlitz L, Payard PA, Copéret C. Heterogeneous alkane dehydrogenation catalysts investigated via a surface organometallic chemistry approach. Chem Soc Rev 2021;50:5806–22. 26. Raman N, Maisel S, Grabau M, Taccardi N, Debuschewitz J, Wolf M, et al. Highly effective propane dehydrogenation using Ga-Rh supported catalytically active liquid metal solutions. ACS Catal 2019;9: 9499–507. 27. Chen S, Chang X, Sun G, Zhang T, Xu Y, Wang Y, et al. Propane dehydrogenation: catalyst development, new chemistry, and emerging technologies. Chem Soc Rev 2021;50:3315–54. 28. Maddah HA. A comparative study between propane dehydrogenation (PDH) technologies and plants in Saudi Arabia. Am Acad Sci Res J Eng, Technol Sci 2018;45:49–63. 29. Cameron AC, Windmeijer FAG. An R-squared measure of goodness of fit for some common nonlinear regression models. J Econom 1997;77:329–42. 30. Chicco D, Warrens MJ, Jurman G. The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Comput Sci 2021;7:1–24. 31. Méndez-Mateos D, Barrio VL, Requies JM, Cambra JF. A study of deactivation by H2S and regeneration of a Ni catalyst supported on Al2O3, during methanation of CO2. Effect of the promoters Co, Cr, Fe and Mo. RSC Adv 2020;10:16551–64. 32. Lamy-Pitara E, Bencharif L, Barbier J. Effect of sulphur on the properties of platinum catalysts as characterized by cyclic voltammetry. Appl Catal 1985;18:117–31. 33. Gao XQ, Li WC, Qiu B, Sheng J, Wu F, Lu AH. Promotion effect of sulfur impurity in alumina support on propane dehydrogenation. J Energy Chem 2022;70:332–9.

John P. Canal*

2 Computational chemistry in the undergraduate inorganic curriculum Abstract: The introduction of computation chemistry has increased in the undergraduate chemistry curriculum. Our method of instruction is centred on an online, self-paced approach where students interact with the material through an instructional handbook, videos, and assignments. In our inorganic undergraduate curriculum students explore computational chemistry though optimization of organometallic complexes, modelling the infrared (IR) and nuclear magnetic resonance (NMR) spectra and investigation of the shape and energy of molecular orbitals. These results are compared to experimentally determined data. The effectiveness of introducing students to computational chemistry to characterize organometallic compounds will be highlighted. Keywords: chemical education; computational chemistry; computer-based learning; laboratory instruction; lower and upper-division undergraduate; organometallic.

2.1 Introduction The adoption of computational chemistry as an education tool in the undergraduate chemistry curriculum has increased in the recent years [1], even though there are still challenges to its greater use, such as the required technical expertise and financial expenditures [2]. Computational chemistry allows students to visually connect with chemical theory and/or experimental data to foster a greater understanding of the material. This level of understanding can be tailored based on the depth of exploration of computational chemistry. Computational chemistry exercises are notable in general first year chemistry [3–8], physical chemistry [9–12], and organic chemistry courses [13–15] with an expansion of the material for the inorganic curriculum required [16–18]. The computational chemistry modules found in the literature tend to focus on the steps and theory of the exercise, with reduced emphasis on the examination of the pedagogical improvements associated with the new method. Evidence of the effectiveness of new computational-based material has focused on either student surveys to evaluate their opinion of the exercise [8–10] and/or an examination of student data for a confirmation of improved learning [4, 8]. Both approaches were adopted in the evaluation of our online, self-paced format to introduce students to computational methods used to analyze organometallic compounds, thus allowing for both a

*Corresponding author: John P. Canal, Department of Chemistry, Simon Fraser University, Burnaby, BC V5A 1S6, Canada, E-mail: [email protected] As per De Gruyter’s policy this article has previously been published in the journal Physical Sciences Reviews. Please cite as: J. P. Canal “Computational chemistry in the undergraduate inorganic curriculum” Physical Sciences Reviews [Online] 2023. DOI: 10.1515/psr-2022-0248 | https://doi.org/10.1515/9783111071428-002

18

2 Chemical education

computational and experimental investigation of the products synthesized in inorganic laboratory courses. Our approach to introduce students to computational analysis of organometallic complexes was developed during the height of the COVID-19 pandemic restrictions, where in-person education was replaced by online approaches [19, 20]. During the Spring 2021 semester at Simon Fraser University, due to strict safety protocols in place because of COVID-19, chemistry laboratory courses were allowed to run a limited number of in-person experiments with the remainder of the course delivered online. This included our third-year inorganic chemistry laboratory course (CHEM 336: Advanced Inorganic Chemistry Laboratory) [21], for which this module was developed. Computational analysis of organometallic complexes was new to our students, and due to the limited in-person opportunities related to the COVID-19 restrictions, an online, self-paced module was developed, and its effectiveness assessed.

2.2 Computational chemistry module This module was developed to increase the use of computational chemistry-based material in the chemistry curriculum and to address the requirements of the online teaching approach adopted during the height of the COVID-19 pandemic. The module was initially run during the Spring 2021 semester and subsequently used in the Spring 2022 semester of CHEM 336 with a total of 50 students completing this unit. CHEM 336 is a traditional advanced inorganic chemistry laboratory course with 8–9 experiments conducted over 13 weeks, in 4-h weekly laboratory periods. Students prepare and isolate coordination complexes which are characterized via methods including nuclear magnetic resonance (NMR), infrared spectroscopy (IR), electron spin resonance (ESR), mass spectrometry (MS) and melting point (MP) determination.

2.2.1 Module Goals/Plan This module strives to introduce students to the computational analysis of organometallic complexes by providing basic instruction on the use and function of computational chemistry. It allows students to explore these complexes through the application of both experimental and computational methods, in order to enhance learning in the inorganic curriculum. The objective of this module is not computational chemistry training for undergraduate students, rather for students to be able to employee computational methods to characterize an organometallic complex through a computational review of the NMR and IR data, the optimization of the structure and molecular orbital evaluation. The focus of the NMR and IR computation analysis was not to predict the peak location, rather to generate similar results to the experimental data, which can be used to aid in the peak assignment.

2.2 Computational chemistry module

– – –

19

The objective of this module includes the development of: Basic computational skills using the GaussView 5.0/Gaussian programs. Models to aid in the interpretation of the IR and NMR spectra, and molecular orbital shapes. Representational competency skills.

The module consists of lower-level calculations to allow easy computation on each student’s personal computer. The GaussView 5.0 [22] and Gaussian [23] programs were chosen as the computational tools since our students have access to these programs on campus. Through the licensing agreement, students were able to download these programs onto their personal devices, allowing them to work on this module remotely. Although we did not investigate the use of alternative free programs, available options included the ORCA [24] and Avogadro [25] programs. The module follows an online, independent learning model and consists of four instructional videos, which focus on specific tasks as well as a detailed step-by-step instructional document. The complex Fe(CO)5 was used as the example complex throughout the instructional document and videos. Students also completed two assignments as part of the program. An analysis of the results from the first assignment and improvements seen in the second assignment allows for an investigation into the effectiveness of our approach. The first step in our evaluation was to determine the students’ previous exposure to computational analysis. This was completed by an anonymous student survey, where students were asked to rate their level of agreement with the following statement: “I did not know about GaussView 5.0/Gaussian before completing the computational chemistry assignment.” Of the 50 students who completed the assignment, 41 replied to the survey. It was determined that 27% of the respondents either agreed or were neutral with the statement, with 73% disagreeing, suggesting that several students had previous experience with computational chemistry. The source of the previous experience was investigated, and it was determined that in another course students were shown the optimization and predicted IR spectrum of CO2 via the GaussView 5.0/Gaussian programs. This was dealt with at a basic level and no higherlevel compounds including organometallic complexes were examined, thus our students were essentially new to the computation method examined in our module and allowed for an examination of the effectiveness of our approach.

2.2.2 Instructional Videos and Manual Four instructional videos were developed to take students through the process of drawing the structure of a complex, optimization of the structure, running the calculations and presentation and interpretation of the data. The complex Fe(CO)5 was used as the example complex throughout the instructional package.

20

2 Chemical education

2.2.2.1 Video 1: GaussView 5.0: introduction The first video of the series [26] introduced students to the basics of the GaussView 5.0 program including a review of the layout of the interface, the significance of the different windows that appear, as well as an explanation of some of the features found in the tool bar. The steps required to construct and manipulate the structure were provided as well as instruction on how to review/set bond lengths and angles. This video was the second longest of the series at 7 min and 45 s (Figure 2.1). This was accompanied by the instructional manual, which summarized all the steps presented in this video (Figure 2.2). 2.2.2.2 Video 2: GaussView 5.0: calculations and IR spectra The second video of the series [27] covered the steps to run the “Optimization and Frequency” (Opt + Freq) calculation required to optimize the structure and provide a data set that can be used to predict the IR spectrum of the complex. Students were provided with a brief explanation of the different types of calculations, such as “Energy”, “Opt + Freq” and “NMR” as well as the different calculations methods. In this module DFT calculations were used to generate all the data. Students were shown how to “add” solvent to their calculation or to run the calculations solvent free. The generation of the IR

Figure 2.1: Screen shot from Video 1 showing the newly constructed Fe(CO)5.

2.2 Computational chemistry module

21

Figure 2.2: Sample of the instructional document highlighting the layout of the GaussView 5.0 interface.

spectrum of the complex was explained as well as how to animate the complex to illustrate the motion associated with each IR signal. This video, which was the longest at 8 min and 6 s (Figure 2.3), was also accompanied by an instructional document which outlined each part of the process (Figure 2.4). 2.2.2.3 Video 3: GaussView 5.0: calculations and NMR spectra The third video of the series [28] expands on the information provided in the second video. Instead of generating the IR spectrum of a complex in this video, the steps to obtain

22

2 Chemical education

Figure 2.3: Screens shot from Video 2 showing the different job types of available in GaussView 5.0.

the NMR spectrum of the complex was outlined. This required a different calculation using the “NMR” job type instead of the “Opt + Freq” method used in Video 2. Using the generated NMR spectrum, the degeneracy within the complex can be illustrated. By selecting an NMR peak, the degenerate atoms within the complex are highlighted in the structure. This video was 5 min and 19 s in length (Figure 2.5). The instructions provided in this video were reviewed in the instructional manual (Figure 2.6). 2.2.2.4 Video 4: molecular orbital The last video of the series was 3 min and 9 s long [29] and focused on the generation and presentation of the molecular orbitals (MO) found in the complex and associated energy levels. This video complements the material presented in the second video as the same output files generated to develop the IR spectrum is used to generate the MOs (Figure 2.7). The video was also supported by the instructional manual (Figure 2.8).

2.2.3 Assignments This module includes two assignments. Students were given one week to complete each assignment, which are the basis for the study of the effectiveness of this approach to teach students computational skills to examine organometallic complexes.

2.2 Computational chemistry module

23

Figure 2.4: Sample from the instructional document showing how to manipulate the generated IR spectrum.

2.2.3.1 Assignment 1 Assignment 1 accompanied the release of the instructional videos and manual on the computational analysis of Fe(CO)5. Students were expected to transfer their newly acquired skill set to analyze Cr(CO)6. This complex was chosen as it allowed for the review of all the skills taught in the videos and manual, but simple enough for the calculation to be quickly completed on the students’ personal computer. A report sheet was completed

24

2 Chemical education

Figure 2.5: Screen shot from Video 3 showing the 13C NMR output text file of Fe(CO)5.

as part of the assignment [30] (Figure 2.9) and it was assigned near the beginning of the semester. The assignment required students to: – Draw and optimize the structure. – Generate the IR spectrum, analyze the motion associated with selected peaks, as well as assign the peaks and report the literature values. – Generate the 13C NMR spectrum, illustrate the degeneracy within the complexes, as well as assign the peaks and report the literature values. – Generate the MO figures and energy level of selected orbitals. – Explain the difference between the calculated and literature spectra values. – Submit all input and output files. 2.2.3.2 Assignment 2 Assignment 2 focused on the analysis of ruthenocene, which was one of the products made in the laboratory portion of the course. Ruthenocene was chosen as the complex to study, so students were able to conduct both an experimental and computational characterization of the analysis data. Due to the more complex structure and high computational demands, students were asked to make the input file but were provided with the output files from the Gaussian calculations [31]. Students were required to correctly use the output file based on the information from the instructional videos and manual. This assignment was completed near the end of the semester, thus allowing students a chance to review their mistakes from assignment 1 and further practice their

2.2 Computational chemistry module

25

Figure 2.6: Sample from the instructional manual showing the 13C NMR spectrum of Fe(CO)5 and the degeneracy within the structure.

computational skills as the instructional videos, manual and programs were made available to students all semester. This assignment was completed as part of a formal laboratory report where students were provided with a specific set of questions to answer [30]. (Figure 2.10) Students were required to: – Draw the structure of ruthenocene. – Generate the IR spectrum, analyze the motion associated with selected peaks, as well as assign the peaks and report the literature values.

26

2 Chemical education

Figure 2.7: Screen shot from Video 4 showing the structure of Fe(CO)5 and selected MO figure.

– –

Generate the 13C and 1H NMR spectra, illustrate the degeneracy within the complexes, as well as assign the peaks and report the literature values. Generate the MO figures and energy level of selected orbitals.

2.3 Results and discussion The analysis of the student results on assignments 1 and 2 allows for an investigation on the effectiveness of our approach. The average grade on assignments 1 and 2 was 86% and 75%, which appears to indicate the students did worse on assignment 2. The use of the average assignment grade is not an accurate indicator of the students’ ability to apply computational methods, as the weight of the components of the assignments were not equal, and marks were given for non-computational aspects. To gain a better understanding of the results both assignments were examined solely on the computational aspects with each skill graded as correctly done or not correctly done. For assignment 1 there were five computational components that were analysed: – The generation of the IR spectrum. – The description of the motion associated with a specific IR peak. – The generation of the 13C NMR spectrum. – The generation of the energy of a specific MO. – The generation of the shape of a specific MO.

2.3 Results and discussion

27

Figure 2.8: Sample from the instructional manual outlining the steps to generate the MO figure.

The five computational based questions were completed by 50 students, thus 250 individual tasks completed. Of these tasks, 36 were done incorrectly, with 11 tasks not completed. It was not determined why the 11 tasks were missed. They could have either been forgotten or the student did not know how to do the task, therefore only an error range for the number of tasks done incorrectly can be generated. For assignment 1, there was an error rate between 15.1% and 18.8%. The lower rate assumes that all the missed

28

2 Chemical education

Figure 2.9: First page of assignment 1.

tasks would have been completed correctly if attempted, thus only 36 incorrect tasks, while the higher rate assumes that all missed tasks would have been done incorrectly, thus 47 (36 + 11) incorrect tasks. A similar analysis was conducted on assignment 2, where the same five computational components conducted in assignment 1 were also completed in assignment 2, with the added task of generating the 1H NMR spectrum. The six computational tasks in assignment 2 that were analysed were:

2.3 Results and discussion

29

Figure 2.10: Sample questions from assignment 2.

– – – – – –

The generation of the IR spectrum. The description of the motion associated with a specific IR peak. The generation of the 13C NMR spectrum. The generation of the 1H NMR spectrum. The generation of the energy of a specific MO. The generation of the shape of a specific MO.

The six computational based questions were also completed by 50 students, thus 300 individual tasks completed. Of these tasks, 47 were done incorrectly, with 10 tasks not

30

2 Chemical education

completed. As with assignment 1, an error rate can be determined for the number of incorrect tasks. For assignment 2, there was an error rate between 8.8% and 12.1%. Comparing the results of assignment 1 and assignment 2, there was an improvement of 35%–41% on the number of correctly completed computational skills between the first and second assignments. Further analysis of the data also showed that only two students did not complete a task on both assignments. No students missed the same task on both assignments, rather the two students missed different parts of the assignment. Also, only four students incorrectly completed the same task on both assignments. Analysis of the assignments did not indicate a specific question that caused issues for all students. Both assignments also allowed students to increase their representational competency skills as they developed the ability to move from the analysis of two-dimensional representations tools (i.e., paper-based IR and NMR spectra, structure of the complex and MO figures) to a three-dimensional representation of these tools [32]. Given the improvement in the error rate and that students did not repeat their mistakes, this approach has proven to be successful. One of the assignment questions asked students to explain the differences between the computational IR and NMR data of Cr(CO)6 with the experimental data. One of the goals of this approach was to highlight the limits of computational results, as mentioned in the instructional videos. On this we had a level of success with 60% of students noting that their results were limited due to the low-level calculations completed, with 56% also recognizing that the computational and experiment analysis was conducted in different solvents, thus partially explaining the difference. To further analyse the success of the module as a teaching tool, students were asked to complete an anonymous survey. The module was examined using a five-point Likert scale “Strongly Disagree” (1) to “Strongly Agree” (5) with 41 of the 50 enrolled students completing the survey. Students were asked to rate their agreement to 3 statements (Table 2.1). Almost all students agreed or strongly agreed to the three statements: Having completed the computational chemistry assignment: – I am more knowledgeable about the GaussView5/Gaussian. – I can apply the ideas of GaussView5/Gaussian to other experiments. – I can apply the ideas of GaussView5/Gaussian to other chemistry courses.

Table .: Student response to assignment evaluation. Rate your level of agreement with the following statement. Having completed the computational chemistry assignment: . I am more knowledgeable about GaussView/Gaussian . I can apply the ideas of GaussView/Gaussian to other experiments. . I can apply the ideas of GaussView/Gaussian to other chemistry courses.

Result (n = ) . . .

2.4 Conclusions

31

A review of the data showed that only two students disagreed with the second statement, with no students disagreeing with the first and third statements, thus indicating that the students felt that this was an effective approach to learn the GaussView 5.0/Gaussian programs and how to apply computational methods to aid in product characterization. Students were also asked if “In your opinion, should the uses of GaussView 5.0/Gaussian in the lab be expanded?” Of the 41 replies received 88% of the students felt that computational work in our laboratory course should be expanded. The student survey comments also provided evidence that the assignments were well received and a few of the verbatim feedback included: – I like that it allows for visualization of the compound and gives lots of information on the compound made. – The videos produced as the pre-assignment lesson were of great quality and made computational chemistry make more sense. It was easy to see how the information gleaned could be used in a laboratory setting. – The visualizations of the vibrational modes of metal complexes provide very useful insight into analysing spectra. The implementation of this into the Ruthenocene experiment was very good. Based on the student feedback and analysis of the assignments, this module was deemed to be effective as students successfully: – Used the GaussView 5.0/Gaussian program. – Modelled the spectral data of inorganic complexes as well as generate an optimized structure and MO information. – Further developed their representational competency skills. The students appreciated learning about how to conduct computational analysis of organometallic complexes, being able to visualize the IR motion and how it relates to a specific IR peak and being able to examine compounds from both a computational and experimental view.

2.4 Conclusions With the transition to online education due to the COVID-19 pandemic, new online approaches were required to present the course material. In our third-year inorganic laboratory course, we developed a computational chemistry module that introduced students to computational analysis of organometallic complexes, an approach that can easily be altered to include coordination complexes. Through an online, self-paced module consisting of four instructional videos and a manual, students were introduced to calculations such as those that predicted IR and NMR spectra as well as MO diagrams of coordination complexes. Through a comparison of the results from the first and second assignment, we observed a 35–41% improvement on their computational skills. Based on

32

2 Chemical education

these results and the positive feedback from the anonymous student survey, this module is deemed to have effectively introduced students to computational analysis of organometallic complexes. Acknowledgments: The author would like to thank the Department of Chemistry at Simon Fraser University (SFU) for financial support and the students of CHEM 336 (Spring 2020 and 2021 semesters) who completed the assignment and provided feedback.

References 1. Snyder HD, Kucukkal TG. Computational chemistry activities with Avogadro and ORCA. J Chem Educ 2021; 98:1335–41. 2. Fortenberry RC, McDonald AR, Shepherd TD, Kennedy M, Sherrill CD. PSI4Education: computational chemistry labs using free software. In: Daus K, Rigsby R, editors. The promise of chemical education: addressing our students’ needs. Washington, DC, USA: American Chemical Society; 2015:85–98 pp. 3. Cody JA, Wiser DC. Laboratory sequences in computational methods for introductory chemistry. J Chem Educ 2003;80:793–5. 4. Ealy JB. Students’ understanding is enhanced through molecular modelling. J Sci Educ Technol 2004;13: 461–71. 5. Feller SE, Dallinger RF, McKinney PC. A program of computational chemistry exercises for the first-semester general chemistry course. J Chem Educ 2004;81:283–7. 6. Linenberger KJ, Cole RS, Sarkar S. Looking beyond Lewis structures: a general chemistry molecular modelling experiment focusing on physical properties and geometry. J Chem Educ 2011;88:962–5. 7. Sharma AK, Asirwatham L. Learning by computing: a first-year honors chemistry curriculum. In: Grushow A, Reeves S, editors. Using computational methods to teach chemical principles. Washington, DC, USA: American Chemical Society; 2019:127–38 pp. 8. Simpson S, Evanoski-Cole A, Gast K, Wedvik MC, Schneider PW, Klingensmith I. Quantum chemical exercise linking computational chemistry to general chemistry topics. Chem Teach Int 2020;1:1–8. 9. Person JK. Introducing the practical aspects of computational chemistry to undergraduate chemistry students. J Chem Educ 2007;84:1323–5. 10. Johnson LE, Engel T. Integrating computational chemistry into physical chemistry curriculum. J Chem Educ 2011;88:569–73. 11. Simpson S, Lonie DC, Chen J, Zurek E. A computational experiment on single-walled carbon nanotubes. J Chem Educ 2013;90:651–5. 12. Miller DP, Phillips A, Ludowieg H, Swihart S, Autschbach J, Zurek E. The computational design of twodimensional materials. J Chem Educ 2019;96:2308–14. 13. Essleman BJ, Hill NJ. Integration of computational chemistry into the undergraduate organic chemistry laboratory curriculum. J Chem Educ 2016;93:932–6. 14. Zdanovskaia MA, Schwarz CE, Habib AD, Hill J, Esselman BJ. Access to computational chemistry for community colleges via WebMO. J Chem Educ 2018;95:1960–5. 15. Ivey MM, Slaughter J, Smith R Jr, Ward DN, Sonnenberg JL. Computational chemistry experiments for an organic chemistry course. Chem Educ 2012;26:14–22. 16. Anderson WP. A Lewis acid-base computational exercise for advanced inorganic chemistry. J Chem Educ 2000;77:209–14. 17. Van Strijdonck GPF, Kamer PCJ, Van Leeuwen PWNM, Van Haaren RJ, Reek JNH, Oevering H, et al. Teaching bonding in organometallic chemistry using computational chemistry. J Chem Educ 2002;79:588–91.

References

33

18. Orenha RP, Galembeck SE. Molecular orbitals of NO, NO+, NO–: a computational quantum chemistry experiment. J Chem Educ 2014;91:1064–9. 19. Canal JP, Goyan RL, Mund G. General chemistry education in a pandemic. Can J Chem 2021;99:964–70. 20. Journal of Chemical Education (JCE). Special issue – insights gained while teaching chemistry in the time of COVID-19. Washington, DC: American Chemical Society. https://pubs.acs.org/toc/jceda8/97/9 [Accessed 15 Sept 2022]. 21. SFU. Department of chemistry course outlines. http://www.sfu.ca/chemistry/undergrad/current/courses/ courses-descriptions.html [Accessed 15 Sept 2022]. 22. Dickson RS, Dobney BJ, Millam J. GaussView, Version 5.0. Wallingford, CT: Semichem Inc.; 2008. 23. Frisch MG, Trucks GW, Schelgel HB, Scuseria GE, Robb MA, Cheeseman JR, et al. Gaussian 09, Revision A03. Wallingford, CT: Gaussian, Inc.; 2016. 24. Welcome to the ORCA forum. https://orcaforum.kofo.mpg.de/app.php/portal [Accessed 15 Sept 2022]. 25. Avogadro. https://avogadro.cc/ [Accessed 15 Sept 2022]. 26. GaussView 5.0. Introduction. https://stream.sfu.ca/Media/Play/a9ee13ad6027460481d0080a5b07dcdf1d [Accessed 15 Sept 2022]. 27. GaussView 5.0. Calculations and IR spectra. https://stream.sfu.ca/Media/Play/ 2163296eecb744d78c85aeb75f14e6571d [Accessed 15 Sept 2022]. 28. GaussView 5.0. Calculations and NMR spectrum. https://stream.sfu.ca/Media/Play/ d18655db48394c6b837f19e4303cdf611d [Accessed 15 Sept 2022]. 29. GaussView 5.0. Molecular orbital diagrams. https://stream.sfu.ca/Media/Play/ 80787b197cda49a7b5b2f029f9ecdc861d [Accessed 15 Sept 2002]. 30. Jalali H, Hanlan L, Canal JP. The use of writing-intensive learning as a communication and learning tool in an inorganic chemistry laboratory course. In: Gupta-Bhowon M, editor Chemistry education in the ICT age. Dordrecht, Netherlands: Springer; 2009:153–60 pp. 31. Harrypersad S, Canal JP. The synthesis of ruthenocene – a methodology appropriate for the inorganic undergraduate curriculum. J Chem Educ 2023;100:1320–5. 32. McCollum BM, Regier L, Leong J, Simpson S, Sterner S. The effects of using touch-screen devices on students’ molecular visualization and representational competence skills. J Chem Educ 2014;91:1810–7.

Francisca Claveria-Cádiz* and Aleksey E. Kuznetsov*

3 Computational design of the novel building blocks for the metal-organic frameworks based on the organic ligand protected Cu4 cluster Abstract: Metal-organic frameworks (MOFs) are tunable porous network compounds composed of inorganic nodes bound by various organic linkers. Here we report the density functional theory (DFT) study of the MOF novel building blocks made of the Cu4 clusters protected by four organic ligands having two phenyl rings and terminated either with Cl or Br atom (precursors 1 and 2, respectively). The research was performed both in the gas phase and with the implicit effects of acetonitrile included, with two functionals, B3LYP and PBE, both with and without the second-order dispersion correction. We analyzed the structural features of the precursors 1 and 2, their electronic structures, molecular electrostatic potential (MEP) distribution, and global reactivity parameters (GRPs). Both functionals resulted in the singlets of the precursors 1 and 2 as the most stable species. The precursor structures optimized with the hybrid functional were found to be quite similar for both halogens, both containing somewhat distorted from planarity Cu4 cluster, with the outer phenyls of the ligands rotated relative to the inner phenyls. With both halogens and both DFT approaches, the frontier molecular orbitals (FMOs) of the precursors 1 and 2 were shown to have quite similar compositions. The change of the substituent from Br to Cl was found to cause slight stabilizations or destabilizations of the HOMOs and LUMOs. The central parts and especially the inner phenyl ring parts of the precursors 1 and 2 were suggested to play a role of nucleophile in various chemical reactions due to the significant accumulation of negative electrostatic potential. Also, weak intermolecular interactions might exist between the ligands of neighboring precursor molecules. Finally, with both substituents the precursors 1 and 2 should be relatively unreactive and demonstrate thermodynamic stability. Further, the precursors 1 and 2 should be quite stable in oxidation reactions and more active in reduction processes. Generally, the substituent nature was shown not to affect significantly the reactivity of the precursors 1 and 2, as well as their other properties.

*Corresponding authors: Francisca Claveria-Cádiz, Programa de Doctorado Conjunto en Ciencias Mención Química, Universidad Técnica Federico Santa María, Avenida España N° 1680, 2390123, Valparaíso, Chile; and Universidad de Valparaíso, Avenida. Gran Bretaña N° 1111, 2360102, Valparaíso, Chile, E-mail: [email protected]; and Aleksey E. Kuznetsov, Departamento de Química, Universidad Técnica Federico Santa María, Av. Santa María 6400, 7660251, Santiago, Chile, E-mail: [email protected]. https://orcid.org/0000-0001-8857-3118 As per De Gruyter’s policy this article has previously been published in the journal Physical Sciences Reviews. Please cite as: F. Claveria-Cádiz and A. E. Kuznetsov “Computational design of the novel building blocks for the metal-organic frameworks based on the organic ligand protected Cu4 cluster” Physical Sciences Reviews [Online] 2023. DOI: 10.1515/psr-2022-0304 | https://doi.org/10. 1515/9783111071428-003

36

3 MOF new building blocks based on the Cu4 cluster with organic ligands

Keywords: building blocks; Cu4 cluster; DFT; FMOs; GRPs; metal-organic frameworks.

3.1 Introduction MOFs are considered to be a novel class of porous crystalline solid compounds composed of metal clusters connected by organic molecular linkers. MOFs are synthesized from various combinations of molecular building blocks giving rise to one-, two-, or threedimensional structures studied by the reticular chemistry which was introduced by Yagi et al. [1] Typically, MOFs are characterized by a large surface area and a high porosity level. It is also worth mentioning that they possess uniform and tunable pores (with sizes within 0.5–2 nm), active sites fully accessible for chemical reactions, and tunable morphologies along with abundant compositions [2, 3]. For example, MOFs can be used as selective molecular sieves for gas separation processes allowing certain molecules to pass but not others [4–6]. These porous structures give rise to promising MOF applications in many important areas: molecular recognition [7], drug delivery [6], catalysis [8–12], magnetism [13], antennae effects [14], luminescence [15], mixed-matrix membranes for gas separations, gas storage, sensors [16], as well as showing enormous potential in energy storage applications [17]. MOFs are generally composed of metal ions/clusters and multidentate organic ligands linked via intermolecular bonding and forming periodic structures, which provides wide possibilities of creation of new materials. The design of new materials presents a powerful research motivation that leads to exploring various design strategies. In this context, MOF chemical modification in order to introduce new functions to enhance further their applications has recently caused considerable research interest. In addition, the diverse choices of metal clusters and organic linkers enable significant flexibility in the design of MOFs with various pore sizes, configurations, morphology, and different physico-chemical properties [18, 19]. Metal cluster-organic ligand frameworks formed from secondary building units [20–23] (SBUs) containing various clusters and linkers are nowadays a topic of significant interest due to their versatile structures, which allow to introduce additional functionalities through the design of clusters, resulting in great structural tunability and thus revealing strategic approaches to designing networks with higher surface areas. Compared with single-metal containing materials, the task of the design of new porous MOFs including metal clusters in SBUs are more appealing, mainly because metal clusters can generally keep their structures during the assembly processing and therefore the final materials are more predictable, which can lead to a higher size of pores and superficial area and thus higher reactivity. The resulting MOFs can retain physicochemical properties of single clusters and metal arrays with different nuclearity, offering a higher range of functionalities. Consequently, the detailed study of these materials attempts to obtain the basic structural knowledge, which further contributes to better development of their possible versatile applications.

3.2 Computational details

37

Computational studies of such metal cluster-containing frameworks comprise detailed research of metal-metal and metal-linker interactions. Preferred use of high nuclearity d10 clusters in these MOFs is due to the ligand-to-metal charge transfer, which in turn is due directly to the bonding MO (ns- and np-type) electronic population [24]. In this sense, the use of closed-shell transition metal cations such as Cu(I) provides a successful pathway towards creating luminescent networks [25–29] with metallophilic interactions. Furthermore, the use of Cu4X4 (X = Br, I) species as tetrahedral nodes provides various possibilities to create networks with cluster SBUs and arene linkers. Taking into account metal–metal interactions and a complete charge transfer pathway in order to gain specific knowledge about properties of molecular MOF structures, as well as proposing suitable models which involves the calculation of binding forces within the host–guest interactions, would allow successful modeling of novel MOFs with versatile applications. Thus, motivated by the above-provided considerations, we decided to focus on the computational studies of the MOF precursors constructed from multidentate organic π-arene derivatives, which exhibit excellent coordination ability in the MOF assembly, and Cu4 clusters [30]. Our DFT studies have been performed on suitable minimal units, or template models (precursors), of the metal-organic framework to be designed, composed of the Cu4 cluster protected/ligated by four organic ligands composed of two arene rings (phenyls) and terminated by Br-or Cl-substituents. We took advantage of the fact that the MOF intrinsic characteristics are largely defined by its inorganic SBU composition and/or structures. The theoretical results obtained would provide a useful understanding that leads to a better design of novel molecular precursors envisaging their more appropriate performance in the design of new MOFs with enhanced properties. The next section contains the computational details, then results and discussion are given, and finally conclusions and perspectives are depicted.

3.2 Computational details DFT studies of the precursors composed of the Cu4 cluster protected/ligated by four organic ligands composed of two arene rings (phenyls) and terminated by Br- or Clsubstituents (precursors one and two, respectively) were done using the Gaussian 16 software [31]. We employed the hybrid density functional B3LYP [32] along with the splitvalence double-zeta basis set 6-31G* [33, 34] with one set of polarization functions (B3LYP/ 6-31G* approach), this basis set was used due to significant computational resources demands. Moreover, for the comparison we did calculations using the hybrid B3LYP functional with the D2 version of Grimme’s dispersion correction included [35] (B3LYP-D2/6-31G* approach), and the generalized gradient approximation (GGA) functional PBE [36] (PBEPBE/6-31G* approach) without and with the D2 version of Grimme’s dispersion correction added [35] (PBEPBE-D2/6-31G* approach). We studied the precursors 1 and 2 both in vacuum and with the implicit effects from acetonitrile CH3CN (dielectric constant ε = 35.688) as a solvent. In the second type of

38

3 MOF new building blocks based on the Cu4 cluster with organic ligands

calculations, the self-reliable IEF-PCM approach [37] with the UFF model as implemented in the Gaussian 16 was used, with the electrostatic scaling factor α = 1.0. The charge distribution analysis was done using the natural bond orbital (NBO) method implemented in the Gaussian [38] with the B3LYP/6-31G* and B3LYP-D2/6-31G* approaches. Frontier molecular orbitals (FMOs) were computed using these approaches with the implicit solvent effects taken into account. Below we present and compare the calculated structural parameters, NBO charges, and FMOs of both precursors. Also, the energies of the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO) were used to compute the GRPs [39–41] (Equations 3.1–3.6). Values of the ionization potential (IP) and electron affinity (EA) were computed according to Equations (3.1) and (3.2): IP = −EHOMO

(3.1)

EA = −E LUMO

(3.2)

For calculating global hardness η and electronegativity X we used Equations (3.3) and (3.4): η=

[IP − EA] [ELUMO − EHOMO ] =− 2 2

(3.3)

X=

[IP + EA] [E LUMO + EHOMO ] =− 2 2

(3.4)

Global electrophilicity ω was computed according to Equation (3.5): ω=

μ2 , 2η

(3.5)

LUMO is the chemical potential of the system. where μ = EHOMO +E 2 Global softness σ value was computed using Equation (3.6):

σ=

1 2η

(3.6)

Open GL version of Molden 5.8.2 software was used for the structures and FMOs visualization [42], and Avogadro software, version 1.1.1, was used to visualize the MEP maps [43, 44].

3.3 Results and discussion 3.3.1 Energetics and structural features Table 3.1 presents the energetics data for the precursors one and two, with Br and Cl, respectively, calculated with the B3LYP/6-31G* and B3LYP-D2/6-31G* approaches and with the effects from the implicit CH3CN. Figures 3.1 and 3.2 show the structures of the precursors one and two, respectively, optimized with both approaches in the implicit acetonitrile.

3.3 Results and discussion

39

Analysis of the results presented in Table 3.1 and Figures 3.1 and 3.2 shows the following important points. (i) With both theoretical approaches and for both halogens, the singlets were calculated to be the lowest in energy structures, whereas the triplets were found to be higher by ca. 49–58 kcal/mol and ca. 58 kcal/mol for Br and Cl, respectively. In the gas phase (vacuum) the singlet-triplet gaps were found to be even larger, ca. 50–60 and 60– 61 kcal/mol for Br and Cl, respectively (see Table S1). Further, with the GGA approaches the singlet-triplet gaps were calculated to be ca. 54–57 and 34–41 kcal/mol for Br and Cl, respectively (PBEPBE/6-31G*), and ca. 44–45 and 44–45 kcal/mol, Br and Cl, respectively (PBEPBE-D2/6-31G*) (see Table S1). (ii) Comparison of the precursor 1 structures obtained with the B3LYP/6-31G* and B3LYP-D2/6-31G* approaches (Figure 3.1a and b) shows the C-Br bond distances to be the same, 1.918 Å, with both approaches. Further, the Cu-C bonds were found to be essentially the same with both approaches as well and the Cu-Cu bonds obtained with the B3LYP-D2/6-31G* approach were calculated to be slightly shorter than those obtained using the B3LYP/6-31G* approach, by mere 0.003 Å. The C-C bond distances

Table .: Precursors  and , with Br and Cl, respectively, studied using the BLYP/-G* and BLYP-D/G* approaches, with the effects from the implicit CHCN. Spin

E, A.U.

E+ZPE, A.U.

ΔE, kcal/mol

E (HOMO/LUMO), A.U.

ΔE, eV/TDDFT, eV

./.

Precursor  BLYP/-G* 

A



A

−,. i −,.

−,.

.

−./−.

−,.

.

−./−. −./−.

BLYP-D/-G* 

A A



−,. −,.

−,. −,.

. .

−./−. −./−. −./−.

./.

−./−. −./−. −./−.

./.

−./−. −./−. −./−.

./.

Precursor  BLYP/-G* 

A A



−,. −,. i

−,. −,.

. .

BLYP-D/-G* 

A A



−,. −,. i

−,. −,.

. .

40

3 MOF new building blocks based on the Cu4 cluster with organic ligands

between two phenyl rings of the ligands obtained with both approaches are also very close, the B3LYP/6-31G* bond distances being 0.004 Å longer than the B3LYP-D2/6-31G* bond distances. However, more significant differences can be seen in dihedral/torsion angles of the structures obtained with both approaches. Thus, the Cu4 cluster in the structure optimized with the B3LYP/6-31G* approach is much more flattened compared to the Cu4 cluster in the structure optimized with the B3LYP-D2/6-31G* approach, as follows from the values of the dihedral angles ∠(Cu1-Cu2-Cu3-Cu4), 2.86° vs. 15.86°. The valence angles within the Cu4 cluster are slightly larger for the B3LYP/6-31G* optimized species, by

Figure 3.1: Structures of the precursor 1, optimized at the B3LYP/6-31G* (a) and B3LYP-D2/6-31G* (b) levels, in the implicit CH3CN.

Figure 3.2: Structures of the precursor 2 with Cl, optimized at the B3LYP/6-31G* (a) and B3LYP-D2/6-31G* (b) levels, in the implicit CH3CN.

3.3 Results and discussion

41

ca. 1°. Next, the valence angles Cu-C-Cu are slightly larger for the B3LYP/6-31G* optimized structure as well, by ca. 2.5°. More significant differences can be observed for the dihedral angles corresponding to the rotation of the ligand outer phenyl rings relative to the inner phenyl rings: in the B3LYP/6-31G* optimized structure these dihedral angles are ca. 35° whereas in the B3LYP-D2/6-31G* optimized structure they have values of ca. 31°, that is, in the second structure the ligands are slightly more flattened compared to the first structure. The most significant difference was found to be for the values of the dihedral angle between the opposite ligands bound to the Cu4 cluster, ∠(C13-C2-C4-C14): while in the B3LYP/6-31G* optimized structure this angle has the value of ca. 7.6°, in the B3LYP-D2/631G* optimized structure the ligands are noticeably tilted relative to each other, with the angle being ca. 39.8°. (iii) Comparison of the precursor 2 structures obtained with the B3LYP/6-31G* and B3LYP-D2/6-31G* approaches (Figure 3.2a and b) shows the C–Cl bond distances to be the same, 1.765 Å, with both approaches. Further, the Cu–C bonds are essentially the same with both approaches as well. The Cu–Cu bonds obtained with the B3LYP-D2/6-31G* approach were found to be slightly shorter than those obtained with the B3LYP/6-31G* approach, by just 0.003 Å. The C-C bond distances between two phenyl rings of the ligands obtained with both approaches are also very close, the B3LYP/6-31G* bond distances being 0.004 Å longer than the B3LYP-D2/6-31G* bond distances. However, again more significant differences can be seen in dihedral/torsion angles of the structures obtained with both approaches. Thus, the Cu4 cluster in the structure optimized with the B3LYP/6-31G* approach is more flattened compared to the Cu4 cluster in the structure optimized with the B3LYP-D2/6-31G* approach, as follows from the absolute values of the dihedral angles ∠(Cu1-Cu2-Cu3-Cu4), 5.28° vs. 15.32°. The valence angles within the Cu4 cluster are very slightly larger for the structure optimized with the B3LYP/6-31G* approach, by less than 1°. Next, the valence angles ∠(Cu-C-Cu) are slightly larger for the structure optimized with the B3LYP/6-31G* approach as well, by ca. 1.4–2.8°. Again, more significant differences can be observed for the dihedral angles corresponding to the tilt of the ligand outer phenyl rings relative to the inner phenyl rings (∠(C5-C6-C7-C8), ∠(C5′-C6′-C7′-C8′), ∠(C9-C10-C11-C12), and ∠(C9′-C10′-C11′-C12′)): in the structure optimized with the B3LYP/6-31G* approach these dihedral angles are ca. 32–37° whereas in the B3LYP-D2/6-31G* optimized structure they have values of ca. 31°, that is, in the second structure the ligands are slightly more flattened compared to the first structure. It should be noticed that in the B3LYP/6-31G* Cl-substituted optimized structure these dihedral angles were calculated to be different from each whereas in the B3LYP/6-31G* Brsubstituted optimized structure their values are quite close to each other. Finally, the most significant difference can be again seen in the values of the dihedral angles between the opposite ligands bound to the Cu4 cluster, ∠(C13-C2-C4-C14): while in the B3LYP/6-31G* optimized structure this angle has the absolute value of ca. 19.5°, in the B3LYP-D2/6-31G* optimized structure the ligands are noticeably tilted relative to each other, with the angle being ca. 37.6°. Again, the difference with the Br-substituted optimized structure can be seen, where this angle is much smaller.

42

3 MOF new building blocks based on the Cu4 cluster with organic ligands

Thus, both hybrid and GGA functionals, either with or without dispersion corrections, without and with implicit solvent effects included, give the singlet structures of the halogen-substituted organic ligand protected Cu4 clusters, precursors one and two, as the most stable species. Furthermore, the structures optimized using the hybrid functional without and with the dispersion correction included are quite similar for both halogens, both contain somewhat distorted from planarity Cu4 cluster, and in both structures the outer phenyls of the ligands are rotated relative to the inner phenyls. The principal differences between the precursors one and two are in the C-halogen bond distances and in the mutual orientation of the ligands located opposite to each other, as can be seen for the structures optimized using the B3LYP/6-31G* approach. We did not consider here geometries of the structures optimized with the GGA functional in order not to overload the text with details, because we are interested in general trends.

3.3.2 Frontier molecular orbitals and NPA charge Figures 3.3 and 3.4 present the FMOs of the precursors one and two, respectively, obtained using the B3LYP/6-31G* and B3LYP-D2/6-31G* approaches with the effects of implicit CH3CN taken into account. Analysis of the compound FMOs shows the following trends. (i) For both halogens and for both DFT approaches, the FMOs look quite similar. The HOMOs have dominating contributions from the bonding–antibonding π-orbitals of the inner phenyl rings of the ligands, with smaller contributions from the bonding–antibonding π-orbitals of the outer phenyl rings of the ligands and p-AOs of the halogen substituents, and the contributions of the Cu4 core are relatively not significant, composed of the d-type AOs of the Cu-centers in antibonding combination with each other and with the bonding– antibonding π-orbitals of the ligand inner phenyls. Clearly, the MOs responsible for bonding between the Cu4 core and ligands are located lower in energy. The LUMOs, on the contrary, are dominated by the contributions from the Cu4 core, composed of the s-type AOs, again in antibonding combination with each other and with the π-orbitals of the ligand inner phenyls, along with the quite significant contributions from ligand bonding– antibonding π-orbitals and much smaller contributions from the p-AOs of the halogen substituents. (ii) Both the HOMO/LUMO and TDDFT gaps are almost unaffected by the

Figure 3.3: Precursor one FMOs calculated with the B3LYP/6-31G* (a) and B3LYP-D2/6-31G* (b) approaches and with the effects of implicit CH3CN taken into account.

3.3 Results and discussion

43

Figure 3.4: Precursor 2 FMOs calculated with the B3LYP/6-31G* (a) and B3LYP-D2/6-31G* (b) approaches and with the effects of implicit CH3CN taken into account.

halogen substituent nature (cf. Table 3.1): for the Br-substituted compound the B3LYP/631G* computed HOMO/LUMO gap is by 0.02 eV smaller than for the Cl-substituted compound and the B3LYP/6-31G* computed TDDFT gap is by 0.03 eV larger than for the Clsubstituted compound. The B3LYP-D2/6-31G* HOMO/LUMO gaps being the same for both compounds and the B3LYP/6-31G* TDDFT gap being larger by 0.01 eV for the Cl-substituted compound. The effect of the DFT approach is quite small as well, in the case of the Brsubstituted compound the B3LYP/6-31G* HOMO/LUMO gap is by 0.05 eV larger than the B3LYP-D2/6-31G* gap, and in the case of the Cl-substituted compound the B3LYP/6-31G* HOMO/LUMO gap is by 0.07 eV larger than the B3LYP-D2/6-31G* gap. Further, for the precursor one the B3LYP/6-31G* TDDFT gap is by 0.05 eV larger than the B3LYP-D2/6-31G* gap, and in the case of the precursor two the B3LYP/6-31G* TDDFT gap is by mere 0.01 eV larger than the B3LYP-D2/6-31G* gap. (iii) When the B3LYP/6-31G* approach is used, the change of the substituent from Br to Cl causes slight HOMO stabilization and slight LUMO destabilization whereas with B3LYP-D2/6-31G* approach this change causes slight destabilization of both HOMO and LUMO of the precursors studied. Thus, with both halogens and both DFT approaches, the FMOs have quite similar compositions. Both HOMO/LUMO and TDDFT gaps are very weakly affected, if at all, by the halogen substituent nature and by the DFT approach. The change of the substituent from Br to Cl causes slight stabilizations or destabilizations of the HOMOs and LUMOs of the compound. Figure 3.5 summarizes the selected atom NPA charges for the precursors 1 and 2 calculated with both approaches in the implicit solvent. Analysis of these charges reveals the following. (i) For both halogen substituents, charges are essentially not affected by the computational approach employed. (ii) In the compounds with both halogen substituents, the Cu4 core atoms have noticeable positive charges, ca. 0.62e, and the ligand carbons bound to the Cu4 core have significant negative charges, ca. −0.64e. (iii) Further, four carbons of the inner phenyl rings not connected to the Cu4 core or to the outer phenyl rings have smaller negative charges, ca. −0.22e and −0.23e. Charges on the carbons of the inner and outer phenyl rings connect to each other are noticeably smaller, ca. −0.05e and −0.06e. Finally, charges on four carbons of the outer phenyl rings not connected to the inner phenyls or to halogens are somewhat similar to charges on the four carbons of

44

3 MOF new building blocks based on the Cu4 cluster with organic ligands

the inner phenyls, ca. −0.21e and −0.25e. (iv) The main difference between two systems is in charges on halogens, different due to their different electronegativity: ca. 0.04e on Brbut ca. −0.02e on Cl-atoms. Also, the carbons connected to the halogens have different negative charges: in the case of the Br-substituents ca. −0.11— −0.12e, and in the case of the Cl-substituents ca. −0.05e. Thus, in the precursors 1 and 2 charges on the selected atoms are mostly not significantly influenced either by halogens or by computational approaches used. The only difference is in charges on the atoms of the C-X moieties, where X is a halogen, due to the Br and Cl electronegativity differences. Also, it is worth noting that there is noticeable charge separation between the Cu4 core and organic ligands which might affect the reactivity and charge transport properties of networks formed by these compounds.

Figure 3.5: NPA charges, e, on the selected atoms of the precursor 1 (top row) and precursor 2 (bottom row) calculated with the B3LYP/6-31G* (a) and B3LYP-D2/6-31G* (b) approaches and with the effects of implicit CH3CN.

3.3 Results and discussion

45

3.3.3 Molecular electrostatic potential (MEP) plots In Figure 3.6 the MEP plots for ligand protected Cu4 cluster with the Br-and Cl-calculated with both computational approaches in the implicit solvents are provided. Analysis of the MEP plots shows the following trends. (i) Generally, the MEP distribution is essentially not affected by the computational approach used. (ii) In both structures, with the Br-and Cl-substituents, significant negative potential accumulation (as indicated by red color) can be noticed over the inner phenyl rings of the ligands and on the C-C link connecting the inner and outer phenyls (cf. Figures 3.6a and b). (iii) Much smaller negative potential accumulation can be noticed on the Cu4 core, and even smaller, if any, accumulation of negative potential can be noticed on the outer phenyls and halogen substituents of the ligands. Thus, the central parts and especially the inner phenyl ring parts of the compounds studied would play role of nucleophile in various chemical reactions due to the significant accumulation of negative electrostatic potential. Also, weak intermolecular (dispersion) interactions might exist between the ligands of neighboring molecules of these compounds.

Figure 3.6: MEP plots of the precursor 1 (top row) and precursor 2 (bottom row) computed at the B3LYP/631G* (a) and B3LYP-D2/6-31G* (b) levels with the effects of implicit CH3CN taken into account.

46

3 MOF new building blocks based on the Cu4 cluster with organic ligands

Table .: Calculated GRPs for the precursors  and  (eV). Halogen Br Cl

IP

EA

Gap

X

η

μ

σ

ω

. .

. .

. .

. .

. .

−. −.

. .

. .

3.3.4 GRP analysis The GRPs, namely, ionization potential (IP), electron affinity (EA), global softness (σ), global electrophilicity (ω), global hardness (η), global electronegativity (X), and chemical potential (μ), were computed based on the FMOs energies (Table 3.1) using Equations (3.1)–(3.6) (Computational details). Their computed values, eV, are presented in Table 3.2. For the simplicity sake, we decided to consider only GRPs computed using the B3LYP functional. Analysis of the values of the GRPs for the compounds with both substituents shows the following trends. (i) For both halogens, the ligand protected Cu4 clusters have significant IP values, 5.05 and 5.06 eV, respectively, however, the EA values are much lower, 1.57 and 1.56 eV, respectively, but still could be considered as relatively high. Therefore, these compounds should be relatively poor electron donors but better electron acceptors, i.e., should be quite stable in oxidation reactions and more active in reduction processes. It is worthwhile noticing that the substituent nature essentially does not have effect on the IP and EA values of the ligand protected Cu4 clusters. (ii) The global electronegativity X values are the same in the case of both substituents and quite high, 3.31 eV, and the global electrophilicity ω values are relatively high as well, 3.148 and 3.130 eV for the Br-and Clsubstituents, respectively. This would imply that the compounds considered should be more active in reduction reactions, which is in line with their relatively high EA values. (iii) Further, global hardness (η) values could be considered as not very high, 1.74/1.75 eV for Br/Cl, respectively. However, global softness (σ) values are quite low, 0.288 and 0.286 eV, respectively. This implies that both precursors should be relatively unreactive and demonstrate thermodynamic stability. This suggestion is supported by their quite noticeable HOMO/LUMO gaps, 3.48 and 3.50 eV, respectively, and quite high absolute chemical potential values, 3.31 eV for both substituents. Thus, with both substituents the ligand protected Cu4 clusters should be relatively unreactive and demonstrate thermodynamic stability. Also, these compounds should be quite stable in oxidation reactions and more active in reduction processes. Generally, the substituent nature was shown not to significantly affect the reactivity of these compounds.

3.4 Conclusions and perspectives

47

3.4 Conclusions and perspectives Metal-organic frameworks have been the object of numerous experimental and computational studies. Motivated by these facts and by the 2018 computational study by Claveria-Cádiz et al. on the organic ligand-protected d10 copper clusters, we have presented the results of the DFT study of the novel building blocks for MOFs built of the Cu4 cluster protected by four organic ligands with two phenyl rings and terminated either with Cl or Br atom, precursors 1 and 2, respectively. The study was performed both in the gas phase (vacuum) and with the implicit effects of acetonitrile included, with the B3LYP and PBE functionals, both with and without the second-order dispersion correction included. We showed that both hybrid and GGA functionals, either with or without dispersion corrections, without and with implicit solvent effects included, results in the singlet structures of the halogen-substituted organic ligand protected Cu4 clusters as the most stable species. Furthermore, the structures optimized employing the B3LYP functional with and without the dispersion correction included were found to be quite similar for both halogens, both contain somewhat distorted from planarity Cu4 cluster, and in both structures the outer phenyls of the ligands are rotated relative to the inner phenyls. The principal differences between the structures with Br-and Cl-substituents are in the C-halogen bond distances and in the mutual orientation of the ligands located opposite to each other, as was observed when the B3LYP/6-31G* approach was employed. We did not consider geometries of the structures optimized with the GGA functional because we were interested in general trends. Further, with both halogens and both DFT approaches, the FMOs of the compounds studied were shown to have quite similar compositions. Both gaps, HOMO/LUMO and TDDFT, were found to be quite weakly affected, if at all, by the halogen substituent nature and by the DFT approach. The change of the substituent from Br to Cl was found to cause slight stabilizations or destabilizations of the HOMOs and LUMOs of the compounds considered. Next, in the compounds studied charges on the selected atoms were shown to be mostly not significantly influenced either by halogens or by computational approaches used. The only difference was shown to exist in charges on the atoms of the C-X moieties, where X is a halogen, due to the Br and Cl electronegativity differences. Also, it is worth noting that there is noticeable charge separation between the Cu4 core and organic ligands which might affect the reactivity and charge transport properties of networks formed by these compounds. The central parts and especially the inner phenyl ring parts of the compounds studied were suggested to play role of nucleophile in various chemical reactions due to the significant accumulation of negative electrostatic potential. Also, weak intermolecular (dispersion) interactions might exist between the ligands of neighboring molecules of these compounds.

48

3 MOF new building blocks based on the Cu4 cluster with organic ligands

Finally, with both substituents the ligand protected Cu4 clusters should be relatively unreactive and demonstrate thermodynamic stability. Also, these compounds should be quite stable in oxidation reactions and more active in reduction processes. Generally, the substituent nature was shown not to significantly affect the reactivity of these compounds, as well as their other properties. Therefore, we might suppose that both precursors could be chosen for the MOF synthesis. However, to provide a more definite response, larger precursor structures should be studied, composed of two or more precursor 1 and 2 molecules. In this respect, reactions between precursor molecules and complex formation among those should be thoroughly considered. In the light of the results and this discussion, the following questions could be suggested to focus on in follow-up studies: (i) Study of chemical bonding in the precursors 1 and 2 including ligand-core cluster interactions; (ii) Studies of MOF precursors with longer ligands containing larger number of phenyl rings; (iii) Studies of MOF precursors where the core Cu clusters are protected by different ligands, including ligands with different halogen substituents and ligands with various substituents in phenyl rings; (iii) Studies of precursor dimers and complexes bound by non-covalent interactions; (iv) Studies of 2D- and 3D-networks formed by these MOF precursors. Some of these studies are currently underway. Supporting information: Supporting Information can be found free of charge online. It contains full table of computational results of the precursors 1 and 2, gas phase//CH3CN.

References 1. Yaghi OM, O’Keeffe M, Ockwig NW, Chae HK, Eddaoudi M, Kim J. Reticular synthesis and the design of new materials. Nature 2003;423:705–14. 2. Bao Z, Wang J, Zhang Z, Xing H, Yang Q, Yang Y, et al. Molecular sieving of ethane from ethylene through the molecular cross-section size differentiation in gallate-based metal–organic frameworks. Angew Chem Int Ed 2018;57:16020–5. 3. Lin RB, Li L, Zhou HL, Wu H, He C, Li S, et al. Molecular sieving of ethylene from ethane using a rigid metal– organic framework. Nat Mater 2018;17:1128–33. 4. He Y, Zhou W, Qian G, Chen B. Methane storage in metal–organic frameworks. Chem Soc Rev 2014;43: 5657–78. 5. Li JR, Sculley J, Zhou HC. Metal–organic frameworks for separations. Chem Rev 2012;112:869–932. 6. Furukawa H, Cordova KE, O’Keeffe M, Yaghi OM. The chemistry and applications of metal-organic frameworks. Science 2013;341:6149. 7. Mizutani N, Hosono N, Le Ouay B, Kitao T, Matsuura R, Kubo T, et al. Recognition of polymer terminus by metal–organic frameworks enabling chromatographic separation of polymers. J Am Chem Soc 2020;142: 3701–5. 8. Zhao M, Yuan K, Wang Y, Li G, Guo J, Gu L, et al. Metal–organic frameworks as selectivity regulators for hydrogenation reactions. Nature 2016;539:76–80.

References

49

9. Samaniyan M, Mirzaei M, Khajavian R, Eshtiagh-Hosseini H, Streb C. Heterogeneous catalysis by polyoxometalates in metal–organic frameworks. ACS Catal 2019;9:10174–91. 10. Wang HF, Chen L, Pang H, Kaskel S, Xu Q. MOF-derived electrocatalysts for oxygen reduction, oxygen evolution and hydrogen evolution reactions. Chem Soc Rev 2020;49:1414–48. 11. Wang L, Yang W, Li Y, Xie Z, Zhu W, Sun ZM. Dynamically controlled one-pot synthesis of heterogeneous core–shell MOF single crystals using guest molecules. Chem Commun 2014;50:11653–6. 12. Daliran S, Oveisi AR, Peng Y, López-Magano A, Khajeh M, Mas-Ballesté R, et al. Metal–organic framework (MOF)-, covalent-organic framework (COF)-, and porous-organic polymers (POP)-catalyzed selective C–H bond activation and functionalization reactions. Chem Soc Rev 2022;51:7810–82. 13. Thorarinsdottir AE, Harris TD. Metal–organic framework magnets. Chem Rev 2020;120:8716–89. 14. Zhao XY, Wang J, Yang QS, Fu DL, Jiang DK. A hydrostable samarium(III)-MOF sensor for the sensitive and selective detection of tryptophan based on a “dual antenna effect”. Anal Methods 2021;13:3994–4000. 15. Samanta P, Let S, Mandal W, Dutta S, Ghosh SK. Luminescent metal–organic frameworks (LMOFs) as potential probes for the recognition of cationic water pollutants. Inorg Chem Front 2020;7:1801–21. 16. Fang X, Zong B, Mao S. Metal–organic framework-based sensors for environmental contaminant sensing. Nano-Micro Lett 2018;10:64. 17. Zhou J, Zeng C, Ou H, Yang Q, Xie Q, Zeb A, et al. Metal–organic framework-based materials for full cell systems: a review. J Mater Chem C 2021;9:11030–58. 18. Liu X, Zhang L, Wang J. Design strategies for MOF-derived porous functional materials: preserving surfaces and nurturing pores. J Mater 2021;7:440–59. 19. Ma Y, Tang X, Chen M, Mishima A, Li L, Hori A, et al. Design of a MOF based on octa-nuclear zinc clusters realizing both thermal stability and structural flexibility. Chem Commun 2022;58:1139–42. 20. Tranchemontagne DJ, Mendoza-Cortés JL, O’Keeffe M, Yaghi OM. Secondary building units, nets and bonding in the chemistry of metal–organic frameworks. Chem Soc Rev 2009;38:1257. 21. Kalmutzki MJ, Hanikel N, Yaghi OM. Secondary building units as the turning point in the development of the reticular chemistry of MOFs. Sci Adv 2018;4. https://doi.org/10.1126/sciadv.aat9180. 22. Schoedel A. Secondary building units of MOFs. In: Metal-organic frameworks for biomedical applications, Elsevier; 2020:11–44 pp. 23. Wang Q, Pengmei Z, Pandharkar R, Gagliardi L, Hupp JT, Notestein JM. Investigating the effect of metal nuclearity on activity for ethylene hydrogenation by metal-organic-framework-supported oxy-Ni(ii) catalysts. J Catal 2022;407:162–73. 24. Claveria-Cadiz F, Arratia-Perez R, Guajardo-Maturana R, Muñoz-Castro A. Survey of short and long cuprophilic d10–d10 contacts for tetranuclear copper clusters. Understanding of bonding and ligand role from a planar superatom perspective. New J Chem 2018;42:8874–81. 25. Kang Y, Wang F, Zhang J, Bu X. Luminescent MTN-type cluster–organic framework with 2.6 nm cages. J Am Chem Soc 2012;134:17881–4. 26. Hayashi T, Kobayashi A, Ohara H, Yoshida M, Matsumoto T, Chang HC, et al. Vapochromic luminescence and flexibility control of porous coordination polymers by substitution of luminescent multinuclear Cu(I) cluster nodes. Inorg Chem 2015;54:8905–13. 27. Perruchas S, Tard C, Le Goff XF, Fargues A, Garcia A, Kahlal S, et al. Thermochromic luminescence of copper iodide clusters: the case of phosphine ligands. Inorg Chem 2011;50:10682–92. 28. Siddiqui MM, Mobin SM, Senkovska I, Kaskel S, Balakrishna MS. Novel zeotype frameworks with soft cyclodiphosphazane linkers and soft Cu4X4 clusters as nodes. Chem Commun 2014;50:12273–6. 29. Vega A, Saillard JY. Bonding in tetrahedral Cu4(μ3-X)4L4 copper(I) clusters: a DFT investigation. Inorg Chem 2004;43:4012–8. 30. Osei MK, Mirzaei S, Bogetti X, Castro E, Rahman MA, Saxena S, et al. Synthesis of square planar Cu4 clusters. Angew Chem Int Ed 2022;61:2–7. 31. Frisch MJ, Trucks GW, Schlegel HB, Scuseria GE, Robb MA, Cheeseman JR, et al. Gaussian 16, revision B.01. Wallingford CT: Gaussian, Inc.; 2016.

50

3 MOF new building blocks based on the Cu4 cluster with organic ligands

32. Axel DB. Density-functional thermochemistry. III. The role of exact exchange. J Chem Phys 1993;98:5648–2. 33. McLean AD, Chandler GS. Contracted Gaussian-basis sets for molecular calculations. 1. 2nd row atoms, Z=11–18. J Chem Phys 1980;72:5639–48. 34. Krishnan R, Binkley JS, Raymond JS, People JA. Self-consistent molecular orbital methods. 20. Basis set for correlated wave-functions. J Chem Phys 1980;72:650–4. 35. Grimme S. Semiempirical GGA-type density functional constructed with a long-range dispersion correction. J Comput Chem 2006;27:1787–99. 36. Perdew JP, Burke K, Ernzerhof M. Generalized gradient approximation made simple. Phys Rev Lett 1996;77: 3865–8. 37. Tomasi J, Mennucci B, Cammi R. Quantum mechanical continuum solvation models. Chem Rev 2005;105: 2999–3093. 38. Reed AE, Curtiss LA, Weinhold F. Intermolecular interactions from a natural bond orbital, donor-acceptor viewpoint. Chem Rev 1988;88:899–926. 39. Geerlings P, De Proft F, Langenaeker W. Conceptual density functional theory. Chem Rev 2003;103: 1793–874. 40. Chakraborty A, Pan S, Chattaraj PK. Biological activity and toxicity: a conceptual DFT approach. Struct Bond 2013;150:143–80. 41. Jorio S, Salah M, El Makarim HA, Tabyaoui M. Reactivity indices related to DFT theory, the electron localization function (ELF) and non-covalent interactions (NCI) calculations in the formation of the nonhalogenated pyruvic esters in solution. Mediterranean J Chem 2019;8:476–85. 42. Schaftenaar G, Noordik JH. Molden: a pre- and post-processing program for molecular and electronic structures. J Comput-Aided Mol Des 2000;14:123–34. 43. Hanwell MD, Curtis DE, Lonie DC, Vandermeersch T, Zurek E, Hutchison GR. Avogadro: an advanced semantic chemical editor, visualization, and analysis platform. J Cheminf 2012;41:1–17. 44. Avogadro: an open-source molecular builder and visualization tool. Version 1.1.1. Available from: http:// avogadro.cc/.

Nadjah Belattar*, Ratiba Mekkiou, Adel Krid and Abdelhamid Djekoun

4 Computational investigation of Arbutus serratifolia Salisb molecules as new potential SARS-CoV-2 inhibitors Abstract: The outbreak of the current pandemic and the evolution of virus resistance against standard drugs led to the emergency of new and potent antiviral agents. Owing to its crucial role in viral replication, the protease enzyme is taken into survey to be a promising target for antiviral drug therapy using computational methods. In order to bring this important class of natural products in the limelight of research for prospective application as chemotherapeutic agents, the anti-SARS-CoV-2 activity of some bioactive molecules obtained from Arbutus serratifolia Salisb which is an Algerian medicinal plant, was investigated using in-silico methods. The molecular docking was performed by AutoDock Vina and UCSF Chimera, as well as ADMET and drug-likeness properties of these molecules were calculated using preADMET web-based application and the Swiss ADME server respectively. The phytochemicals (from Pr(1) to Pr(12)) were tested for their pharmacokinetic properties and docked into the main protease binding site on (PDB ID: 6Y84) in order to find a promising antiviral ligand. All tested molecules induced binding affinities into the binding pocket of (PDB ID: 6Y84) with energy scores ranging from moderate to better (from −6.4 to −8.00 kcal/ mol). It is worthy to note that both Pr(2): (1S,5R,6S,8S,9S)-6,8-Dihydroxy-8-methyl-1,5,6,7,8,9hexahydrocyclopenta [c] pyran-1-yl-β-D-glucopyranoside and Pr(7): ((1S,5S,6S,9S)-1-(β-D-Glucopyranosyloxy)-14-oxo-1,5,6,9-tetrahydro-1H-2,15-dioxacyclopenta [cd] inden-8-yl) methyl acetate, were found to be the best inhibitors with binding affinities (−7.7 kcal/mol and −8.0 kcal/mol), respectively, by virtue of the fact that all these tested molecules exhibited good binding affinities compared with those of Ritonavir and Nirmatrelvir (−1.73 and −1.93 kcal/mol), respectively, which are used as standard antiviral drugs to prevent viral growth. The amino acids: His-163; Glu-166; Arg-188; Thr-190 and Gln-192 represent the key residues of the interaction of SARS-CoV-2 main protease with Pr(7). Furthermore, the results of pharmacodynamic and pharmacokinetic investigations revealed that Pr(6), Pr(8) and Pr(9) uphold the drug-likeness criteria and more particularly, these substances can be absorbed by the human intestine. In addition, all these molecules were shown to be neither hepatotoxic nor

*Corresponding author: Nadjah Belattar, Pharmaceutical Sciences Research Center (CRSP), Ali Mendjli, Constantine, 25000, Algeria; and Research Unit of Valorisation of Natural Resources, Bioactive Molecules and Physicochemical and Biological Analyses (VARENBIOMOL), Mentouri Brothers University, Constantine -1, Algeria, E-mail: [email protected] Ratiba Mekkiou, Research Unit of Valorisation of Natural Resources, Bioactive Molecules and Physicochemical and Biological Analyses (VARENBIOMOL), Mentouri Brothers University, Constantine -1, Algeria Adel Krid and Abdelhamid Djekoun, Pharmaceutical Sciences Research Center (CRSP), Ali Mendjli, Constantine, 25000, Algeria As per De Gruyter’s policy this article has previously been published in the journal Physical Sciences Reviews. Please cite as: N. Belattar, R. Mekkiou, A. Krid and A. Djekoun “Computational investigation of Arbutus serratifolia Salisb molecules as new potential SARS-CoV-2 inhibitors” Physical Sciences Reviews [Online] 2023. DOI: 10.1515/psr-2022-0240 | https://doi.org/10.1515/ 9783111071428-004

52

4 Arbutus serratifolia Salisb as new SARS-CoV-2 inhibitor

significantly noxious to human organism. These natural products are therefore promising inhibitor candidates of viral main protease. However, further in-vitro, in-vivo and even clinical assays are required to probe their functional mechanisms and then to assess their antiviral potency against COVID-19. Keywords: Arbutus serratifolia Salisb; binding affinities; drug-likeness; molecular docking; SARS-CoV-2.

4.1 Introduction An infectious respiratory complication An infectious respiratory complication whose responsible agent is SARS-CoV-2 virus has mainly affected 215 countries, resulting in a severe socioeconomic and global health crisis insofar as more than 360,578,392 total cases and 5,620,865 cumulative deaths were reported on January 27th, 2022 [1]. Hence, the majority of cases only present chillness, fever, dry cough, weakness, and hard breathing with loss of taste and smell senses, while a small minority (0.2%–5%) of cases develop pneumonia and multiorgan failure which can be fatal, particularly in the absence of secondary healthcare assistance [2, 3]. Generally, four structural proteins: nucleocapsid (N), membrane (M), envelope (E), and spike (S) combine to generate SARS-CoV-2 in which only the proteins (E), (M), and (S) can be visible on the outer surface of the virus’s particle, and more particularly the spike protein occurs as a knob like design, bigger than the other structural proteins despite the fact that all of these proteins contribute to the viral production [4]. Moreover, the SARS-CoV-2 spike protein enhances the adherent and invasive ability by inducing the virus binding to receptors and then cross into host cells. Since it represents the most exposed showcase of viral architecture, the spike protein serves as an immunogen stimulus as it is necessary for receptor-mediated viral reproduction. The novel SARS-CoV-2 alpha [5] and beta [6] variants were discovered at the end of 2020, followed by gamma variant [7] and the recent SARS-CoV-2 delta/delta+ variant which emerged in 2021 (B.1.617/AY.1) [7–9] resulting in a new worldwide infection. New vaccinations must be extremely developed, although with cutting-edge technologies such as in the case of mRNA assimilation the timeframe is still affordable [10]. Therefore, it is important to improve new pharmacological strategies and other therapeutic modalities for making the pandemic control very easier in the future [11, 12]. Due to the urgent need to promote and screen an efficient substance that is able to prevent the SARS-CoV-2 outbreak, many molecules are now undergoing clinical trials to examine their therapeutic potential. Nirmatrelvir was used as a therapeutic drug against SARS-CoV-2 in some countries at the beginning of the pandemic, but its use has declined when some vaccinations have been created. Ritonavir, saquinavir, darunavir, indinavir, and ASC 09 are only a few of the additional drugs, both known and novel drugs were investigated in clinical studies [13, 14]. To enhance their immune system against COVID-19, the Algerian society uses several preparations of traditional medicinal herbs, among which Arbutus serratifolia Salisb,

4.2 Part 1: Materials and methods

53

belonging to Ericaceae family, is prescribed in traditional medicine to cure a variety of illnesses and problems corresponding to colds, coughs, and respiratory difficulties. The current work aims to identify the active compounds against the main viral protease using molecular docking analysis after testing their pharmacokinetic and pharmacodynamic properties. For this reason, the following natural products: Pr(1):(1S,5R,6R,8S,9S)-6,8-Dihydroxy-8-methyl-1,5,6,7,8,9-hexahydrocyclopenta [c] pyran-1-yl β-D-glucopyranoside. Pr(2):(1S,5R,6S,8S,9S)-6,8-Dihydroxy-8-methyl-1,5,6,7,8,9-hexahydrocyclopenta [c] pyran-1-yl β-D-glucopyranoside. Pr(3):Methyl((1R,5R,6S,8R,9R)-6,8-Dihydroxy-8-methyl-1-(β-D-glucopyranosyloxy)1,5,6,7,8,9-hexahydrocyclopenta [c] pyran-4-yl) carboxylate. Pr(4):Methyl((1S,5S,8S,9S)-1-(β-D-glucopyranosyloxy)-8-hydroxy-8-(hydroxymethyl)1,5,8,9-tetrahydrocyclopenta [c] pyran-4-yl) carboxylate. Pr(5):Methyl((1S,5S,8S,9S)-1-(β-D-glucopyranosyloxy)-8-hydroxy-8-(hydroxymethyl)-1,5,6,7,8,9-hexahydrocyclopenta [c] pyran-4-yl) carboxylate. Pr(6):(1S,5S,6S,8S,9R)-8-Methyl-11-oxo-1,5,6,7,8,9-hexahydro-4H-2,12-dioxacyclopenta [cd] inden-1-yl-β-D-glucopyranoside. Pr(7):((1S,5S,6S,9S)-1-(β-D-Glucopyranosyloxy)-14-oxo-1,5,6,9-tetrahydro-1H-2,15dioxacyclopenta [cd] inden-8-yl) methyl acetate. Pr(8):Methyl((1S,5S,9S)-1-(β-D-glucopyranosyloxy)-8-(hydroxymethyl)-1,5,6,9-tetrahydrocyclopenta [c] pyran-4-yl) carboxylate. Pr(9):(1R,5S,6R,7R,8S,9R)-6-Hydroxy-1-(β-D-glucopyranosyloxy)-1,5,6,7,8,9-hexahydrooxireno [2’’,3’’:4”’,5”’] cyclopenta [c] pyran. Pr(10):((1S,5S,8R,9S)-1-(β-D-Glucopyranosyloxy)-8-hydroxy-8-(hydroxymethyl)1,5,8,9-tetrahydrocyclopenta [c] pyran-4-yl) carboxylic acid. Pr(11):((1S,5S,6R,8R,9S)-6,8-Dihydroxy-8-(hydroxymethyl)-1-(β,D-glucopyranosyloxy)1,5,6,7,8,9-hexahydrocyclopenta [c] pyran-4-yl) carboxylic acid. Pr(12):(1R,5R,6S,7R,8S,9S)-5,6-Dihydroxy-1-(β-D-glucopyranosyloxy)-1,5,6,7,8,9-hexahydrooxireno [2’’,3’’:4”’,5”’] cyclopenta [c] pyran. (Table 4.1), were selected for molecular docking with SARS-CoV-2 main protease (PDB ID: 6Y84), using Autodock Vina and UCSF Chimera software.

4.2 Part 1: Materials and methods 4.2.1 Phytochemical study 4.2.1.1 Extraction of secondary metabolites of A. serratifolia Salisb A. serratifolia Salisb species was harvested in El-Milia Forest (Jijel region) in October 2017 and a reference specimen has been deposited at the Botanical Herbarium of our research unit VARENBIOMOL at Constantine University.

54

4 Arbutus serratifolia Salisb as new SARS-CoV-2 inhibitor

Table .: The nomenclatures of the studied molecules. Molecules Pr() Pr() Pr() Pr() Pr() Pr() Pr() Pr() Pr() Pr() Pr() Pr()

Nomenclatures (S,R,R,S,S)-,-Dihydroxy--methyl-,,,,,-hexahydrocyclopenta [c] pyran--yl-β-Dglucopyranoside. (S,R,S,S,S)-,-Dihydroxy--methyl-,,,,,-hexahydrocyclopenta [c] pyran--yl-β-Dglucopyranoside. Methyl((R,R,S,R,R)-,-Dihydroxy--methyl--(β-D-glucopyranosyloxy)-,,,,,-hexahydrocyclopenta [c] pyran--yl) carboxylate. Methyl((S,S,S,S)--(β-D-glucopyranosyloxy)--hydroxy--(hydroxymethyl)-,,,-tetrahydrocyclopenta [c] pyran--yl) carboxylate. Methyl((S,S,S,S)--(β-D-glucopyranosyloxy)--hydroxy--(hydroxymethyl)-,,,,,-hexahydrocyclopenta [c] pyran--yl) carboxylate. (S,S,S,S,R)--Methyl--oxo-,,,,,-hexahydro-H-,-dioxacyclopenta [cd] inden--ylβ-D-glucopyranoside. ((S,S,S,S)--(β-D-Glucopyranosyloxy)--oxo-,,,-tetrahydro-H-,-dioxacyclopenta [cd] inden--yl) methyl acetate. Methyl((S,S,S)--(β-D-glucopyranosyloxy)--(hydroxymethyl)-,,,-tetrahydrocyclopenta [c] pyran--yl) carboxylate. (R,S,R,R,S,R)--Hydroxy--(β-D-glucopyranosyloxy)-,,,,,-hexahydrooxireno [’’,’’:”’,”’] cyclopenta [c] pyran. ((S,S,R,S)--(β-D-Glucopyranosyloxy)--hydroxy--(hydroxymethyl)-,,,-tetrahydrocyclopenta [c] pyran--yl) carboxylic acid. ((S,S,R,R,S)-,-Dihydroxy--(hydroxymethyl)--(β,D-glucopyranosyloxy)-,,,,,-hexahydrocyclopenta [c] pyran--yl) carboxylic acid. (R,R,S,R,S,S)-,-Dihydroxy--(β-D-glucopyranosyloxy)-,,,,,-hexahydrooxireno [’’,’’:”’,”’] cyclopenta [c] pyran.

The aerial parts (leaves, flowers and stems: 477 g) of this plant were extracted three times, at room temperature with MeOH-H2O (80:20) during three days. The extraction with different solvents in gradient polarity led to obtain: petroleum ether (2.23 g), CHCl3 (2.38 g), EtOAc (2.86 g) and n-BuOH (2.94 g) fractions. 4.2.1.2 Separation and purification of bioactive molecules Ethyl acetate fraction (2.86 g) was submitted to chromatographic separation into silica gel column using the (CHCl3/MeOH) eluent from which 33 fractions were separated and collected in the same conditions. Using a chromatographic column, the fractions 496–513 (163.7 mg) were deposited on silica gel support, prepared in CHCl3 solvent and the elution was carried out using (CHCl3/ EtOAc/MeOH) eluent with increasing polarities. Chromatographic separation into a silica gel column was monitored using UV lamp (254 and 365 nm), and pooling of the total final fractions was performed based on silica gel thin layer chromatography (TLC) analysis, and the following products were isolated:

4.2 Part 1: Materials and methods

55

4.2.1.2.1 (1S,5R,6R,8S,9S)-6,8-Dihydroxy-8-methyl-1,5,6,7,8,9-hexahydrocyclopenta [c] pyran-1-yl-β-D-glucopyranoside (Figure 4.1).

Figure 4.1: Chemical structure of Pr(1).

4.2.1.2.1.1 Separation. Pr(1) (10.4 mg) was obtained as white amorphous powder by chromatographic separation into sephadex LH-20 column using MeOH eluent. 4.2.1.2.1.2 Detection. Pr(1) resulted in bluish-violet coloration with sulfuric vanillin reagent prepared from a solution of (0.5 mL) H2SO4 dissolved in (9 mL) EtOH and (0.1 g) vanillin dissolved in (10 mL) EtOH, the mixture was subjected to heat at 100 °C for 10 min [15]. 4.2.1.2.1.3 Spectroscopic data 4.2.1.2.1.3.1 UV (MeOH) λmax (nm): 230 characteristic of unsaturated enol-ether system. 4.2.1.2.1.3.2 IR (1% KBr) νmax (cm−1): 3375 (OH), 1642 (C=C): olefinic group, 2984 (C–H): methyl group, 3036 (HC=C): methylene group, 1082 (C–O): ether group. 4.2.1.2.1.3.3 1H NMR (DMSO-d6, 400 MHz). δ 5.47 ppm (1H, d, J = 2.53 Hz, H-1), δ 6.14 ppm (1H, d, J = 1.98 Hz, H-3), δ 4.83 ppm (1H, dd, J = 6.24 Hz and 3.06 Hz, H-4), δ 2.46 ppm (1H, m, H-5), δ 3.88 ppm (1H, m, H-6), δ 4.81 ppm (1H, d, J = 3.96 Hz, OH-6), δ 1.8 ppm(1H, dd, J = 13.38 Hz and 5.78 Hz, H-7a), δ 2.04 ppm (1H, dd, J = 13.24 Hz and 5.64 Hz, H-7b), δ 4.53 ppm (1H, s, OH-8), δ 2.49 ppm (1H, dd, J = 9.58 Hz and 1.05 Hz, H-9), δ 1.33 ppm (3H, s, H-10), δ 4.52 ppm (1H, d, J = 7.86 Hz, H-1′), δ 3.18 ppm (1H, ddd, J = 9.16 Hz, 8.2 Hz and 4.17 Hz, H-2′), δ 4.82 ppm (1H, d, J = 4.2 Hz, OH-2′), δ 3.34 ppm (1H, ddd, J = 9.4 Hz, 8.58 Hz and 4.08 Hz, H-3′), δ 4.67 ppm (1H, d, J = 3.95 Hz, OH-3′), δ 3.21 ppm (1H, ddd, J = 9.05 Hz,8.25 Hz and 4.12 Hz, H-4′), δ 4.9 ppm (1H, d, J = 4.16 Hz, OH-4′), δ 3.27 ppm (1H, m, H-5′), δ 3.84 ppm (1H, ddd, J = 12.18 Hz, 4.32 Hz and 2.4 Hz, H-6′a), δ 3.71 ppm (1H, ddd, J = 12.23 Hz, 5.6 Hz and 4.27 Hz, H-6′b), δ 3.97 ppm (1H, dd, J = 3.4 Hz and 3.10 Hz, OH-6′). 4.2.1.2.1.3.4 13C NMR (DMSO-d6, 100.3 MHz). δ 93.5 ppm (C-1), δ 138.9 ppm (C-3), δ 106.1 ppm (C-4), δ 39.5 ppm (C-5), δ 76.4 ppm (C-6), δ 48.8 ppm (C-7), δ 78.9 ppm (C-8), δ 50.6 ppm (C-9), δ 25.2 ppm (C-10), δ 98.6 ppm (C-1′), δ 73.5 ppm (C-2′), δ 78.1 ppm (C-3′), δ 70.8 ppm (C-4′), δ 77.8 ppm (C-5′), δ 63.4 ppm (C-6′). 4.2.1.2.2 (1S,5R,6S,8S,9S)-6,8-Dihydroxy-8-methyl-1,5,6,7,8,9-hexahydrocyclopenta [c] pyran-1-yl-β-D-glucopyranoside (Figure 4.2).

56

4 Arbutus serratifolia Salisb as new SARS-CoV-2 inhibitor

Figure 4.2: Chemical structure of Pr(2).

4.2.1.2.2.1 Separation. Pr(2) (7.8 mg) was obtained as a white amorphous powder by chromatographic separation into sephadex LH-20 column using MeOH eluent. 4.2.1.2.2.2 Detection. Pr(2) which represents the 6α-hydroxy epimer of Pr (1), resulted in a blue-violet coloration with sulfuric vanillin reagent prepared from a solution of (0.5 mL) H2SO4 dissolved in (9 mL) EtOH and (0.1 g) vanillin dissolved in (10 mL) EtOH, the mixture was submitted to heat at 100 °C for 10 min [15]. 4.2.1.2.2.3 Spectroscopic data 4.2.1.2.2.3.1 UV (MeOH) λmax (nm): 227 characteristic of unsaturated enol-ether system. 4.2.1.2.2.3.2 IR (1% KBr) νmax (cm−1): 3389 (OH), 1648 (C=C): olefinic group, 2977 (C–H): methyl group, 3052 (HC=C): methylene group, 1098 (C–O): ether group. 4.2.1.2.2.3.3 1H NMR (DMSO-d6, 400 MHz). δ 5.51 ppm (1H, d, J = 2.46 Hz, H-1), δ 6.18 ppm (1H, d, J = 1.77 Hz, H-3), δ 4.79 ppm (1H, dd, J = 6.14 Hz and 2.85 Hz, H-4), δ 2.53 ppm (1H, m, H-5), δ 3.94 ppm (1H, m, H-6), δ 4.83 ppm (1H, d, J = 4.02 Hz, OH-6), δ 2.05 ppm (1H, dd, J = 13.22 Hz and 6.03 Hz, H-7a), δ 2.1 ppm (1H, dd, J = 13.12 Hz and 6.01 Hz, H-7b), δ 4.56 ppm (1H, s, OH-8), δ 2.54 ppm (1H, dd, J = 9.35 Hz and 2.51 Hz, H-9), δ 1.48 ppm (3H, s, H-10), δ 4.66 ppm (1H, d, J = 8.12 Hz, H-1′), δ 3.2 ppm (1H, ddd, J = 8.94 Hz, 8.13 Hz and 4.22 Hz, H-2′), δ 4.78 ppm (1H, d, J = 4.21 Hz, OH-2′), δ 3.4 ppm (1H, ddd, J = 9.24 Hz, 8.58 Hz and 4.03 Hz, H-3′), δ 4.71 ppm (1H, d, J = 3.9 Hz, OH-3′), δ 3.36 ppm (1H, ddd, J = 8.95 Hz, 8.12 Hz and 4.21 Hz, H-4′), δ 4.85 ppm (1H, d, J = 4.19 Hz, OH-4′), δ 3.34 ppm (1H, m, H-5′), δ 3.76 ppm (1H, ddd, J = 11.96 Hz, 2.36 Hz and 4.38 Hz, H-6′a), δ 3.69 ppm (1H, ddd, J = 12.15 Hz, 5.52 Hz and 4.24 Hz, H-6′b), δ 4.05 ppm (1H, dd, J = 3.36 Hz and 3.14 Hz, OH-6′). 4.2.1.2.2.3.4 13C NMR (DMSO-d6, 100.3 MHz). δ 94.2 ppm (C-1), δ 141.3 ppm (C-3), δ 106.7 ppm (C-4), δ 42.2 ppm (C-5), δ 77.8 ppm (C-6), δ 50.2 ppm (C-7), δ 80.2 ppm (C-8), δ

4.2 Part 1: Materials and methods

57

52.3 ppm (C-9), δ 26.4 ppm (C-10), δ 98.9 ppm (C-1′), δ 74.1 ppm (C-2′), δ 77.8 ppm (C-3′), δ 71.2 ppm (C-4′), δ 78.1 ppm (C-5′), δ 64.2 ppm (C-6′). 4.2.1.2.3 Methyl((1R,5R,6S,8R,9R)-6,8-Dihydroxy-8-methyl-1-(β-D-glucopyranosyloxy)1,5,6,7,8,9-hexahydrocyclopenta [c] pyran-4-yl) carboxylate (Figure 4.3).

Figure 4.3: Chemical structure of Pr(3).

4.2.1.2.3.1 Separation. The solid mixture (526–528) (88.5 mg) obtained from the chromatographic separation of ethyl acetate fraction was purified into sephadex LH-20 column using MeOH eluent yielding Pr(3) product (8.3 mg). 4.2.1.2.3.2 Detection. Pr(3) resulted in a dark-purple coloration with the sulfuric vanillin reagent (5% H2SO4 in EtOH and 1% vanillin in EtOH) and then heated at 100 °C for 10 min [15]. 4.2.1.2.3.3 Spectroscopic data 4.2.1.2.3.3.1 UV (MeOH) λmax (nm): 233 characteristic of an unsaturated enol-ether system, λmax (nm) = 312 characteristic of carbonyl functionality. 4.2.1.2.3.3.2 IR (1% KBr) νmax (cm−1): 3364 (OH), 1710 (C=O), 1650 (C=C): olefinic group, 2981 (C–H): methyl group, 3047 (HC=C): methylene group, 1074 (C–O): ether group. 4.2.1.2.3.3.3 1H NMR (DMSO-d6, 400 MHz). δ 5.62 ppm (1H, d, J = 3.12 Hz, H-1), δ 7.05 ppm (1H, d, J = 1.49 Hz, H-3), δ 3.42 ppm (1H, dd, J = 9.87 Hz and 3.93 Hz, H-5), δ 3.89 ppm (1H, m, H-6), δ 4.85 ppm (1H, d, J = 4.04 Hz, OH-6), δ 2.24 ppm (1H, dd, J = 13.5 Hz and 5.61 Hz, H-7a), δ 1.85 ppm (1H, dd, J = 13.42 Hz and 5.12 Hz, H-7b), δ 4.52 ppm (1H, s, OH-8), δ 2.63 ppm (1H, dd, J = 9.8 Hz and 3.96 Hz, H-9), δ 1.32 ppm (3H, s, H-10), δ 3.54 ppm (3H, s, H-12), δ 4.68 ppm (1H, d, J = 7.96 Hz, H-1′), δ 3.24 ppm (1H, ddd, J = 8.45 Hz, 7.95 Hz and 4.19 Hz, H-2′), δ 4.84 ppm (1H, d, J = 4.23 Hz, OH-2′), δ 3.37 ppm (1H, ddd, J = 9.02 Hz, 8.13 Hz and 3.92 Hz, H-3′), δ 4.65 ppm (1H, d, J = 3.88 Hz, OH-3′), δ 3.29 ppm (1H, ddd, J = 9.06 Hz, 8.04 Hz and

58

4 Arbutus serratifolia Salisb as new SARS-CoV-2 inhibitor

4.25 Hz, H-4′), δ 4.81 ppm (1H, d, J = 4.2 Hz, OH-4′), δ 3.41 ppm (1H, m, H-5′), δ 3.84 ppm (1H, ddd, J = 12.18 Hz, 4.4 Hz and 2.94 Hz, H-6′a), δ 3.68 ppm (1H, ddd, J = 12.3 Hz, 5.88 Hz and 4.25 Hz, H-6′b), δ 3.98 ppm (1H, dd, J = 3.35 Hz and 3.11 Hz, OH-6′). 4.2.1.2.3.3.4 13C NMR (DMSO-d6, 100.3 MHz). δ 95.1 ppm (C-1), δ 154.7 ppm (C-3), δ 108.6 ppm (C-4), δ 39.5 ppm (C-5), δ 78.9 ppm (C-6), δ 49.2 ppm (C-7), δ 78.6 ppm (C-8), δ 53.4 ppm (C-9), δ 27.3 ppm (C-10), δ 167.8 ppm (C-11), δ 53.4 ppm (C-12), δ 98.8 ppm (C-1′), δ 75.3 ppm (C-2′), δ 77.9 ppm (C-3′), δ 71.3 ppm (C-4′), δ 76.8 ppm (C-5′), δ 63.2 ppm (C-6′). 4.2.1.2.4 Methyl((1S,5S,8S,9S)-1-(β-D-glucopyranosyloxy)-8-hydroxy-8-(hydroxymethyl)-1,5,8,9-tetrahydrocyclopenta [c] pyran-4-yl) carboxylate (Figure 4.4).

Figure 4.4: Chemical structure of Pr(4).

4.2.1.2.4.1 Separation. The solid mixture (545–555) (140 mg) obtained from the chromatographic separation of ethyl acetate fraction was submitted to further separation into silica gel column using EtOAc-MeOH-H2O (8:1:1) eluent yielding the following products Pr(4) (12.3 mg) and its C8 epimer Pr(5) (10.7 mg). 4.2.1.2.4.2 Detection. Pr(4) resulted in a dark-purple coloration with the sulfuric vanillin reagent (5% H2SO4 in EtOH and 1% vanillin in EtOH) and then heated at 100 °C for 10 min [15]. 4.2.1.2.4.3 Spectroscopic data 4.2.1.2.4.3.1 UV (MeOH) λmax(nm): 226 characteristic of an unsaturated enol-ether system, λmax (nm) = 315 characteristic of carbonyl functionality. 4.2.1.2.4.3.2 IR (1% KBr) νmax(cm−1): 3375 (OH), 1718 (C=O), 1641 (C=C) olefinic group, 2974 (C–H): methyl group, 3052 (HC=C): methylene group, 1089 (C–O): ether group. 4.2.1.2.4.3.3 1H NMR (DMSO-d6, 400 MHz). δ 5.74 ppm (1H, d, J = 3.86 Hz, H-1), δ 7.17 ppm (1H, d, J = 1.36 Hz, H-3), δ 4.65 ppm (1H, dd, J = 8.76 Hz and 4.05 Hz, H-5), δ 6.18 ppm (1H, dd,

4.2 Part 1: Materials and methods

59

J = 5.54 and 2.79 Hz, H-6), δ 6.05 ppm (1H, d, J = 5.72 Hz, H-7), δ 5.18 ppm (1H, s, OH-8), δ 3.15 ppm (1H, dd, J = 9.66 Hz and 3.82 Hz, H-9), δ 3.83 ppm (1H, dd, J = 13.5 Hz and 3.86 Hz, H-10a), δ 3.91 ppm (1H, dd, J = 13.46 Hz and 3.9 Hz, H-10b), δ 5.02 ppm (1H, dd, J = 4.23 Hz and 4.41 Hz, OH-10), δ 3.57 ppm (3H, s, H-12), δ 4.73 ppm (1H, d, J = 7.81 Hz, H-1′), δ 3.44 ppm (1H, ddd, J = 8.65 Hz, 8.02 Hz and 4.21 Hz, H-2′), δ 4.89 ppm (1H, d, J = 4.25 Hz, OH-2′), δ 3.62 ppm (1H, ddd, J = 9.15 Hz, 8.33 Hz and 4.03 Hz, H-3′), δ 4.7 ppm (1H, d, J = 3.9 Hz, OH-3′), δ 3.47 ppm (1H, ddd, J = 9.1 Hz, 8.12 Hz and 4.3 Hz, H-4′), δ 4.77 ppm (1H, d, J = 4.24 Hz, OH-4′), δ 3.52 ppm (1H, m, H-5′), δ 3.96 ppm (1H, ddd, J = 12.23 Hz, 3.39 Hz and 2.87 Hz, H-6′a), δ 3.73 ppm (1H, ddd, J = 12.5 Hz, 5.64 Hz and 3.2 Hz, H-6′b), δ 3.93 ppm (1H, dd, J = 3.41 Hz and 3.17 Hz, OH-6′). 4.2.1.2.4.3.4 13C NMR (DMSO-d6, 100.3 MHz). δ 94.7 ppm (C-1), δ 153.6 ppm (C-3), δ 109.8 ppm (C-4), δ 39.6 ppm (C-5), δ 134.2 ppm (C-6), δ 135.4 ppm (C-7), δ 83.2 ppm (C-8), δ 52.9 ppm (C-9), δ 66.3 ppm (C-10), δ 168.2 ppm (C-11), δ 53.2 ppm (C-12), δ 97.6 ppm (C-1′), δ 74.9 ppm (C-2′), δ 75.4 ppm (C-3′), δ 72.1 ppm (C-4′), δ 75.8 ppm (C-5′), δ 63.4 ppm (C-6′). 4.2.1.2.5 Methyl((1S,5S,8S,9S)-1-(β-D-glucopyranosyloxy)-8-hydroxy-8-(hydroxymethyl)-1,5,6,7,8,9-hexahydrocyclopenta [c] pyran-4-yl) carboxylate (Figure 4.5).

Figure 4.5: Chemical structure of Pr(5).

4.2.1.2.5.1 Separation. Pr(5) (10.7 mg) representing the C8-hydroxy epimer of Pr(4), was also separated into silica gel column using EtOAc-MeOH-H2O (8:1:1) eluent. 4.2.1.2.5.2 Detection. Pr(5) resulted in a dark-purple coloration with the sulfuric vanillin reagent (5% H2SO4 in EtOH and 1% vanillin in EtOH), the solution was heated at 100 °C for 10 min [15].

60

4 Arbutus serratifolia Salisb as new SARS-CoV-2 inhibitor

4.2.1.2.5.3 Spectroscopic data 4.2.1.2.5.3.1 UV (MeOH) λmax (nm): 224 characteristic of an unsaturated enol-ether system, λmax (nm) = 318 characteristic of carbonyl functionality. 4.2.1.2.5.3.2 IR (1% KBr) νmax (cm−1): 3373 (OH), 1715 (C=O), 1639 (C=C) olefinic group, 2972 (C–H): methyl group, 3047 (HC=C): methylene group, 1093 (C–O): ether group. 4.2.1.2.5.3.3 1H NMR (DMSO-d6, 400 MHz). δ 5.71 ppm (1H, d, J = 3.62 Hz, H-1), δ 7.52 ppm (1H, d, J = 1.84 Hz, H-3), δ 4.63 ppm (1H, dd, J = 8.72 Hz and 4.12 Hz, H-5), δ 6.23 ppm (1H, dd, J = 5.58 and 2.81 Hz, H-6), δ 6.14 ppm (1H, d, J = 5.66 Hz, H-7), δ 5.07 ppm (1H, s, OH-8), δ 3.21 ppm (1H, dd, J = 9.62 Hz and 3.77 Hz, H-9), δ 3.75 ppm (1H, dd, J = 13.48 Hz and 4.34 Hz, H-10a), δ 3.83 ppm (1H, dd, J = 13.42 Hz and 4.26 Hz, H-10b), δ 5.02 ppm (1H, dd, J = 4.22 Hz and 4.37 Hz, OH-10), δ 3.6 ppm (3H, s, H-12), δ 4.69 ppm (1H, d, J = 7.78 Hz, H-1′), δ 3.41 ppm (1H, ddd, J = 8.75 Hz, 8.14 Hz and 4.23 Hz, H-2′), δ 4.93 ppm (1H, d, J = 4.27 Hz, OH-2′), δ 3.57 ppm (1H, ddd, J = 9.18 Hz, 8.37 Hz and 4.02 Hz, H-3′), δ 4.73 ppm (1H, d, J = 3.86 Hz, OH-3′), δ 3.44 ppm (1H, ddd, J = 9.08 Hz and 8.16 Hz, H-4′), δ 4.79 ppm (1H, d, J = 4.26 Hz, OH-4′), δ 3.46 ppm (1H, m, H-5′), δ 3.92 ppm (1H, ddd, J = 12.3 Hz, 4.06 Hz and 3.42 Hz, H-6′a), δ 3.66 ppm (1H, ddd, J = 12.57 Hz, 5.68 Hz and 3.26 Hz, H-6′b), δ 3.97 ppm (1H, dd, J = 3.37 Hz and 3.2 Hz, OH-6′). 4.2.1.2.5.3.4 13C NMR (DMSO-d6, 100.3 MHz). δ 95.2 ppm (C-1), δ 153.7 ppm (C-3), δ 108.9 ppm (C-4), δ 39.5 ppm (C-5), δ 135.9 ppm (C-6), δ 133.8 ppm (C-7), δ 82.9 ppm (C-8), δ 46.1 ppm (C-9), δ 67.7 ppm (C-10), δ 167.9 ppm (C-11), δ 53.1 ppm (C-12), δ 97.6 ppm (C-1′), δ 75.1 ppm (C-2′), δ 75.5 ppm (C-3′), δ 72.3 ppm (C-4′), δ 76.1 ppm (C-5′), δ 63.6 ppm (C-6′). 4.2.1.2.6 Pr(6):(1S,5S,6S,8S,9R)-8-Methyl-11-oxo-1,5,6,7,8,9-hexahydro-4H-2,12-dioxacyclopenta [cd] inden-1-yl-β-D-glucopyranoside (Figure 4.6).

Figure 4.6: Chemical structure of Pr(6).

4.2 Part 1: Materials and methods

61

4.2.1.2.6.1 Separation. The solid mixture (530–534) (77.1 mg) obtained from the chromatographic separation of ethyl acetate fraction was subjected to further separation through a flash chromatography column using EtOAc-MeOH (6:4) eluent yielding Pr(6) product (16.8 mg) as a white powder. 4.2.1.2.6.2 Detection. Pr(6) resulted in a light-purple coloration with the sulfuric vanillin reagent (5% H2SO4 in EtOH and 1% vanillin in EtOH) and then heated at 100 °C for 10 min [15]. 4.2.1.2.6.3 Spectroscopic data 4.2.1.2.6.3.1 UV (MeOH) λmax (nm) 244: characteristic of an unsaturated enol-ether system, λmax (nm) = 326 characteristic of carbonyl functionality. 4.2.1.2.6.3.2 IR (1% KBr) νmax (cm−1): 3378 (OH), 1715 (C=O), 1642 (C=C) olefinic group, 2977 (C–H): methyl group, 3051 (HC=C): methylene group, 1086 (C–O): ether group. 4.2.1.2.6.3.3 1H NMR (DMSO-d6, 400 MHz). δ 5.42 ppm (1H, d, J = 3.94 Hz, H-1), δ 7.66 ppm (1H, s, H-3), δ 3.28 ppm (1H, dd, J = 6.78 Hz and 7.41 Hz, H-5), δ 5.13 ppm (1H, m, H-6), δ 1.62 ppm (1H, ddd, J7a-7b = 13.92 Hz, J7a-8 = 10.3 Hz and J7a-6 = 8.18 Hz, H-7a), δ 1.75 ppm (1H, ddd, J7b-7a = 13.86 Hz, J7b-8 = 7.35 Hz, J7b-6 = 8.23 Hz, H-7b), δ 1.93 ppm (1H, m, H-8), δ 2.15 ppm (1H, m, H-9), δ 1.17 ppm (3H, d, J = 6.36 Hz, H-10), δ 4.73 ppm (1H, d, J = 7.8 Hz, H-1′), δ 3.46 ppm (1H, ddd, J = 8.24 Hz, 9.1 Hz and 4.26 Hz, H-2′), δ 4.87 ppm (1H, d, J = 4.3 Hz, OH-2′), δ 3.54 ppm (1H, ddd, J = 9.15 Hz, 8.63 Hz and 3.92 Hz, H-3′), δ 4.78 ppm (1H, d, J = 3.89 Hz, OH-3′), δ 3.38 ppm (1H, ddd, J = 8.57 Hz, 9.23 Hz and 4.25 Hz, H-4′), δ 4.77 ppm (1H, d, J = 4.21 Hz, OH-4′), δ 3.40 ppm (1H, m, H-5′), δ 3.68 ppm (1H, ddd, J6′a-6′b = 12.85 Hz, J6′a5′ = 5.18 Hz and J6′a-OH = 3.42 Hz, H-6′a), δ 3.87 ppm (1H, ddd, J6′b-6′a = 12.73 Hz, J6′b-5′ = 3.52 Hz and J6′b-OH = 3.31 Hz, H-6′b), δ 3.94 ppm (1H, dd, J6′a-OH = 3.38 Hz and J6′b-OH = 3.24 Hz, OH-6′). 4.2.1.2.6.3.4 13C NMR (DMSO-d6, 100.3 MHz). δ 96.6 ppm (C-1), δ 151.4 ppm (C-3), δ 106.3 ppm (C-4), δ 36.9 ppm (C-5), δ 85.3 ppm (C-6), δ 42.5 ppm (C-7), δ 34.8 ppm (C-8), δ 46.2 ppm (C-9), δ 18.5 ppm (C-10), δ 176.2 ppm (C-11), δ 98.8 ppm (C-1′), δ 74.8 ppm (C-2′), δ 75.6 ppm (C-3′), δ 72.5 ppm (C-4′), δ 77.2 ppm (C-5′), δ 63.5 ppm (C-6′). – Pr(7) and Pr(8) identifications The solid mixture (515–523) (120.4 mg) obtained from the chromatographic separation of ethyl acetate fraction was submitted to further separation into silica gel column using CHCl3-MeOH (9:1) eluent yielding the following products Pr(7) (14.2 mg) and Pr(8) (28.65 mg). 4.2.1.2.7 Pr(7):[(6S,9S,1S,5S)-1-(β-D-Glucopyranosyloxy) −13-oxo-1,5,6,9-tetrahydro1H-2,14-dioxacyclopenta [cd] inden-8-yl] methyl acetate (Figure 4.7).

62

4 Arbutus serratifolia Salisb as new SARS-CoV-2 inhibitor

Figure 4.7: Chemical structure of Pr(7).

4.2.1.2.7.1 Separation. Pr(7) was isolated as white needles whose melting point was estimated at 126–132 °C. 4.2.1.2.7.2 Detection. Pr(7) resulted in a blue-violet coloration after being developed on TLC plate which was dried and sprayed with the sulfuric vanillin reagent (5% H2SO4 in EtOH and 1% vanillin in EtOH) and then heated at 100 °C for 10 min [15]. 4.2.1.2.7.3 Spectroscopic data 4.2.1.2.7.3.1 UV (MeOH) λmax (nm): 238 characteristic of unsaturated enol-ether system, λmax (nm) = 321 characteristic of carbonyl functionality. 4.2.1.2.7.3.2 IR (1% KBr) νmax (cm−1): 3382 (OH), 1716 (C=O), 1637 (C=C) olefinic group, 2971 (C–H): methyl group, 3054 (HC=C): methylene group, 1094 (C–O): ether group. 4.2.1.2.7.3.3 1H NMR (DMSO-d6, 400 MHz). δ 5.47 ppm (1H, d, J = 4.06 Hz, H-1), δ 7.28 ppm (1H, s, H-3), δ 3.42 ppm (1H, dd, J5-6 = 7.12 Hz and J5-9 = 6.86 Hz, H-5), δ 5.34 ppm (1H, dd, J6-5 = 7.4 Hz and J6-7 = 6.72 Hz, H-6), δ 5.73 ppm (1H, broad doublet, J6-7 = 6.67 Hz, H-7), δ 3.26 ppm (1H, dd, J9-1 = 4.15 Hz and J9-5 = 6.94 Hz, H-9), δ 4.61 ppm (1H, d, J10a-10b = 14.2 Hz, H-10a), δ 4.56 ppm (1H, d, J10b-10a = 14.63 Hz, H-10b), δ 2.13 ppm (3H, s, H-12), δ 4.62 ppm (1H, d, J = 7.73 Hz, H-1′), δ 4.83 ppm (1H, ddd, J = 8.5 Hz, 9.23 Hz and 4.29 Hz, H-2′), δ 4.83 ppm (1H, d, J = 4.32 Hz, OH-2′), δ 4.96 ppm (1H, ddd, J = 8.42 Hz, 9.3 Hz and 4.03 Hz, H-3′), δ 4.74 ppm (1H, d, J = 3.93 Hz, OH-3′), δ 4.74 ppm (1H, ddd, J = 8.35 Hz, 9.21 Hz and 4.33 Hz, H-4′), δ 4.81 ppm (1H, d, J = 4.27 Hz, OH-4′), δ 4.95 ppm (1H, m, H-5′), δ 3.89 ppm (1H, ddd, J6′a-6′b = 12.7 Hz, J6′a-5′ = 5.22 Hz and J6′a-OH = 3.42 Hz, H-6′a), δ 3.97 ppm (1H, ddd, J6′b-6′a = 12.66 Hz, J6′b-5′ = 4.03 Hz and J6′b-OH = 3.21 Hz, H-6′b), δ 3.89 ppm (1H, dd, J6′a-OH = 3.45 Hz and J6′b-OH = 3.23 Hz, OH-6′). 4.2.1.2.7.3.4 13C NMR (DMSO-d6, 100.3 MHz). δ 92.8 ppm (C-1), δ 151.2 ppm (C-3), δ 105.7 ppm (C-4), δ 38.4 ppm (C-5), δ 87.5 ppm (C-6), δ 130.3 ppm (C-7), δ 145.1 ppm (C-8), δ 46.3 ppm (C-9), δ 63.4 ppm (C-10), δ 172.6 ppm (C-11), δ 21.3 ppm (C-12), δ 174.4 ppm (C-13), δ

4.2 Part 1: Materials and methods

63

99.9 ppm (C-1′), δ 74.6 ppm (C-2′), δ 77.9 ppm (C-3′), δ 72.1 ppm (C-4′), δ 78.2 ppm (C-5′), δ 63.1 ppm (C-6′). 4.2.1.2.8 Pr(8):Methyl((1S,5S,9S)-1-(β-D-glucopyranosyloxy)-8-(hydroxymethyl)1,5,6,9-tetrahydrocyclopenta [c] pyran-4-yl) carboxylate (Figure 4.8).

Figure 4.8: Chemical structure of Pr(8).

4.2.1.2.8.1 Separation. Pr(8) was isolated as a yellow gum product whose UV absorbance is characteristic of λmax (MeOH) = 236.5 nm. 4.2.1.2.8.2 Detection. Pr(8) led to a violet coloration after being developed on TLC plate which was dried and sprayed with the sulfuric vanillin reagent (5% H2SO4 in EtOH and 1% vanillin in EtOH) and then heated at 100 °C for 10 min [15]. 4.2.1.2.8.3 Spectroscopic data 4.2.1.2.8.3.1 UV (MeOH) λmax (nm): 235 characteristic of an unsaturated enol-ether system, λmax (nm) = 330 characteristic of carbonyl functionality. 4.2.1.2.8.3.2 IR (1% KBr) νmax (cm−1): 3389 (OH), 1715 (C=O), 1643 (C=C) olefinic group, 2977 (C–H): methyl group, 3062 (HC=C): methylene group, 1098 (C–O): ether group. 4.2.1.2.8.3.3 1H NMR (DMSO-d6, 400 MHz). δ 5.36 ppm (1H, d, J = 4.12 Hz, H-1), δ 7.43 ppm (1H, s, H-3), δ 3.34 ppm (1H, m, H-5), δ 2.64 ppm (1H, ddd, J6a-6b = 13.14 Hz, J6a-7 = 5.13 Hz and J6a-5 = 2.2 Hz, H-6a), δ 2.81 ppm (1H, ddd, J6b-6a = 13.10 Hz, J6b-7 = 3.24 Hz and J6b-5 = 1.98 Hz, H-6b), δ 5.91 ppm (1H, dd, J7-6a = 5.16 Hz and J7–6b = 3.86 Hz, H-7), δ 2.53 ppm (1H, dd, J91 = 2.51 Hz and J9-5 = 6.39 Hz, H-9), 3.86 ppm (1H, dd, J10a-10b = 14.97 Hz and J10a-OH = 4.37 Hz, H-10a), δ 4.02 ppm (1H, dd, J10b-10a = 15.2 Hz and J10b-OH = 4.25 Hz, H-10b), δ 5.62 ppm (1H, dd, J10a-OH = 4.41 Hz and J10b-OH = 4.23 Hz, OH-10), δ 3.58 ppm (3H, s, H-12), δ 4.52 ppm (1H, d, J = 7.79 Hz, H-1′), δ 3.34 ppm (1H, ddd, J = 8.13 Hz, 9.05 Hz and 4.31 Hz, H-2′), δ 4.87 ppm (1H, d, J = 4.29 Hz, OH-2′), δ 3.29 ppm (1H, ddd, J = 9.24 Hz, 8.78 Hz and 3.9 Hz, H-3′), δ 4.77 ppm (1H,

64

4 Arbutus serratifolia Salisb as new SARS-CoV-2 inhibitor

d, J = 3.87 Hz, OH-3′), δ 3.12 ppm (1H, ddd, J = 9.4 Hz, 8.96 Hz and 4.18 Hz, H-4′), δ 4.83 ppm (1H, d, J = 4.23 Hz, OH-4′), δ 3.2 ppm (1H, m, H-5′), δ 3.53 ppm (1H, ddd, J6′a-6′b = 13.2 Hz, J6′a5′ = 5.56 Hz and J6′a-OH = 3.47 Hz, H-6′a), δ 3.97 ppm (1H, ddd, J6′b-6′a = 12.8 Hz, J6′b-5′ = 3.92 Hz and J6′b-OH = 3.24 Hz, H-6′b), δ 3.92 ppm (1H, dd, J6′a-OH = 3.49 Hz and J6′b-OH = 3.2 Hz, OH-6′). 4.2.1.2.8.3.4 13C NMR (DMSO-d6, 100.3 MHz). δ 96.3 ppm (C-1), δ 152.1 ppm (C-3), δ 112.0 ppm (C-4), δ 36.4 ppm (C-5), δ 38.5 ppm (C-6), δ 126.4 ppm (C-7), δ 143.8 ppm (C-8), δ 47.2 ppm (C-9), δ 57.9 ppm (C-10), δ 168.5 ppm (C-11), δ 52.2 ppm (C-12), δ 99.2 ppm (C-1′), δ 74.6 ppm (C-2′), δ 76.4 ppm (C-3′), δ 72.3 ppm (C-4′), δ 78.4 ppm (C-5′), δ 62.5 ppm (C-6′). 4.2.1.2.9 Pr(9): (1R,5S,6R,7R,8S,9R)-6-Hydroxy-1-(β-D-glucopyranosyloxy)-1,5,6,7,8,9hexahydrooxireno [2’’,3’’:4”’,5”’] cyclopenta [c] pyran (Figure 4.9).

Figure 4.9: Chemical structure of Pr(9).

4.2.1.2.9.1 Separation. The aqueous EtOAc phase introduced a limpid residue (10.8 g) whose chromatographic separation into a polygoprep column led to collection of two fractions, which were monitored using 1H NMR analysis: Fraction 1 (210.6 mg) contained sugars and

4.2 Part 1: Materials and methods

65

mineral residues. Fraction 2 (123.2 mg) was chromatographed into silica gel column using MeOH-H2O (9:1) eluent and resulted in Pr(9) product (57 mg) as pure white crystals. 4.2.1.2.9.2 Detection. Pr(9) led to a dark-purple coloration after being developed into TLC plate which was dried and sprayed with the sulfuric vanillin reagent (5% H2SO4 in EtOH and 1% vanillin in EtOH) and then heated at 100 °C for 10 min [15]. 4.2.1.2.9.3 Spectroscopic data 4.2.1.2.9.3.1 UV (MeOH) λmax (nm): 233 characteristic of an unsaturated enol-ether system. 4.2.1.2.9.3.2 IR (1% KBr)max (cm−1): 3392 (OH), 1643 (C=C) olefinic group, 3062 (HC=C): methylene group, 1098 (C–O): ether group, 1278 (C–O): epoxide ring. 4.2.1.2.9.3.3 1H NMR (DMSO-d6, 400 MHz). δ 4.93 ppm (1H, d, J = 3.78 Hz, H-1), δ 6.62 ppm (1H, d, J = 6.02 Hz, H-3), δ 5.18 ppm (1H, dd, J4-3 = 6.41 Hz and J4-5 = 4.32 Hz, H-4), δ 2.23 ppm (1H, ddd, J5-4 = 4.43 Hz, J5-6 = 7.32 Hz and J5-9 = 8.24 Hz, H-5), δ 4.14 ppm (1H, ddd, J6-5 = 7.6 Hz, J6-7 = 1.25 Hz and J6-OH = 3.98 Hz, H-6), δ 5.41 ppm (1H, d, J = 4.03 Hz, OH-6), δ 3.56 ppm (1H, dd, J7-6 = 1.28 Hz and J7-8 = 3.2 Hz, H-7), δ 3.84 ppm (1H, dd, J8-9 = 7.62 Hz and J8-7 = 3.22 Hz, H-8), δ 2.49 ppm (1H, ddd, J9-1 = 3.78 Hz, J9-5 = 8.19 Hz and J9-8 = 7.51 Hz, H-9), δ 4.98 ppm (1H, d, J = 8.2 Hz, H-1′), δ 4.42 ppm (1H, ddd, J = 8.24 Hz, 9.12 Hz and 4.24 Hz, H-2′), δ 4.84 ppm (1H, d, J = 4.27 Hz, OH-2′), δ 5.12 ppm (1H, ddd, J = 8.52 Hz, 9.2 Hz and 3.78 Hz, H-3′), δ 4.75 ppm (1H, d, J = 3.84 Hz, OH-3′), δ 4.72 ppm (1H, ddd, J = 8.08 Hz, 9.15 Hz and 4.32 Hz, H-4′), δ 4.84 ppm (1H, d, J = 4.29 Hz, OH-4′), δ 5.63 ppm (1H, m, H-5′), δ 3.59 ppm (1H, ddd, J6′a-6′b = 12.74 Hz, J6′a-5′ = 5.2 Hz and J6′a-OH = 3.53 Hz, H-6′a), δ 3.73 ppm (1H, ddd, J6′b-6′a = 12.61 Hz, J6′b-5′ = 3.84 Hz and J6′b-OH = 3.22 Hz, H-6′b), δ 4.05 ppm (1H, dd, J6′a-OH = 3.5 Hz and J6′b-OH = 3.26 Hz, OH-6′). 4.2.1.2.9.3.4 13C NMR (DMSO-d6, 100.3 MHz). δ 97.2 ppm (C-1), δ 142.4 ppm (C-3), δ 106.3 ppm (C-4), δ 38.5 ppm (C-5), δ 82.6 ppm (C-6), δ 63.2 ppm (C-7), δ 57.7 ppm (C-8), δ 43.6 ppm (C-9), δ 102.3 ppm (C-1′), δ 74.2 ppm (C-2′), δ 77.4 ppm (C-3′), δ 68.9 ppm (C-4′), δ 76.7 ppm (C-5′), δ 62.3 ppm (C-6′). – Pr(10), Pr(11), and Pr(12) identifications Paper chromatography was conducted on Whatman 3 MM paper, which was eluted with n-BuOH-HOAc-H2O (56:3:20) and visualized with vanillin reagent showed three separated strips: Pr(10) product (violet, Rf: 0.28), Pr(11) product (gray violet, Rf: 0.17), and Pr(12) product (dark purple, Rf: 0.12). The strips were cut into small pieces and eluted in MeOH solvent, as well as concentrated under reduced pressure. Decolorizing charcoal (82 g) was added, and the suspensions (7 g) were stacked by silica gel layer on the Gooch funnel. MeOH-H2O (5:95) eluent was used to remove the mono and disaccharides. 4.2.1.2.10 Pr(10):((1S,5S,8R,9S)-1-(β-D-Glucopyranosyloxy)-8-hydroxy-8-(hydroxymethyl)-1,5,8,9-tetrahydrocyclopenta [c] pyran-4-yl) carboxylic acid (Figure 4.10).

66

4 Arbutus serratifolia Salisb as new SARS-CoV-2 inhibitor

Figure 4.10: Chemical structure of Pr(10).

4.2.1.2.10.1 Separation. Pr(10) (28 mg) was separated as a white crystalline product (m.p. 161–165 °C, from MeOH-H2O) from the corresponding strip after being chromatographed on acidic silica gel using n-BuOH-H2O (1:9) eluent. 4.2.1.2.10.2 Detection. Pr(10) is characterized by acidic properties (pH = 2.2); its reaction with sulfuric vanillin resulted in a blue-violet coloration. 4.2.1.2.10.3 Spectroscopic data 4.2.1.2.10.3.1 UV (MeOH) λmax (nm): 234 characteristic of an unsaturated enol-ether system, λmax (nm) = 325 characteristic of carbonyl functionality. 4.2.1.2.10.3.2 IR (1% KBr) νmax (cm−1): 3377 (OH), 1713 (C=O), 1640 (C=C): olefinic group, 3059 (HC=C): methylene group, 1086 (C–O): ether group. 4.2.1.2.10.3.3 1H NMR (DMSO-d6, 400 MHz). δ 5.62 ppm (1H, d, J = 3.24 Hz, H-1), δ 7.4 ppm (1H, s, H-3), δ 3.46 ppm (1H, m, H-5), δ 6.24 ppm (1H, dd, J6-5 = 2.78 Hz and J6-7 = 6.03 Hz, H-6), δ 5.71 ppm (1H, dd, J7-5 = 1.69 Hz and J7-6 = 6.06 Hz, H-7), δ 5.24 ppm (1H, s, OH-8), δ 2.64 ppm (1H, dd, J9-1 = 2.1 Hz and J9-5 = 7.86 Hz, H-9), δ 3.65 ppm (1H, dd, J10a-10b = 14.67 Hz and J10a-OH = 4.09 Hz, H-10a), δ 3.89 ppm (1H, dd, J10b-10a = 14.81 Hz and J10b-OH = 4.17 Hz, H-10b), δ 4.96 ppm (1H, dd, J10a-OH = 4.11 Hz and J10b-OH = 4.20 Hz, OH-10), δ 12.54 ppm (1H, s, OH-11), δ 4.56 ppm (1H, d, J = 7.6 Hz, H-1′), δ 3.45 ppm (1H, ddd, J = 8.22 Hz and 8.87 Hz, H-2′), δ 4.89 ppm (1H, d, J = 4.25 Hz, OH-2′), δ 3.39 ppm (1H, ddd, J = 8.93 Hz, 8.76 Hz and 3.78 Hz, H-3′), δ 4.73 ppm (1H, d, J = 3.81 Hz, OH-3′), δ 3.27 ppm (1H, ddd, J = 9.32 Hz, 8.91 Hz and 4.27 Hz, H-4′), δ 4.82 ppm (1H, d, J = 4.34 Hz, OH-4′), δ 3.34 ppm (1H, m, H-5′), δ 3.48 ppm (1H, ddd, J6′a-6′b = 12.98 Hz, J6′a-5′ = 5.52 Hz and J6′aOH = 3.91 Hz, H-6′a), δ 3.77 ppm (1H, ddd, J6′b-6′a = 12.76 Hz, J6′b-5′ = 3.89 Hz and J6′b-OH = 3.3 Hz, H-6′ b), δ 4.11 ppm (1H, dd, J6′a-OH = 3.5 Hz and J6′b-OH = 3.26 Hz, OH-6′). 4.2.1.2.10.3.4 13C NMR (DMSO-d6, 100.3 MHz). δ 95.3 ppm (C-1), δ 149.8 ppm (C-3), δ 111.0 ppm (C-4), δ 38.1 ppm (C-5), δ 137.8 ppm (C-6), δ 133.2 ppm (C-7), δ 85.5 ppm (C-8), δ

4.2 Part 1: Materials and methods

67

45.2 ppm (C-9), δ 67.0 ppm (C-10), δ 172.0 ppm (C-11), δ 99.0 ppm (C-1′), δ 74.1 ppm (C-2′), δ 76.6 ppm (C-3′), δ 69.9 ppm (C-4′), δ 77.2 ppm (C-5′), δ 62.0 ppm (C-6′). 4.2.1.2.11 Pr(11):((1S,5S,6R,8R,9S)-6,8-Dihydroxy-8-(hydroxymethyl)-1-(β,D-glucopyranosyloxy)-1,5,6,7,8,9-hexahydrocyclopenta [c] pyran-4-yl) carboxylic acid (Figure 4.11).

Figure 4.11: Chemical structure of Pr(11).

4.2.1.2.11.1 Separation. Pr(11) (34 mg) was isolated as a dusky powder from the corresponding strip after being chromatographed on acidic silica gel using CHCl3-MeOH-H2O (14:6:1) eluent. 4.2.1.2.11.2 Detection. Pr(11) is characterized by acidic properties (pH = 2.3), as well as it resulted in a grizzle-violet coloration with acidic vanillin. 4.2.1.2.11.3 Spectroscopic data 4.2.1.2.11.3.1 UV (MeOH) λmax (nm): 230 characteristic of an unsaturated enol-ether system, λmax (nm) = 324 characteristic of carbonyl functionality. 4.2.1.2.11.3.2 IR (1% KBr) νmax (cm−1): 3379 (OH), 1711 (C=O), 1638 (C=C) olefinic group, 3065 (HC=C): methylene group, 1084 (C–O): ether group. 4.2.1.2.11.3.3 1H NMR (DMSO-d6, 400 MHz). δ 5.58 ppm (1H, d, J = 3.16 Hz, H-1), δ 7.66 ppm (1H, s, H-3), δ 2.86 ppm (1H, dd, J5-6 = 3.41 Hz and J5-9 = 9.65 Hz, H-5), δ 4.53 ppm (1H, m, H-6), δ 4.8 ppm (1H, d, J = 4.12 Hz, OH-6), δ 1.76 ppm (1H, dd, J7a-7b = 13.8 Hz and J7a-6 = 8.34 Hz, H-7a), δ 1.94 ppm (1H, dd, J7b-7a = 13.86 Hz and J7b-6 = 7.98 Hz, H-7b), δ 5.13 ppm (1H, s, OH-8), δ 2.76 ppm (1H, dd, J9-1 = 2.47 Hz and J9-5 = 9.63 Hz, H-9), δ 3.71 ppm (1H, dd, J10a-10b = 13.7 Hz and

68

4 Arbutus serratifolia Salisb as new SARS-CoV-2 inhibitor

J10a-OH = 4.19 Hz, H-10a), δ 3.84 ppm (1H, dd, J10b-10a = 13.92 and J10b-OH = 4.21 Hz, H-10b), δ 4.92 ppm (1H, dd, J10a-OH = 4.15 Hz and J10b-OH = 4.23 Hz, OH-10), δ 4.89 ppm (1H, d, J = 7.97 Hz, H-1′), δ 4.53 ppm (1H, ddd, J = 8.12 Hz, 9.3 Hz and 4.25 Hz, H-2′), δ 4.91 ppm (1H, d, J = 4.28 Hz, OH-2′), δ 5.10 ppm (1H, ddd, J = 8.4 Hz, 9.18 Hz and 3.79 Hz, H-3′), δ 4.71 ppm (1H, d, J = 3.84 Hz, OH-3′), δ 4.76 ppm (1H, ddd, J = 8.44 Hz, 9.2 Hz and 4.42 Hz, H-4′), δ 4.8 ppm (1H, d, J = 4.39 Hz, OH-4′), δ 4.72 ppm (1H, m, H-5′), δ 3.51 ppm (1H, ddd, J6′a-6′b = 13.06 Hz, J6′a-5′ = 5.6 Hz and J6′a-OH′ = 3.51 Hz, H-6′a), δ 3.65 ppm (1H, ddd, J6′b-6′a = 12.71 Hz, J6′b-5′ = 3.8 Hz and J6′b-OH′ = 3.29 Hz, H-6′b), δ 3.97 ppm (1H, dd, J6′a-OH = 3.47 Hz and J6′b-OH = 3.3 Hz, OH-6′). 4.2.1.2.11.3.4 13C NMR (DMSO-d6, 100.3 MHz). δ 95.2 ppm (C-1), δ 152.4 ppm (C-3), δ 111.0 ppm (C-4), δ 37.9 ppm (C-5), δ 128.0 ppm (C-6), δ 132.8 ppm (C-7), δ 85.7 ppm (C-8), δ 44.8 ppm (C-9), δ 67.4 ppm (C-10), δ 171.2 ppm (C-11), δ 99.1 ppm (C-1′), δ 73.6 ppm (C-2′), δ 76.5 ppm (C-3′), δ 70.4 ppm (C-4′), δ 77.2 ppm (C-5′), δ 61.5 ppm (C-6′). 4.2.1.2.12 Pr(12):(1R,5R,6S,7R,8S,9S)-5,6-Dihydroxy-1-(β-D-glucopyranosyloxy)1,5,6,7,8,9-hexahydrooxireno [2’’,3’’:4”’,5”’] cyclopenta [c] pyran (Figure 4.12).

Figure 4.12: Chemical structure of Pr(12).

4.2.1.2.12.1 Separation. Pr(12) (14 mg) was obtained as a brown powder from the corresponding strip after being chromatographed on acidic silica gel using n-BuOH-AcOH-H2O (2:8:15) eluent. 4.2.1.2.12.2 Detection. Pr(12) led to grizzle-purple coloration after being developed on TLC plate which was dried and sprayed with the sulfuric vanillin reagent (5% H2SO4 in EtOH and 1% vanillin in EtOH) and then heated at 100 °C for 10 min [15].

4.2 Part 1: Materials and methods

69

4.2.1.2.12.3 Spectroscopic data 4.2.1.2.12.3.1 UV (MeOH) λmax (nm): 234 characteristic of an unsaturated enol-ether system. 4.2.1.2.12.3.2 IR (1% KBr) νmax (cm−1): 3397 (OH), 1641 (C=C) olefinic group, 3064 (HC=C): methylene group, 1104 (C–O): ether group, 1279 (C–O): epoxide ring. 4.2.1.2.12.3.3 1H NMR (DMSO-d6, 400 MHz). δ 4.87 ppm (1H, d, J = 3.81 Hz, H-1), δ 6.56 ppm (1H, d, J = 6.09 Hz, H-3), δ 5.27 ppm (1H, d, J4-3 = 6.38 Hz, H-4), δ 2.65 ppm (1H, ddd, J5-4 = 4.38 Hz, J5-6 = 7.21 Hz and J5-9 = 7.86 Hz, H-5), δ 4.24 ppm (1H, s, OH-5), δ 4.31 ppm (1H, dd, J6-7 = 1.44 Hz and J6-OH = 4.11 Hz, H-6), δ 5.74 ppm (1H, d, J6-OH = 4.15 Hz, OH-6), δ 3.73 ppm (1H, dd, J7-6 = 1.36 Hz and J7-8 = 3.15 Hz, H-7), δ 3.87 ppm (1H, dd, J8-9 = 7.56 Hz and J87 = 3.17 Hz, H-8), δ 2.55 ppm (1H, dd, J9-1 = 3.78 Hz and J9-8 = 7.51 Hz, H-9), δ 4.93 ppm (1H, d, J = 8.25 Hz, H-1′), δ 4.47 ppm (1H, ddd, J = 8.31 Hz, 9.07 Hz and 4.3 Hz, H-2′), δ 4.95 ppm (1H, d, J = 4.26 Hz, OH-2′), δ 5.16 ppm (1H, ddd, J = 8.48 Hz, 9.26 Hz and 3.82, H-3′), δ 4.74 ppm (1H, d, J = 3.78 Hz, OH-3′), δ 4.66 ppm (1H, ddd, J = 7.96 Hz, 9.12 Hz and 4.38 Hz, H-4′), δ 4.76 ppm (1H, d, J = 4.35 Hz, OH-4′), δ 5.57 ppm (1H, m, H-5′), δ 3.62 ppm (1H, ddd, J6′a-6′b = 12.7 Hz, J6′a5′ = 5.17 Hz and J6′a-OH = 3.57 Hz, H-6′a), δ 3.68 ppm (1H, ddd, J6′b-6′a = 12.56 Hz, J6′b-5′ = 3.9 Hz and J6′b-OH = 3.19 Hz, H-6′b), δ 3.94 ppm (1H, dd, J6′a-OH = 3.53 Hz and J6′b-OH = 3.22 Hz, OH-6′). 4.2.1.2.12.3.4 13C NMR (DMSO-d6, 100.3 MHz). δ 97.5 ppm (C-1), δ 142.5 ppm (C-3), δ 106.3 ppm (C-4), δ 40.3 ppm (C-5), δ 82.8 ppm (C-6), δ 64.1 ppm (C-7), δ 57.7 ppm (C-8), δ 45.3 ppm (C-9), δ 102.3 ppm (C-1′), δ 73.2 ppm (C-2′), δ 77.5 ppm (C-3′), δ 69.1 ppm (C-4′), δ 76.7 ppm (C-5′), δ 62.4 ppm (C-6′). 4.2.2 In-silico assessment Molecular docking is one of the most crucial approaches for drug discovery and design, which is used to analyze the type of interaction of the ligand and its protein enzyme, as well as the blind docking method entails examining the full surface of the macromolecule for binding sites. Therefore, using specific currently available drugs and some potent bioactive substances, blind molecular docking experiments were carried out and tested using SARS-CoV-2-main protease. 4.2.3 Ligand optimization The 2D structures of A. serratifolia Salisb molecules: Pr(1), Pr(2), Pr(3), Pr(4), Pr(5), Pr(6), Pr(7), Pr(8), Pr(9), Pr(10), Pr(11) and Pr(12) were drawn using MarvinSketch and optimized via Avogadro software. The obtained 2D SDF file format was transformed to 3D SDF format using the SMILES converter and structure file generator [16, 17].

70

4 Arbutus serratifolia Salisb as new SARS-CoV-2 inhibitor

4.2.3.1 Drug likeness and ADMET calculations Using PreADMET, a web-based application, the pharmacokinetic properties and the pharmacological efficiency of the tested molecules were checked. Meanwhile, Pre ADMET predicts the main ADME parameters corresponding to ADME (absorption, distribution, metabolism, and extraction) and toxicity properties. The drug-likeness parameters were computed using the Swiss ADME web server. According to Lipinski, Egan, Ghose, Muegge and Veber rules [18], the drug-likeness properties of A. serratifolia Salisb molecules: Pr(1), Pr(2), Pr(3), Pr(4), Pr(5), Pr(6), Pr(7), Pr(8), Pr(9), Pr(10), Pr(11) and Pr(12) were examined to check their deprivation from any breaches of the aforementioned rules. 4.2.3.2 Preparation of receptor and its binding site The essential viral components involved in viral particle attachment, replication, and reproduction in host cells, including the main protease, spike protein, RNA binding protein, and the N-terminal RNA binding domain, were visualized in 3D structure of SARS-CoV-2. These protein entities were discovered as the convenient targets for viral life cycle inhibition in human host cells. Generally, the 3D structure of the SARS-CoV-2 main protease [PDB ID: 6Y84] was retrieved from the RCSB PDB database. The binding affinities arising from the ligand and the receptor interactions were measured using the estimated amino acids with their binding pockets from the Q-site finder server [17]. 4.2.3.3 Molecular docking Using AutoDock Vina, a blind docking procedure was applied to dock the prepared SDF structures of A. serratifolia molecules. This procedure consists of searching the binding pocket of these ligands into the main protein. Hence, the predicted binding site of the specified protein target was docked with the following natural products: Pr(1), Pr(2), Pr(3), Pr(4), Pr(5), Pr(6), Pr(7), Pr(8), Pr(9), Pr(10), Pr(11), and Pr(12) reported in (Table 4.1). The following steps summarize the most used methodology in dynamic docking procedure. 4.2.3.3.1 Protein preparation. The interactions of the proposed molecules with the coronavirus targets were established through the protein (PDB ID: 6Y84). In our work, the UCSF Chimera software application was used to minimize the energy of the crystal structure and thus all the proteins’ heteroatoms and water molecules were eliminated using AutoDock vina. Hence, the optimization of H-bond and the insertion of Kollmann charges were performed, and saved as pdbqt charges. 4.2.3.3.2 Determination of active site and grid box. 1000 steps of the MMFF94 force field were applied, with a Vander Waals scaling factor of 1.00 and a charge cut-off of 0.25. A grid box of size (X, Y, Z): (33.42Å, 63.72Å, 60.08Å) was used as the bounding box for the docking research, with center parameters (X, Y, Z): (12.9Å, 1.31Å, 5.8Å). Critical analysis and

4.3 Results and discussions

71

molecular docking are two technical words commonly used to characterize the type and the energy of binding process. Typically, the Drug Discovery Studio version determines the active sites which will be used as ligand’s coordinates in the original target protein grids (BIOVIA Dassault Systems), and using Discovery Studios software, the researcher must locate the non-covalent interactions [19]. 4.2.3.3.3 Ligand-receptor interactions. The pose view of Lead was used to examine how A. serratifolia Salisb molecules could interact with their SARS-CoV-2 protein targets in the docked complex [20]. These molecules were established in 2D and 3D pose views, and their lead was studied. The interactions of these molecules with the protein target were depicted in the attached figures using LigPLOT+ software.

4.3 Results and discussions 4.3.1 Drug likeness and ADMET calculations The results of drug-likeness calculations of the following molecules: Pr(1), Pr(2), Pr(3), Pr(4), Pr(5), Pr(6), Pr(7), Pr(8), Pr(9), Pr(10), Pr(11) and Pr(12) using Swiss ADME server showed that Pr(6) possessing a molecular weight of 358.34 Da, the number of hydrogen bond acceptor and donor are nine and four respectively. Pr(6) also is endowed with excellent topological polar surface area (TPSA) which was determined with value 134.91 Å2, lipophilicity (iLog P) and water solubility (Log S ESOL) with values of 2.12 and −1.28 respectively. These findings uphold the Lipinski, Veber, and Muegge rules that are necessary for optimizing drug-likeness properties. Furthermore, the druglikeness properties of Pr(8) and Pr(9) were found in agreement with Lipinski rule (Table 4.2). On the other hand, ADME characteristics of these molecules calculated using preADMET web-based application revealed that Pr(6) exhibits good pharmacokinetic properties corresponding to absorption, bioavailability and distribution parameters such as HIA with value 38.89%, the pure water solubility with value 18.79 mg/mL, and plasma protein binding with value 34.98%, as shown in (Table 4.3).

4.3.2 Docking study The results of the molecular docking study of different molecules with main protease target using AutoDock Vina software revealed that the binding affinities are ranging from (−6.5 kcal/mol) to (−8.0 kcal/mol) as shown in (Table 4.4). Pr(7) performed the highest binding affinity (−8.0 kcal/mol) with SARS-CoV-2 main protease which is necessary for replication and reproduction of SARS-CoV-2 virus, and its interactions are induced by different residues including the following amino-acids:

72

4 Arbutus serratifolia Salisb as new SARS-CoV-2 inhibitor

Table .: Predicted physicochemical properties of the studied molecules. Molecule Pr() Pr() Pr() Pr() Pr() Pr() Pr() Pr() Pr() Pr() Pr() Pr()

MW (Da)

nHA

nRB

nHBA

nHBD

MR (m/mol)

TPSA (Å)

i LogP

. . . . . . . . . . . .

           

           

           

           

. . . . . . . . . . . .

. . . . . . . . . . . .

. . . . . . . . . . . .

MW, molecular weight; nHA, number of heavy atoms; nRB, number of rotatable bonds; nHBA, number of H-bond acceptors; nHBD, number of H-bond donors; MR, molar refractivity; TPSA, topological polar surface area; i LogP, lipophiliciy.

Table .: ADMET properties of the studied molecules. Molecule

Pr() Pr() Pr() Pr() Pr() Pr() Pr() Pr() Pr() Pr() Pr() Pr()

Absorption

Bioavailability

HIA (%)

Caco- CP (nm/s)

MDCK CP (nm/s)

SP (logKp) (cm/s)

. . . . . . . . . . . .

. . . . . . . . . . . .

. . . . . . . . . . . .

−. −. −. −. −. −. −. −. −. −. −. −.

BS (mg/ PWS (mg/ mL) mL) . . . . . . . . . . . .

. . . . . . . . . . . .

Distribution PPB (%)

BBB penetration

. . . . . . . . . . . .

. . . . . . . . . . . .

HIA, human intestinal absorption; Caco- CP, cancer coli or colon cancer cell permeability (https://en.wikipedia.org/wiki/Caco); MDCK CP, Madin-Darby Canine Kidney cell permeability; SP (logKp), skin permeability; expressed as logarithmic skin permeability (logKp) predicted by quantitative structure-activity relationship (predicted logKp) against values experimentally observed (observed logKp); BS, buffer solubility; PWS, pure water solubility; PPB, plasma protein binding; BBB, blood-brain barrier. The bold values represent the best binding energy of the corresponding product.

His-163; Arg-188; Gln-192; Thr-190; Glu-166 which form hydrogen-bonded interactions and other amino-acids: Cys-145; Asn-142; Ser-46; Gln-189; Pro-168; Leu-167; Met-49/165 and Phe-140 generating non-bonded interactions (Table 4.4). The results of docking interactions of all studied molecules were summarized in Table 4.5 and the orientations of their docking poses at the active site (PDB ID: 6Y84) were depicted in Table 4.4).

4.3 Results and discussions

Table .: The docking results of the studied molecules with COVID- main protein (PDB ID: Y). Molecule

Docking score (kcal/mol)

Pr()

−.

Pr()

−.

Pr()

−.

Docking interactions

73

74

4 Arbutus serratifolia Salisb as new SARS-CoV-2 inhibitor

Table .: (continued) Molecule

Docking score (kcal/mol)

Pr()

−.

Pr()

−.

Pr()

−.

Docking interactions

4.3 Results and discussions

Table .: (continued) Molecule

Docking score (kcal/mol)

Pr()

−.

Pr()

−.

Pr()

−.

Docking interactions

75

76

4 Arbutus serratifolia Salisb as new SARS-CoV-2 inhibitor

Table .: (continued) Molecule

Docking score (kcal/mol)

Pr()

−.

Pr()

−.

Pr()

−.

Docking interactions

The bold value represents the best binding energy of the corresponding product.

4.3 Results and discussions

77

Table .: Different amino-acids detected during the interactions of molecules with the main protein target of COVID-. Compound

ΔG (kcal/mol)

H-bonded interactions (Å)

Non-bonded interactions His-; Cys-; Thr-; Asn-; Gly-; Met- Met-; Met-; Ser-; Cys-; Cys-; Thr-; Gly-; His-; Met-; Met-; Asn-; Cys-; Cys-; Thr-; Phe-; Leu-; Gly- Met-; Met-; Cys-; Ser-; Thr-; Thr-; Cys- Thr-; Met-; Met-; Ser-; His-; Cys-; Thr-; Cys-; Asn-; Gly- Thr-; Thr-; His-; His-; Met-; Met-; Cys-; Cys-;

Pr()

−.

His-(.); His-(.); Glu-(.); Thr-(./.)

Pr()

−.

Glu-(.); His-(.)

Pr()

−.

His-(./.); His-(.); Glu-(./.); Thr-(.)

Pr()

−.

Pr()

−.

His-(.); His-(./.); Glu-(./.); Thr-(.); Gly-(.); Asn-(.); Leu-(.) His-(.); Glu-(.); Thr-(.)

Pr()

−.

Thr-(./.); Glu-(.); Gly-(.)

78

4 Arbutus serratifolia Salisb as new SARS-CoV-2 inhibitor

Table .: (continued) Compound

ΔG (kcal/mol)

H-bonded interactions (Å)

Pr()

−.

His-(.); Arg-(./.); Gln-(.); Thr-(./.); Glu-(.)

Pr()

−.

His-(./.); Cys-(.); Glu-(./.); Asn-(.); Gly-(.)

Pr()

−.

Cys-(.); Thr-(.); Asn-(././.)

Pr()

−.

His-(.); Glu-(.); Thr-(.)

Pr()

−.

His-(.); Cys-(.); Cys-(.); Thr-(.); Ser-(.); Leu-(.)

Non-bonded interactions Leu-; Leu-; Asn-; Ser-; Cys-; Asn-; Ser-; Gln-; Pro-; Leu-; Met-; Met-; Phe- Thr-; Thr-; Thr-; Met-; Met-; Cys-; Leu- Glu-; Cys-; Met-; Met-; His-; His- Met-; Met-; His-; Cys-; Cys-; Ser-; Thr-; Thr-; Gly- Glu-; His-; Met-; Met-; Gln-; Thr-; Gly-; Asn-

References

79

Table .: (continued) Compound

ΔG (kcal/mol)

Pr()

−.

H-bonded interactions (Å)

Non-bonded interactions

His-(.); Glu-(.); Gly-(./.)

His-; Thr-; Asn-; Met-; Cys-

The bold value represents the best binding energy of the corresponding product.

4.4 Conclusions The current research work was devoted to identify the efficient SARS-CoV-2 inhibitors among these natural products: Pr(1), Pr(2), Pr(3), Pr(4), Pr(5), Pr(6), Pr(7), Pr(8), Pr(9), Pr(10), Pr(11) and Pr(12), which were isolated from A. serratifolia Salisb species. The following products: Pr(6):(1S,5S,6S,8S,9R)-8-Methyl-11-oxo-1,5,6,7,8,9-hexahydro4H-2,12-dioxacyclopenta [cd] inden-1-yl-β-D-glucopyranoside followed by Pr(8):Methyl ((1S,5S,9S)-1-(β-D-glucopyranosyloxy)-8-(hydroxymethyl)-1,5,6,9-tetrahydrocyclopenta [c] pyran-4-yl) carboxylate and Pr(9):(1R,5S,6R,7R,8S,9R)-6-Hydroxy-1-(β-D-glucopyranosyloxy)1,5,6,7,8,9-hexahydrooxireno [2’’,3’’:4”’,5”’] cyclopenta [c] pyran are endowed with excellent drug-likeness and good ADME pharmacokinetic properties with nil violations of different standard rules. Pr(7):((1S,5S,6S,9S)-1-(β-D-Glucopyranosyloxy)-14-oxo-1,5,6,9-tetrahydro-1H-2,15dioxacyclopenta [cd] inden-8-yl) methyl acetate and Pr(2):(1S,5R,6S,8S,9S)-6,8-Dihydroxy-8-methyl-1,5,6,7,8,9-hexahydrocyclopenta [c] pyran-1-yl-β-D-glucopyranoside were found to be the best inhibitors of SARS-CoV-2 main protease, showing better binding affinities of −8.0 kcal/mol and −7.70 kcal/mol respectively. Furthermore, these molecules induced good hydrogen-bonding interactions with the protein active site in molecular docking analysis and caused a significant conformational rearrangement in the ligand-binding site. These results which were compared with those of the antiviral drugs: Ritonavir and Nirmatrelvir (−1.73 and −1.93 kcal/mol), respectively [21] led to the suggestion of these phytochemicals as promising candidate inhibitors of SARS-CoV-2 protease. However, other experimental tests in-vitro and in-vivo must be investigated to promote their pharmacological activities.

References 1. WHO coronavirus (COVID-19) dashboard; 2002. https://covid19.who.int [Accessed 05 Aug 2022]. 2. O’Driscoll M, Dos Santos GR, Wang L, Cummings DAT, Azman AS, Paireau J, et al. Age-specific mortality and immunity patterns of SARS-CoV-2. Nature 2021;590:140–5.

80

4 Arbutus serratifolia Salisb as new SARS-CoV-2 inhibitor

3. Jukič M, Kores K, Janežič D, Bren U. Repurposing of drugs for SARS-CoV-2 using inverse docking fingerprints. Front Chem 2021;9:757826. 4. Sternberg A, Naujokat C. Structural features of coronavirus SARS-CoV-2 spike protein: targets for vaccination. Life Sci 2020;257:118056. 5. Volz E, Mishra S, Chand M, Barrett JC, Johnson R, Geidelberg L, et al. Assessing transmissibility of SARS-CoV2 lineage B.1.1.7 in England. Nature 2021;593:266–9. 6. Tegally H, Wilkinson E, Giovanetti M, Iranzadeh A, Fonseca V, Giandhari J, et al. Detection of a SARS-CoV-2 variant of concern in South Africa. Nature 2021;592:438–43. 7. Faria NR, Mellan TA, Whittaker C, Claro IM, Candido DS, Mishra S, et al. Genomics and epidemiology of the P.1 SARS-CoV-2 lineage in Manaus, Brazil. Science 2021;372:815–21. 8. Faria NR, Mellan TA, Whittaker C, Claro IM, Candido DD, Mishra S, et al. Genomics and epidemiology of a novel SARS-CoV-2 lineage in Manaus, Brazil. WHO database on COVID-19. Science 2021;372:815–21. 9. Moelling K. Within-host and between-host evolution in SARS-CoV-2-new variant’s source. Viruses 2021;13: 751. 10. Badgujar KC, Badgujar VC, Badgujar SB. Vaccine development against coronavirus (2003 to present): an overview, recent advances, current scenario, opportunities and challenges. Diabetes Metab Syndrome 2020;14:1361–76. 11. Kaddoura M, Al Ibrahim M, Hijazi G, Soudani N, Audi A, Alkalamouni H, et al. COVID-19 therapeutic options under investigation. Front Pharmacol 2020;11:1196. 12. Pooladanda V, Thatikonda S, Godugu C. The current understanding and potential therapeutic options to combat COVID-19. Life Sci 2020;254:117765. 13. Mengist HM, Dilnessa T, Jin T. Structural basis of potential inhibitors targeting SARS-CoV-2 main protease. Front Chem 2021;12:622898. 14. Mahase E. Covid-19: Pfizer’s paxlovid is 89% effective in patients at risk of serious illness, company reports. BMJ 2021;375:n2713. 15. Yadav D, Gupta MM. Isolation and HPTLC analysis of iridoids in Premna integrifolia, an important ingredient of ayurvedic drug Dashmool. JPC-J Planar Chromatogr–Mod TLC 2013;26:260–6. 16. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, et al. The protein data bank, 1999. Int Tables Crystallogr 2006;24:675–84. 17. Laurie AT, Jackson RM. Q-SiteFinder: an energy-based method for the prediction of protein–ligand binding sites. Bioinformatics 2005;21:1908–16. 18. Goodsell DS, Morris GM, Olson AJ. Automated docking of flexible ligands: applications of AutoDock. J Mol Recogn 1996;9:1–5. 19. Biovia DS. Discovery studio modeling environment; 2017. https://www.3ds.com/products-services/biovia [Accessed 25 January 2022]. 20. Sharma A, Vora J, Patel D, Sinha S, Jha PC, Shrivastava N. Identification of natural inhibitors against prime targets of SARS-CoV-2 using molecular docking, molecular dynamics simulation and MM-PBSA approaches. J Biomol Struct Dyn 2022;40:3296–311. 21. Siva Kumar B, Anuragh S, Kammala AK, Ilango K. Computer aided drug design approach to screen phytoconstituents of Adhatoda vasica as potential inhibitors of SARS-CoV-2 main protease enzyme. Life 2022;12:315.

Rudo Zhou*, Pamhidzai Dzomba* and Luke Gwatidzo

5 Formulation of a herbal topical cream against Tinea capitis using flavonoids glycosides from Dicerocaryum senecioides and Diospyros mespiliformis Abstract: Topical fungal infections including, Tinea capitis with escalating resistance to conventional therapies are a rising concern globally. Studies have shown substantial in vitro efficacy of plant compounds against fungal pathogens. This study utilized flavonoid glycosides from Dicerocaryum senecioides and Diospyros mespiliformis as active compounds to formulate a topical cream against Tinea capitis. The in vitro test utilized disc diffusion assay prepared from fungal isolates obtained from individuals showing resistance to topical miconazole. Clinical trials were performed using volunteers. Both isolated strains exhibited substantial in vitro susceptibility to the cream formulation with inhibition zones ranging between 10 and 18 mm. MIC values for both test organisms ranged between 85 mg/ml and 120 mg/ml. The cream showed stability both physico-chemically and against microbial contamination. Physicochemical parameters evaluated include colour, pH, appearance, particle size, phase separation, phase inversion, creaming, spread-ability, electrical conductivity and in vitro occlusivity test and were within the accepted range. In limited clinical trials using volunteers, Tinea capitis started disappearing as from day 5 by topically applying the cream twice per day. All the patients were completely healed by the 7th day. The results of the study showed that flavonoid glycosides from D. senecioides and D. mespiliformis are good candidates to be utilized as active natural compounds against Tinea capitis resistant strains. Therefore more clinical trials and structural elucidations are recommended. Keywords: Dicerocaryum senecioides and Diospyros mespiliformis; flavonoids; fungal resistance; Tinea capitis.

5.1 Introduction Dermatophytosis, a type of superficial fungal infection is quite common. It negatively impacts on the quality of life of the affected people regardless of age [1]. Approximately

*Corresponding authors: Rudo Zhou and Pamhidzai Dzomba, Department of Chemistry, Bindura University of Science Education, Bindura, Zimbabwe, E-mail: [email protected] (R. Zhou), [email protected] (P. Dzomba). Luke Gwatidzo, Department of Chemistry, Bindura University of Science Education, Bindura, Zimbabwe, E-mail: [email protected] As per De Gruyter’s policy this article has previously been published in the journal Physical Sciences Reviews. Please cite as: R. Zhou, P. Dzomba and L. Gwatidzo “Formulation of a herbal topical cream against Tinea capitis using flavonoids glycosides from Dicerocaryum senecioides and Diospyros mespiliformis” Physical Sciences Reviews [Online] 2023. DOI: 10.1515/psr-2022-0273 | https://doi.org/10.1515/9783111071428-005

82

5 Formulation of a herbal topical cream

20–25% of the global population suffers from skin fungal infections. Antifungal drugs that are currently available exhibit diverse mechanisms of action including disrupting the of synthesis of fungal cell membrane or cell wall components (echinocandins) [2], permeability of cell membrane (amphotericin-B, azoles and allylamines), nucleic acid synthesis (flucytosine) and microtubule or mitotic spindle functioning (griseofulvin) [3]. Antifungal agents that disrupt fungal cell wall or cell membrane are predominantly fungicidal while those that inhibit fungal cell division are fungi-static. The minimum inhibitory concentrations (MIC) of a given antifungal agent determines its fungicidal properties [4]. There are numerous oral or topical antifungal agents that are used in managing dermatophytosis. However, it customarily need long-term therapy, when using allylamines like terbinafine and azoles [5] such as ketoconazole or miconazole. Frequently, successful treatment of dermatophytosis is achieved by using topically applied antifungal agents. Improper treatment of these infections results in them becoming chronic thus requiring oral antifungal treatment. This is mostly linked to hepatotoxicity [6]. Additionally, onset of other complications like bacterial superinfection and lichenification are imminent [1]. Hence the need for effectuate and concrete treatment of dermatophytosis. Topical (also called transdermal) drug delivery pertains to drug delivery through the skin. It is an aesthetical preference to customary methods of oral and intravenous routes. Its advantages are, the noninvasive delivery nature, slur of first-pass metabolism, notably far-reaching abidance of action, reduced frequency of dosing, low pharmaceutical toxicity and enhanced patient compliance [7]. Fungi have become fundamental causes of acute and chronic deep-seated infections in the human population especially recurrent cutaneous infections that are often severe in etiolate or immunosuppressed individuals [8]. Drugs available for treatment of fungal infections are mostly fungi-static. Therefore, emerging multidrug resistance has indelibly fostered the search for alternative low cost low toxicity traditional therapies and natural products [9]. Contemporary antifungal therapies with semblance of novel mechanisms are needed to curb fungal diseases and combat multidrug resistance as well as complementing existing therapies. This work focuses on formulation and evaluation of a topical cream with flavonoid glycosides from Dicerocaryum senecioides and Diospyros mespiliformis as an alternative therapy to manage Tinea capitis.

5.2 Materials and methodology 5.2.1 47 Chemicals and reagents Analytical reagent grade solvents, chemicals and reagents were used. TLC plates (60F254 20 × 20 cm) used for analytical TLC were supplied by Merck and (60F254 20 × 20 cm) used for preparative TLC were supplied by Sigma-Aldrich.

83

5.2 Materials and methodology

5.2.2 Plant material Fresh plant materials were collected in Mberengwa, Zimbabwe, on 4 February 2021. The leaves of D. senecioides and D. mespiliformis were cleaned and dried in air for two weeks under a shade. D. mespiliformis fruits were de-seeded and used as fresh samples. Identification and authentication was done at the National Herbarium and Botanical Gardens in Harare. Voucher specimens were archived at the Bindura University of Science Education Natural Products section. 5.2.2.1 Preparation of plant extract: Extraction of phytochemicals, thin layer chromatographybioautography and preparative thin layer chromatography were done as described by [10] without any modifications.

5.2.3 Preparation of cream formulations Table 5.1 illustrates the formulation of test creams.

5.2.4 Preliminary stability tests 5.2.4.1 Centrifugation test: Creams were centrifuged at 3000 rpm (GPC Model, India) for 20 min at 25 °C after 24 h of preparation. Macroscopic analyses were used to evaluate homogeneity and organoleptic characteristics [11]. 5.2.4.2 [12] Thermal stress: Cream formulations were placed in a heated thermostatic bath at 40 and 60 °C, holding for 20 min at each temperature. Organoleptic characteristics were evaluated before during and after exposure to thermal stress [13]. 5.2.4.3 Freeze and thaw cycles: Cream samples were kept at 4 °C for 24 h followed by another 24 h at 40 °C (single cycle). Three cycles were carried out. Organoleptic characteristics of the samples were evaluated [14]. Table .: Composition of cream formulations. Percent (%) ingredient in formulation Ingredient

Role

C

C

C

C

Cetostearyl alcohol Paraffin oil Glycerine Propylene glycol SLES Germaben Vitamin E Extract Xanthan gum Water

Stabilizer, opacifier and viscosity increasing agent Softener and film-forming agent Humectant Emollient Surfactant and abbrassive Preservative Oil preservation Active ingredient Stabilizer Solvent

    . . .  . q.s



. . . 

    . . . 

    . . . 

q.s

q.s

q.s



84

5 Formulation of a herbal topical cream

Organoleptic characteristics evaluated were electrical conductivity, appearance, colour and pH. To determine pH, 1.0 g of cream was placed in 9 g distilled water and homogenised. A digital pH meter was used to determine the pH value by inserting the electrode directly into the cream sample. Electrical conductivity was measured at 25 °C by inserting the electrode into the sample.

5.2.5 Accelerated stability test The sample proved to be stable by preliminary tests was stored under the following conditions: 4 °C, room temperature and 40 °C. The sample was kept at these conditions for 60 days. Macroscopic analyses, electrical conductivity and pH were evaluated [14].

5.2.6 Long term stability tests 5.2.6.1 Spread ability: Spread ability was evaluated by the parallel plate method by sandwiching the formulation between two slides measuring 20 cm × 20 cm. A 100 g mass was placed uniformly on the upper slide such that the cream was pressed forming a uniform thin layer for 2 min. The radius of the cream was measured and recorded. Area (A) covered by the cream was calculated [15]. The experiment was done in triplicate. Equation (5.1) was used to calculate the spread ability. spread ability = A = πr2

(5.1)

Where r = radius of the cream 5.2.6.2 [16] Measurement of pH: A digital pH meter was used to measure the pH of cream formulation C1 at 25 °C at the intervals of 2 weeks thereafter for 6 months [12]. The average of triplicate measurements were calculated and recorded. 5.2.6.3 In vitro occlusivity test: The test was performed in a desiccator at 30 °C and 65% relative humidity. 400 mg of formulation were evenly distributed on the surface of a No. 1 Whatman filter papers. 20 g of distilled water were placed in small beaker. The beaker was sealed with the smeared filter paper. Another set up was done under same conditions with no smeared filter paper. The sets of experiments were left for 48 h. The procedure was done for all the cream formulation C1. The occlusion factor, F was determined as follows: F=

A−B × 100% A

(5.2)

Where A = Water flux through uncovered filter (% water loss), B = Water flux through covered filter (% water loss) 5.2.6.4 Phase separation: The cream samples were kept in closed containers at room temperature. Separation of phases was carefully observed every 2 weeks for six months. 5.2.6.5 Particle size and phase inversion: Morphology of the emulsions and their particle size were determined using an optical microscope (Olympus, Japan). Micrographs were collected using an Itel A56 Pro camera at 100× magnification immediately after preparation and thereafter monthly for 6 months. Briefly, 1 ml of cream sample was diluted with a factor of 10 using glycerol. Four drops of the diluted sample were transferred on to a glass slide, stained with aniline blue and covered with a cover slip and

5.2 Materials and methodology

85

focused on a microscope. The diameter of particles was randomly determined using the eyepiece micrometer [17]. 5.2.6.6 Creaming index: The creaming index of cream formulation C1 was determined by centrifuging 5 ml of cream at 25 °C and 4000 rpm for 15 min. The tube was refrigerated at approximately 5 °C for 2 week undisturbed [18]. Thereafter, the total height of emulsion and height of cream were measured. Creaming index was calculated using equation (5.3) %CI =

C × 100% CT

(5.3)

Where C = height of subnatant layer, CT = total height of emulsion

5.2.7 In vitro antifungal assay 5.2.7.1 Determination of MIC: Prior to evaluation of the MIC of the cream, preliminary susceptibility test was done to see if the dermatophytes were susceptible to the cream using disc diffusion assay. Susceptibility of test organisms to cream formulation and control was then assayed using broth macro-dilution method adopted from [19] with modifications. Briefly, triplicate suspensions of the spores were prepared in 0.85% NaCl and diluted to appropriate densities using L-glutamine and phenol red as the indicator. The suspensions were placed into 12 × 75 mm test tubes. Two-fold serial dilutions of cream formulation (21.25–340 mg/ml) in dimethyl sulfoxide (DMSO) were added to the suspensions. Miconazole and DMSO were used as positive and negative controls respectively. The experiments were incubated at 25 °C for 7 days. The MIC was determined as the lowest concentration showing 100% growth inhibition. Procedure was done at the beginning and at the end of the 6 month period [8].

5.2.8 Microbiological assessment Enumeration of total viable bacteria count was done at the end of every month for the 6 month period: To 90 ml of buffer peptone water, 10 g of cream formulation were added and the mixture was homogenized. Serial dilutions of the mixture were prepared to 10−2 following standard protocol. A 0.1 ml aliquot of each non-filtrable suspension from the 10−2 dilution was used as the inoculum in nutrient agar to enumerate the total viable bacteria count for Escherichia coli, Staphylococcus aureus and Pseudomonas spp. The triplicate sets of plates were incubated for 24 h at 37 °C [20].

5.2.9 Clinical trials A randomized, placebo-controlled approach adopted from [21] and modified was used. The inclusion criteria was that subjects must provide signed informed consent form and consent for the participating subjects to provide photographs of affected areas before during and after study. Subjects who received any oral or topical treatment of tinea or had taken any drug a week before commencement or during the course of the study were excluded. Other criteria for exclusion were pregnancy, lactation and any history of drug allergy or intolerance. The enrolled subjects were given instruction at baseline (T0) to apply the cream under study twice daily (for 7 days) to the selected target area with infection. If the subject exhibited visible improvement at T1 (after 7 days), then further instruction to extend the application for another 7 days (T2) was given. A follow-up for two months from the time treatment ended was done on all participants.

86

5 Formulation of a herbal topical cream

5.2.10 Data analysis All results herein are presented as mean ± standard deviation (SD). One-way analysis of variance (ANOVA) was used to analyse the data obtained using IBM SPSS VERSION 20 software at significance level p < 0.05.

5.3 Results 5.3.1 Stability tests results The results obtained from preliminary stability tests are shown in Table 5.2. A summary of accelerated stability tests for cream formulation C1 is represented in Table 5.3. Table 5.4 gives a summary of the results of parameters evaluated in long-term stability test. Figure 5.1a–d represents variation of pH, occlusivity, particle size and spread-ability of formulation C1 over the period of the study.

5.3.2 In vitro antifungal assay Figure 5.2 illustrates the results of the preliminary fungal susceptibility tests to the cream formulation. Table 5.5 shows the inhibition zones obtained during preliminary susceptibility test. Table 5.6 shows MIC values obtained from the assays done initially and at the end of the 6 month period.

5.3.3 Microbial assessment The results of microbial stability assessment of cream formulation C1 are presented in Table 5.7.

5.3.4 Clinical trials results Figure 5.3a–b shows positive culture test results for selected hair samples of some participants taken before commencement of treatment (Table 5.8).

87

5.4 Discussion

Table .: Results of preliminary stability tests of the cream formulations. Parameters evaluated After centrifuging Appearance Colour PH Electrical conductivity (µS/cm)

Formulation code C

C

C

C

No phase separation White . ± .  ± 

Phase separation observed . ± .  ± 

Phase separation observed . ± .  ± 

Phase separation observed . ± .  ± 

No phase separation White . ± .  ± 

Phase separation observed Off-white . ± .  ± 

Phase separation observed Off-white . ± .  ± 

Phase separation observed Off-white . ± .  ± 

No phase separation White . ± .  ± 

Phase separation observed . ± .  ± 

Phase separation observed . ± .  ± 

Phase separation observed . ± .  ± 

After subjection to thermal stress Appearance Colour PH Electrical conductivity (µS/cm) After freeze-thaw cycles Appearance Colour PH Electrical conductivity (µS/cm)

Table .: Results for accelerated stability tests of C. Parameter

Electrical conductivity (µS/cm) Appearance Colour PH

After  days

After  days

 °C

 °C

 °C

 °C

 °C

 °C

 ±  No change White . ± .

 ±  No change White . ± .

 ±  No change White . ± .

 ±  No change White . ± .

 ±  No change White . ± .

 ±  No change White . ± .

5.4 Discussion There are a variety of paramount properties that are evaluated when a cosmeceutical or dermaceutical product is formulated. Emollients are chosen with special attention as their physic-chemical properties such as molecular weight, length of chain and polarity

88

Table .: Long term stability tests results for C. Parameter

Occlusivity (%) Phase separation

C Control C Control C Control

Particle size C (µm) Control Phase C inversion Control Creaming index (%) Spread ability (cm)

C Control C Control

























. ± . . ± . . ± . . ± . No separation No separation . ± . . ± . No inversion No inversion . . . Not evaluated

. ± . . ± . . ± . . ± . No separation No separation . ± . . ± . No inversion No inversion . . . Not evaluated

. ± . . ± . . ± . . ± . No separation No separation . ± . . ± . No inversion No inversion . . . Not evaluated

. ± . . ± . . ± . . ± . No separation No separation . ± . . ± . No inversion No inversion . . . Not evaluated

. ± . . ± . . ± . . ± . No separation No separation . ± . . ± . No inversion No inversion . . . Not evaluated

. ± . . ± . . ± . . ± . No separation No separation . ± . . ± . No inversion No inversion . . . Not evaluated

. ± . . ± . . ± . . ± . No separation No separation . ± . . ± . No inversion No inversion . . . Not evaluated

. ± . . ± . . ± . . ± . No separation No separation . ± . . ± . No inversion No inversion . . . Not evaluated

. ± . . ± . . ± . . ± . No separation No separation . ± . . ± . No inversion No inversion . . . Not evaluated

. ± . . ± . . ± . . ± . No separation No separation . ± . . ± . No inversion No inversion . . . Not evaluated

. ± . . ± . . ± . . ± . No separation No separation . ± . . ± . No inversion No inversion . . . Not evaluated

. ± . . ± . . ± . . ± . No separation No separation . ± . . ± . No inversion No inversion . . . Not evaluated

5 Formulation of a herbal topical cream

PH

Time (weeks)

5.4 Discussion

89

Figure 5.1: Variation in pH, occlusivity, particle size and spreadability of the cream formulation C1 over the period of study. a–d: Variation of pH, occlusivity, particle size and spread-ability over 24 weeks.

90

5 Formulation of a herbal topical cream

Figure 5.2: Preliminary antifungal activity of the cream formulation using disc diffusion assay against T. rubrum isolates 1 and 2. Table .: Inhibition zones for isolates  and . Test organism

Inhibition zone (mm)

T. rubrum isolate  T. rubrum isolate 









 

 

 

 

Table .: Minimum inhibitory concentrations for isolates  and . Test organism

MIC (mg/ml)

T. rubrum isolate  T. rubrum isolate 

Initially

After  months

. .

. .

Table .: Prevalence of pathogenic microorganisms in the cream formulation. Time (wks)

     

Test organism’s CFU/g E. coli

S. aureus

Pseudomonas spp.

     

     

     

Figure 5.3: Picture of positive fungal culture test for hair samples obtained from participants. a–b: Positive fungal culture test for hair samples obtained from participants.

5.4 Discussion

91

N

Baseline Placebo 

Day  C 

Day  Placebo C  

Clinical evaluation . ± . . ± . . ± . . ± . Lesion mean diameter (cm) Hair growth mean score     Mycological evaluation Positive dermatophyte culture (%)     Positive potassium hydroxide test (%)     Key for hair growth mean score •  – no hair growth •  – very little hair growth •  – little hair growth •  – evident hair growth •  – good hair growth.

Day  Placebo C  

Day 

Placebo C  

Placebo 

Not evident . ± . Not evident . ± . Not evident . ± . 

 

 



 

   

   

 

5 Formulation of a herbal topical cream

C 

92

Table .: Results for clinical and mycological evaluation of participants in the study.

5.4 Discussion

93

tend to influence sensory properties which include spread ability and the final product’s sensory properties at large. High molecular weight polymers are pseudo-plastic resulting in high viscosity thus increasing stability of the incessant phase. Therefore, addition of xanthan gum increased stability and reduce particle aggregation [22]. The prepared cream formulations were white, homogenous and viscous. They were subjected to centrifugation, freeze-thaw cycles and thermal stress after 24 h of preparation so as to choose best formulation because they are the usual stress parameters assessed in testing stability of emulsions. Cream C1 was the most determinate since creaming, pH change and phase discontinuity were not observed during preliminary stability testing stage (Table 5.2). The pH value for C1 was stable at 6.00. Electrical conductivity is a common parameter applied in the investigation of phase inversion. When the process of phase inversion occurs in an oil/water emulsion, its electrical conductivity shows a strong decrease. It assists in identifying the form of emulsion. Very high values of the parameter characterize oil/water emulsions as observed in the other three formulations. Conductivity values for C1 formulation were between 75 and 81 μS/cm and were statistically not different. C1 was a water/oil emulsion. Some scholars perceived conductivity changes in emulsions but regarded them stable because macroscopically, they were stable [23]. It is reportedly formidable to determine stability of a formulation basing on electrical conductivity only because of lack of linearity in the relationship between this parameter and instability. It is an established fact that stable emulsions have small particles because Brownian movement is more pronounced than gravitational force. Particle size is commonly used to assess stability of emulsions [23]. Particle size impedes aggregation of emulsion into a flocculant and conjugate mass. It is mandatory for cream formulations to remain physically stable for the duration of their shelf life [23]. Therefore no or negligible variation in the particle size array is critical. The cream stability is closely related to the particle size distribution. A large particle size may enhance Ostwald ripening thereby increasing droplet size leading to coalescence and creaming. The emulsion droplets under study showed spherical shape. The shape of the particles after 6 months storage at room temperature did not change. The mean droplet size of the emulsion under study was found to be within micro-emulsion range (1–100 µm) for the whole period of study when kept room temperature (Table 5.4). The mean droplet size for the cream was 2.95 ± 1.20 µm. After the completion of the study, the droplet size was 3.62 ± 1.13 µm. There was no noteworthy variation (p > 0.05) in average droplet size when stored at 25 °C (Figure 5.2c). Droplet size is an important characteristics of topical formulations that accounts for their physical stability. The small droplet size prevents droplet coalescence and sedimentation against gravitational force. Droplet size analysis in the current study showed that the emulsion had a small droplet size in the range desire able for topical emulsions. High creaming index renders the emulsion unstable. In this study, creaming index of C1 was zero indicating the formulation was highly stable. Two features are involved in the progression of creaming index: if oil phase is rich in oil extract, then creaming rate decreases and if there is high emulsifier

94

5 Formulation of a herbal topical cream

concentration, creaming rate also decreases [24]. The percentage of the occlusion for C1 was investigated against a control during the study period of 6 months. The values of the occlusion factor for the cream under study showed no significant difference in comparison with the control throughout the period of study (Figure 5.2b). The percentage of water loss of the cream was found to be 5.65 ± 0.01–5.55 ± 0.01% while that of the control was 5.59 ± 0.01. The water run varies with the occlusivity of membrane proffered by the formulation. Good occlusivity of test cream was mainly due to the fatty composition of formulation which prevented evaporation of water. Occlusivity test is indicative of ability to prevent water loss [25]. Moisture-retainers like propylene glycol and glycerol play vital role in water-retention. These two are soluble in water but paraffin is an oil which forms a thin oily layer on the skin. The two phases are come together as an emulsion due to the action stearic acid, a surfactant. Cosmeceutical and dermaceutical formulations contain nutrients which support microbial growth, hence the need for them to be sterile. The prevalence of skin disease due to poor sanitation and consumption of microbiologically contaminated water is common in developing countries. Therefore, cosmeceutical products should be free from microbial contamination and total bacterial load should be below specified limits so that they do not aggravate infection. To minimise the chances of skin infection, microbiological assessment of the finished product is a necessity in order to obtain standard quality product [20]. In this study the formulated cream was analysed for microbial contamination against E. coli, S. aureus and Pseudomonas spp. Through-out the study period, the cream exhibited zero microbial contamination partly due to the use of a preservative. Antibacterial activity of the plant active principle used in the formulation cannot be ruled out completely and is subject to study. The antifungal activity was preliminarily determined using disc diffusion assay and results from the preliminary assay were satisfactory. Inhibition zones ranged between 10 and 18 mm (Table 5.5). There was evidence that the test organisms were significantly susceptible to the cream formulation. There was significant inhibition of mycelial growth by the cream. MIC value for the cream was further determined and it was found to be 85 mg/ml for isolate 1 and 120 mg/ml for isolate 2. The cream therefore has an inhibitory effect against the two test organisms. Fowora et al. reported MIC values of 0.32 and 0.64 µL using limonene and Ferulago capillaris against T.rubrum [19]. Sajjad et al. also reported values of 200 and 400 μg/ml against 2 strains of T.rubrum [26]. Therefore, the results obtained herein are acceptable. The pilot clinical study indicates that the antifungal cream formulation using phytochemicals as active ingredients is an efficacious treatment approach with 100% recovery of patients with Tinea capitis. There was no record of serious side effects and relapse of the infection for all subjects who completed the study. It is important to note that a two-month follow-up on complete response was done on all subject. This suggests that the phytochemical active principles used in the formulation of the study cream have potential for prolonged activity that may delay relapse which commonly resurfaces shortly after therapy is stopped. The results obtained from the study cream on Tinea capitis can be attributed to possible combination of multiple

5.5 Conclusion

95

mechanisms of action of the active principles. The mechanism of action of the active compound is open for further study. Most anti-fungal agents are chemical in nature and they are similar to the emerging laser-based treatments in that they both cause momentous side effects. Although the formulated cream has exhibited promising potency and safety, there is need for large scale multi-centre and randomised studies on the cream while benchmarking it against more than one standard antifungal creams. Nevertheless, the current study confirms that flavonoid glycosides from D. senecioides and D. mespiliformis used in formulation of topical antifungal cream are potentially safe and efficacious. This represents a natural option in managing tinea. While terbinafine and sertaconazole gave a complete cure in 100% subjects with Tinea cruris [27] and Tinea corporis after 3 weeks of treatment [16], cream C1 resulted in complete healing in all subjects after 2 weeks. Both topical and oral formulations of antifungal agents are available. Greater preference is given to topical treatments in superficial tinea management owing to their high cure rates of 2–4 weeks of therapy. The cream under study falls on the threshold in terms of duration of treatment.

5.5 Conclusion In this study, four creams were formulated with flavonoids glycosides from D. senecioides and D. mespiliformis as antifungal agents. Only one cream was found to be a good candidate for further studies. It was selected for further evaluation basing on its physical properties in relation to preliminary stability studies. The greatest achievement in the current study is that the integrity, appearance, texture, pH, microbial stability and antifungal activity of the selected product were maintained for 6 months. Another major finding of the investigation is that the test cream is a potent alternative in managing fungal infections with 100% recovery of patients with Tinea capitis within 2 weeks of application twice a day. There was no record of serious side effects and recurrence for all participants who completed the study as determined by a six-month follow-up on subjects after cessation of treatment. The minimum inhibitory concentrations in vitro were found to be 85 mg/ml against isolate 1 and 120 mg/ml against isolate 2 for the cream. For a more realistic comparison between the formulated cream and marketed antifungal creams, macro clinical trials on various skin conditions related to Tinea capitis are recommended.

References 1. Parrish N, Fisher SL, Gartling A, Craig D, Boire N, Khuvis J, et al. Activity of various essential oils against clinical dermatophytes of microsporum and Trichophyton. Front Cell Infect Microbiol 2020;10:567. 2. Ghannoum MA, Rice LB. Antifungal agents: mode of action, mechanisms of resistance, and correlation of these mechanisms with bacterial resistance 1999;12:501–17.

96

5 Formulation of a herbal topical cream

3. Bvumbi C, Chi GF, Stevens MY, Mombeshora M, Mukanganyama S. The effects of Tormentic acid and extracts from callistemon Citrinus on Candida Albicans and Candida Tropicalis growth and inhibition of Ergosterol biosynthesis in Candida Albicans. Sci World J 2021;2021:1–3. 4. Cavaleiro C, Pinto E, Gonc MJ, Salgueiro L. Antifungal activity of Juniperus essential oils against dermatophyte, Aspergillus and Candida strains. J Appl Microbiol 2006;100:1333–8. 5. Mahmoud YAG, Metwally MA, Mubarak HH, Zewawy NE. Treatment of tinea versicolor caused by Malassezia furfur with dill seed extract: an experimental study. Int J Pharm Pharmaceut Sci 2015;7:1–7. 6. Ezeokoli OT, Gcilitshana O, Pohl CH. Risk factors for fungal co-infections in critically ill COVID-19 patients, with a focus on immunosuppressants. J Fungi 2021;7:545. 7. Bolla PK, Clark BA, Juluri A, Cheruvu HS, Renukuntla J. Evaluation of formulation parameters on permeation of Ibuprofen from topical formulations using Strat-M ® membrane. Pharmaceutics 2020;12:151. 8. Pinto E, Hrimpeng K, Lopes G, Vaz S, Cavaleiro C, Salgueiro L. Antifungal activity of Ferulago capillaris essential oil against Candida, Cryptococcus, Aspergillus and dermatophyte species. Eur J Clin Microbiol Infect Dis 2013;32:1311–20. 9. Lopes G, Pinto E, Salgueiro L. Natural products: an alternative to conventional therapy for dermatophytosis? Mycopathologia 2017;182:143–67. 10. Zhou R, Dzomba P, Goredema M, Gwatidzo L, Mupawose K. Formulation and evaluation of a herbal shampoo using flavonoid glycosides from Dicerocaryum Senecioides. East Afr J Sci Technol Innov 2022;3: 1–12. 11. Tasic M. The influence of polar and non-polar emollients on the structure and skin moisturizing potential of the emulsions. Acta Med Median 2016;55:25–30. 12. Kim J, Lee K, Jerng UM, Choi G. Global comparison of stability testing parameters and testing methods for finished herbal products. Evid base Compl Alternative Med 2019;2019. https://doi.org/10.1155/2019/ 7348929. 13. Emulsions PO, Liu N, Chen Q, Li G, Zhu Z, Yi J, Li C. Properties and stability of Perilla seed protein-stabilized oil-in-water emulsions: influence of protein concentration, pH, NaCl concentration and thermal treatment. Molecules 2018;23:1533. 14. Gyawali R, Gupta RK, Shrestha S, Joshi R, Paudel PN. Formulation and evaluation of polyherbal cream containing cinnamomum Zeylanicum blume, Glycyrrhiza Glabra L and Azadirachta indica A. Juss. J Chin Inst Food Sci Technol 2020;25:61–71. 15. Kumar TP, Eswaraiah MC. Formulation and evaluation of topical hydrogel containing antifungal drug. Pharm Pharmacol Int J 2020;8:249–54. 16. Lamie C, Elmowafy E, Ragaie MH, Attia DA, Nahed D. Assessment of antifungal efficacy of Itraconazole loaded Aspasomal cream: comparative clinical study comparative clinical study. Drug Deliv 2022;29: 1345–57. 17. Salehi N. Investigating the changes in cream properties following topical application and their influence on the product efficiency investigating the changes in cream properties following topical application and their influence on the product efficiency. Iran J Pharm Res 2022;21. https://doi.org/10.5812/ijpr.123946. 18. Eleonore G, Tchienou D, Karole R, Tsague T, Florence T, Pega M, et al. Multi-response optimization in the formulation of a topical cream from natural ingredients. Cosmetics 2018;5:1–14. 19. Fowora MA, Onyeaghasiri FU, Olanlege ALO, Edu-muyideen IO, Adebesin OO. In Vitro susceptibility of dermatophytes to anti-fungal drugs and aqueous Acacia Nilotica leaf extract in Lagos, Nigeria. J Biomed Sci Eng 2021;14:74–82. 20. Aleem A, Khan M, Abid U. Microbial analysis of selected brands of whitening creams. Saudi J Med Pharm Sci 2020;6:178–82. 21. Parekh M, Ramaiah G, Pashilkar P, Ramanujam R, Johnston P, Ilag LL. A pilot single centre, double blind, placebo controlled, randomized, parallel Study of Calmagen ® Dermaceutical cream and lotion for the topical treatment of Tinea and Onychomycosis. BMC Compl Alternative Med 2017;17:1–11.

References

97

22. Audilene M, Freitas D, Cosmo J, Idalina A, Alves S, Assis FD, et al. Use of the natural products from the leaves of the fruitfull tree Persea Americana against Candida Sp. biofilms using acrylic resin discs. Sci Total Environ 2020;703:134779. 23. De Azevedo Ribeiro RC, Barreto SMAG, Ostrosky EA, Da Rocha-Filho PA, Veríssimo LM, Ferrari M. Production and characterization of cosmetic nanoemulsions containing opuntia ficus-indica (L.) mill extract as moisturizing agent. Molecules 2015;20:2492–509. 24. Varka E, Tsatsaroni E, Xristoforidou N, Darda A, Al ET. Stability study of O/W cosmetic emulsions using Rosmarinus officinalis and calendula officinalis extracts. Molecules 2012;2012:139–45. 25. Hamishehkar H, Same S, Adibkia K, Zarza K, Shokri J. A comparative histological study on the skin occlusion performance of a cream made of solid lipid nanoparticles and vaseline. Open J Appl Sci 2015;10:378–87. 26. Sajjad M, Khan A, Ahmad I. Antifungal activity of essential oils and their synergy with fluconazole against drug-resistant strains of Aspergillus fumigatus and Trichophyton rubrum. Res Pharmaceut Sci 2011;90: 1083–94. 27. Zhao D, Chen B, Wang Y, Jiao C. Topical Clotrimazole cream for the treatment of Tinea Cruris. Medicine 2020;99:1–4.

Supplementary Material: This article contains supplementary material (https://doi.org/10.1515/PSR-20220273).

Yetunde Bunmi Oyeyiola* and Beatrice Olutoyin Opeolu

6 Immediate effects of atrazine application on soil organic carbon and selected macronutrients and amelioration by sawdust biochar pretreatment Abstract: Increasing use of herbicides has contributed immensely to current soil and water degradation in the tropics. Published works on effects of herbicides on soil organic carbon (SOC) – a major indicator for soil health and macronutrients and their enhancement by biochar are scarce for soils in Africa despite heavy herbicide applications every cropping season. This incubation trial evaluated immediate effects of atrazine application on SOC and selected soil macronutrients. The potential of sawdust (SD) biochar to mitigate associated SOC and macronutrients depletion was also assessed. A total of 950 g soil was placed in each leaching column (20 cm length and 7 cm diameter). The experiment was a factorial combination of four SD biochar types: SD + poultry manure (PM) pyrolyzed at 350 °C, SD-PM at 350 °C, SD + PM at 450 °C and SD-PM at 450 °C applied at two rates of 5 and 10 t/ha equivalent to 2.38 and 4.76 g/950 g soil, respectively. Atrazine alone and absolute control (AC) that received neither biochar nor atrazine were included for comparison. The treatments were replicated thrice in completely randomized design. Appropriate biochar was applied within 5 cm soil depth, moistened to field capacity, and left to equilibrate for 2 weeks. Thereafter, 20 mL atrazine solution was applied at 2.5 kg a.i/ha (achieved through 6.75 g atrazine powder/l of distilled water). Basal NPK 15:15:15 fertilizer mixed with urea at 0.1 and 0.03 g/900 g soil, respectively, was applied to mimic farmers’ practice on atrazine treated fields. Maize seeds were thereafter sown in the treated soils and nurtured for 2 weeks. Data taken on soil pH, SOC, exchangeable bases, available phosphorus, and dry biomass weight (DBW) of maize seedlings at the expiration of the trial were subjected to two-way analysis of variance using Genstat Statistical Package with means separated using LSD at 5% probability level. There were significant reductions in soil pH (5.8%), SOC (31%), and Ex. Ca (27%) in atrazine alone soil compared to AC. Contrarily, similar atrazine treated soil pretreated with SD biochar had increased soil pH, SOC, exchangeable Ca, available P, and DBW by 5.6 (in SD + PM@450 °C), 73.6 (SD-PM@450 °C), 84 (SD + PM@450 °C), 2,338.4 (SD + PM@450 °C), and 154.8% (SD + PM@350 °C), respectively, dominantly at 10 t/ha compared to AC. Sole atrazine treated soil was, however, higher in soil available P (23.8 mg/kg) and TDBW

*Corresponding author: Yetunde Bunmi Oyeyiola, Department of Crop Production and Soil Science, Ladoke Akintola University of Technology, Ogbomoso, Nigeria, E-mail: [email protected] Beatrice Olutoyin Opeolu, Faculty of Applied Sciences, Cape Peninsula University of Technology, Cape Town, South Africa, E-mail: [email protected] As per De Gruyter’s policy this article has previously been published in the journal Physical Sciences Reviews. Please cite as: Y. B. Oyeyiola and B. O. Opeolu “Immediate effects of atrazine application on soil organic carbon and selected macronutrients and amelioration by sawdust biochar pretreatment” Physical Sciences Reviews [Online] 2023. DOI: 10.1515/psr-2022-0241 | https://doi.org/ 10.1515/9783111071428-006

100

6 Immediate effects of atrazine application on soil organic carbon

(0.56 g) against 5.42 mg/kg and 0.42 g from AC, respectively. Biochar pH and organic carbon were the most influential biochar properties contributing significantly to SOC sequestration and macronutrient enrichment in the atrazine treated soil. Pretreatment of soils with sawdust biochar prior to atrazine application is, therefore, recommended for mitigating associated organic carbon and macronutrient depletion in the soils for enhanced maize production. Keywords: atrazine; biochar pretreatment; macronutrient enrichment; sawdust biochar; soil organic carbon depletion.

6.1 Introduction Global crop production is bedeviled by diverse challenges that have brought about diverse approaches for their mitigations. Weed infestation is a major challenge farmers battle every cropping season if gains would be made from their cropping activities. Weeds are plants growing where they are not wanted. Common weeds in Africa include Imperata cylindrica, Chromolaena odorata, Euphorbia heterophylla, Digitaria abyssinica, Striga genus, Tridax procumbens, Pennisetum purpureum, and Tithonia diversifolia. Weed infestation is capable of causing total crop loss arising from their keen competition for space, nutrient, and water with the main crops. This menace is managed dominantly by hoe weeding (on small plots under traditional cropping system) and use of herbicides (on larger plots and commercial farms). Herbicide use for weed management involves the use of specially formulated chemical mixture capable of killing plants through altering normal biochemical processes in the target plants. Commonly used herbicides in Africa are metolachlor, butachlor, diuron, glufosinate, paraquat, glyphosate, and atrazine. Use of herbicide in the control of weeds is perhaps the most favored choice of weed management among farmers in the world due to herbicides’ fast killing action on weeds and ease of application. Herbicide use is not without demerits of which little attention had been given to in most developing countries of the world. The adverse effects associated with herbicide use are both point and nonpoint application effects on soils, crops, and water. Only a small proportion of the applied herbicides kill the target weeds while the remaining larger portions are either left in the soil or washed into surface and underground water where they serve as potential toxicant to nontarget organisms [1–3]. Atrazine (2-chloro-4-ethylamino-6-isopropylamino-1,3,5-triazine) is an important broad-spectrum herbicide effective in the control of both broad and narrow leafed weeds [4, 5]. It is very popular among farmers because of its ready availability, low cost of procurement, presentation in both powder and liquid forms, and high killing efficacy on target plants when applied pre- or postemergently [6]. Atrazine use has been proscribed in many developed countries of the world due to its high concentrations annually leached into surface and subsurface water [5, 7, 8] and injury done to cultivated crops. It is

6.1 Introduction

101

moderately soluble in water [5], thus making its leached residues potential carcinogenic materials and health disorder agent to humans and threat to wellness of aquatic lives [5, 8, 9]. Atrazine is mainly dissipated through biodegradation in organic matter rich soils by microbes such as Actinobacteria and Proteobacteria spp. [10] that utilize the high N contents in atrazine as source of nutrients [11–13]. Other dissipation channels are through run off, leaching [9] and sorption unto organic and inorganic soil colloids [6, 14, 15]. Complete atrazine dissipation was reported in the adsorption trial of atrazine using woodchip bioreactor [16]. The work of Zhu et al. [17] tested bacterial strains isolated from soil as a microbial agent for remediation of atrazine-degraded soils. Soil organic carbon (SOC) generally indexed as major indicator for soil health and productivity has been severely depleted in most tropical soils annually receiving herbicide for weed eradication [18, 19]. It plays important role in herbicide sorption in soils and was reported as the most severely affected soil parameter in soils receiving indiscriminate atrazine doses [18]. The SOC as well as soil microbial activities and population had been used as tools for assessing pesticide bioactivity in soils [4, 11, 18, 20]. The organic carbon in soils treated with atrazine was found to have negative correlations with bioactivity of atrazine in soils [21]. Presence of atrazine in soils has also been found to reduce activity of soil microbes and enzymes [11, 21]. Chemical characteristics of herbicides affect their reactions in soil. Atrazine is known for its low adsortion capacity to negatively charged sites of soil and shows resistance to biodegradation [18, 22]. These properties encourage enhanced bioactivity of atrazine within the soil solution and eventual susceptibility to leachate loses, which adversely affects nontarget organisms. Soils low in pH and rich in organic matter and sesquioxides are, however, favorable for atrazine sorption and reduced bioavailability [6, 14]. Soils with innate characteristics that favor increases in atrazine bioavailability in soil solution such as low SOC, clay content, and high phosphorus reservation and sand fraction (which are peculiar with the soils of this study area) are, therefore, susceptible to higher atrazine-induced crop injury and growth reduction. Biochar is a carbon-rich solid product from pyrolysis of biomass. It is widely used as amendment in problematic soils for improving SOC, water and nutrient retention, and mitigating greenhouse gas emission and nutrient leaching from soils. Biochar’s large surface area, pore volume, high carbon, and ash contents can, therefore, be explored for improving organic carbon, herbicide sorption, and biodegradation in soils. Liu et al. [23] and Dutta et al. [20] had previously indicated positive correlation among atrazine sorption, alkyl and carbonyl carbon in SOM fractions.. Increasing annual field application of atrazine has been implicated to contribute significantly to depletion of organic matter with abounding reports on its adverse effects on microbial population and activities in soils of the world [4, 11, 21, 24, 25]. Published works on fate of organic carbon and macronutrient concentrations in Nigerian soils annually receiving atrazine are scarce despite heavy atrazine use every cropping season. Co-application of atrazine with N-based inorganic fertilizers such as urea is a popular

102

6 Immediate effects of atrazine application on soil organic carbon

practice among local farmers in Nigeria and these inorganic N-fertilizers have been found to reduce atrazine degradation in soils by microbes [11, 13]. From the foregoing, Nigerian soils managed this way are, therefore, susceptible to enhanced atrazine persistence with adverse cumulative effects on the biological, chemical, and physical properties of the soil. Atrazine persistence in soil further predisposes water resources to contamination via leaching. Abrupt stoppage of atrazine use may be difficult to achieve among Nigerian farmers due to its popularity and efficacy for weed eradication. Need to develop environmentfriendly pretreatment (such as biochar) for use on herbicide-treated soils is paramount to achieve sustainable management of soil and water resources. This work, therefore, evaluated the immediate effects of atrazine application with or without biochar pretreatment on organic C and selected macronutrient concentrations in a nutrientdegraded sandy soil under leaching condition. The potentials of sawdust biochar types and rates of application to mitigate organic carbon and macronutrient depletion arising from atrazine immediate application were also evaluated. Lastly, dominant biochar nutrient properties controlling enhancement of SOC and selected macronutrients in atrazine-treated soil were determined at the two biochar application rates.

6.2 Materials and methods 6.2.1 Description of the experimental soil The soil studied was obtained from the Teaching and Research Farm, Ladoke Akintola University of Technology located in the derived guinea savanna agro ecology of Nigeria. The field was sampled at 0–15 cm depth using a soil auger. The soil was air dried, crushed, sieved appropriately, and analyzed routinely for pH, organic C, available P, total N, exchangeable bases, and particle size distribution following standard procedures by [26]. The soil is classified as Alfisols [27] and locally as Gambari soil series [28]. The soil is slightly alkaline and severely depleted in organic carbon, available P, total N and has marginal exchangeable bases concentrations (Table 6.1). The soil has higher proportion of sand fraction over silt and clay.

6.2.2 Biochar preparation The biochars tested were produced from Gmelina arborea sawdust (SD) with or without poultry manure (PM). The feedstocks were preheated at 105 °C for 24 h to remove nonflammable components such as moisture and CO2 [29]. The moisture-free feedstock were thereafter pulverized and pyrolyzed in the muffled furnace. A total of 20 g SD (in triplicate) pyrolyzed at either 350 or 450 °C for 20 min represented sole sawdust (SD-PM) biochar types, while 10 g SD pyrolyzed with 10 g PM at either 350 or 450 °C

6.2 Materials and methods

103

Table .: Selected characteristics of the soil studied. Parameters Soil pH (HO) Available P (Mehlich mg/kg) Total N (g/kg) Organic C (%) Ex. cations (cmol/kg) Ca Mg K Na Particle sizes (g/kg) Sand Silt Clay

Values . . . . . . . .   

represented SD + PM biochar types. Four biochar types SD + PM@350, SD + PM@450, SD-PM@350, and SD-PM@450 were produced. The prepared biochar were crushed and analyzed proximately for nutrient contents following standard procedures for plant nutrients determination [26, 29].

6.2.3 Treatments, design, and experimental set up The leaching column trial was conducted between June and August, 2021. Each leaching column made from polyvinyl chloride (PVC) pipe measured 20 cm long and 7 cm diameter. Each leaching column was lined at one end with three-fold oven sterilized cotton mesh mounted over a leachate collection cup. Each leaching column contained 950 g soil. The trial was a factorial combination of four biochar types: SD + PM@350, SD + PM@450, SD-PM@350, and SD-PM@450 applied at two rates: 5 and 10 t/ha equivalent to 2.37 and 4.75 g per 950 g soil, respectively. Similar triplicate sets of soil-filled leaching columns that received no biochar but atrazine only (tagged Atrazine alone) and a set that received neither biochar nor atrazine (tagged Absolute control) were included. Appropriate biochar types and rates were mixed within the 5 cm soil depth in each leaching column and moistened to field capacity with 250 mL distilled water. The whole set up was kept covered under aluminum foil sheet and left to equilibrate in the dark for 2 weeks. Similar moistening, equilibration, and incubation were done for the atrazine alone and absolute control soils. Atrazine stock solution was thereafter prepared by dissolving 6.75 g of atrazine powder in 1000 mL flask made up to the mark with of distilled water to achieve the 2.5 kg a.i/ha recommended rate for soils in the study area. A total of

104

6 Immediate effects of atrazine application on soil organic carbon

20 mL of the atrazine stock solution was applied into all the incubated soil (except the absolute control) with the aid of a 20 mL syringe. This was immediately followed by basal chemical fertilizer application using 0.1 and 0.03 g/950 g soil of NPK 15:15:15 and urea, respectively, to achieve 60 kg N, 30 kg P, and 30 kg K/ha. This mimicked farmers’ practice and achieved half recommended rate of chemical fertilizer for maize production in the study area (since the maize plants were not nurtured to maturity). Two maize seeds were thereafter sown per column and thinned to one a week after sowing. One leaching exercise was carried out on the soils a week after atrazine application using 250 mL distilled water to mimic soil receiving rainfall few days after atrazine application under field conditions. Data on leachate collected were, however, not presented in this paper. The maize plants were nurtured for 2 weeks at room temperature that fluctuated around 25–27 °C.

6.2.4 Data collection Soil and plant data were taken at maize seedling harvesting. Soil sampled within 5 cm soil depth from each soil were air dried, crushed, sieved, and analyzed for pH, organic carbon, available P, and exchangeable Ca, Mg, K, and Na contents. Soil pH was determined in soil solution obtained after shaking 10 g of soil in 20 mL distilled water using a pH meter earlier buffered in standard solutions 4, 7, and 9. Organic carbon was determined by the dichromate wet oxidation method described by Walkley and Black [30]. Around 1 g of soil weighed into a 500 mL volumetric flask was treated with 10 mL of 1N potassium dichromate followed by addition of 20 mL of concentrated sulfuric acid and gentle swirling for 1 min 200 mL of distilled water was thereafter added in each solution after standing for 30 min on the laboratory bench. This was followed by addition of 10 mL of concentrated phosphoric acid and five drops of diphenylamine indicator. Each soil mixture was titrated with 0.5N ferrous sulfate solution until the color changed to maroon green. Mehlich-3 procedure was followed for the determination of available P. Around 3 g soil was extracted with 30 mL Mehlich-3 extractant in 50 mL centrifuge tubes shaken for 5 min. After allowing the solution to stand for 10 min, the solution was centrifuged at 3000 rpm for 5 min. Phosphorus content in each 1 mL aliquot was determined by Molybdate blue method using Murphy and Riley solution read on the spectrophotometer. Exchangeable cations (Ca, Mg, K, and Na) were extracted using neutral 1N NH4OAc at soil: extracting solution ratio of 1:10 after 15 min shaking on the mechanical shaker. Atomic Absorption Spectrophotometer (Buck Scientific model 211) was used to read the concentrations of Ca and Mg while K and Na were read on the Flame Photometer (Buck Scientific model 410). The 2 weeks old maize seedlings harvested from each treated soil were oven dried at 65 °C for 24 h for the estimation of dry biomass weight.

6.3 Results

105

6.2.5 Data analysis All the soil and plant data were subjected to two-way analysis of variance using Genstat statistical package. Means were separated by LSD at 5% probability level. Regression analysis was employed for the estimation of percentage contributions of selected biochar nutrient components to changes in selected soil and plant parameters from the atrazinetreated soil.

6.3 Results 6.3.1 The pH and nutrient characteristics of the biochar produced and tested The pH and nutrient composition of the biochar produced and tested are shown in Table 6.2. The SD biochar co-pyrolyzed with PM were higher in pH and all the nutrient parameters except organic carbon content, which was higher in sole SD biochar. Higher pyrolysis temperature (450 °C) increased the concentrations of all the nutrient parameters and pH except organic C regardless the feedstock combination. Organic C content decreased by 39.2% in SD + PM@450 biochar while macronutrients N and P contents increased by 94.3 and 876%, respectively, compared to SD + PM@350. This gave rise to biochar with lower C/N and C/P ratios of 43 and 23, respectively, in SD + PM@450 compared to 117 and 315 respective ratios from SD + PM@350. Similarly, in SD-PM@450 Table .: The pH and nutrient characteristics of the biochar types tested. Biochar types SD + PM

SD-PM

Biochar parameters

 °C

 °C

 °C

 °C

pH Ash (%) Ca (%) Mg (%) K (%) Na (%) Organic C (%) N (%) P (%) C/N C/P

. . . . . . . . . . .

. . . . . . . . . . .

. . . . . . . . . . .

. . . . . . . . . . .

SD is sawdust and PM is poultry manure.

106

6 Immediate effects of atrazine application on soil organic carbon

biochar, organic C reduced by 8.2% while N and P increased by 35.5 and 128.6%, respectively, relative to SD-PM@350. This thereafter produced biochar with lower C/N and C/P ratios of 119 and 312, respectively, in SD-PM@450 compared to 174 and 773 respective ratios from SD-PM@350.

6.3.2 Immediate effects of atrazine and biochar pretreatment on soil organic carbon Organic carbon content differed significantly with or without biochar pretreatment in the atrazine-treated soils (Figure 6.1). There were immediate severe decreases in organic carbon content in soil that received only atrazine within the 2 weeks of observation. The organic carbon in atrazine alone treated soil decreased by 31% compared to AC. Conversely, similar atrazine-treated soil pretreated with biochar did not show evidence of SOC depletion. Biochar pretreatment increased SOC from initial 1.47% to a range of 1.57 (in SD + PM@350 applied at 10 t/ha) to 2.57% (in SD-PM@450 applied at 10 t/ha). The differences were significantly influenced by the biochar type while the variations controlled by biochars’ application rates were not significant. Nevertheless, lower application rate of 5 t/ha produced higher organic carbon contents in soil pretreated with

Figure 6.1: Immediate effects of atrazine and biochar pretreatment on soil organic carbon. BT is biochar type; BR is biochar rate; SD + PM@350 and SD + PM@450 are sawdust biochar co-pyrolyzed with poultry manure at 350 and 450 °C, respectively; SD-PM@350 and SD-PM@450 are sole sawdust biochar pyrolyzed at 350 and 450 °C, respectively. ***Significance at p < 0.001, ns is not significant.

6.3 Results

107

SD + PM based biochar. Higher application rate of 10 t/ha on the other hand encouraged higher SOC in SD-PM based biochar treatments. All the biochar treatments regardless the application rate (except SD + PM@450 applied at 10 t/ha) produced SOC contents that were significantly higher than atrazine alone (1.02%) and absolute control (1.48%) soils.

6.3.3 Immediate effects of atrazine and biochar pretreatment on soil pH Atrazine alone treatment immediately reduced soil pH from the initial 7.28 to 6.15 within 2 weeks residence in the soil representing 5.8 and 15.5% reductions compared to absolute control and initial soil, respectively (Figure 6.2). Biochar pretreatment of similar atrazine-treated soil improved the soil to a pH range of 6.33 (in SD + PM@350 at 5 t/ha) to 6.95 (in SD + PM@450 at 5 t/ha). The observed pH changes among the different biochar pretreatments did not vary significantly with the biochar type and application rate. The SD + PM biochar types were generally superior to SD-PM biochars in achieving pH shift to near neutral values in atrazine treated soil.

Figure 6.2: Immediate effects of atrazine and biochar pretreatment on soil pH. BT is biochar type; BR is biochar rate; SD + PM@350 and SD + PM@450 are sawdust biochar co-pyrolyzed with poultry manure at 350 and 450 °C, respectively; SD-PM@350 and SD-PM@450 are sole sawdust biochar pyrolyzed at 350 and 450 °C, respectively; ns is not significant.

108

6 Immediate effects of atrazine application on soil organic carbon

6.3.4 Immediate effects of atrazine and biochar pretreatment on soil available P Phosphorus availability in the atrazine-treated soils was significantly affected by biochar types and rates of application (Figure 6.3). Biochar pretreatment of the soil encouraged higher soil available P (ranging from 38.77 to 132.16 mg/kg) compared to atrazine alone (23.8 mg/kg) and absolute control (5.42 mg/kg) soils. Sawdust biochar co-pyrolyzed with PM supported higher soil P over sole SD biochar regardless the pyrolysis temperatures and rates of application. While higher pyrolysis temperature of 450 °C favored higher P availability in soils pretreated with SD + PM at both application rates, lower pyrolysis temperature of 350 °C favored higher P availability in SD-PM biochar. The SD + PM@450 biochar pretreatment was responsible for highest soil available P at both application rates in the atrazine-treated soils. This represented about three- and five-fold increases at 5 and 10 t/ha rates, respectively, over atrazine alone soil. It was surprising, however, to observe higher (23.80 mg/kg) soil available P in atrazine alone soil within 2 weeks of atrazine application over absolute control (5.42 mg/kg).

Figure 6.3: Immediate effects of atrazine and biochar pretreatment on soil available P. BT is biochar type; BR is biochar rate; SD + PM@350 and SD + PM@450 are sawdust biochar co-pyrolyzed with poultry manure at 350 and 450 °C, respectively; SD-PM@350 and SD-PM@450 are sole sawdust biochar pyrolyzed at 350 and 450 °C, respectively. ***Significance at p < 0.001.

109

6.3 Results

6.3.5 Immediate effects of atrazine and biochar pretreatment on exchangeable bases in the soil Exchangeable Ca was the most severely depleted by atrazine among the basic cations considered within 2 weeks residence of atrazine in the soil (Table 6.3). Atrazine significantly reduced Ex. Ca by 27% compared to absolute control. The other Ex. Bases: Mg, K, and Na concentrations from atrazine alone soil were, however, above the concentrations observed from the absolute control although with values significantly lower than those from soils pretreated with biochar. Atrazine application increased concentrations of Ex. Mg, K, and Na by about 4, 58, and 25%, respectively, compared to absolute control. Drastic increases in Ex. Ca, Mg, K, and Na were noted from soils treated with sawdust biochar prior to atrazine application. Their concentrations, however, differed significantly with the biochar types and application rates. The SD + PM@450 biochar supported higher concentrations of these bases, while least concentrations of Ex. Ca, K, and Na were from soil amended with SD-PM@350 and Ex. Mg from SD-PM@450. The SD + PM@450 biochar pretreatment at 10 t/ha in atrazine-treated soil increased Ex. Ca, Mg, K, and Na by 84, 74, 210, and 88%, respectively, compared to absolute control.

Table .: Immediate effects of atrazine and biochar pretreatment on exchangeable bases in the soil. Ca

Mg

K

Na

cmol/kg Biochar rates

Biochar rates

Biochar rates

Biochar rates

Biochar types

 t/ha

 t/ha

Mean

 t/ha

 t/ha

Mean

 t/ha

 t/ha

Mean

 t/ha

 t/ha

Mean

SD + PM@ SD + PM@ SD-PM@ SD-PM@ Mean BT(LSD) BR(LSD) BT × BR (LSD) Checks Absolute control Atrazine alone

. . . . . *** *** ***

. . . . .

. . . .

. . . . . *** *** ***

. . . . .

. . . .

. . . . . *** *** ***

. . . . .

. . . .

. . . . . *** *** ***

. . . . .

. . . .

. .

. .

. .

. .

***Significant at p < .. BT is biochar type; BR is biochar rate; SD + PM@ and SD + PM@ are sawdust biochar co-pyrolyzed with poultry manure at  and  °C, respectively; SD-PM@ and SD-PM@ are sole sawdust biochar pyrolyzed at  and  °C, respectively.

110

6 Immediate effects of atrazine application on soil organic carbon

6.3.6 Immediate effects of atrazine and biochar pretreatment on dry biomass weight of maize seedlings Atrazine application with or without SD biochar pretreatment did not significantly affect dry biomass weight (DBW) of the maize seedlings (Figure 6.4). Nevertheless, soils treated with biochar prior to atrazine application produced larger DBW that ranged from 0.78 (in SD + PM@350 at 5 t/ha) to 1.07 g/pot (in SD + PM@350 at 10 t/ha) compared to 0.56 and 0.42 g/pot from atrazine alone and absolute control soils, respectively. Generally, maize seedlings DBW were larger in SD + PM biochar applied at 10 t/ha while lower application rate of 5 t/ha produced larger biomass weights in SD-PM biochar.

6.3.7 Regression analysis indicating contributions of selected biochar nutrient properties to organic carbon and macronutrient contents in the atrazine-treated soil The selected biochar nutrient characteristics at high and low application rates contributed differently to the enhanced nutrient status of the soil treated with atrazine. At high biochar application rate, biochar ash, Ca, Mg, K, N, and P contents consistently had positive correlation with all the soil characteristics considered except SOC

Figure 6.4: Immediate effects of atrazine and biochar pretreatment on biomass weight of maize seedlings. BT is biochar type; BR is biochar rate; SD + PM@350 and SD + PM@450 are sawdust biochar co-pyrolyzed with poultry manure at 350 and 450 °C, respectively; SD-PM@350 and SD-PM@450 are sole sawdust biochar pyrolyzed at 350 and 450 °C, respectively; ns is not significant.

6.3 Results

111

(Table 6.4a and b). Only biochar organic C content had positive relationship (R2 = 0.55) with SOC. Furthermore, all the biochar nutrient properties except Na accounted highly for variations in soil Ex. Ca (75–98%) and available P (46–88%) over other soil nutrient properties. Significant contributions to Ex. Ca was accounted for by pH (R2 = 0.97**), Ca (R2 = 0.94*), K (R2 = 0.92*), and organic C (R2 = 0.98**) contents of the biochar tested, while only biochar Ca (R2 = 0.88*) content significantly contributed to available P increases at higher application rate. This indicated biochar Ca contents alone to significantly account for 87, 94, and 84% increases in available P, Ex. Ca, and Mg contents, respectively. Similarly, 97, 92, and 98% variations in soil Ex. Ca were significantly explained by the respective pH, K, and organic C contents of the biochar tested. Table .: Contributions of selected biochar properties at high ( t/ha) application rate to organic carbon and macronutrient contents in atrazine-treated soil.  t/ha biochar application rate Soil parameters

Regression equations

R

F Prob

. . . . .

. .** . . .*

. . . . .

. .* . .* .

. . . . .

. . . . .

. . . . .

. . . . .

Biochar organic C content SOC Ex. Ca Ex. Mg Ex. K pH

y = .x + . y = −.x + . y = −.x + . y = −.x + . y = −.x + . Biochar Ca content

SOC Avail. P Ex. Ca Ex. Mg pH

y = −.x + . y = .x + . y = .x + . y = .x + . y = .x + . Biochar ash content

SOC Avail. P Ex. Ca Ex. Mg pH

y = −.x + . y = .x + . y = .x + . y = .x + . y = .x + . Mg content

Avail. P Ex. Ca Ex. Mg Ex. K pH

y = .x + . y = .x + . y = .x + . y = .x + . y = .x + .

112

6 Immediate effects of atrazine application on soil organic carbon

Table .: (continued)  t/ha biochar application rate Soil parameters

Regression equations

R

F Prob

.

.

. . . . . .

. . .* . . .

. . . . .

. . . . .

. . . . .

. . . . .

. . . . . . .

. . .** . . . .

Biochar Na content Ex. Na

y = .x + . Biochar K content

SOC Avail. P Ex. Ca Ex. Mg Ex. K pH

y = −.x + . y = .x − . y = .x − . y = .x + . y = .x + . y = .x + . Biochar N content

Avail. P Ex. Ca Ex. Mg Ex. K pH

y = .x − . y = .x − . y = .x + . y = .x + . y = .x + . Biochar P content

SOC Avail. P Ex. Ca Ex. Mg pH

y = −.x + . y = .x + . y = .x + . y = .x + . y = .x + . Biochar pH

SOC Avail. P Ex. Ca Ex. Mg Ex. K Ex. Na pH

y = −.x − . y = .x − . y = .x − . y = .x − . y = .x − . y = .x − . y = .x + .

*** and * are significant levels at p < . and ., respectively. ** and * are significant levels at p < . and ., respectively.

At low application rate, all the selected biochar properties accounted for variation in SOC and macronutrient contents at different magnitudes except Na (Table 6.5a and b). Increasing concentrations of these biochar properties increased SOC and macronutrient contents. Exception was indicated by biochar organic carbon content (R2 = −0.15) that had

6.3 Results

113

Table .: Contributions of selected biochar properties at low ( t/ha) application rate to organic carbon and macronutrient contents in atrazine-treated soil.  t/ha biochar application rate Soil and plant parameters

Regression equations

R

F Prob

. . . . . . .

. . .* .* .** . .

. . . . . . .

. .* . . . . .

. . . . . . .

. . . . . . .

. . . . . . .

.* . . . . . .

. . .

. . .*

Biochar organic C content SOC Avail. P Ex. Ca Ex. Mg Ex. K Ex. Na DBW

y = −.x + . y = −.x + . y = −.x + . y = −.x + . y = −.x + . y = −.x + . y = .x + . Ca content

SOC Avail. P Ex. Ca Ex. Mg Ex. K Ex. Na pH

y = .x + . y = .x + . y = .x + . y = .x + . y = .x + . y = .x + . y = .x + . Biochar ash content

SOC Avail. P Ex. Ca Ex. Mg Ex. K Ex. Na pH

y = .x + . y = .x + . y = .x + . y = .x + . y = .x + . y = .x + . y = .x + . Biochar Mg content

SOC Avail. P Ex. Ca Ex. Mg Ex. K Ex. Na pH

y = .x + . y = .x + . y = .x + . y = .x + . y = .x + . y = .x + . y = .x + . Biochar K content

SOC Avail. P Ex. Ca

y = .x + . y = .x − . y = .x − .

114

6 Immediate effects of atrazine application on soil organic carbon

Table .: (continued)  t/ha biochar application rate Soil and plant parameters

Regression equations

Ex. Mg Ex. K Ex. Na pH

y = .x − . y = .x − . y = .x − . y = .x − .

R

F Prob

. . . .

. . . .

. . . . . . .

. . . . . . .

. . . . . . .

. . . . . . .

. . . . . . . .

. . .** . .* . . .

.

.

Biochar N Content SOC Avail. P Ex. Ca Ex. Mg Ex. K Ex. Na pH

y = .x + . y = .x + . y = .x + . y = .x + . y = .x + . y = .x + . y = .x + . Biochar P content

SOC Avail. P Ex. Ca Ex. Mg Ex. K Ex. Na pH

y = .x + . y = .x + . y = .x + . y = .x + . y = .x + . y = .x + . y = .x + . Biochar pH

SOC Avail. P Ex. Ca Ex. Mg Ex. K Ex. Na pH DBW

y = .x − . y = .x − . y = .x − . y = .x − . y = .x − . y = .x − . y = .x + . y = −. + . Biochar Na content

Avail. P

y = −,x + .

** and * are significant levels at p < . and ., respectively. ** and * are significant levels at p < . and ., respectively.

negative insignificant relationship with SOC. Only biochar N content significantly accounted for highest (86%) increases in organic C in the soil treated with atrazine followed by Mg (82%) > P (70%) > Ash (68%) > K (64%) > Ca (42%) > pH (24%) > organic C (15%).

6.4 Discussion

115

It is important to note that of all the biochars’ properties tested at both application rates, only the biochar pH contributed generally to variations observed in all the soil properties and DBW of the test crop. None of the biochars’ properties accounted for any variation in the DBW of maize seedlings except biochars’ pH and organic C at low application rate. Summarily, all the biochar properties accounted poorly to SOC at high application rate with a range of 5% by K to 55% by biochar’s organic C content while low application rate accounted for more variations in SOC with 70, 82, and 86% explained by biochar P, Mg and N contents, respectively. Generally, the selected biochar properties at low application rate had decreasing contribution order of SOC > Avail. P > Ex. Ca while higher application rate contributed more to Ex. Ca > Avail. P > Ex. Mg.

6.4 Discussion Immediate contributions of atrazine to organic carbon pool and selected macronutrients in a nutrient-degraded soil were evaluated in this trial. Soil organic carbon, pH, and Ex. Ca were the most severely depleted by atrazine within its 2 weeks residence in the soil. The SOC, pH, and Ex. Ca decreased by 31, 5.8, and 27%, respectively, compared to absolute control that received neither atrazine nor biochar pretreatment. The severe reduction in Ex. Ca in the atrazine alone soil caused acidification of the soil from its initial 7.28 to 6.15. Atrazine was also found to reduce soil Ex. Ca and Mg by 26 and 25%, respectively, in a continuously cropped soil in Nigeria [19]. This increasing acidity soil condition inhibited optimal biological activities in the soil leading to the observed severe reduction in SOC. Microbial decomposition of atrazine active material in the atrazine alone soil was hindered at this new soil pH that is below the critical pH range for atrazine degrading microbes reported by Muhammad et al. [31] and Li et al. [32]. The work of Dehghani et al. [5] revealed increasing microbial atrazine degradation with increasing SOC and pH of up to 7.0. Previous studies have also shown atrazine to mineralize more carbon (above 137%) from soil than the quantities it added at the end of its complete metabolizes in the soil [13]. Soil organic matter indices such as microbial diversity and carbon source utilization have been indicated to be adversely affected by atrazine application [4]. Immediate reduction of dehydrogenase activities in a Nigerian soil as a result of immediate reduction of soil organic matter following application of herbicide were revealed in the work of Sebiomo et al. [33]. Evidences of these herbicides limiting enzymatic activities in their early weeks of application are in Emurotu and Anyanwu [34] where atrazine conferred more toxic effects on soil microflora and enzymatic processes over butachlor. Other workers, however, had observed later increases in microbial activities and SOC contents after the initial decreases of these soil biological properties [5, 9, 19]. These authors opined that atrazine metabolites are rich in N and C, which later serve as substrate for the native microbes to thrive on.

116

6 Immediate effects of atrazine application on soil organic carbon

Findings from this study showcase biochar pretreatment as a solution to the immediate severe carbon depletion observed with atrazine usage in this nutrient degraded soil. All the biochar (except SD + PM@450 at 10 t/ha) increased SOC from the initial 1.47% to a range of 1.57–2.57%. This observation was influenced by the carbon content of the biochar tested as impacted by the feedstock and pyrolysis temperature employed in the biochar production. Higher SOC concentrations in the atrazine-treated soil were supported by biochar with higher carbon content and lesser soil macronutrient enrichment tendencies. Generally, SD biochar that supported higher pH, available P, and Ex. bases in the atrazine treated soil as found in soil pretreated with SD + PM biochar types produced lower SOC compared to SD-PM biochar pretreated soils. Therefore, SD biochar potentials to sequester higher carbon over macronutrients in this atrazinetreated soil increases with increasing C/N and C/P ratios of the biochar and vice versa. The biochars utilized multiple mechanisms to mitigate atrazine adverse effects in the soil. This work identified biochar ability to sequester carbon, increase exchangeable basic cations, available P, and redistribute soil ions to achieve optimal pH range of 6.33–6.95 from the initial 7.28. The new pH range brought about by biochar pretreatment was reported to be optimal for soil microbes involved in atrazine degradation [32, 35]. This is also consistent with the submission of Su et al. [36] where enhanced N and P nutrients in organic carbon enriched soil were found to support faster atrazine degradation and reduced its ecotoxicity tendencies. The enhanced SOM and favorable pH brought about by the biochar pretreatment further encouraged atrazine adsorption unto the active sites of the soil organic matter and served as source of nourishment for optimal microbial decomposition [37]. Evidences of atrazine sorption on the functional groups conferred on soil organic matter by biochar such as carbonyl, carboxylic, amine, amide, and hydroxyl groups were reported by Penn et al. [14]. These dissipation processes eventually led to reduced atrazine toxicity in the soil that allowed production of more vigorous and higher dry biomass weight of maize seedlings from biochar pretreated soils compared to atrazine alone and absolute control soils. This is consistent with the submissions of Takeshita et al. [38]. The unexpected increases in available P content (23.80 mg/kg) in atrazine alone soil over absolute control (5.42 mg/kg) within 2 weeks of atrazine application demands further studies and explanation. This is against higher P mineralization, availability, and utilization by soil microbes reported for herbicides like glyphosate and butachlor after complete degradation in soils [9, 34]. Elias and Bernot [39] suggested development of adaptation mechanisms by microbes involved in nitrate and phosphate transformation in soils under continuous treatment with certain herbicides like atrazine. The soil studied was from the arable field with record of indiscriminate atrazine application over the years. This elevated P concentrations could have been contributed from the intense SOC mineralization initiated by the atrazine in the soil studied. Similar significant increases in available P from 14.39 mg/kg in control to 38.19 mg/kg in soil under 5 years continuous use of atrazine was observed by Oladele and Ayodele [19]. The increased available P in the atrazine alone soil may, however, be short lived if the soil pH continues to reduce below

6.5 Conclusions

117

6.0. Soil pH below 6.0 encourages solubility of aluminum oxides in the soil, which leads to P fixation. Only biochar organic C content at 10 t/ha application rate had positive relationship 2 (R = 0.55) with SOC, while other biochar properties consistently maintained negative relationship with SOC. Furthermore, the consistent negative correlation the biochar organic C had with the soil pH and all the macronutrients in the soil at both application rates explains mineralization of these nutrients into the soil pool from the biochar organic C. The relationship was high and significant with Ex. Ca (R2 = −0.98**) and pH (R2 = −0.83*) at high application rate and with Ex. K (R2 = −0.96*) and Ca (R2 = −0.90*) at low application rate. This can be substantiated by the slightly lower organic C contents observed in biochar pretreated soils that supported higher available P and exchangeable bases. This indicates the inability of the same biochar type to simultaneously sustain higher organic C and macronutrient concentrations in the atrazine-treated soil. Generally, all the biochars’ properties considered contributed to the SOC and macronutrient enrichment while only biochar pH and organic carbon content at 5 t/ha application rate accounted for variations in the DBW of the maize seedling. This further emphasized pH and organic carbon contents of biochar as the most influential biochar properties controlling biochar mitigation process in the atrazine-treated soil. This is consistent with the submissions of James et al. [37] and Gondar et al. [40]. The distinct differences in the directions of the relationship among biochar and soil properties at the two application rates as indicated by the regression analysis pointed toward the need for strategic modification of the C/N and C/P ratios and application rates of biochar. It also suggested suitability of each biochar type as pretreatment in atrazine-polluted soil. Higher SOC is sequestered by biochar modified for high in ash, Ca, pH, and P and low organic C when applied at 5 t/ha as seen with SD + PM biochars while biochar higher in organic C but lower ash, Ca, pH, and P composition favored optimal SOC sequestration at 10 t/ha as observed with SD-PM biochars. Conclusively, biochar pH and organic carbon were the dominant parameters involved in biochar potentials to enhance SOC and macronutrients. This was indicated by the consistently higher contributions of up to 97 and 98% by pH at 10 and 5 t/ha, respectively, and 98 and 96% at 10 and 5 t/ha, respectively, compared to contributions by other biochar nutrient properties. Biochar with high pH are known for their ability to improve catalytic reactions of alkali needed for accelerated herbicide hydrolysis [23, 41].

6.5 Conclusions The immediate effects of atrazine application were studied within 2 weeks in a nutrientdegraded soil. Sole atrazine usage is a potential threat to sustaining optimal organic carbon and exchangeable Ca in the soil especially in soils not receiving organic matter amendments. Atrazine severely decreased the soil pH and depleted the soil of its organic

118

6 Immediate effects of atrazine application on soil organic carbon

carbon and calcium. Sawdust biochar showed potential suitability for use as pretreatment in mitigating atrazine side effects on SOC, macronutrients, and pH in atrazinetreated soil. Sole sawdust biochar encouraged higher organic carbon build up while sawdust biochar co-pyrolyzed with poultry manure was superior in macronutrient enrichment in the soil studied. The dominant biochar parameters responsible for mitigating adverse atrazine effects on the soil were pH and organic carbon contents. Thus, their proper modification through feedstock combinations, pyrolysis temperature variations, and application rates would help achieve production of useful biochar pretreatment for atrazine-treated soils. Repetition of this trial under field condition for evaluation of atrazine active ingredient efficacy on weeds following use of sawdust biochar pretreatment is recommended to concretize these preliminary findings.

References 1. Qi Y, Li J, Guan X, Yan B, Fu G, He J, et al. Effects of herbicides on non-target plant species diversity and the community composition of fallow fields in northern China. Sci Rep 2020;10:9967. 2. Marcato AC, Pereira de Souza C, Fontanetti CS. Herbicide 2,4-D: a review of toxicity on non-target organisms. Water Air Soil Pollut 2017;228:120. 3. Thiour-Mauprives C, Martin-Laurent F, Calvayrac C, Barthelmebs L. Effects of herbicide on non-target microorganisms: towards a new class of biomarkers. Sci Total Environ 2019;684:314–25. 4. Yang F, Gao M, Lu H, Wei Y, Chi H, Yang T, et al. Effects of atrazine on chernozem microbial communities evaluated by traditional detection and modern sequencing technology. Microorganisms 2021;9:1832. 5. Dehghani M, Nasseri S, Hashemi H. Study of the bioremediation of atrazine under variable carbon and nitrogen sources by mixed bacterial consortium isolated from corn field in Fars province of China. J Environ Public Health 2013:7:973164. 6. Czaplicka M, Barchanska H, Jaworek K, Kaczmarczyk B. The interaction between atrazine and the mineral horizon of soil: a spectroscopic study. J Soils Sediment 2018;18:827–34. 7. Albuquerque FP, De Oliveira JL, Moschini-Carlos V, Fraceto LF. An overview of the potential impacts of atrazine in aquatic environments: perspectives for tailored solutions based on nanotechnology. Sci Total Environ 2020;700:134868. 8. Pathnak RK, Dikshit AK. Atrazine and human health. Int J Ecosys 2011;1:14–23. 9. Wolmarans K, Swart WJ. Influence of glyphosate, other herbicides and genetically modified herbicideresistant crops on soil microbiota: a review. S Afr J Plant Soil 2014;31:177–86. 10. Gao ZC, Song PP, Wang GY, Wang JH, Zhu LS, Wang J. Responses of atrazine degradation and native bacterial community in soil to Arthrobacter sp. Starin HB-5. Ecotoxicol Environ Saf 2018;159:317–23. 11. Fernandes AF, Wang P, Staley C, Moretto JA, Altarugio LM, Campanharo SC, et al. Impact of atrazine exposure on the microbial community structure in a Brazilian tropical latosol soil. Microb Environ 2020;35. https://doi.org/10.1264/jsme2.ME19143. 12. Dutta A, Singh N. Degradation of atrazine in mineral salts medium and soil using enrichment culture. J Environ Sci Health Part B 2013;48:860–8. 13. Penn CJ, Gonzalez JM, Chagas I. Investigation of atrazine sorption to biochar with titration calorimetry and flow-through analysis: Implications for design of pollution-control structure. Front Chem 2018;6:307. 14. Dorado J, Tinoco P, Almendros G. Soil parameters related with the sorption of 2,4-D and atrazine. Commun Soil Sci Plant Anal 2003;34:1119–33.

References

119

15. Ranaivoson A, Rice P, Moncrief JF, Feyereisen GW, Dittrich M. Acetochlor and atrazine dissipation in wood chip denitrifying bioreactor: a comparison of experimental results with model estimates. Int J Hydrol 2019; 3:286–306. 16. Zhu J, Fu L, Meng Z, Jin C. Characteristics of an atrazine degrading bacterium and the construction of microbial agent for effective atrazine degradation. Water Environ J 2019;35:7–17. 17. Liu Y, Fan X, Zhang T, Sui X, Song F. Effects of atrazine application on soil aggregates, soil organic carbon and glomalin-related soil proteins. Plant Soil Environ 2021;67:173–81. 18. Oladele S, Ayodele A. Glyphosate, 1,1′-dimethyl-4,4′-bipyridinium dichloride and atrazine induces changes in soil organic carbon, bacterial and fungal communities in a tropical alfisol. Eurasian J Soil Sci 2017;6: 238–48. 19. Dutta A, Mandal A, Manna S, Singh SB, Berns AE, Singh N. Effect of organic carbon chemistry on sorption of atrazine and metsulfuron- methyl as determined by (13) C-NMR and IR spectroscopy. Environ Monit Assess 2015;187:620. 20. Liu YF, Fan XX, Zhang T, He WY, Song FQ. Effects of the long –term application of atrazine on soil enzyme activity and bacterial community structure in farmlands in China. Environ Pollut 2020;262:114264. 21. Fan XX, Chang W, Sui X, Liu YF, Song G, Song FQ, et al. Changes in rhizobacterial community mediating atrazine dissipation by arbuscular mycorrhiza. Chemosphere 2020;256:127046. 22. Cao B, Zhang Y, Wang ZY, Li MY, Yang F, Jiang D, et al. Insight into the variation of bacterial structure in atrazine contaminated soil regulating by potential phytoremediator: Pennisetum americanum (L.) K. Schum. Front Microbiol 2018;9:00864. 23. Liu Y, Lonappan L, Brar SK, Yang S. Impact of biochar amendment in agricultural soils on the sorption, desorption and degradation of pesticides: a review. Sci Total Environ 2018;645:60–70. 24. Chen Q, Yang B, Wang H, He F, Gao Y, Scheel RA. Soil microbial community toxic response to atrazine and its residue under atrazine and lead contamination. Environ Sci Pollut Control Ser 2015;22:997–1007. 25. International Institute of Tropical Agriculture (IITA). Selected methods for soil and plant analysis. Manual series No. 1, 2nd ed.; 1978:1–24 pp. 26. Soil Survey Staff. Keys to Soil Taxonomy. A basic system of soil classification for making and interpreting soil surveys, 9th ed. Washington, D.C.: United State Department of Agriculture. NRCS; 2003:281 p. 27. Olatunji OO. Forms and distribution of sesquioxides and effects on phosphate sorption in some soil on basesment complex of south-western Nigeria [Ph.D. Thesis]. Ogbomoso, Nigeria: LAUTECH; 2011:192 p. 28. Oyeyiola YB, Ewetola EA, Olaitan OE, Arogundade MI. Biochar yield and quality enhancement by poultry manure spiking at varied slow pyrolysis heating regimes. J Solid Waste Technol Manag 2021;47:705–16. 29. Walkley AJ, Black IA. Estimation of soil organic carbon by chromic acid titration method. Soil Sci 1934;37: 29–38. 30. Muhammad F, Yusuf F, Ahmad FA, Shehu U, Yakasai HM. Optimising the effects of pH and temperature on atrazine degradation by Bacillus safensis strain BUK_BCH_BTE6 an efficient atrazine tolerating bacterial from an agricultural soil in Kura Local Government Area of Kano State, Nigeria. Nigerian J Biotechnol 2021; 38:98–100. 31. Li Y, Liang D, Sha J, Zhang J, Gao J. Isolating and identifying the atrazine degrading strain Arthrobacter sp LY-1 and applying it for the bioremediation of atrazine-contaminated soil. Pol J Environ Stud 2019;28: 1267–75. 32. Sebiomo A, Ogundero VW, Bankole SA. Effect of four herbicides on microbial population, soil organic matter and dehydrogenase activity. Afr J Biotechnol 2011;10:770–8. 33. Su J, Zhu L, Li X, Wang J, Xie H, Wang J, et al. Effects of nitrogen and phosphorus fertilizers on atrazine degradation and detoxification by degrading strain HB-5. Environ Sci 2010;31910:210–5. 34. James TK, Ghanizadeh H, Harrington KC, Bolan NS. Degradation of atrazine and bromacil in two forestry waste products. Sci Rep 2021;11:3284. 35. Elias D, Bernot MJ. Effects of atrazine, metolachlor, carbaryl and chlorothalonil on benthic microbes and their nutrient dynamics. PLoS One 2014;9:e0109190.

120

6 Immediate effects of atrazine application on soil organic carbon

36. Gondar D, Lopez R, Antelo J, Fiol S, Arce F. Adsorption of paraquat on soil organic matter: effect of exchangeable cations. J Hazard Mater 2012;235:218–33. 37. Cara IG, Topa D, Puiu I, Jitareanu G. Biochar a promising strategy for pesticide contaminated soils. Agriculture (MDPI) 2022;12:1578. 38. Zhang P, Huang P, Ma M, Meng X, Hao V, Sun H. Chapter 11 – Effects of biochar on the environmental behavior of pesticides. In: Tsang DC, Ok YS, editors. Biochar in Agriculture for achieving sustainable development goals. Cambridge: Academic Press; 2022:129–38 pp. 39. Enurotu MO, Auyanwu CU. Effect of atrazine and butachlor on some soil enzymes activities at different concentrations. Eur J Exp Biol 2016;6:9–16. 40. Harney RL, Senseman SA, Krytz LJ, Hons FM. Soil carbon and nitrogen mineralization as affected by atrazine and glyphosate. Biol Fertil Soils 2002;35:35–40. 41. Tekeshita V, Mendes KF, Alonso FG, Tornisielo VL. Effect of organic matter on the behavior and control effectiveness of herbicides in soil. Planta Daninha 2019;37:e019214401.

Benton Otieno*, Mervyn Khune, John Kabuba and Peter Osifo

7 Process configuration of combined ozonolysis and anaerobic digestion for wastewater treatment Abstract: Industrial activities and increased human population have made wastewater streams not entirely amenable to conventional treatment methods. Anaerobic digestion (AD) can treat such wastewaters with the advantage of bioresource recovery. However, the presence of solids and recalcitrant compounds in most wastewater streams may affect the AD process. Thus, combining AD with advanced oxidation processes (AOPs) such as ozonolysis is necessary. Ozonolysis can improve the biodegradability of wastewater substrates or eliminate biorecalcitrant pollutants that escape the AD process. This study combined ozonolysis with AD to treat waste activated sludge (WAS) and distillery wastewater (DWW). When applied as a pre-treatment, ozonolysis caused the rigid cell walls in WAS to rupture and solubilised the extracellular polymeric substances (EPS), leading to increased biodegradability. For the DWW, ozonolysis pre-treatment reduced the biorecalcitrant aromatic compounds to simple aliphatic compounds, thereby increasing biodegradability. In the ensuing anaerobic process, the WAS pre-treatment improved TSS and COD reductions and a 230% increase in cumulative biogas production. For the DWW, the ozonolysis pre-treatment did not significantly impact COD reduction or biogas production; however, ozonolysis as a posttreatment removed the color causing biorecalcitrant melanoidins from the anaerobically digested effluent and solubilised the sludge (TSS) washed out from the AD unit. Therefore, the AD-ozonolysis process configuration depends on the substrate being treated. Ozonolysis is best applied pre-AD for WAS treatment and post-AD for DWW. Keywords: distillery wastewater; post-treatment; pre-treatment; sludge solubilisation; waste activated sludge.

*Corresponding author: Benton Otieno, Research Centre for Renewable Energy and Water, Vaal University of Technology, Vanderbijlpark, South Africa; and Department of Chemical Engineering, Vaal University of Technology, Vanderbijlpark, South Africa, E-mail: [email protected]. https://orcid.org/0000-00021763-1584 Mervyn Khune, John Kabuba and Peter Osifo, Department of Chemical Engineering, Vaal University of Technology, Vanderbijlpark, South Africa As per De Gruyter’s policy this article has previously been published in the journal Physical Sciences Reviews. Please cite as: B. Otieno, M. Khune, J. Kabuba and P. Osifo “Process configuration of combined ozonolysis and anaerobic digestion for wastewater treatment” Physical Sciences Reviews [Online] 2023. DOI: 10.1515/psr-2022-0340 | https://doi.org/10.1515/9783111071428-007

122

7 Configuration of combined anaerobic and ozonolysis

Abbreviations AD AOPs AST BOD COD DWW EPS HMW HRT LMW OLR TOC TS TSS UASB UV VFAs WAS

anaerobic digestion advanced oxidation processes activated sludge treatment Biochemical oxygen demand chemical oxygen demand distillery wastewater extracellular polymeric substances high molecular weight hydraulic retention time Low molecular weight organic loading rate total organic carbon total solids total suspended solids upward flow anaerobic sludge blanket ultraviolet light volatile fatty acids waste activated sludge

7.1 Introduction Activated sludge is the flocculent culture of organisms developed in aeration tanks due to wastewater treatment under controlled conditions [1]. Industrial wastewater or sewage is treated such that biological floc is formed from organisms in the water by bubbling atmospheric air, thus reducing the sewage’s organic content [2]. Once this water has been treated, the overflow of the mixed liquor is sent into settling tanks, and the treated liquor is released for further treatment before discharge [3]. The excess sludge that accumulates is called waste activated sludge (WAS) which is ultimately removed from the treatment process and stored in storage tanks away from the primary treatment process. The WAS consists of readily decomposable organic matter, pathogens, heavy metals, and toxic chemicals, posing the risk of secondary environmental pollution [4]. WAS remediation is a significant legal, ecological, and economic challenge [5–8]. WAS management and treatment accounted for as high as 60% of the total treatment costs incurred by municipal wastewater plants [7, 9]. Anaerobic digestion (AD) of WAS for solids reduction, energy recovery, and stabilisation has long been considered [10, 11]. However, the unique characteristics of WAS restricted the AD process by slowing digestion, resulting in low solids reduction and low biogas production [12]. WAS is always considered a nuisance even though the sludge granules are rich in organic matter and nutrients. It is thus essential to introduce a pre-treatment process to

7.2 Methodology

123

enhance the biodegradability of WAS, thereby improving biogas production and solids reduction [7]. Distilleries (alcohol producing) are one of the leading environmental polluters, as close to 90% of the utilised raw material (mainly molasses) results in wastewater [13, 14]. Distillery wastewater (DWW) is characterised by a dark color, bad smell, and a high organic load as indicated by a biochemical oxygen demand (BOD) of 45–60 and chemical oxygen demand (COD) of 70–120 g/L [13, 15]. Discharging the DWW into receiving streams such as rivers and lakes can lead to eutrophication and hinder photosynthesis by aquatic flora [16, 17]. The anaerobic digestion treatment is often preferred for DWW. However, the biorecalcitrant color-causing melanoidin compounds can sometimes hinder the first step (hydrolysis) of AD, thereby lowering the overall rate. Moreover, during AD, the color-causing melanoidin compounds repolymerises easily, intensifying the color of the anaerobically digested DWW [14]. Combining the biological anaerobic process with ozonolysis, which is an advanced oxidation process, is important for effective wastewater treatment [18–20]. For DWW, ozonolysis pre-treatment can break down the color-causing biorecalcitrant polymeric high molecular weight (HMW) melanoidins into low molecular weight (LMW) compounds that are easily biodegradable [21, 22]. For WAS, ozonolysis has been an ideal pretreatment method for solubilising the solids, leading to improved biodegradability [23]. Additionally, the advantages of integrating ozonolysis as a pre-treatment process are that it generates low-inhibitory compounds and operates at ambient temperature and pressure. The ozone can be generated on-site and utilised directly, avoiding chemical supply and storage issues [24]. Alternatively, ozonolysis can be applied as a posttreatment to eliminate recalcitrant compounds that have escaped the AD process. The present study investigated the application of an integrated AD-AOP system to treat WAS and DWW. Ozonolysis was applied as a pre-or post-treatment to AD to determine the best system configuration. The integrated system was evaluated based on biodegradability enhancement, biogas production, color and COD reductions, and sludge solubilisation to determine the best process configuration.

7.2 Methodology 7.2.1 Materials Chemical of reagent grade including sodium thiosulphate (Na2S2O3), phosphoric acid (H3PO4), methanol (CH3OH), sulphuric acid (H2SO4), potassium iodide (KI), silver sulfate (AG2SO4), hydrochloric acid (HCl), sodium hydroxide (NaOH), and potassium dichromate (K2Cr2O7) were all obtained from Merck Limited in South Africa. All the sourced chemicals were used as received.

124

7 Configuration of combined anaerobic and ozonolysis

Table .: Physical and chemical characteristics of WAS before and after ozonolysis. Parameter

pH TSS DOC Total COD (CODT) Soluble (CODS) BOD BOD:COD Sulfate Phosphate Total alkalinity Volatile fatty acids

Units

– (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) – (mg/L) (mg/L) (mg/L) (mg/L)

Value Before ozonolysis

After ozonolysis

. .  ,   .    

. .  ,   .    

7.2.2 Distillery wastewater and waste activated sludge The waste activated sludge (WAS) used in the current study was obtained from the secondary settling tank of a wastewater treatment (WWT) plant in a local municipality, Vanderbijlpark, South Africa. Distillery wastewater (DWW) was collected from a molasses-based alcohol distillation plant in Durban, South Africa, and stored at 4 °C until used. The chemical and physical characteristics of the WAS and DWW are given in Tables 7.1 and 7.2, respectively.

7.2.3 Ozonolysis pre-treatment process for WAS and DWW The WAS and DWW substrates were pretreated in a 5 L fluidised ozone reactor (made of glass) to improve sludge solubilisation and enhance biodegradability before Table .: Physical and chemical characteristics of DWW before and after ozonolysis. Parameter

pH CODT CODS DOC BOD BOD/COD Absorbance at  nm Absorbance at  nm

Units

– (mg/L) (mg/L) (mg/L) (mg/L) – (a.u.) (a.u.)

Value Before ozonolysis

After ozonolysis

. , ,   . . .

. , ,   . . .

7.2 Methodology

125

Figure 7.1: Schematic diagram of the ozonation (a) and anaerobic units (b).

AD. Briefly, ozone gas was bubbled through the wastewater substrate contained in the ozone reactor at a constant dosage of 45 mgO3/L/min for 1 h. Ozone was supplied by the ozone generator (Biozone Manufacturing). The generated ozone gas was bubbled via a gas diffuser placed at the bottom of the reactor. The ozonolysis set-up is schematically given in Figure 7.1a.

7.2.4 Anaerobic digestion of WAS and DWW Anaerobic digestion of the two wastewater substrates was carried out in two separate UASB reactors. For the DWW, the AD reactor (Figure 7.1b) of 2 L working volume was operated at an optimum organic loading rate (OLR) after a successful digester start-up. The digester was operated stepwise during start-up from an OLR of 1.2 until the optimum OLR of 15 kg COD/M3/day was attained. For the start-up, the UASB digester was inoculated with active anaerobic sludge granules obtained from a digester treating breweries wastewater. Distillery wastewater was added in small amounts, increasing the amount added whenever reactor stability had been attained, as indicated by near constant COD reduction and biogas production. The start-up period lasted for 33 days. Afterwards, the digester was operated semi-continuously from day 34 to 54 while feeding non-pretreated DWW and from day 55 to 74 with ozone pretreated DWW. For the anaerobic digestion of WAS, an already active UASB reactor of 3 L working volume, of which 1 L consisted of sludge granules from an anaerobic digester treating municipal wastewater, was used. Anaerobic digestion of WAS was done in semi-batch mode with manual sampling and feeding. Each batch lasted six days (as determined by near-constant COD reduction and diminished biogas production after the 6th day), after which a new feed was introduced. The digestion temperature was maintained at 37 °C using a heating tape wrapped around the reactor. The reactor was fed raw WAS

126

7 Configuration of combined anaerobic and ozonolysis

(no pre-treatment) in the first three batches, while ozone pretreated WAS was fed in the subsequent three batches. Daily biogas production was monitored. Also, samples were collected and analysed for COD and TSS.

7.2.5 Ozonolysis post-treatment of anaerobically digested DWW The effluent from the UASB reactor treating DWW was diluted with water by a factor of two and then subjected to ozonolysis in the ozone reactor to remove the biorecalcitrant color. The optimum ozone dosage was determined by varying the amount of applied ozone. The amount of ozone used up in the reaction (ozone transfer) was determined following Equation (1); O3 T =

O3 S × 100 O3 U

(7.1)

where O3 S, O3 T, and O3 U are the supplied ozone (mg), ozone transfer (%), and utilized ozone (mg), respectively.

7.2.6 Physical and chemical analysis Samples withdrawn at pre-determined time intervals during the anaerobic and ozonolysis processes were analysed for pH, color, COD, dissolved organic carbon (DOC), and BOD. The COD, DOC, pH, and BOD were determined following standard methods [25]. Aromaticity and color were determined by UV absorption measurement, while the concentration of the cations was determined by Ion Chromatography [26].

7.3 Results and discussion 7.3.1 Characteristics of WAS and DWW before and after ozonolysis pre-treatment Tables 7.1 and 7.2 give the physicochemical characteristics of the WAS and DWW, respectively, before and after ozone pre-treatment. During WAS pre-treatment, the TSS was reduced by 29% from 20.9 to 14.9 mg/L, indicating the solubilisation of the suspended solids [27, 28]. The solubilisation released the suspended COD into the aqueous phase leading to the observed increase in DOC and soluble COD from 1700 to 2300 and 155–245 mg/L, respectively [9]. On the other hand, the total COD slightly decreased from 24,500 to 21,600 mg/L, ensuring adequate substrate retention for the ensuing anaerobic process. In a previous study by Chen et al. [29], ozonation of activated sludge led to increases in soluble COD, DOC, and soluble total nitrogen. Through ozonolysis, the hard

7.3 Results and discussion

127

cell walls contained in WAS were ruptured, and the extracellular polymeric substances (EPS) solubilised, releasing the cellular contents. The increased concentration of nitrates (0–115 mg/L) and sulfates (18–75 mg/L) in the supernatant confirmed the release of cellular contents [7]. The BOD5 of the pre-treated WAS increased from 1520 to 3520 mg/L contributing to an overall 2.6-fold increase in biodegradability in the BOD5:COD (0.06–0.16). For the DWW (Table 7.2), ozonation pre-treatment increased biodegradability, as shown by the increase of 21% in the BOD5:COD ratio. The complex color-causing biorecalcitrant aromatic compounds such as melanoidins are broken down into biodegradable acidic intermediates and simple aliphatic compounds through ozonation. The reduction of the aromatics contributed to the reduced absorbances at 254 nm for aromaticity (3.85–1.75 a.u.) and 475 nm for color (16.38–12.88 a.u.). Also, the ozonation process achieved solubilisation of suspended organic solids, releasing them into the aqueous face, as shown by the reduction in total COD but with an increase in soluble COD. However, the total COD reduction was very low (15,000 to 14,400 mg/L), ensuring adequate biomass retention for the ensuing AD.

7.3.2 Effect of ozone pre-treatment on anaerobic digestion of WAS The COD and TSS reductions during anaerobic digestion of raw WAS and ozone pretreated WAS are given in Figure 7.2a and b, respectively. The pre-treated WAS had better COD and TSS reductions during anaerobic digestion than the raw WAS. Ozonolysis pre-treatment led to the rupture of the hard cell walls and partial solubilisation (as shown by the 29% reduction in TSS in Table 7.1) of the sludge, availing the cellular contents and leading to improved degradation by the microorganisms [30]. The most significant effect of the pre-treatment process on AD was observed in biogas production, as shown in Figure 7.3. A comparison of the biogas production profiles (Figure 7.3a) showed that the pretreated WAS had more than double the daily biogas production than the raw WAS.

Figure 7.2: Reduction in (a) COD and (b) TSS during anaerobic digestion of raw (Δ) and ozone pretreated (:) waste activated sludge.

128

7 Configuration of combined anaerobic and ozonolysis

Figure 7.3: Biogas production, (a) daily and (b) cumulative, during anaerobic digestion of raw (█) and ozone pretreated (░) waste activated sludge.

The daily biogas production was higher for the pretreated WAS at the beginning of digestion and remained relatively high with continued digestion than the raw WAS which had a significantly diminished biogas production after the first day. The pretreated WAS had a higher cumulative biogas production of 4.75 L after six days of digestion than the raw WAS which had 1.48 L (Figure 7.3b). Ozonolysis pre-treatment solubilised part of the organic matter, which was then easily converted to biogas. The significant increase in biogas production indicates that a major fraction of the solubilised matter was biodegradable [31, 32]. Table 7.3 shows the overall evaluation of pre-treatment methods with respect to the results of the subsequent anaerobic digestion. Ozonolysis pre-treatment (from the current study) led to the highest observed increase (230%) in biogas production in comparison to the other methods. The remarkable increase could be a result of the 50% reduction in TSS, resulting in a 25% increase in DOC during pre-treatment. Ozonolysis pre-treatment is a feasible solution and can overcome the limitations associated with other technologies such as catalyst recovery (catalytic oxidation), high energy and cost (ultrasonic and microwave), excess water use (wet oxidation), and low acidic medium (Fenton) [33].

7.3.3 Effect of ozone pre-treatment on anaerobic digestion of DWW The non-pre-treated DWW and the ozonated DWW were subjected to AD, and the changes in COD (Figure 7.4a), biogas production (Figure 7.4b), and color (Figure 7.4c) were monitored. The COD removal during the digestion of raw DWW increased from 32% on day 34 to around 63% by the 37th day. The low COD reductions observed during the initial stages (days 34–36) were partly due to the microorganisms acclimatizing to the

7.3 Results and discussion

129

Table .: Comparison of ozonolysis with other pre-treatment methods. Pre-treatment Method

Results

Bacterial enzymatica Fentona Thermala

Extracellular polymeric substance (EPS) decreased to  mg/L Sludge disintegration increased by % Increase in soluble carbon, nitrogen, and phosphorus concentrations by , , and %. Sludge viscosity reduced Sludge disintegration increased by % Increased soluble COD from . to . mg/L

Alkalia Low thermala UV-photocatalysisa

Ozonolysisb

Results of subsequent anaerobic treatment Suspended solids reduction = .% COD solubilization = .% NR Methane production increased by % Methane production increased by %

Methane production increased by % Methane production increased by % Methane production of . mL/L sludge, VS reduction of .%, and total COD reduction of .% % reduction in TSS, DOC increased by Improved TSS and COD reductions and a %, nitrates increased from  to  mg/L, % increase in the cumulative biogas production carbohydrates increased four-fold

References; a[] and bcurrent study.

new semi-continuous feeding regime (from batch). The highest reduction in COD averaged around 70% from the 42nd to the 53rd day. To investigate the effect of pre-treatment, ozone pretreated DWW was fed into the reactor from day 55–74 at the same OLR of 15 kg/ m3/d. During this period, the average COD removal remained constant at 71%, indicating that ozone pre-treatment did not significantly affect the AD process, despite the significant increase in biodegradability of the pretreated DWW (Table 7.2). A similar observation was made with the daily biogas production, which averaged 10 L/day for raw and ozone pretreated DWW (Figure 7.4b). The lack of observable differences in the anaerobic digestion of the raw and pretreated substrates could be because of the relatively high biodegradability of the raw DWW, with only 2% being biorecalcitrant [34]. However, the negative color reduction indicated increased color intensity (Figure 7.4c) during the anaerobic digestion of the raw and ozone pretreated DWW substrates. Under the mesophilic conditions of the UASB reactor employed, the biorecalcitrant melanoidins were repolymerised into high molecular weight (usually >5.0 kDa) long-chain organics, thereby increasing the effluent’s color intensity [35, 36]. Ozone pre-treatment was expected to eliminate the melanoidins before AD and improve color reduction during AD. However, the increased color intensity points to incomplete removal of the melanoidins during ozonation pre-treatment. The melanoidins that remained after ozonolysis easily re-polymerised during AD, increasing the color intensity. Ozone pre-treatment is, therefore, ineffective in fully reducing the biorecalcitrant melanoidins into easily biodegradable intermediates/compounds and ensuring their complete elimination in the ensuing anaerobic process.

130

7 Configuration of combined anaerobic and ozonolysis

Figure 7.4: Reduction in (a) COD, (b) daily biogas production, and (c) change in colour intensity during anaerobic digestion of raw and ozone pretreated DWW.

7.3.4 Ozonolysis of anaerobically digested DWW (post-treatment) 7.3.4.1 Characteristics of the anaerobically digested DWW Table 7.4 shows that the DWW had a significant organic load before anaerobic digestion (BOD5 7.3 g/L, COD 15 g/L). The BOD:COD ratio of 0.48 indicated that the DWW substrate was highly biodegradable (a ratio of 0.4 and above is recommended). Up to 75% of the COD and 95% of the BOD5 were eliminated during biodegradation, although the color intensity was enhanced by 40%. After AD, there was still a sizeable quantity of COD present (3560 mg/L), responsible for the biorecalcitrant component, mainly melanoidin compounds that were the source of the intense color of the AD effluent. The BOD5:COD ratio for the anaerobic effluent was about 0.05, confirming the elimination of all the biodegradable organics. Sludge washout from the digester was indicated by the twofold increase in the total suspended solids. To remove the color and solubilise the solids (sludge), ozonolysis post-treatment was applied to the anaerobically digested DWW effluent.

7.3 Results and discussion

131

Table .: Characteristics of DWW before and after AD. Parameter pH COD DOC TSS BOD BOD:COD Absorbance (colour)

Units (g/L) (g/L) (mg/L) (g/L) (a.u.)

Before

After

Change, %

.  . . . . .

. . . . . . .

+ − − + − − +

7.3.4.2 Optimisation of the ozone transfer during post-treatment The evaluation of ozone transfer and color removal at different ozone flow rates is shown in Figure 7.5. Since most organic pollutants are removed during AD (up to 95% and 76% BOD5 and COD reductions, respectively), it is important to optimise the ozone dosage to achieve maximum color removal and ozone transfer during post-treatment. The ozone dosages were varied between 45 and 135 mg/L/min. With increased ozone dosages (from 45 to 135 mg/L/min), the color reduction remained relatively constant between 80 and 85%, while the ozone transfer significantly reduced from 83 to 47%. At higher dosages, the amount of ozone transferred is less; the reason for this reduction is hat the dissolved ozone concentration is approaching the maximum solubility. Moreover, at higher ozone dosages (flowrates), the supplied gas forms big bubbles minimising contact with the organic pollutants. Ultimately, most of the ozone provided quickly escapes in the off-gas. At lower dosages (flowrates), the formation of smaller bubbles allows for greater interfacial surface area for ozone mass transfer [37].

Figure 7.5: Color reduction and ozone transfer at different dosages.

132

7 Configuration of combined anaerobic and ozonolysis

7.3.4.3 Changes in COD, BOD5, and solids concentration The ozonolysis post-treatment of the anaerobically digested AD resulted in up to 80% color removal and a 14% reduction in DOC (Figure 7.6). The oxidation of the melanoidin molecules and the mineralisation of biorecalcitrant components led to reductions in color (Figure 7.6a) and DOC (Figure 7.6b). Color-causing organic compounds with aromatic rings, functional groups like OCH3 and OH, carbon double bonds (C=C), and atoms like N, P, O, and S (negatively charged) are selectively attacked by ozone following the Criegee mechanism [38, 39]. The attack can lead to a rapid disappearance in the color but with the possible formation of other stable end products or intermediates such as carboxylic acids, which are not easily oxidized by ozone [40]. The products formed are still detectable as DOC; thus, the observed low DOC removal of 14%. Ozone can also break down, resulting in the generation of highly reactive hydroxyl radicals (OH·), that can potentially attack (unselectively) all the organic compounds, leading to their total mineralisation via chain degradation reactions [25, 41, 42]. Figure 7.7a, b, and c show the changes in COD (soluble – CODS, total – CODT), total dissolved solids (TDS), and the BOD5:COD ratio, respectively, during ozonolysis posttreatment of the AD effluent. Total, soluble, and suspended COD can be used to investigate the fate of sludge during ozonation. The CODT reduced to 1250 from 1438 mg/L (13% reduction), while the CODS was unchanged. The CODT decrease was attributed to the solubilisation of sludge washed out (suspended COD) from the AD reactor, also indicated by the increase in TDS. After 1 h of ozonolysis, the CODT and CODS levels were nearly similar; indicating that up to 88% of the sludge had been solubilised. Previous investigations on the treatment of DWW with ozonolysis found low CODT decreases within the first hour of treatment [43, 44]. The suspended COD (TSS) is dissolved during ozonolysis resulting in constant CODs, but with a reducing CODT. After the post-treatment, the anaerobically digested effluent had a twofold (0.05–0.11) increase in the BOD5:COD indicating improved biodegradability [45].

Figure 7.6: Color (a) and DOC (b) reductions during ozonolysis of anaerobically digested DWW.

7.4 Conclusions

133

Figure 7.7: Change in (a) COD (total (○) and soluble (Δ)), (b) BOD5:COD ratio, and (c) TDS during ozonolysis of anaerobically digested DWW.

7.4 Conclusions UASB reactors were successfully started up and used for the anaerobic digestion of sludge and distillery wastewater. Ozonolysis was introduced as a pre-or post-treatment process to enhance the digestion process. Ozonolysis pre-treatment of WAS solubilised the sludge and led to a two-fold increase in cumulative biogas production in the ensuing anaerobic process. In the case of distillery wastewater, ozonolysis pre-treatment did not significantly impact COD reduction or biogas production; however, when applied as a post-treatment to AD, ozonolysis effectively eliminated the biorecalcitrant melanoidin responsible for the intense color of the anaerobic effluent and solubilised the TSS (sludge) washed out. Integrating ozonolysis with anaerobic digestion is a promising technique for treating high-strength wastewater. For effective treatment, the process integration should first be configured since it is substrate specific. Ozonolysis should precede AD when treating WAS, while for DWW, ozonolysis should be applied as a posttreatment to AD. Also, kinetics and energy analyses should be determined to guide designing an integrated AD-ozonation process for WAS and DWW treatment.

134

7 Configuration of combined anaerobic and ozonolysis

References 1. Ouyang J, Li C, Wei L, Wei D, Zhao M, Zhao Z, et al. Activated sludge and other aerobic suspended culture processes. Water Environ Res 2020;92:1717–25. 2. Skouteris G, Rodriguez-Garcia G, Reinecke SF, Hampel U. The use of pure oxygen for aeration in aerobic wastewater treatment: a review of its potential and limitations. Bioresour Technol 2020;312:123595. 3. Asiwal RS, Sar SK, Singh S, Sahu M. Wastewater treatment by effluent treatment plants. Int J Civ Eng 2016;3: 19–24. 4. Yuan H, Yu B, Cheng P, Zhu N, Yin C, Ying L. Pilot-scale study of enhanced anaerobic digestion of waste activated sludge by electrochemical and sodium hypochlorite combination pre-treatment. Int Biodeterior Biodegrad 2016;110:227–34. 5. Manterola G, Uriarte I, Sancho L. The effect of operational parameters of the process of sludge ozonation on the solubilisation of organic and nitrogenous compounds. Water Res 2008;42:3191–7. 6. Ozgun H. Anaerobic Digestion Model No. 1 (ADM1) for mathematical modeling of full-scale sludge digester performance in a municipal wastewater treatment plant. Biodegradation 2019;30:27–36. 7. Otieno B, Apollo S, Kabuba J, Naidoo B, Simate G, Ochieng A. Ozonolysis pre-treatment of waste activated sludge for solubilization and biodegradability enhancement. J Environ Chem Eng 2019;7:102945. 8. Delanka-Pedige HMK, Zhang Y, Nirmalakhandan N. Algal wastewater treatment integrated with carbon adsorption and ozonation for water reclamation: multi-criteria comparison with conventional pathways. Algal Res 2022;61:102581. 9. Du H, Wu Y, Wu H, Li F. Effect of ozone pre-treatment on characteristics of dissolved organic matter formed in aerobic and anaerobic digestion of waste-activated sludge. Environ Sci Pollut Res 2021;28:2779–90. 10. Silvestre G, Ruiz B, Fiter M, Ferrer C, Berlanga JG, Alonso S, et al. Ozonation as a pre-treatment for anaerobic digestion of waste-activated sludge: effect of the ozone doses. Ozone Sci Eng 2015;37:316–22. 11. Akcakaya M, Tuncay S, Icgen B. Two-stage anaerobic digestion of ozonated sewage sludge predominantly took over by acetotrophic methanogens with increased biogas and methane production. Fuel 2022;317: 123434. 12. Liang T, Elmaadawy K, Liu B, Hu J, Hou H, Yang J. Anaerobic fermentation of waste activated sludge for volatile fatty acid production: recent updates of pre-treatment methods and the potential effect of humic and nutrients substances. Process Saf Environ Protect 2021;145:321–39. 13. Otieno BO, Apollo SO, Naidoo BE, Ochieng A. Photodecolorisation of melanoidins in vinasse with illuminated TiO2–ZnO/activated carbon composite. J Environ Sci Heal Part A 2017;52:1–8. 14. Mabuza J, Otieno B, Apollo S, Matshediso B, Ochieng A. Investigating the synergy of integrated anaerobic digestion and photodegradation using hybrid photocatalyst for molasses wastewater treatment. EuroMediterr J Environ Integr 2017;2:17. 15. Apollo S, Aoyi O. Combined anaerobic digestion and photocatalytic treatment of distillery effluent in fluidized bed reactors focusing on energy conservation. Environ Technol 2016;37:1–9. 16. Otieno B, Apollo S, Naidoo B, Ochieng A. Photodegradation of molasses wastewater using TiO2–ZnO nanohybrid photocatalyst supported on activated carbon. Chem Eng Commun 2016;6445:1443–54. 17. Navgire M, Yelwande A, Tayde D, Arbad B, Lande M. Photodegradation of molasses by a MoO3–TiO2 nanocrystalline composite material. Chin J Catal 2012;33:261–6. 18. Yuan R, Qin Y, He C, Wang Z, Bai L, Zhao H, et al. Fe–Mn–Cu–Ce/Al2O3 as an efficient catalyst for catalytic ozonation of bio-treated coking wastewater: characteristics, efficiency, and mechanism. Arab J Chem 2022; 16:104415. 19. Lee W, Marcotullio S, Yeom H, Son H, Kim TH, Lee Y. Reaction kinetics and degradation efficiency of halogenated methylparabens during ozonation and UV/H2O2 treatment of drinking water and wastewater effluent. J Hazard Mater 2022;427:127878.

References

135

20. Palomares-Reyna D, Carrera-Crespo JE, Sosa-Rodríguez FS, García-Pérez UM, Fuentes-Camargo I, Lartundo-Rojas L, et al. Photo-electrochemical and ozonation process to degrade ciprofloxacin in synthetic municipal wastewater, using C,N-codoped TiO2 with high visible-light absorption. J Environ Chem Eng 2022; 10:107380. 21. Santos DC, Silva L, Albuquerque A, Simões R, Gomes AC. Biodegradability enhancement and detoxification of cork processing wastewater molecular size fractions by ozone. Bioresour Technol 2013;147:143–51. 22. Amaral-Silva N, Martins RC, Castro-Silva S, Quinta-Ferreira RM. Ozonation and perozonation on the biodegradability improvement of a landfill leachate. J Environ Chem Eng 2016;4:527–33. 23. Otieno B, Apollo S, Kabuba J, Naidoo B, Ochieng A. Ozonolysis post-treatment of anaerobically digested distillery wastewater effluent. Ozone Sci Eng 2019;41:551–61. 24. Otieno B, Apollo S, Naidoo B, Ochieng A. Modeling ozonation pre-treatment parameters of distillery wastewater for improved biodegradability. J Environ Sci Heal Part A 2019;54:1066–74. 25. Mecha AC, Onyango MS, Ochieng A, Momba MNB. Impact of ozonation in removing organic micropollutants in primary and secondary municipal wastewater: effect of process parameters. Water Sci Technol 2016;74:756–65. 26. Navgire M, Yelwande A, Tayde D, Arbad B, Lande M. Photodegradation of molasses by a MoO3–TiO2 nanocrystalline composite material. Chin J Catal 2012;33:261–6. 27. Gomes AC, Silva L, Simões R, Canto N, Albuquerque A. Toxicity reduction and biodegradability enhancement of cork processing wastewaters by ozonation. Water Sci Technol 2013;68:2214–9. 28. Ariunbaatar J, Panico A, Frunzo L, Esposito G, Lens PNL, Pirozzi F. Enhanced anaerobic digestion of food waste by thermal and ozonation pre-treatment methods. J Environ Manag 2014;146:142–9. 29. Chen S, Xue B, Wang X, Dong B, Xu Z. Partial ozonation enhanced the content and aromatization of humic substances in activated sludge through chemical and microbial effects. J Clean Prod 2022;378:134531. 30. Tuncay S, Akcakaya M, Icgen B. Ozonation of sewage sludge prior to anaerobic digestion led to Methanosaeta dominated biomethanation. Fuel 2022;313:122690. 31. Kim DH, Cho SK, Lee MK, Kim MS. Increased solubilization of excess sludge does not always result in enhanced anaerobic digestion efficiency. Bioresour Technol 2013;143:660–4. 32. Sosnowski P, Klepacz-Smolka A, Kaczorek K, Ledakowicz S. Kinetic investigations of methane cofermentation of sewage sludge and organic fraction of municipal solid wastes. Bioresour Technol 2008;99: 5731–7. 33. Anjum M, Al-Makishah NH, Barakat MA. Wastewater sludge stabilization using pre-treatment methods. Process Saf Environ Protect 2016;102:615–32. 34. Chavan MN, Kulkarni MV, Zope VP, Mahulikar PP. Microbial degradation of melanoidins in distillery spent wash by an indigenous isolate. Indian J Biotechnol 2006;5:416–21. 35. Liang Z, Wang Y, Zhou Y, Liu H. Coagulation removal of melanoidins from biologically treated molasses wastewater using ferric chloride. Chem Eng J 2009;152:88–94. 36. Liu M, Zhu H, Dong B, Zheng Y, Yu S, Gao C. Submerged nanofiltration of biologically treated molasses fermentation wastewater for the removal of melanoidins. Chem Eng J 2013;223:388–94. 37. Mazzei AL, Meyer RM, Bollyky LJ. Mass transfer of high concentration ozone with high efficiency injectors and degassing separators. Int Oozne Assoc Pan Am Gr 1995;1–15. https://www.luftozone.co.uk/wpcontent/uploads/2019/05/tworzenie_instalacji.pdf. 38. Bar Oz Y, Mamane H, Menashe O, Cohen-Yaniv V, Kumar R, Iasur Kruh L, et al. Treatment of olive mill wastewater using ozonation followed by an encapsulated acclimated biomass. J Environ Chem Eng 2018;6: 5014–23. 39. Clem V, de Mendonça HV. Ozone reactor combined with ultrafiltration membrane: a new tertiary wastewater treatment system for reuse purpose. J Environ Manag 2022;315:115166. 40. Peña M, Coca M, González G, Rioja R, García MT. Chemical oxidation of wastewater from molasses fermentation with ozone. Chemosphere 2003;51:893–900.

136

7 Configuration of combined anaerobic and ozonolysis

41. Kasiri MB, Modirshahla N, Mansouri H. Decolorization of organic dye solution by ozonation; Optimization with response surface methodology. Int J Ind Chem 2013;4:3. 42. Wang J, Yuan R, Feng Z, Ma F, Zhou B, Chen H. The advanced treatment of textile printing and dyeing wastewater by hydrodynamic cavitation and ozone: degradation, mechanism, and transformation of dissolved organic matter. Environ Res 2022;215:114300. 43. Zeng YF, Liu ZL, Qin ZZ. Decolorization of molasses fermentation wastewater by SnO2-catalyzed ozonation. J Hazard Mater 2009;162:682–7. 44. Sangave PC, Gogate PR, Pandit AB. Combination of ozonation with conventional aerobic oxidation for distillery wastewater treatment. Chemosphere 2007;68:32–41. 45. Venkatesh S, Quaff a. R, Pandey ND, Venkatesh K. Impact of ozonation on decolorization and mineralization of azo dyes: biodegradability enhancement, by-products formation, required energy and cost. Ozone Sci Eng 2015;37:420–30.

Patrick Leonard Omokpariola, Patrice A. C. Okoye, Victor U. Okechukwu and Daniel Omeodisemi Omokpariola*

8 Concentration levels and risk assessment of organochlorine and organophosphate pesticide residue in selected cereals and legumes sold in Anambra State, southeastern Nigeria Abstract: The levels of organochlorine and organophosphate pesticide residues in selected cereal crops (beans, cowpea, millet, maize, sorghum, and rice) purchased from major markets in Anambra, south-eastern Nigeria, were assessed and compared with established MRLs. The QuEChERS (quick, easy, cheap, effective, rugged, and safe) method was used for extraction and clean-up of pesticide residues. Thereafter detection and quantification were done using GC/MS. The result reveals that the analysed grain samples contained some organochlorine pesticides and organophosphates. The organochlorine was most dominant followed by the organophosphates. Organochlorine pesticide residues varied from 0.048 to 0.298 mg/kg in beans, BDL to 0.398 mg/kg in cowpea, 0.018–0.337 mg/kg in maize, 0.023–0.375 mg/kg in millet, 0.058–0.415 mg/kg in sorghum and 0.045–0.442 mg/kg in rice while organophosphate pesticide residue varied from BDL to 0.315 mg/kg in beans, BDL to 0.113 mg/kg in cowpea, BDL to 0.228 mg/kg in maize, BDL to 0.253 mg/kg in millet, BDL to 0.218 mg/kg in sorghum and BDL to 2.1 35 mg/kg in rice. Highest concentration of endosulphan II (0.442 mg/kg) was detected in rice, followed by aldrin (0.415 mg kg−1) in sorghum and endosulphan II (0.40 mg/kg) in sorghum. The pesticide toxicity index (PTI) was above one (1), whereas health index (HI) was less than one (1) and cancer risk were within USEPA reference guideline for crops indicating children will have greater health effect than adults. Hence, strict monitoring and control of pesticide residues in agricultural products is advocated. Keywords: Cancer ratio; Cereals and Legumes; Health risk; Organochlorine pesticides; Organophosphate pesticides; South-eastern Nigeria.

*Corresponding author: Daniel Omeodisemi Omokpariola, Department of Pure and Industrial Chemistry, Faculty of Physical Science, Nnamdi Azikiwe University, Awka, Anambra, 420261, Nigeria, E-mail: [email protected]. https://orcid.org/0000-0003-1360-4340 Patrick Leonard Omokpariola, Chemical Evaluation and Regulation, National Agency for Food and Drug Administration and Control, Isolo Industrial Estate, Oshodi Expressway, Isolo, Lagos, 101263, Nigeria. https://orcid.org/0000-0002-4983-2719 Patrice A. C. Okoye and Victor U. Okechukwu, Department of Pure and Industrial Chemistry, Nnamdi Azikiwe University, Awka, Anambra, Nigeria. https://orcid.org/0000-0002-0706-8898 (P.A.C. Okoye) As per De Gruyter’s policy this article has previously been published in the journal Physical Sciences Reviews. Please cite as: P. L. Omokpariola, P. A. C. Okoye, V. U. Okechukwu and D. O. Omokpariola “Concentration levels and risk assessment of organochlorine and organophosphate pesticide residue in selected cereals and legumes sold in Anambra State, south-eastern Nigeria” Physical Sciences Reviews [Online] 2023. DOI: 10.1515/psr-2022-0319 | https://doi.org/10.1515/9783111071428-008

138

8 Assessment of OCPs and OPPs in cereals and legumes in Anambra State

8.1 Introduction Pollution of environmental matrices (water, soil, and air) together with foodstuffs by pesticides have rapidly increased worldwide and have been of major concern regarding its potentially carcinogenic residues remaining in the food chain, global transport, and its persistent in the environment because of their high chemical stability and lipid solubility [1]. This can be traced to anthropogenic activities such as mining, increased agricultural production, urbanization, and industrial processes. Pesticides are chemical compounds or mixture of substances aimed at preventing, combating, repelling, or mitigating the effect of pests and vectors on agricultural plants, domestic animal, and human beings. Pesticides are design to be toxic and are primarily designed to kill insects, fungi and weeds hat can threaten public health and the economy [2, 3]. They include insecticides, herbicides, nematicides, avicides, rodenticides, pesticides, bactericides, insect repellent, antimicrobial, and fungicides. Most pesticides are applied directly on the crop or soil, while some other are injected into the soil or applied as granules. Pesticide applications depend on the crop type and age, target, formulation, application technique, and weather condition. Plants may accumulate pesticides with different pathways such as from the root uptake, contamination of foliage and fruits by soil particles, and deposition from air particles [4]. Basically, human exposure to pesticides can occur through several pathways, including dermal absorption and inhalation of air particulates, but the ingestion of contaminated agricultural food accounts for more than 90% of total exposure [5, 6]. The human health effects associated with short term exposure to hazardous pesticides includes headache, dizziness, nausea, vomiting, and convulsion while the long-term exposure is associated with a broad spectrum of possible health effects in human that include neurotoxicity, reproductive toxicity, hepatotoxicity, Parkinson disease, and cancer [7]. Despite the impact of pesticide to increase agricultural production and control insect vectors, however its use may be quite contentious in small quantity of pesticide may remain in the crops. Hence, these pesticide residues may have harmful impact because of its ability to persist for long period causing toxic effect to living organisms in the food chain [1]. Studies have showed that presence of pesticide residues could be harmful to human health and the environment [7–9]. Most of these food crops are consumed in the communities where they are being planted and sold in the open markets. Legumes and cereals especially beans, maize, and rice are food crop that requires much preservation due to its unavailability in most part of Nigeria, and to effectively control weevils and beetles when storing these products [10], consequently, they are most likely to contain high levels of pesticide residues. In the past era, cereals and vegetable farmers have often sought ways to preserve their products, beans inclusive, with the application of red dry peppers rather than pesticides, but today, one means of cereal preservation is the use of poisonous/banned chemicals and wrong application not minding the effects [11]. As a result, most of the crops have high pesticide residues. In South-East of Nigeria, farmers have been widely used pesticides to achieve good productivity in cereal crops. There is limited study carried out to ascertain the levels of

8.2 Materials and methods

139

pesticide residues, the risk assessment, and the human health impact. Therefore, this study aims to assess the levels of organochlorine and organophosphate pesticide residues in selected legumes and cereal crops (beans, cowpea, millet, maize, sorghum, and rice) sold in major markets in Anambra, south-eastern Nigeria, as well as its health risk.

8.2 Materials and methods 8.2.1 Sample collection and preparation Samples of six agricultural products which include rice (Oriza sativa), millet (panicium spp), sorghum (Sorghum bicolor), beans (Phaseolus spp), maize (Zea mays), and cowpea (Vigna unguiculata) were purchased in September 2019 from major markets (Amansea, Amawbia, Ebenebe, Ifite, Isiago, Isi-Aniocha, Mbaukwu, Mgbakwu, Nibo, Nise, Okpuno, and Umuokpu) in Anambra state, South-eastern Nigeria (Figure 8.1). These agro products were chosen because of their wide consumption by the populace and the impact of major plant pests and disease on them both at the pre-harvest and post-harvest stages. The grain samples were cleaned by picking out stones, weevils, and other nonvaluable constituents. Each sample

Figure 8.1: Map of anambra state showing market outlets (Google – Copernicus Data).

140

8 Assessment of OCPs and OPPs in cereals and legumes in Anambra State

were then milled/blended separately, first using a mortar and pestle and then finally grounded to powder using mechanical grinding machine. The individual grain samples were stored at a temperature not less than 40 °C till further analysis.

8.2.2 Chemicals All organic solvents used in the study were pesticide grade or HPLC grade (“Determination of Multiclass Pesticides in Dry Herbs Using GC-ECD”) Pesticide reference standards of organochlorine pesticides (alpha-HCH, beta-HCH, lindane, chlorothalonil, delta-HCH, heptachlor, aldrin, heptachlor epoxide, endosulphan I, dieldrin, endrin, endosulphan II, p, p’ –DDD, endosulphan sulfate, p,p’ – DDT), pyrethroids (lambda-cyhalothrin and permethrin), and organophospate pesticides (diclorvos, mevinfos, diazinon, dimethoate, diclofenthion, phosphamidon, pirimophos-methyl, chlorpyrifos, parathion, fenthion, isofenfos, bromophos, and ethion) were obtained from pesticide reference standards (purity >95%) from Dr. Ehrensdorfer (Augsburg, Germany). Reference standard solutions (1000 μg/mL) of all the analysed pesticides were prepared in methanol and kept at −4 °C. Acetonitrile (Lab-Scan; GC, assay >99%), methanol (Merck, 99.9% HPLC grade), ethyl acetate (LabScan, Dublin, Ireland; pesticide grade), acetone, dichloromethane and petroleum ether (Lab-Scan, Pestiscan), formic acid (Riedel–de Haen; 98–100%), ammonia solution (Riedel–de Haen; 33%), sodium chloride (Riedel–de Haen; 99%), disodium hydrogen-citrate sesquihydrate (Fluka, 99%), trisodium citrate dihydrate (Fluka), sodium chloride and anhydrous magnesium sulfate (Merck), and anhydrous sodium sulfate (Riedel-deHaen) were used for experiments, and deionized water was obtained using a Milli-Q unit (Millipore Corporation, USA).

8.2.3 Extraction of pesticide residues from samples The QuEChERS (quick, easy, cheap, effective, rugged and safe) method as describe by Anastassiades et al. [12] and Mekonnen et al. [13] was used in the extraction of the pesticide residue in which 10 g of homogenized composite cereal and legume samples was weighed using an electronic weighing balance, into a 250 mL beaker. The sample was Soxhlet extracted for 4 h using dichloromethane as the extraction solvent. The extract was then concentrated by distilling-off the solvent on a rotary evaporator at about 41 °C. The reduced extract was then conserved for clean-up.

8.2.4 Clean-up Cleanup of the extract was done using a column of about 15 cm (length) × 1 cm (internal diameter) which was packed first with glass wool and then 5 g activated silica gel prepared in a slurry form in dichloromethane. About 5 g of anhydrous sodium sulfate was placed on top of the column to absorb any water in the sample or the solvent. The pre-elution was done with 15 mL of dichloromethane without exposing the sodium sulfate layer to air to prevent the cracking of packed silica gel adsorbent. “The reduced extracts were run through the column and allowed to sink below the sodium sulfate layer. Elution was done with 3– 10 mL portions of dichloromethane. The eluents were collected and accompanying solvent was then evaporated to dryness under a stream of pure nitrogen and taken for GC/MS analysis.

8.2.5 Analysis of organochlorine and organophosphate pesticides Detection and determination of the pesticide residues were done by reconstituting the dried sample eluents with 2 mL n-hexane before injecting 1 μL of the purified and cleaned up eluents into the injection

8.2 Materials and methods

141

port of an Agilent 6890A Gas Chromatography system equipped with electron capture detector (ECD) (“Dietary exposure assessment of organochlorine pesticides in two …”) The separation was performed on a fused silica capillary column (DB-17, 30 m, 0.25 mm internal diameter and film thickness of 0.25 μm). The temperatures of the injector and detector were 250 °C and 290 °C, respectively (“Effects of Environmental Conditions and Methanol Feeding Strategy on …”) Oven temperatures programme started from 150 °C and increased to 280 °C at 6 °C per minute. The injection was carried on a spitless injector, carrier gas was helium at a flow rate of 2 mL/min, and make up gas was nitrogen. The run time was 21.667 min. Quantification of the OCPs and OPPs was based on external calibrations curves prepared from the standard solutions of each of the OCPs and OPPs. Pesticide residues in the extracts were identified using the retention times of the reference standards as described by Oyeyiola et al. [14] and Mekonnen et al. [13].

8.2.6 Quality control All reagents used during the analysis were exposed to the same extraction procedures. Solvents used were run to verify any interfering substances within the runtime. In all batches reagent blanks and samples were fortified with mixed OCP and OPP standards for quality control checks. All the samples were analysed in triplicates. Routine recoveries were analysed concurrently with each batch of analytical extracts. Fortification level of 0.05 mg/kg was chosen based on the limit of determination. Organochlorine and organophosphate pesticide residues were recovered in the range of 69–119%. Calibration standards was prepared at different concentrations by dilution of the composite stock standard solution with hexane, corresponding to the expected range of concentrations found in the samples and it was used to calibrate (retention time and peak area) the instrument response with respect to analyte concentration. Calibration mixtures of concentration levels 0.005, 0.01, 0.05, 0.1, and 0.5 μg/kg were prepared in methanol: ammonium formate buffer 10 mM pH 4 (1:1) and kept at −4 °C, which produced a regression of range of 0.8975–1.0000. The LOD and LOQ were between 0.07 and 0.75 μg/kg and 0.3–2.3 μg/kg, respectively.

8.2.7 Statistical analysis All analysis was carried out in triplicates and Results were expressed as mean ± standard deviation. All the results obtained were explain using statistical package for social sciences (SPSS version 17.0) and Microsoft Excel.

8.2.8 Pesticide toxicity Index The pesticide toxicity index is the cumulative risk from each toxicity quotient (TQ) evaluated using the sample concentration and the corresponding maximum resident limits (MRLs) [15] using the formula [16]: PTI = ∑ TQ =

C MRL

(8.1)

where: C is the concentration of pesticide residue individually (mg/kg), MRL is the EU maximum residue limit (mg/kg) [15]. The acceptable PTI target with no risk to humans is lower than one (1) and vice versa with probable health risk above one (1).

142

8 Assessment of OCPs and OPPs in cereals and legumes in Anambra State

8.2.9 Health and exposure risk assessment Health and exposure risk assessment was determined using the United States Environmental Agency’s risk models [17] that assess the health implication of organochlorine and organophosphate exposure to humans (adults and children) using certain assumptions based on guidelines. 8.2.9.1 Non-carcinogenic assessment: The non-carcinogenic health risk estimates for each of the organochlorine and organophosphate pesticides residues in cereals were calculated as the chronic daily intake (CDI) and the health risk index (HI). The CDI was obtained by multiplying the mean residual pesticide concentration (mg/kg) in each cereal and the food consumption rate (g/person/day), exposure frequency (days/year), exposure duration (year) and conversion factor, and dividing by body weight (kg) and average time of exposure (using exposure by 365 days) as shown in equation (8.2). The noncarcinogenic health risk was assessed by calculating the health index (HI) which was evaluated by dividing the CDI by their corresponding values of RfD as shown in equation (8.3). When the hazard index is > 1, the food involved is considered unacceptable and could pose a health threat to the consumers; when the hazard index is < 1, the food involved is considered acceptable with no health threats to the consumers [17, 18]. C × FC × EF × ED × CF AT × BW n CDI CDI1 CDI2 CDI3 CDIn NC Hazard Index (HI) = ∑ = + + …+ RfD1 RfD2 RfD3 RfDn i=0 RfD CDINC (mg kg−1 ∙ kg−1 ) =

(8.2) (8.3)

Where CDINC is the calculated non-carcinogenic chronic daily intake; C is the concentration of the pesticide residue in the grain samples (mg/kg), FC is the food consumption rate (mean consumption rate of cereals (rice, maize, millet, sorghum) is 291.7 g/person/day, while legumes (beans and cowpea) is 17.6 g/person/day) WHO – GEM [19]; EF is the exposure frequency = 350 days/year; ED is the exposure duration (26 years for adults and 6 years for children); CF is the conversion factor (maximum acceptable risk level (1 × 10−6) that represents the increased probability of developing cancer over the lifetime as a result of exposure to the pesticide residue, AT is the average time of exposure (ED × 365 days); BW is average body weight (children = 15 kg; adults = 70 kg), RfD is the reference dose, as the values were obtained from the US-EPA – IRIS [17, 18, 20]; Gray [21] and Forkuoh [22] are shown in Table 8.1. Table .: Reference value for organochlorine and organophosphate. Organochlorine Alpha-HCH Beta-HCH Lindane (gamma-HCH) Chlorothalonil Delta-HCH Heptachlor Aldrin Heptachlor epoxide Endosulphan I Dieldrin Endrin

RfD . . . . . . . . . . .

CSF

Organophosphate

RfD

. . . . . .  . NA  NA

Diclorvos Mevinfos Diazinon Dimethoate Diclofenthion Phosphamidon Pirimophos-methyl Chlorpyrifos Parathion Fenthion Isofenphos

. . . . . . . . . . .

CSF NA NA NA NA NA NA NA NA NA NA NA

8.3 Results and discussion

143

Table .: (continued) Organochlorine Endosulphan II p, p’–DDD Endosulphan sulfate p, p’ – DDT

RfD

CSF

Organophosphate

. . . .

NA . NA .

Bromophos Ethion

RfD . .

CSF NA NA

[–]. RfD, reference dose (mg/kg/day); CSF, cancer slope factor (mg/kg/day); NA, no reference value/data.

8.2.9.2 Carcinogenic assessment: For carcinogenic risk assessment, the cancer benchmark concentration (CBC), CDI, and cancer ratio (CR) were estimated using equation (8.4)–(8.6), respectively. The hazard ratio (HR) was calculated by dividing the CDI by the cancer benchmark concentration (CBC). The CBC for carcinogenic effect is derived by setting the risk to 1 in 1,000,000 due to a lifetime exposure to the pesticide residue, which is BW FC × CSF C × FC × EF × ED × CF CDIC (mg/kg/day) = AT × BW n CDI CDI1 CDI2 CDI3 CDIn c CR = ∑ = + + …+ CBC1 CBC2 CBC3 CBCn i=0 CBCc CBC =

(8.4) (8.5) (8.6)

where CDIC is the calculated carcinogenic chronic daily intake; C is the concentration of the pesticide residue in the grain samples (mg/kg), FC is the food consumption rate (mean consumption rate of cereals (rice, maize, millet, and sorghum) is 291.7 g/person/day, while legumes (beans and cowpea) is 17.6 g/person/ day); EF is the exposure frequency = 350 days/year; ED is the exposure duration (26 years for adults and 6 years for children); CF is the conversion factor (maximum acceptable risk level (1 × 10−6) that represents the increased probability of developing cancer over the lifetime as a result of exposure to the pesticide residue, AT is the average time of exposure (70 × 365 days), BW is the body weight (kg), FC is the cereals and legumes consumption rate (g/person/day), and CSF is the cancer slope factor (mg/kg/day) [23]. Cancer slope factor values were obtained from the integrated risk information system (IRIS) as shown in Table 8.1 [20, 23]. When CR is less than 1 × 10−4, it indicates that there is potential risk to human health, while the maximum acceptable risk level is 1 × 10−6 which implies less risk of one in a million because of lifetime exposure.

8.3 Results and discussion 8.3.1 Mean Concentration of organochlorine and organophosphate pesticides residues Tables 8.2 and 8.3 reveals the mean concentrations of organochlorine and organophosphate pesticide residues detected and their MRLs in food grains sold in major markets in Awka, Nigeria, as shown in Appendix. Fifteen organochlorine and thirteen organophosphate pesticide residues were detected in the sampled food crops. Generally, there was a slight disparity in the concentration of the different organochlorine and organophosphate

Pesticide residue

. ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .

Cowpea . ± . . ± . . ± . . ± . . ± . . ± . BDL BDL . ± . . ± . . ± . . ± . . ± . . ± . . ± .

Maize

Millet

Sorghum

Rice

MRLs level []

. ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .

. ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .

. ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .

. ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .

. . .  . . . . . . . . . . .

Values are mean ± standard deviation of triplicate determination. BDl, below detection limits of .–. mg/kg; MRL, maximum residue limit.

8 Assessment of OCPs and OPPs in cereals and legumes in Anambra State

α-HCH β-HCH γ-HCH Chlorothalonil δ-HCH Heptachlor Aldrin Heptachlor epoxide Endosulphan I Dieldrin Endrin Endosulphan II p, p’ –DDD Endosulphan sulfate p, p’ – DDT

Beans

144

Table .: Mean Concentration (mg/kg) of organochlorine pesticide residues in grain samples.

Table .: Mean Concentration (mg/Kg) of organophosphate pesticide residues in grain samples. Pesticide residue

Beans

Cowpea

Maize

Millet

Sorghum

Rice

Diclorvos Mevinfos Diazinon Dimethoate Diclofenthion Phosphamidon Pirimophos-methyl Chlorpyrifos Parathion Fenthion Isofenfos Bromophos Ethion

. ± . . ± . . ± . . . . ± . . ± . BDL . ± . . ± . BDL . ± . . ± . . ± .

. ± . . ± . BDL BDL BDL BDL . ± . BDL BDL BDL BDL BDL BDL

. ± . . ± . BDL . ± . . ± . . ± . . ± . . ± . BDL . ± . . ± . BDL . ± .

. ± . BDL . ± . . ± . . ± . . ± . . ± . BDL . ± . . ± . BDL . ± . BDL

. ± . BDL BDL BDL BDL BDL BDL . ± . BDL BDL BDL BDL BDL

. ± . BDL BDL BDL BDL BDL BDL . ± . BDL BDL . ± . BDL BDL

EU MRLs [] . . . . . . . . . . . . .

Values are mean ± standard deviation of triplicate determination. BDL, below detection limits of .–. mg kg−; MRL, maximum residue limit.

8.3 Results and discussion

145

146

8 Assessment of OCPs and OPPs in cereals and legumes in Anambra State

pesticide residue in the studied food crops. The organochlorine pesticides (Table 8.2) detected include α-HCH, β-HCH, γ-HCH, chlorothalonil, δ-HCH, heptachlor, aldrin, heptachlor epoxide, endosulphan I, dieldrin, endrin, endosulphan II, p, p’ –DDD, endosulphan sulfate, and p, p’ – DDT. Quantifiable residues of p, p’-DDT and its metabolite p, p’-DDD were present in all the grain samples. This is indicative of widespread contamination of the grains sold in markets within the study areas. Among the HCH isomers, α-HCH was the residue with the highest concentration in the study area. The mean concentration of the HCH isomers in the studied grains are in the order α HCH > γ-HCH > δ-HCH > β-HCH. Organochlorine pesticide residues ranged from 0.048 to 0.298 mg/kg in beans, BDL to 0.398 mg/kg in cowpea, 0.018–0.337 mg/kg in maize, 0.023–0.375 mg/kg in millet, 0.058–0.415 mg/kg in sorghum, and 0.045–0.442 mg/kg in rice while organophosphate pesticide residue ranged from BDL to 0.315 mg/kg in beans, BDL to 0.113 mg/kg in cowpea, BDL to 0.228 mg/kg in maize, BDL to 0.253 mg/kg in millet, BDL to 0.218 mg/kg in sorghum and BDL to 2.135 in rice. Highest concentration of endosulphan II (0.442 mg/kg) was detected in rice, followed by aldrin (0.415 mg/kg) in sorghum and endosulphan II (0.40 mg/kg) in sorghum. The disparity in the residue levels of the studied grains suggested that the residues undergo different degree of degradation. The differences in concentrations of p, p’- DDT, p, p’ - DDD could be because the chemicals degrade at different rates from application mode and time on crops. The variations in the residue levels among the individual metabolite were expected because most agricultural products dealers use different pesticide concentrations at different stages of the life cycle of the food products. The results of the present study are similar with those of other studies by Lozowicka et al. [24] which revealed the presence of organochlorine pesticides such as aldrin, dieldrin, p,p’ DDE, p,p’ DDD, and so on from samples of beans, cucumber, lupine, tomatoes, carrots, celery, and parsley collected from Kazakhstan and Poland and study by Sosan and Oyekunle [25] which also shows the presence of similar organochlorine pesticides in cowpeas from markets in Ile-ife, Nigeria. Concentrations of organochlorine pesticides levels found in this study were higher than those reported from other researchers from Nigeria and beyond [26–28]. This may be due to increased use and wrong application of pesticides. All the detected pesticides were higher than EU MRLs as shown in Tables 8.2 and 8.3. This might be due to wrong application/usage of pesticides in the production of food grains in the study area or could be because of occurrence and persistence of these compounds in the environment. The presence of dieldrin in all the grains suggests photodegradation of aldrin to its degradation product. Dieldrin has a higher mean concentration than aldrin (aldrin 0.048 mg/kg and dieldrin 0.067 mg/kg) for beans. All the concentration of aldrin and its metabolite are higher than the expected MRLs. Adeleye et al. [29] detected the presence of organochlorine pesticides such as endosulfan sulfate, endrin aldrin in vegetable and fruit samples at concentrations higher than the MRLs set by UK/EU and studies by [15, 30, 31] have detected the presence of organochlorine pesticides such as aldrin, dieldrin, and DDT at concentrations higher than the MRL, set by the European

8.3 Results and discussion

147

Union, in bean samples from Maiduguri and Lagos, respectively. However, if a residue in a food sample exceeds the MRLs, the food commodity is assumed to be unsafe for consumption. The relatively high concentration of Heptachlor in maize grains implied that heptachlor was an active ingredient applied for maize insect control. The high concentration of its residues assessed may be as a result of past usage which resulted in bioaccumulation, or it might have arisen from previously contaminated soils due to past misuse of the insecticide. Heptachlor is used primarily by farmers to kill nematodes, termites, ants, and soil insects in seed grains and on crops, as well as by exterminators and homeowners to kill termites. On the other hand, the concentration of heptachlor epoxide was highest with sorghum having the mean concentration of 0.322 mg/kg. The variation between heptachlor and heptachlor epoxide is also indicative of long-term effect of these residues in the environment. Heptachlor has been banned in Nigeria, and is expected to have been phased out, it is, however, may still being sold under different names or labels or may be added as one of the active ingredients in other insecticides currently sold in Nigerian. Erhunmwunse et al. [32], and Ogar et al. [31], obtained results for pesticide residues in several food grains which were in line with the results obtained in this study. Endosulphan 1 were detected at concentrations of 0.34, 0.225, 0.222 and 0.175 mg/kg, in samples of rice, sorghum, beans, and cowpea, respectively and endosulphan II at concentrations of 0.442, 0.40, 0.375, 0.277, 0.227, and 0.137 mg/kg, in samples of rice, sorghum, millet, cowpea, beans, and maize, respectively, from markets in Awka. This is startling due to the fact the residue is at a higher concentration than the MRLs. This is similar with the study of Iliya et al. [33] and Otitoju et al. [35] where they reported the presence of endosulphan at high concentrations in samples of beans within Jos, Nigeria. This is indicative of high use of organochlorine pesticides in the storage and or cultivation of the crop. The presence of pesticide residues in cereal grains is one important concern for consumers due to their possible long adverse health effects; especially for children, as they consume a higher proportion of cereal grains and its products in relation to their body weight and are more susceptible to chemicals since they are in early developmental stages [35, 36]. Organochlorine pesticides and their metabolites are highly toxic and have been implicated in a wide range of adverse health effects such as cancer, neurological damage, reproductive system deformities, birth defect, and damage to the immune system signifying that they can affect human growth and reproduction [8, 24, 25]. Table 8.3 reveals the organophosphate pesticides residues detected include dichlorvos, mevinfos, diazinon, dimethoate, diclofenthion, phosphamidon, pirimophosmethyl, chlorpyrifos, parathion, fenthion, isofenfos, bromophos, and ethion, wherein the studied legumes and cereals generally contain lower mean concentration of organophosphate residues. It was observed that dichlorvos and mevinfos were found in most of the grains, whereas other organophosphate residues were below detectable limit. The residues of organophosphates were most dominant in beans, maize, and millet while the other three crops have relatively low levels of the organophosphate residues.

148

8 Assessment of OCPs and OPPs in cereals and legumes in Anambra State

Organophosphates are highly potent compounds used majorly as insecticides in the control of storage insects in food crops. They are harmful and more often involved in acute poisoning than other classes of pesticides [31, 36]. In the present study, wide variations were observed for mean concentrations of residues in cowpea, maize, sorghum, millet, beans, and rice. The mean concentration of organophosphate residues in the samples indicates very high contamination of most of the food grains with residues of diclorvos, mevinfos, and chlorpyrifos. The least level for diclorvos was detected in millet (0.055 mg/kg). The highest residue levels of diclorvos, mevinfos, and chlorpyrifos may be due to its repeated use in pre and pro harvest. Results of organophosphate pesticide residues detected from this study were higher than the acceptable MRLs as shown in Table 8.2 except bromophos, parathion, chlorpyrifos and pirimphos-methyl. Previous studies detected chlopyrifos in samples of beans within Jos and Taraba, Nigeria [33, 34]. These residues were found to be below their respective MRLs as compared with the present study. Several factors such as non-availability to farmers and or non-application of these insecticides during the period of study, and from low concentration levels below the limits of quantitation [37], may be responsible of the non-detection of other metabolites of pesticides. Organophosphate pesticides are known to be highly toxic, health effects in adults are cancer, respiratory illnesses, and liver and renal injuries [36, 38].

8.3.2 Pesticide toxicity index Tables 8.4 and 8.5 shows the toxicity quotient (TQ) of each grain samples analysed in tandem with pesticide residue and EU MRL value as computed. The pesticides toxicity index (PTI) is the mixture of pesticides, as it critical because of continual exposure to these food sources that is grown for human consumption. Data in Tables 8.4–8.5 gives the cumulative risk associated with cumulative risk (PTI) that were all above one (1), ranging from 180.41 (beans) – 250.21 (sorghum) in organochlorine residues and 15.53 (cowpea) – 92.34 (beans) in organophosphate residues. The TQ of chlorothalonil (organochlorine) in all grain samples were below one (1) implying no risk, as the cumulative TQ (PTI) were contributed by α-HCH, β-HCH, γ-HCH, δ-HCH, heptachlor, and endrine, respectively. The TQ of diclorvos were major contribution to PTI of organophosphate. The data agreed with Shala by et al. [16], pesticide assessment in vegetables in Egypt with values ranging from 30.16 – 128.44 in mixed pesticides matrices and different chemical groups; also, Chaikasem and Na Roi-et [39] assessment showed similar health concerns about vegetables contamination with pesticides in Thailand with PTI > 1.0, therefore they inferred that accumulation could take place with time leading to chronic health effects to populace. Other authors [40–44], also reported similar concerns that demonstrate the possibility for pesticide residues to pose significant health risk, as there is critical need to survey the pesticide concentration to standardize the amount applicable in pest, disease and insect controls in all agricultural practices and preservation. So

8.3 Results and discussion

149

Table .: Toxicity quotient of organochlorine pesticide residues in grain samples. Pesticide residue

Beans

Cowpea

Maize

Millet

Sorghum

Rice

α-HCH β-HCH γ-HCH Chlorothalonil δ-HCH Heptachlor Aldrin Heptachlor epoxide Endosulphan I Dieldrin Endrin Endosulphan II p, p’ –DDD Endosulphan sulfate p, p’ – DDT PTI

. . . . . . . . . . . . . . . .

. . . . . . NA NA . . . . . . . .

. . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . .

NA, no analytical data; ΣTQp, sum-total of toxicity in organochlorine residues; PTI, pesticide toxicity index across grain samples.

Table .: Toxicity quotient of organophosphate pesticide residues in grain samples. Pesticide residue

Beans

Cowpea

Maize

Millet

Sorghum

Rice

Diclorvos Mevinfos Diazinon Dimethoate Diclofenthion Phosphamidon Pirimophos-methyl Chlorpyrifos Parathion Fenthion Isofenfos Bromophos Ethion TQI

. . . . . . ND . . ND . . . .

. . ND ND ND ND . ND ND ND ND ND ND .

. . ND . . . . . ND . . ND . .

. ND . . . . . ND . . ND . ND .

. ND ND ND ND ND ND . ND ND ND ND ND .

. ND ND ND ND ND ND . ND ND . ND ND .

NA - no analytical data; ΣTQp, sum-total of toxicity in organophosphate residues; PTI, pesticide toxicity index across grain samples.

therefore, although organophosphate concentration was lower than organochlorine pesticides, there is possibility for bioaccumulation to take place from continual oral exposure in humans leading to adverse – sub chronic – chronic health issues over a period (usually one’s lifetime).

150

8 Assessment of OCPs and OPPs in cereals and legumes in Anambra State

8.3.3 Health risk assessment Assessing chronic exposure is an indication of evaluated levels of pesticides over a lifetime and likely health effect of a populace [16], which has been well developed by the US Environmental Protection Agency and the WHO. The chronic daily intake (CDI) of dietary intake for both non-carcinogen and carcinogen were evaluated to assess the Hazard index (HI) and cancer ratio (CR) associated with continual exposure to organochlorine and organophosphate pesticides for adult and children respectively. Table 8.6 reveals the hazard index for organochlorine pesticides in decreasing order are sorghum > millet > maize > rice > beans > cowpea was less than one (1) signifying no adverse health risk associated to adults and children population. Table 8.7 reveals the hazard index for organophosphate pesticides in decreasing order are maize > millet > rice > sorghum > beans > cowpea. An empirical review of the data (Tables 8.6 and 8.7) shows that children have 82.35% probability to have certain health concerns than adults (17.65%) from the current study. Table 8.8 shows the cancer ratio (CR) evaluated from CDI dietary intake divided by cancer benchmark concentration (CBC), which was possible for organochlorine pesticides due to presence of cancer slope factor (CSF), as organophosphate had no CSF for CR evaluation. The organochlorine pesticides in decreasing ratio are sorghum > millet > rice > maize > beans > cowpea, which showed an 81.25% cancer ratio for children to adults across the cumulative values (Table 8.8). Using USEPA reference standard (1.0E-04 – 1.0E-06) showed that the cumulative cereals and legumes were within relative levels for carcinogenic risk assessment for a population, as children have tendencies to have cancer related illness over a period of 70 years. Hence the organochlorine pesticide residue of food crops in the present study may be considered to pose significant risk to the consumers. The present study corroborates with previous work by Li et al. [45] and Taiwo et al. [46] whose research on health risk analysis of some commonly used pesticides found in food indicated that about 30% of the calculated pesticide theoretical maximum dose intake values were greater than the acceptable average daily intake (ADI) values (Table 8.8). The presence of some residues detected in this study having a HI less than 1 is suggests little or no adverse health risk from grain consumption by adults and children within the study area [20, 23]. Watt [36] in his review stated that children are exposed to these pesticides right from women pregnancy stage, breast feeding, and dietary exposure as food as they develop essential body organs and systems, which are neurological, respiratory, immune, thyroid, endocrine, and metabolism systems. As the WHO [47, 48] affirmed that since children have a long year ahead of them than adults, they have appreciable time for chronic diseases to initiate by early exposure due to long latency period. It is imperative to note that pesticide chemicals have been known to trigger diseases from biochemical interference or cellular modification across diverse bodily functions leading to epigenetic effect [49, 50], neurological diseases such as epilepsy, reduced IQ, Tourette syndrome,

151

8.3 Results and discussion

Table .: Hazard Index of organochlorine pesticides in cereals and legumes for adults and children. Adult

Children

Pesticide residue

Beans

Cowpea

Maize

Millet

Sorghum

Rice

α-HCH β-HCH γ-HCH Chlorothalonil δ-HCH Heptachlor Aldrin Heptachlor epoxide Endosulphan I Dieldrin Endrin Endosulphan II p, p’ –DDD Endosulphan sulfate p, p’ – DDT HI

.E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E-

.E- .E- .E- .E- .E- .E- – – .E- .E- .E- .E- .E- .E- .E- .E-

.E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E-

.E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E-

.E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E-

.E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E-

Pesticide residue

Beans

Cowpea

Maize

Millet

Sorghum

Rice

α-HCH β-HCH γ-HCH Chlorothalonil δ-HCH Heptachlor Aldrin Heptachlor epoxide Endosulphan I Dieldrin Endrin Endosulphan II p, p’ –DDD Endosulphan sulfate p, p’ – DDT HI

.E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E-

.E- .E- .E- .E- .E- .E- – – .E- .E- .E- .E- .E- .E- .E- .E-

.E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E-

.E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E-

.E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E-

.E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E-

autistic disorder, dementia, Parkinson disease, amyotrophic lateral sclerosis [36], abnormal immunity, reproductive abnormality, and behavioral tendencies [36, 50]. Other health effects associated with organophosphate and organochlorine pesticides include acute poisoning (fatigue, dizziness, blurred vision, nausea, itchy skin, stinging eyes, paralysis, seizures, disorientation, and death) [51, 52], birth defect and congenital conditions (deformity, cerebral palsy, health disease, turner syndrome, skeletal abnormality,

152

8 Assessment of OCPs and OPPs in cereals and legumes in Anambra State

Table .: Hazard index of organophosphate pesticides in cereals and legumes for adults and children. Adult

Children

Pesticide residue

Beans

Cowpea

Maize

Millet

Sorghum

Rice

Diclorvos Mevinfos Diazinon Dimethoate Diclofenthion Phosphamidon Pirimophos-methyl Chlorpyrifos Parathion Fenthion Isofenfos Bromophos Ethion HI

.E- .E- .E- .E- .E- .E- – .E- .E- – .E- .E- .E- .E-

.E- .E- – – – – .E- – – – – – – .E-

.E- .E- – .E- .E- .E- .E- .E- – .E- .E- – .E- .E-

.E- .E+ .E- .E- .E- .E- .E- – .E- .E- .E+ .E- – .E-

.E- – – – – – – .E- – – – – – .E-

.E- – – – – – – .E- – – .E- – – .E-

Pesticide residue

Beans

Cowpea

Maize

Millet

Sorghum

Rice

Diclorvos Mevinfos Diazinon Dimethoate Diclofenthion Phosphamidon Pirimophos-methyl Chlorpyrifos Parathion Fenthion Isofenfos Bromophos Ethion HI

.E- .E- .E- .E- .E- .E- – .E- .E- – .E- .E- .E- .E-

.E- .E- – – – – .E- – – – – – – .E-

.E- .E- .E+ .E- .E- .E- .E- .E- – .E- .E- – .E- .E-

.E- .E+ .E- .E- .E- .E- .E- – .E- .E- – .E- – .E-

.E- – – – – – – .E- – – – – – .E-

.E- – – – – – – .E- – – .E- – – .E-

stillbirth, and neonatal death) [51–53], and endocrine diseases (kidney enlargement, and liver lesions) [54–56]. Although, international chemical conventions, protocols, and treaties (Rotterdam, Stockholm, chemical weapons and Montreal, amongst others) has critically banned the use, manufacture of these chemicals [57–60] that are precursors to pesticides, herbicides and insecticides is still produced or imported into Nigeria, where there are no proper monitoring, product handling, and sparingly management by regulatory agencies and government policies, which makes it nearly impossible provide protection for children that are likely sources. So therefore, there are issues that requires a holistic attention of all stakeholders in healthcare, agriculture, food and beverages, chemical

153

8.4 Conclusions

Table .: Cancer ratio of organochlorine pesticides in cereals and legumes for adults and children. Adult

Children

Pesticide residue

Beans

Cowpea

Maize

Millet

Sorghum

Rice

α-HCH β-HCH γ-HCH Chlorothalonil δ-HCH Heptachlor Aldrin Heptachlor epoxide Dieldrin p, p’ –DDD p, p’ – DDT cCR

.E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E-

.E- .E- .E- .E- .E- .E- – – .E- .E- .E- .E-

.E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E-

.E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E-

.E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E-

.E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E-

Pesticide residue

Beans

Cowpea

Maize

Millet

Sorghum

Rice

α-HCH β-HCH γ-HCH Chlorothalonil δ-HCH Heptachlor Aldrin Heptachlor epoxide Dieldrin p, p’ –DDD p, p’ – DDT cCR

.E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E-

.E- .E- .E- .E- .E- .E- – – .E- .E- .E- .E-

.E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E-

.E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E-

.E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E-

.E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E-

cCR, cumulative cancer risk; endosuphan I, II, endrin, endosulphan sulfate, no slope data available.

manufacturers, distributors, importers, and so on to design framework, procedures, policy tools, and precautionary principle to protect children from harm to their health, as they are very vulnerable and have greater exposure matrix from actions of adults knowingly or vice versa.

8.4 Conclusions The study investigates the presence of organochlorine and organophosphate pesticide residue in six selected grains sold in major markets of Anambra state, South-eastern Nigeria. Most of the samples analysed in this study indicates presence of one or more of the pesticide residues, with most occurring at concentrations higher than their respective EU/UK maximum residue limits (MRLs). The organochlorine was most dominant followed by the organophosphates, as the pesticide toxicity index (PTI) > 1 was evaluated using

154

8 Assessment of OCPs and OPPs in cereals and legumes in Anambra State

MRL showed that there are probable health concerns to populace over a period. Health risk assessment of organochlorine and some of organophosphate pesticide residues in selected cereal crops indicate health threat, thus hazard index (HI) values were less than one (1), as cancer risk were within USEPA reference guideline. It indicates certain levels of non-carcinogenic/carcinogenic risk associated with the lifetime consumption of cereals sold within the study area. Organochlorine/organophosphate pesticides detected are indicative of continuous use of obsolete banned pesticides and misuse in the cultivation and storage of the studied grains. Hence, there is need for enforcement of pesticide regulations, strict monitoring in addition to training of farmers on the hazards of using banned pesticides in agricultural production.

References 1. Zhou Y, Guan J, Gao W, Lv S, Ge M. Quantification and confirmation of fifteen carbamate pesticide residues by multiple reaction monitoring and enhanced product ion scan modes via Lc-MS/MS QTRAP system. Molecules 2018;23:2488–96. 2. Rahmawati S, Kirana LC, Yoneda M, Oginawati DK. Risk analysis on organochlorine pesticides residue in potato and carrot from conventional and organic farms in citarum watershed area, west java province, Indonesia. J Teknologi Lingkungan 2017;9:1–15. 3. Cox C, Surgan M. Unidentified inert ingredient in pesticides: implication for human developmental health. Perspectives 2006;111:377–82. 4. Gonzales M, Miglioranza KS, Aizpún de Moreno JE, Moreno VJ, Aizpun de Moreno JE. Occurrence and distribution of organochlorine pepticides (OSPs) in tomato (Lycopersion esculentum) crops from organic production. J Agric Food Chem 2003;51:1353–9. 5. Ward MH, Colt JS, Metayer C, Gunier RB, Lubin J, Crouse V, et al. Residential exposure to polychlorinated biphenyls and organochlorine pesticides and risk of childhood leukemia. Health Perspect 2009;117: 1007–13. 6. Okechukwu VU, Omokpariola DO, Onwukeme VI, Nweke EN, Omokpariola PL. Pollution investigation and risk assessment of polycyclic aromatic hydrocarbons in soil and water from selected dumpsite locations in rivers and Bayelsa State, Nigeria. Environ Health Anal Toxicol 2021;36:e2021023. 7. USEPA-IRIS. Integrated risk information system (IRIS) assessments: list A to Z chemical assessments. Washington D.C: Environmental Protection Agency; 2019. Available from: https://cfpub.epa.gov/ncea/iris_ drafts/atoz.cfm. 8. Zahid A, Muhammad S, Ali S, Mujeeb R, Soomro A, Zaib J, et al. Farmers’ perceptions about effects of pesticides use in vegetables in taluka usta Muhammad of Jaffarabad district. J Nat Sci Res 2016;6:22–9. 9. Abdullahi AE. Aguoru CU, Ogbonna IO, Olasan JO, Umar ND. Assessment of pesticide residues in some commonly cultivated vegetables in doma metropolis, Nasarawa state, Nigeria. Int J Agric Environ Biotechnol 2019;4:797–804. 10. Falaju J. How to preserve beans, grains without poisonous chemicals; 2019. Available from: http://www. google.com/amp/s/guardian.ng/Features/BusinessAgrohow-to-preserve-beans-without-poisonouschemicals [Accessed 24 Jun 2019]. 11. Vincent AY, Safina B. Effective ways to control beans, maize storage pests; 2019. Available from: www. dailytrust.com.ng/effective-ways-to-control-beans-maizestorage-pest [Accessed 24 Jun 2019].

References

155

12. Anastassiades M, Lehotay SJ, Stajnbaher D, Schenck FJ, Stajnbaher D. Fast and easy multiresidue method employing acetonitrile extraction/partitioning and dispersive solid-phase extraction for the determination of pesticide residues in produce. J AOAC Int 2003;86:412–31. 13. Mekonnen B, Siraj J, Negash S. Determination of pesticide residues in food premises using QuECHERS method in Bench-Sheko zone, southwest Ethiopia. BioMed Res Int 2021;6612096:1–13. 14. Oyeyiola AO, Fatunsin OT, Akanbi LM, Fadahunsi DE, Moshood MO. Human health risk of organochlorine pesticides in foods grown in Nigeria. J Health Pollut 2017;7:63–70. 15. UK/EC. Maximum residue levels in commodities set by United Kingdom and European commission; 2008. Available from: https://secure.pesticides.gov.uk/MRLs/search.asp. 16. Shalaby SEM, Abou GY, El-Metwally IM, Abou-elella GMA. Health risk assessment of pesticide residues in vegetables collected from Dakahlia, Egypt. J Plant Protect Res 2021;61:254–64. 17. USEPA. Risk assessment guidance for superfund: volume III—part A, process for conducting probabilistic risk assessment. Washington, DC, USA: US Environmental Protection Agency; 2001. Available from: https:// www.epa.gov/sites/production/files/2015-09/documents/rags3adt_com. 18. USEPA. Child-specific exposure factors handbook (final report). Washington D.C.: Environmental Protection Agency; 2008. Available from: https://cfpub.epa.gov/ncea/risk/recordisplay.cfm?deid=199243 [Accessed 12 Nov 2021]. 19. WHO. GEMS/food regional diets. Regional per capita consumption of raw and semi-processed agricultural commodities; 2009. Available from: http://www.who.int/foodsafety/publications/chem/regional_diets/ en/ [Accessed 12 Nov 2021]. 20. USEPA-IRIS. Integrated risk information system (IRIS) assessments: list A to Z chemical assessments. Washington D.C: Environmental Protection Agency; 2019. Available from: https://cfpub.epa.gov/ncea/iris_ drafts/atoz.cfm?list_type¼alpha [Accessed on 12 Nov 2021]. 21. Gray GM. The precautionary principle in practice: comparing US EPA and WHO pesticide risk assessments. Harvard center for risk analysis: risk in perspective. Using decision science to promote reasoned responses to health. Saf Environ Risk 2004;12:1. 22. Forkuoh F, Boadi NO, Borquaye LS, Afful S. Risk of human dietary exposure to organochlorine pesticide residues in fruits from Ghana. Sci Rep 2018;8:16686. 23. USEPA. Slope factors (SF) for carcinogens from US EPA. Washington, D.C: Environmental Protection Agency; 2007. Available from: http://www.popstoolkit.com/tools/HHRA/SF_USEPA.aspx. 24. Lozowicka B, Kaczynski P, Wolejko E, Piekutin J, Sagitov A, Toleubayev K, et al. Evaluation of organochlorine pesticide residues in soil and plants from east Europe and Central Asia. Desalination Water Treat 2017;57: 1310–21. 25. Sosan MB, Oyekunle JAO. Organochlorine pesticide residue levels and potential human risks in kolanut from selected markets in Osun State, Southwestern Nigeria. Asian J Chem Sci 2017;2:1–11. 26. Opuni KFM, Asare-Nkansah S, Osei-Fosu P, Akonnor A, Bekoe SO, Dodoo ANO. Monitoring and risk assessment of pesticide residues in selected herbal medicinal products in Ghana. Environ Monit Assess 2021;193:1–14. 27. Otitoju O, Lewis CC. Health risk assessment of pesticide residues in bean samples from Wukari, Taraba State, Nigeria. J Environ Chem Ecotoxicol 2021;12:1–13. 28. Nardelli V, D’Amico V, Ingegno M, Rovere I, Iammarino M, Casamassima F, et al. Pesticides contamination of cereals and legumes: monitoring of samples marketed in Italy as a contribution to risk assessment. Appl Sci 2021;11:1–12. 29. Adeleye AO, Sosan MB, Oyekunle AO. Dietary exposure assessment of organochlorine pesticides in two commonly grown leafy vegetables in South-western Nigeria. Heliyon 2019;5:e01895. 30. Obida MG, Stephen SH, Goni AD, Victor OO. Pesticide residues in bean samples from Northeastern Nigeria. ARPN J Sci Technol 2012;2:34–7. 31. Ogar CO, Tettey J, Coker HB, Adepoju-Bello AA. Analysis of organochlorine pesticide residues in beans from markets in Lagos Sate, Nigeria. West Afr J Pharm 2012;23:60–8.

156

8 Assessment of OCPs and OPPs in cereals and legumes in Anambra State

32. Erhunmwunse NO, Dirisu A, Olomukoro JO. Implications of pesticide usage in Nigeria. Trop Freshw Biol 2012;21:15–25. 33. Iliya HA, Wannang NN, Falang KD, Nehemiah JA. Evaluation of some pesticide residues in stored beans in Jos, Nigeria. W Afr J Pharmacol Drug Res 2012;28:1–5. 34. Otitoju O, Lewis CC. Health risk assessment of pesticide residues in bean samples from Wukari, Taraba State, Nigeria (“Health risk assessment of pesticide residues in bean samples from…”). J Environ Chem Ecotoxicol 2021;12:1–13. 35. Zawiyah S, Cheman YB, Nazimah SAH, Chin CK, Tsukamoto I, Hamanyza AH, et al. Determination of organochlorine and pyrethroid pesticides in fruit and vegetables using SAX/PSA cleanup column. Food Chem 2007;102:98–103. 36. Watts M. Poisoning our future: children and pesticides. Pesticide action network Asia and the Pacific, 10850 Penang, Malaysia; 2013. e-ISBN: 978-983-9381-62-7. 37. Arowora KA, Imo C, Yakubu OE, Kukoyi AJ, Ugwuoke KC, Igwe EO. Nutritional composition and pesticide residue levels of Some cereal grains sold in Wukari, Taraba state. FUW Trends Sci Technol J 2020;5:111–6. 38. EPA. Hired darm workers and well-being at risk. Wukari, Taraba State: US General Accounting Office Report to Congressional Requesters; 1998. 39. Chaikasem S. Roi-et Na V. Health risk assessment of pesticide residues in vegetables from river basin area. Environ Res 2020;42:46–61. 40. Fantke P, Gillespie BW, Juraske R, Jolliet O. Estimating half-lives for pesticide dissipation from plants. Environ Sci Technol 2014;48:8588–602. 41. Fantke P, Juraske R. Variability of pesticide dissipation half-lives in plants. Environ Sci Technol 2013;47: 3548–62. 42. Malhat F, Kasiotis KM, Shalaby EM. Magnitude of cyantraniliprole residues in tomato following open field application: a prelude to risk assessment. Environ Monit Assess 2018;190:116. 43. Khan N, Yaqub G, Hafeez T, Tariq M. Assessment of health risk due to pesticide residues in fruits, vegetables, soil, and water. J Chem 2020;2020:1–7. 44. Hossain MS, Fakhruddin ANM, Chowdhury AZM, Rahman MA, Alam KM. Health risk assessment of selected pesticide residues in locally produced vegetables of Bangladesh. Int J Food Res Technol 2015;22:110–5. 45. Li XH, Wang W, Wang J, Cao XL, Wang XF, Liu JC, et al. Contamination of soils with organochlorine pesticides in urban parks in Beijing, China. Chemosphere 2008;70:1660–8. 46. Taiwo AM, Talabi OP, Akintola AA, Babatunde ET, Olanrewaju MO, Adegbaju BH, et al. Evaluating the potential health risk of organochlorine pesticides in selected protein foods from Abeokuta southwestern Nigeria. Environ Pollut Bioavailab 2020;32:131–45. 47. WHO. Principles for evaluating health risks in children associated with exposure to chemicals. (“Summary of principles for evaluating health risks in children …”). In: Environmental health criteria, 237. Geneva: World Health Organization; 2006. 48. WHO. Persistent organic pollutants: impact on child health. Geneva: World Health Organization; 2010. 49. Skinner MK, Manikkam M, Guerrero-Bosagna C. Epigenetic transgenerational actions of endocrine disruptors. Reprod Toxicol 2011;31:337–43. 50. CEH. Pesticide exposure in children. Pediatrics 2012;130:e1757–63. 51. Bornman R, de Jager C, Worku Z, Farias P, Reif S. DDT and urogenital malformations in newborn boys in a malarial area. (“DDT and urogenital malformations in newborn boys in a malarial area”). BJU Int 2010;106: 405–11. 52. Gaspari L, Paris F, Jandel C, Kalfa N, Orsini M, Daurès JP, et al. Prenatal environmental risk factors for genital malformations in a population of 1442 French male newborns: a nested case-control study. Hum Reprod 2011;26:3155–62. 53. Gaspari L, Paris FO, Jeandel C, Sultan C. Peripheral precocious puberty in a 4-month-old girl: role of pesticides? Gynecol Endocrinol 2011;27:721–4.

References

157

54. Medina-Pastor P, Triacchini G, EFSA (European Food Safety Authority). The 2018 European union report on pesticide residues in food. EFSA J 2020;18:6057. 55. Adeoluwa OA, Mosudi BS, John A, Oyedele O. Occurrence and Human Health Risk of Dichlorodiphenyltrichloroethane (DDT) and Hexachlorocyclohexane (HCH) Pesticide Residues in Commonly Consumed Vegetables in Southwestern Nigeria. J Health Pollut 2019;9:190909. 56. Nardelli V, D’Amico V, Ingegno M, Della Rovere I, Iammarino M, Casamassima F, et al. (“Pesticides Contamination of Cereals and Legumes: Monitoring of Samples…”) Pesticides contamination of cereals and legumes: monitoring of samples marketed in Italy as a contribution to risk assessment. (“Pesticides Contamination of Cereals and Legumes: Monitoring of Samples…”). Appl Sci 2021;11:1–12. 57. Omokpariola PL, Omokpariola DO. Institutional framework for the sound management of chemicals and chemical industries in Nigeria. ACS Chem Health Saf 2021;28:457–67. 58. Omokpariola DO, Omokpariola PL. Health and Exposure risk assessment of heavy metals in rainwater samples from selected locations in Rivers State, Nigeria. In: Ramasami P, editor. Green and sustainable processing. Berlin: De Gruyter; 2021;2:16–23. 59. Mekonnen B, Siraj J, Negash S. Determination of Pesticide Residues in Food Premises Using QuECHERS Method in Bench-Sheko Zone, Southwest Ethiopia. BioMed Res Int 2021;2021:6612096. 60. Fatunsin OT, Oyeyiola AO, Moshood MO, Akanbi LM, Fadahunsi DE. Dietary risk assessment of organophosphate and carbamate pesticide residues in commonly eaten food crops. Sci Afr 2020;8:e00442.

Supplementary Material: This article contains supplementary material (https://doi.org/10.1515/PSR-20220319).

Babasanmi Oluwole Abioye*, Aderonke Adetutu Okoya and Abimbola Bankole Akinyele

9 Adsorption of trichloroacetic acid from drinking water using polyethylene terephthalate waste carbon and periwinkle shells–based chitosan Abstract: Toxins are formed because of massive anthropogenic activities, polluting freshwater bodies. Most disinfectants used in water purification produce disinfection by-products (DBPs) such as trichloroacetic acid (TCA). TCA is a strong acid, and TCA uptake could harm gastrointestinal tract tissues or result in systemic acidosis. Activated carbons were investigated to remove TCA from drinking water in this study. Elemental and Energy Dispersive X-ray (EDX) and scanning electron microscope methodologies were employed to characterize the surface morphological features of the activated carbons (SEM). Activated carbons’ chemical functional groups were identified through using Fourier transform-infrared (FT-IR) spectroscopy technique. Using a UV-vis spectrophotometer, the TCA concentrations in water samples were examined at 530 nm. The levels of TCA in raw and conventionally treated water were 0.9900 and 2.8900 mg/L, respectively. The polyethylene terephthalate activated carbon (PETAC), polyethylene terephthalate modified activated carbon (PETMAC), and commercial activated carbon (CAC) gave mean TCA removal efficiencies of 80.80%, 90.90%, and 90.90% for raw water and 95.16%, 96.13%, and 100% for conventionally treated water, respectively. The reusability efficiencies of PETAC and PETMAC were 78.4% and 82.4%, respectively. The PETAC with R2 = 0.9377 showed that Langmuir model best fit the TCA adsorption in the isotherm models. According to the findings, PETAC was effective at removing TCA from water sources and could be improved by incorporating chitosan. Keywords: activated carbon; adsorption; polyethylene terephthalate; trichloroacetic acid.

*Corresponding author: Babasanmi Oluwole Abioye, Institute of Ecology and Environmental Studies, Obafemi Awolowo University, Ile-Ife, Nigeria, E-mail: [email protected] Aderonke Adetutu Okoya, Institute of Ecology and Environmental Studies, Obafemi Awolowo University, Ile-Ife, Nigeria Abimbola Bankole Akinyele, Pure and Industrial Chemistry Department, Nnamdi Azikwe University, Awka, Anambra State, Nigeria As per De Gruyter’s policy this article has previously been published in the journal Physical Sciences Reviews. Please cite as: B. O. Abioye, A. A. Okoya and A. B. Akinyele “Adsorption of trichloroacetic acid from drinking water using polyethylene terephthalate waste carbon and periwinkle shells–based chitosan” Physical Sciences Reviews [Online] 2023. DOI: 10.1515/psr-20220295 | https://doi.org/10.1515/9783111071428-009

160

9 Adsorption of trichloroacetic acid from drinking water

9.1 Introduction Water covers three-quarters of the earth’s surface (75%), and it remains very important. Notably, water is a common substance existing naturally in three common states of matter [1]. It is a key requisite of life and enormously significant for existence of all living organisms [2]. However, extensive human activities have led to the accumulation of toxic wastes, which pollute fresh water bodies [3]. Water is classically denoted as unfit for use domestically or in support of aquatic life, when compromised by humanderived or other waste. Consequently, diverse approaches have been exploited to remove pollutants from water; these include sedimentation, precipitation, oxidation, carbon adsorption, disinfection, ion-exchange, and membrane filtration, among others [4]. Disinfectants used for potable water comprises chlorine (Cl), chloramines (NH2Cl), ozone (O3), chlorine dioxide (ClO2), and ultraviolet radiation, all of which kill or prevent replication of microorganisms in water supply [5]. Chlorine treatment causes significant disruption to bacterial cells, such cell permeability disruption, nucleic acid damage, and enzyme degradation [6]. Chemical disinfectants when in excess could by means of organic and inorganic contaminants lead to the materialization of other hazardous by-products (disinfection by-products) [7]. Disinfection by-products (DBPs), such as trichloroacetic acid (TCA), could be hazardous to human health, because they are hydrophilic and highly acidic [8]. Trichloroacetic acids and other DBPs can bring about adverse effects in humans if ingested through drinking water. Humans could likewise be exposed to TCA through the consumption of food, and some beverages made with water that is disinfected with chlorine. The presence and quantities of DBPs have been discovered in the water supply, restricted because of this concern with an extreme contamination of approximately 200 μg/L [9]. Consumption water especially in Nigeria comes in polyethylene pack, known as table water, and it is one of the booming businesses in Nigeria. Over the past few years, polyethylene materials have substituted leaves, glasses, and metals as a low cost and more efficient means of packaging. However, on the downside, the polyethylene materials are nonbiodegradable, which make them persist in the urban area, clogging drains, threatening small animals, soil detriment, and oceans pollution [10]. Different treatment machineries already established for the evacuation of the polyethylene material leftover. Currently, two options are available for the disposal of polyethylene material, i.e., incineration or landfilling. However, these can lead to serious environmental pollution consequences [11]. Because carbon materials derived from polyethylene wastes are used often in green energy technologies and sustainable environmental practices, the conversion of polyethylene wastes into carbon-based functional materials is particularly appealing. Anoxic pyrolysis, pressure carbonization, and catalytic carbonization are a few of the thermal handling techniques used to create carbon-based products from waste polyethylene (pollutant adsorption and CO2 capture) [11]. Adsorption techniques using

9.2 Material and methods

161

activated carbon (AC) remains commonly recognized method to get rid of hazardous substances present in water. In addition, AC surface modification becomes of interest, as modified AC gives better adsorption efficiency than commercially available AC [12]. Currently, chitosan (produced by deacetylation of periwinkle shells chitin) has high utilization for hazardous compounds removal, the presence of OH− and NH2 make it coordination and electrostatic interaction site [13]. As a result, the goal of this research is to find out more about adsorption performances of chitosan modified activated carbon produced from polyethylene terephthalate and periwinkle shells on trichloroacetic acids.

9.2 Material and methods 9.2.1 Collection of materials Polyethylene terephthalate (PET) bottle wastes used for this study were obtained from the Campus of Obafemi Awolowo University’s, Ile-Ife, Osun State, Nigeria. The cocoa husks (CHs) were obtained from the Teaching and Research Farm of Obafemi Awolowo University, Ile-Ife. The Periwinkle shells were collected from Eket main Market, Eket in Akwa-Ibom State, Nigeria. Trichloroacetic acid (TCA, 99.5%), pyridine, and toluene were purchased from Sigma-Aldrich (Merck Company) and of analytical quality. Potassium hydroxide used was produced from Cocoa Pod ash.

9.2.2 Preparation of caustic alkali from cocoa husk ash 9.2.2.1 Preparation of cocoa husk ash The CHs were ashed following the process developed by Okoya et al., 2020. A weighted clean-dried crucible containing pulverized CHs (7 kg) was placed in a muffle furnace (Carbolite 12/65 tube furnace Essen Germany) for ash production at 600 °C and was maintained for 1 h. The crucibles containing ash material were taken out of the furnace after it has been allowed to cool to room temperature and riddled to < 150 µm unit size [14]. 9.2.2.2 Calculation of ash yield from cocoa husk The percentage ash produced from the CHs was calculated by using Equation (9.1) below. This procedure was replicated in order to obtain the average ash yield. Yield (%) =

Weight of cocoa ash × 100 Weight of cocoa husk

(9.1)

162

9 Adsorption of trichloroacetic acid from drinking water

The caustic alkali was extracted from the cocoa husk ash by mixing the cocoa husk ash with distilled water in ratio 1:100 (w/v). The mixture was thoroughly mixed and swirled. It was then filtered with Whatman filter paper (No. 1), to collect caustic alkali as the filtrate [14].

9.2.3 Chemical activation of the carbon 9.2.3.1 Activation of PET with caustic alkali Caustic alkali solution of 600 mL was poured into a 1000 mL beaker and 57 g of shredded PET was added. The combination was stirred, desiccated at 95 °C on magnetic stirrer [12]. 9.2.3.2 Carbonization of the activated PET Caustic alkali impregnated PET (2.8 kg) was kept at a temperature of 500 °C for 1 h in a muffled furnace. When the furnace was off, cool to room temperature before the carbonized sample was removed. The carbon yield was calculated by weighing the cooled carbonized material (activated carbon) against the caustic alkali impregnated PET [12]. The percentage carbon yield was determined using the following equation: Yield (%) =

Weight of Char × 100 Weight of activated PET

(9.2)

9.2.3.3 Determination of the pH of the activated (PET) carbon In a 200 ml beaker, the activated carbon (2.0 g) was weighed into 100 ml distilled water with the mixture was gently boiled for 10 min. After allowing the solution to cool to ambient temperature, it was diluted to 100 ml with distilled water. Finally, it was gently mixed using glass rod for a few minutes before the pH was measured using calibrated pH/EC/TDS meter [12]. 9.2.3.4 Determination of ash content of activated (PET) carbon The activated carbon was burned for 4 h at 500 °C in a muffled furnace to obtain the ash. The obtained ash was weighed after cooling down in a desiccator [12]. The percentage ash content was determined with Equation (9.3). Weight of ash (%) =

Weight of ash × 100 Weight of activated carbon (g)

(9.3)

9.2 Material and methods

163

9.2.4 Preparation of chitosan from Periwinkle shell 9.2.4.1 Deproteinization of periwinkle shell In a 500 ml beaker, 50 g powdered periwinkle shell was weighed and 200 ml caustic alkali solution (4% w/v) was added. The beaker’s contents were filtered, and the filtrate was soaked and washed with distilled–deionized water until it was base-free (using litmus paper on the filtrate) and then dried for 2 h at 110 °C to reduce moisture [15]. 9.2.4.2 Demineralization of deproteinized periwinkle shell Using 250 ml conical flask, deproteinized periwinkle shell was combined with 3% (v/v) 1 M HCl of 100 ml, and the mixture was stirred for 3 h at 30 °C on a magnetic stirrer. The filtrate was rinsed in distilled–deionized water to acid-free solution (using litmus paper on the filtrate). The acid-free filtrate was then desiccated in the oven at 90 °C for 1 h [15]. 9.2.4.3 Decolorization of demineralized–deproteinized periwinkle shell The demineralized–deproteinized periwinkle shell was decolorized for 3 h in acetone at 60 °C. After, the residue (chitin) was filtered out and air-dried for further processing [15]. 9.2.4.4 Deacetylation of chitin The chitin was treated with a 50% (w/v) NaOH solution in a 250 ml conical flask and maintained at 30 °C for 4 h on a magnetic stirrer. The residue (chitosan) (2-acetamido2-deoxy-D-glucose-N-(acetylglucosamin was soaked and washed with distilled–deionized water and then oven dried for 1 h at 90 °C [15]. 9.2.4.5 Preparation of chitosan gel A total of 5 g of chitosan was gently combined with 100 ml of 10% oxalic acid (v/v) with continuous stirring. For homogeneity, the temperature of the combination was raised to 45 °C and chitosan gel was obtained [15].

9.2.5 Determination of chitosan yield Chitosan yield that was derived from periwinkle shell was a proof for periwinkle as a good raw material for chitosan. As a result, quantifying the chitosan isolated from periwinkle shell is not out of place [15]. The yield of chitosan as a percentage was estimated using the following equation:

164

9 Adsorption of trichloroacetic acid from drinking water

Chitosan yield (%) =

Weight of chitosan × 100 Weight of Periwinkle granules (g)

(9.4)

9.2.6 Determination of moisture content of chitosan Gravimetric analysis was used to decide the moisture content. A paper weight was measured and recorded. Chitosan of 0.3 g sample was taken into the piece of paper and the weight was recorded. The sample was dried for 3 h in an oven at 110 °C. The process was repeated until there was no difference in weight between two successive measurements [15]. Moisture content (%) =

Initial weight − after drying weight Initial weight

(9.5)

9.2.7 Determination of ash content of chitosan The ash content (AC) was determined using AC =

M1 × 100 M × (100 − X)/100

(9.6)

where M is the amount of chitosan used in the experiment and M1 is the mass of ash collected multiplied by the moisture content of the chitosan sample used in the experiment.

9.2.8 Modification of PET activated carbon The chitosan gel (100 mL) was heated to 50 °C after becoming diluted with water (approximately 500 mL). Roughly 50 g of caustic alkali activated PET carbon was weighed and gently added to the diluted gel in a separate container, where it had been mechanically agitated for 24 h at 200 osc/min with a mechanical shaker. The adsorbents were cleaned using distilled–dionised water and placed in an oven to dry up after being coated with chitosan gel. After that, the chitosan-coated adsorbent was soaked for 3 h in a 0.5% (w/v) NaOH solution, and thoroughly washed with distilled water before being dried at 102 °C for 2 h, ventilated to room temperature, and preserved in a desiccator for further use [15].

9.2 Material and methods

165

9.2.9 Characterization of activated PET and chitosan modified activated PET carbon The surface morphopolgies and elemental content of activated carbons were analyzed by subjecting it to scanning electron microscope fixed with energy dispersive X-ray (SEM and EDX, Carl Zeiss). The functional groups in modified and unmodified caustic alkali activated PET carbon were also confirmed with the (SHIMADZU-FTIR-8400) Fourier Transform Infrared spectrometer (FT-IR). 9.2.9.1 Preparation of samples for SEM/EDX analysis A pair of pasty carbon strips were utilized to adhere alkali-activated polyethylene terephthalate (PETAC) and chitosan-modified activated polyethylene terephthalate carbon (PETMAC) to the aluminum receptacle stub. Samples for sorting were placed with carbon and electrically held. Also, the samples were electrically amplified using silver paint. The samples were then allowed to completely dry for approximately 3 h in the drying oven at 60 °C. Upon inserting the samples in the SEM holder, the instrument was turned on. The samples were then placed by the SEM apparatus in a chamber with a comparable high pressure, a small working area, and differential pumping of the electron optical support to maintain a low enough vacuum at the electron gun. Then imaging was achieved. The distributing energy dispersive X-ray acceleration voltage was then set to 20 kV with a working distance of 14 mm, and the detector was moved to 45 mm by rotating the knob, the samples were focused, and the X-ray spectrum was collected and saved in pgt fifile. 9.2.9.2 Preparation of samples for FT-IR analysis The FT-IR KBr technique was used to analyze the materials. On the surface of a very fine KBr plate, a globule of the liquid was placed. In order to spread the liquid out in a thin layer stuck between the plates and fastened, a second plate was placed on top of the first plate. After wiping any liquid from the plate’s edge, the sample plate was mounted into a sample holder that was connected to a recording device and then examined.

9.2.10 Trichloroacetic acid analysis TCA levels in water samples were measured by pipetting 1 ml of water samples into test tubes and adding 2.5 mL of 7.8 M potassium hydroxide solution, 5.0 ml pyridine, and 0.5 mL toluene in that order. It was shacked vigorously for 1 min and then allowed the test tubes to stand for 5 min to allow the layers to separate. Incubation was performed on the combination by inserting the test tube into a flask and placed in a water bath at 65 °C for 50 min. After the sample solution was allowed to cool to room temperature, a

166

9 Adsorption of trichloroacetic acid from drinking water

pyridine layer was formed, and 3.0 mL of the layer was pipetted into a 10 mL test tube and mixed with 0.6 mL distilled water. In an alkaline media, TCA in solution interacted with pyridine. The extinction of the red reaction product is determined using ultraviolent-visible spectrophotometer. The absorbance of the sample was measured 20 min later using a UV-vis spectrophotometer at 530 nm in a cuvette with a path length of 10 nm after the pyridine layer and distilled water were thoroughly mixed. The calibration curve was used to compute the TCA concentration in mg/L in the water sample, which matched the measured absorbance value of the water sample [16, 17]. A blank sample was determined by replacing the 1 mL of the water sample with 1 mL of distilled water following the above process.

9.2.11 Batch adsorption experiment Batch adsorption studies were conducted using Activated Polyethylene Terephthalate Carbon (PETAC) and Chitosan Modified Activated Polyethylene Terephthalate Carbon (PETMAC) as adsorbents. Each of the simulated solutions (50 mL) of trichloroacetic acid (0.5, 1.0, 1.5, 2.0, 2.5, 3.0, and 3.5 mg/L) were prepared in different conical flasks with constant shaking using an orbital shaker operated at 120 osc/min, and the following parameters such as adsorbent dosage (0.01–0.1 g), contact time (1–6 min), and pH were investigated for adsorption efficiencies [12]. After batch adsorption experiment, filtration was performed using Whatman filter paper (No. 1). The residual filtrate was prepared following photometry determination method of ref. [16, 17].

9.2.12 Water sampling for TCA analysis Water samples were taken from dam that supply Obafemi Awolowo University water treatment plant Ile-Ife, Osun State. Treated water was also collected from the distribution outlet. All the samples were collected in replicates by submerging the 40 mL amber glass bottles into ½ to 1 foot below the water surface and the taps, respectively. Filled bottles were tightly sealed with the lid after NH4Cl was added as a dechlorinating agent to convert free chlorine to monochloramine, as monochloramine has reactivity lower than free chlorine [18, 19]. Also, to avoid DBPs formation during sample transportation and storage.

9.2.13 Recovery experiment for photometry determination of TCA standard To determine the recovery rate, 50 mL of raw and treated water were spiked with 5 mL of 0.00875 mg/L (calculated from the optimum TCA concentration). The influence of matrix on the analytical technique was also explored by testing the recovery of

9.3 Results and discussion

167

known TCA concentrations in distilled water samples. The residual concentration equation for the recovery experiment is provided in equation below [20]. Recovery (%) =

Spiked sample result − unspiked sample result × 100 Known spike added concentration

(9.7)

9.2.14 Reusability potential of the adsorbent The reusability of solid adsorbent is one of the most important features described in practical applications for organic contaminants from wastewater. Batch mode was used for ten (10) cycles in the TCA adsorption/desorption investigation. Weighing 0.06 g of the adsorbents into a solution (50 mL) containing 2.5 mg/L of TCA concentration and agitating on a rotary shaker at 120 osc/min for 4 min assessed the reusability efficiency of PETAC and PETMAC. After that, filtration was done with Whatman filter paper (No 1.). A UV-visible spectrophotometer was used to examine the filtrates after necessary preparation. The residual adsorbents were desorbed in 25 ml distilled water with 10 ml toluene and H2SO4 mixture (9:1 v/v) to enhance dissociation of TCA from the adsorbent binding sites. After, it was agitated for 1 h. The mixture containing the adsorbent, toluene and H2SO4, was filtered after agitation, and the leachates were tested for TCA content. Following the same technique, the adsorbents were completely rinsed with distilled water and reused [21].

9.3 Results and discussion 9.3.1 Cocoa husk ash The cocoa husk ash powder weight and percentage ash yield were 616.4 ± 0.41 g and 8.81 ± 0.51%, respectively. While the weight before carbonization was 7,000 ± 0.070. The reduction in weight can be attributed to the cleavage of carbon bonds and loss of moisture content [22].

9.3.2 Caustic alkali from cocoa husk ash Caustic alkali filtrate of approximately 850 ml was obtained from 1,000 ml cocoa husk ash solution after filtration, and 38.4 g of caustic alkali salt was obtained after evaporation of the solution to dryness [22].

168

9 Adsorption of trichloroacetic acid from drinking water

9.3.3 Physicochemical properties of polyethylene terephthalate activated carbon (PETAC) When compared to other solid wastes, PETAC has an advantage due to its high carbon return rate [20]. Low carbon yield because it might be one of the industries’ revenue streams. The proven fact that high carbon yield of plastic waste is analytical of effective adsorption is the result of the fact that high carbon yield and low ash content give better characteristics of pore structures. A substrate with a pH of 6.9 is indicative of neutrality. Since activated carbon is often amphoteric in nature, the pH of the solution may affect whether it is positively or negatively charged. Lower pH values result in more positively charged surfaces, which facilitates the adoption of additional anionic groups due to greater electrostatic attraction between anions and the positively charged surfaces activated carbon and vice versa [20].

9.3.4 Physico-chemical properties of chitosan from periwinkle shell The chitosan yield, moisture content, and ash content were 18.4 ± 0.12, 8.30 ± 0.03, and 1.40 ± 0.05%, respectively. Chitosan yield estimates varied from 18.6% to 20% by ref. [23, 24]. Response time has a positive effect on yield and is related to the change in yield. In general, chitosan yield values drop with increasing heating temperature. This is possibly because at extreme heat, the chitosan molecules depolymerize, which leads to a reduction in the material’s molecular weight [25]. Commercial chitosan products, according to Suryawanshi (2019), have less than 10% moisture content, however [26], found that chitosan powder moisture levels ranged from 5% to 11% (w/w). The original moisture content and storage conditions, notably temperature and relative humidity, have a strong influence on how much water is absorbed [27].

9.3.5 Characterization of PET activated carbon and chitosan modified activated carbon with SEM-EDX before adsorption The adsorbents PETAC and PETMAC were characterized using SEM-EDX to determine the morphological properties of each adsorbent’s surface and its elemental composition. Figure 9.1 shows cross-sectional SEM of PETAC at ×7,000 and PETMAC at ×9,000 before adsorption. The surface texture and morphological properties of the adsorbents are readily visible in scanning electron micrographs. The elements detected in both PETAC and PETMAC before adsorption include carbon and oxygen (Table 9.1). The result shows that carbon (C) had percentage weight of 84.87 and 79.65 for PETMAC and PETAC, respectively while oxygen (O) had percentage weight of 15.13 and 20.35 for PETMAC and PETAC, respectively. Therefore, the heteroatoms on the carbon surfaces are oxygen containing functional groups [23].

9.3 Results and discussion

169

Figure 9.1: Cross-sectional scanning electron microscope image of polyethylene terephthalate activated carbon and polyethylene terephthalate modified activated carbon before adsorption. Table .: Elemental composition of activated carbon before and after adsorption. Elements

Carbon Oxygen Iron

Before adsorption weight (%)

After adsorption weight (%) Raw

After adsorption weight (%) Treated

PETAC

PETMAC

PETAC

PETMAC

PETAC

PETMAC

. . –

. . –

. . .

. . .

. . –

. . –

170

9 Adsorption of trichloroacetic acid from drinking water

9.3.6 Functional groups of polyethylene terephthalate activated carbon (PETAC) and polyethylene terephthalate modified activated carbon (PETMAC) The functional groups of the adsorbent were identified using Fourier transform infrared (FT-IR) transmission spectrometry on PETAC and PETMAC. Both PETAC and PETMAC exhibit a broad band covering the range of around 3,049.56 to 3,435.34 cm−1, which could be ascribed to the hydroxyl group (–OH) after pretreatment with caustic alkali [28, 29] as seen in Figures 9.2A and B. The existence of band 3,308.03 cm−1 is connected to amine (-NH2) group, which could be as a result of chitosan modification on the PETMAC [15], and the stretching of methylene (–CH2) and methyl CH3 result in tiny adsorption band around 2,912.61 cm−1 [30]. The prominent signal at 1,622.19 cm−1 indicates C=C aromatic skeletal stretching [29].

9.3.7 Characterization of water samples The water samples from the OAU water treatment plant were analyzed using UV-spectrophotometer to measure the concentration of TCA. The results showed that the concentration of TCA in the raw water and the conventionally treated water were 0.9900 ± 0.0224 mg/L and 2.8900 ± 0.0012 mg/L, respectively, which are above the WHO maximum permissible limits of 200 μg/L [31].

9.3.8 Recovery experiment for TCA photometric determination As shown in Table 9.2, TCA shows a recovery percentage of 89.03 ± 0.0013% for raw water, 88.34 ± 0.0001% for treated water, and 97.71 ± 0.0012% for distilled water.

9.3.9 Parametric studies on the TCA removal from aqueous solution The utilization of two adsorbents for TCA adsorption was investigated. By comparing the adsorbents (both modified and unmodified) of the same particle sizes to each other and the parameters impacting adsorption were investigated. The adsorption performances of modified and unmodified activated carbons generated from polyethylene terephthalate wastes for TCA are as presented. 9.3.9.1 Effects of contact time for TCA adsorption in simulated solution Figure 9.3 showed the results of a study of TCA adsorption utilizing PETAC and PETMAC with varying contact times (1–6 min). At the beginning of the contact time, the percentage

171

9.3 Results and discussion

Figure 9.2: Fourier transform infrared spectrum of (A) polyethylene terephthalate activated carbon and (B) polyethylene terephthalate modified activated carbon before adsorption.

Table .: Mean percentage recoveries of trichloroacetic acid spiked distilled, raw, and treated water samples. Sample ID Raw water sample Treated water sample Distilled water

Spiked concentration

Unspiked concentration

% Recovery

. ± . . ± . . ± .

. ± . . ± . . ± .

. ± . . ± . . ± .

172

9 Adsorption of trichloroacetic acid from drinking water

removal of PETAC and PETMAC increased from 24.6 to 80.3% and 95.7–98.3%, respectively. This was followed by a decreased percentage removal after optimum contact time (4 min) from 80.3 to 23.1% and 98.3 to 93.6% for PETAC and PETMAC, respectively. The initial rapid removal efficiencies could be because at the start of the adsorption process, all the active sites on the adsorbents were unoccupied [32]. After 4 min of contact time, PETAC’s percentage removal reduced sharply, while PETMAC’s percentage removal reduced somewhat from 98.3 to 93.6%. This is due to the fact that as time passes, more pollutant molecules adhere to the adsorption sites. When all pores are virtually filled at equilibrium, there is no considerable uptake of polluting species [33]. 9.3.9.2 Effect of adsorbent dosage on the adsorption of TCA in its simulated solution The result of the adsorbent dosage (0.01–0.1 g) for the removal of TCA was presented in Figure 9.4. The results showed that removal efficiencies of PETAC and PETMAC dosages increased from 44.57 to 93.14% and 73.14–97.43%, respectively, as the dosage increases from 0.01 to 0.06 g. This can be due to availability of adsorption sites with an increasing quantity of adsorbent [34]. It was also revealed that the PETMAC dosage had better removal efficiency than the PETAC because the adsorbent’s performance was improved as a result of the modification [35]. [36] reported a high adsorption efficiency of 82.3% after modification of carbon from rice husk with chitosan.

Figure 9.3: Effect of time on the percentage removal of trichloroacetic acid onto polyethylene terephthalate activated carbon and polyethylene terephthalate modified activated carbon.

9.3 Results and discussion

173

Figure 9.4: Effect of adsorbent dosage on percentage removal of trichloroacetic acid onto polyethylene terephthalate activated carbon and polyethylene terephthalate modified activated carbon.

9.3.9.3 Effect of pH on TCA adsorption in its simulated solution Figure 9.5 presents the result of TCA adsorption for PETAC and PETMAC at various pH levels. The results reveal that when the pH of the TCA solution increased from 5 to 9, the percentage removal of TCA increased, whereas at higher pH, the percentage removal of TCA decreased. The percentage removal of PETAC increased from 44.6 to 97.4% when the pH of the TCA solution was raised from 5 to 9. At pH 9, both PETAC and PETMAC exhibit an optimal percentage removal of 97.4%. This shows that the effects of pH on the adsorbents were predominant at high pH levels, due to the distribution of the carbonyl group and oxygen of the TCA [35, 37]. [38] had shown that the increase of total acidic groups on adsorbent surface could significantly decrease the adsorption capacity. 9.3.9.4 Effect of the initial concentration on TCA adsorption in a simulated solution Figure 9.6 shows that as the initial TCA concentration rises, the percentage of TCA removed by PETAC and PETMAC decreases. For PETAC and PETMAC, the percentage removal of TCA reduced from 75 to 34.6% and 91 to 34.56%, respectively. This could be because the adsorbate only had a limited number of binding sites on the adsorbents [39].

174

9 Adsorption of trichloroacetic acid from drinking water

Figure 9.5: Effect of pH on the percentage removal of trichloroacetic acid onto polyethylene terephthalate activated carbon and polyethylene terephthalate modified activated carbon.

Figure 9.6: Effect of initial concentration on the percentage removal of trichloroacetic acid onto polyethylene terephthalate activated carbon and polyethylene terephthalate modified activated carbon.

9.3.10 Adsorption of TCA from raw water and conventionally treated water An adsorption study was carried out on the incoming raw water and water that has been treated at Obafemi Awolowo University water treatment facility in Ile-Ife. The influent

175

9.3 Results and discussion

raw water was treated using PETAC and PETMAC, as well as commercial activated carbon (CAC). The water samples were treated using the optimal pH, adsorbent dosage, and contact time parameters from the modeling experiment. The adsorption efficiency of PETAC, PETMAC, and CAC for TCA adsorption is shown in Table 9.3. The PETAC shows remarkable adsorption efficiency for both raw (80.80%) and treated water (95.16%). Also, PETMAC adsorption efficiency were 90.90% and 96.13% for raw and treated water, respectively, compared to commercial activated carbon of 90.90% and 100.00% for raw and treated water, respectively. This confirms that the structure of the pores of PETAC and PETMAC is good for adsorption of TCA from potable water [40]. The results of the oneway analysis of variance showed that there was significant difference in the mean adsorption efficiencies of the three adsorbents (PETAC, PETMAC, CAC) (P < 0.01) for conventional treated water.

9.3.11 Characterization of PETAC and PETMAC with SEM-EDX after adsorption The scanning electron micrograph vividly shows the adsorbents’ surface morphological characteristics. PETAC and PETMAC after adsorption on raw water sample (Figures 9.7A and B) as well as PETAC and PETMAC on conventionally treated water (Figure 9.7C and D) are as presented. The pores of the adsorbent were clogged after being subjected to TCA adsorption (Figure 9.7) compared to clearer pores seen before adsorption (Figure 9.1). The elemental compositions of the adsorbents are shown in Table 9.4, and carbon (C), oxygen (O), and iron (Fe) were (74.96, 25.67, and 4.34%) and (75.20, 20.48, and 4.32%) in both the PETAC and PETMAC, respectively, for raw water. [38] found that the Fe–O groups in Fe3O4 acted as nucleation site for the enhanced adsorption of cephalexin by magnetic PET-based activated carbon. Oxygen-containing functional groups are the major adsorption sites [39].

Table .: Adsorption efficiency of polyethylene terephthalate activated carbon, polyethylene terephthalate modified activated carbon, and commercial activated carbon on raw and treated water samples using the optimum simulation conditions. Contaminant

TCA

Adsorbents

PETAC PETMAC CAC

Adsorption efficiency (%) Raw water

Treated water

. ± . . ± . . ± .

. ± . . ± .  ± .

176

9 Adsorption of trichloroacetic acid from drinking water

Table .: Characteristic parameters of isotherm constants for trichloroacetic acid onto polyethylene terephthalate activated carbon and polyethylene terephthalate modified activated carbon. Isotherm models Langmuir qm (mg/g) KL (L/g) R Freundlich Kf (mg/g) n R Tempkin B bT R Dubinin Radushkevich Β R

TCA onto PETAC

TCA onto PETMAC

. . .

. . .

. . .

. . .

. , .

. . .

−. .

−. .

Figure 9.7: Scanning electron microscope image of (a) polyethylene terephthalate activated carbon after adsorption on raw water sample (×7,000), (b) polyethylene terephthalate modified activated carbon after adsorption on raw water sample (×9,000), (c) polyethylene terephthalate activated carbon after adsorption on raw water sample (×7,000), and (d) polyethylene terephthalate activated carbon after adsorption on treated water sample (×9,000).

9.3 Results and discussion

177

9.3.12 Adsorption equilibrium isotherm The characteristic parameters for the isotherm constants of PETAC and PETMAC are shown in Table 9.4 below. The Langmuir model has a R2 of 0.94 for PETAC, which is significantly higher than 0.45 seen for PETMAC. The Freundlich isotherm also gave R2 of 0.82 for PETAC compared to 0.14 witnessed by PETMAC. The Tempkin isotherm takes into account interactions between adsorbents and TCA and assumes that adsorption free energy is proportional to surface coverage [40]. The Tempkin isotherm does not adequately describe TCA adsorption on adsorbents, but PETMAC showed a better correlation coefficient of 0.6421 compared to lower value of 0.5452 gotten for PETAC. Also, Dubinin Radushkevich adsorption isotherm gave a better R2 of 0.5013 for PETAC while PETMAC gave a significantly lower value of 0.0042. Overall, the TCA adsorption onto PETAC is favorable using Langmuir isotherm due to the closeness of R2 to 1 [12, 41], as the surface adsorption mechanisms may be responsible for TCA adsorption on the PETAC, and Tempkin isotherm best fit TCA adsorption onto PETMAC as a result of R2 value.

9.3.13 Reusability potential of the adsorbent The results showed that the PETAC and PETMAC are easily recyclable and reusable, with little loss in adsorption efficiency, as the adsorption efficiency remained higher (78.4%) even after the fifth batch of adsorption–desorption cycles (Figure 9.8). Adsorption efficiency (94.4–90.4%) across consecutive adsorptions from 1 to 5 cycles and lower

Figure 9.8: The reusability efficiency of polyethylene terephthalate activated carbon and polyethylene terephthalate modified activated carbon on trichloroacetic acids adsorption at 2.5 mg/L initial concentration.

178

9 Adsorption of trichloroacetic acid from drinking water

adsorption efficiency (around 60–40%) from 6 to 10 cycles of adsorption were observed for PETAC. PETMAC gave higher adsorption efficiency of 100% and maintained as high as 82.4% up to fifth desorption–adsorption cycle and decreased from 62.4 to 40.4% for 6 to 10 cycles. [21] reported a similar result when employing magnetic graphene oxide modified zeolite for effective methylene blue removal from aqueous solution. After five cycles, the clearance effectiveness of methylene blue dropped from 89.59 to 76.86%, according to these authors.

9.4 Conclusions PETMAC and PETAC display well-developed carbon content and pores through large surface area; this offers ample space for adsorption and consequently efficient for an adsorbate (TCA) uptake. This research provides a platform for the application of PET and periwinkle shell–based solid waste for the production of adsorbents that are efficient for the removal of TCA in drinking water supply.

References 1. Hossain MZ. Water: the most precious resource of our life. Glob J Adv Res 2015;2:1436–45. 2. Gupta S, Deshpande R. Water for India in 2050: first-order assessment of available options. Curr Sci 2004; 86:1216–24. 3. World Health Organisation, WHO. Hardness in drinking water. In: Guidelines for drinking water quality, 4th ed.. World Health Organization; 2011:564 p. 4. Okoya AA, Adenekan A, Ajadi FA, Ayodele SO. Assessment of chitosan coated Aspergillusniger as biosorbent for dye removal and its impact on the heavy metal and physicochemical parameters of textile wastewater. Afr J Environ Sci Technol 2020;14:281–9. 5. Mezzanotte V, Antonelli M, Citterio S, Nurizzo C. Wastewater disinfection alternatives: chlorine, ozone, peracetic acid, and UV light. Water Environ Res 2007;79:2373–9. 6. Mitch WA, Gerecke AC, Sedlak DL. A N-nitrosodimethylamine (NDMA) precursor analysis for chlorination of water and wastewater. Water Res 2003;37:3733–41. 7. Blount BC, Backer LC, Aylward LL, Hays SM, Lakind JS. Human exposure assessment for DBPs: factors influencing blood trihalomethane levels. In: Nriagu JO, editors. Encyclopedia of environmental health. Amsterdam, The Netherlands: Elsevier Science; 2011:100–7 pp. 8. Dickenson ER, Summers RS, Croué J, Gallard H. Haloacetic acid and trihalomethane formation from the chlorination and bromination of aliphatic β-dicarbonyl acid model compounds. Environ Sci Technol 2008; 42:3226–33. 9. Tao L, Li Y, Kramer PM, Wang W, Pereira MA. Hypomethylation of DNA and the insulin-like growth factor-II gene in dichloroacetic and trichloroacetic acid-promoted mouse liver tumors. Toxicology 2004;196:127–36. 10. Manisalidis L, Stavropoulou E, Stavropoulos A, Bezirtzoglou E. Environmental and health impacts of air pollution: a review. Front Public Health 2020;8:14. 11. Chen Z, Wei W, Ni B, Chen H. Plastic wastes derived carbon materials for green energy and sustainable environmental applications. Environ Funct Mater 2022;1:34–48.

References

179

12. Okoya A, Adeodun S. Adsorption efficiency of chitosan-coated carbon for industrial effluent treatment. Int J Sci Eng Res 2017;8:2229–5518. 13. Ding P, Huang K, Li G, Zeng W. Mechanisms and kinetics of chelating reaction between novel chitosan derivatives and Zn (II). J Hazard Mater 2007;146:58–64. 14. Okoya AA, Akinola OE, Adegbaju OS, Akinyele AB, Amuda OS. Quantification and removal of trichloromethane in clorinated water using coconut shell activated carbon. Ghana J Sci 2020;61:1–14. 15. Okoya AA, Akinyele AB, Amuda OS, Ofoezie IE. Chitosan-grafted carbon for the sequestration of heavy metals in aqueous solution. Am Chem Sci J 2016;11:1–14. 16. Tanaka S, Ikeda M. A method for determination of trichloroethanol and trichloroacetic acid in urine. Occup Environ Med 1968;25:214–9. 17. Moss M, Rylance H. The Fujiwara reaction: some observations on the mechanism. Nature 1966;210:945–6. 18. Ademoroti C. Standard methods for water and effluents analysis. Ibadan: Foludex Press Ltd.; 1996. 19. Bagalwa M. Environmental impact of land use change on water quality of inflowing tributaries of Lake Kivu. In: Proceedings of the 11th world lakes conference – proceedings; 2006. 20. Okoya AA, Olalekan O, Akinyele AB, Ochor NO. Efficacy of Moringa oleifera seed husk as adsorptive agent for trihalomethanes from a water treatment plant in southwestern, Nigeria. J Chem 2020;2020:1–11. 21. Huang C, Li H, Chen C. Effect of surface acidic oxides of activated carbon on adsorption of ammonia. J Hazard Mater 2008;159:523–7. 22. Hatta ZM. Chemical composition and morphological of cocoa pod husk and cassava peels for pulp and paper production. Aust J Basic Appl Sci 2013;7:406–11. 23. Suryawanshi N, Jujjavarapu SE, Ayothiraman S. Marine shell industrial wastes- an abundant source of chitin and its derivatives: constituents, pretreatment, fermentation, and pleiotropic applications-a revisit. Int J Environ Sci Technol 2019;16:3877–98. 24. Casadidio C, Peregrina DV, Gigliobianco MR, Deng S, Censi R, Di Martino P. Chitin and chitosan: characteristics, eco-friendly processes, and applications in cosmetic science. Mar Drugs 2019;17:369. 25. Patria A. Production and characterization of chitosan from shrimp shells waste. Int J Bioflux Soc 2013;6: 339–44. 26. Rege PR, Shukla DJ, Block LH. Chitosans as tableting excipients for modified release delivery systems. Int J Pharm 1999;181:49–60. 27. Hossain MS, Igbal A. Production and characterization of Chitosan from shrimp waste. J Bangladesh Agric Univ 2014;12:153–60. 28. Zhang D, Lin X, Zhang Q, Ren X, Yu W, Cai H. Catalytic pyrolysis of wood-plastic composite waste over activated carbon catalyst for aromatics production: effect of preparation process of activated carbon. Energy 2020;212:118983. 29. RehmanPark AM, Park S. Current progress on the surface chemical modification of carbonaceous materials. Coatings 2019;9:103. 30. Ahangar FA, Rashid U, Ahmad J, Tsubota T, Alsalme A. Conversion of waste polyethylene terephthalate (Pet) polymer into activated carbon and its feasibility to produce green fuel. Polymers 2021;13:3952. 31. Pan S, An W, Li H, Su M, Zhang J, Yang M. Cancer risk assessment on trihalomethanes and haloacetic acids in drinking water of China using disability-adjusted life years. J Hazard Mater 2014;280:288–94. 32. Farombi AG, Amuda OS, Alade AO, Okoya AA, Adebisi SA. Central composition design for optimization of preparation conditions and characterization of hydroxyapatite produced from catfish bones. Beni-Suef Univ J Basic Appl Sci 2018;7:474–80. 33. Rashid RA, Jawad AH, Ishak AZM, Kasim NN. KOH-activated carbon developed from biomass waste: adsorption equilibrium, kinetic and thermodynamic studies for methylene blue uptake. Desalination Water Treat 2016;57:27226–36. 34. Vakili M, Deng S, Cagnetta G, Wang W, Meng P, Liu D, et al. Regeneration of chitosan-based adsorbents used in heavy metal adsorption: a review. Separ Purif Technol 2019;224:373–87.

180

9 Adsorption of trichloroacetic acid from drinking water

35. Leng L, Xu S, Liu R, Yu T, Zhuo X, Leng S, et al. Nitrogen containing functional groups of biochar: an overview. Bioresour Technol 2020;298:122286. 36. Ighalo JO, Adeniyi AG. A mini-review of the morphological properties of biosorbents derived from plant leaves. SN Appl Sci 2020;2:1–16. 37. Plaza MG, Arenillas A, Rubiera F, Pis J. CO2 capture by adsorption with nitrogen enriched carbons. Fuel 2007;86:2204–12. 38. Rai P, Singh KP. Valorization of poly (ethylene) terephthalate (PET) waste into magnetic carbon for adsorption of antibiotic from water: characterization and application. J Environ Manag 2018;207:249–61. 39. Singh E, Kumar A, Mishra R, You S, Singh L, Kumar S, et al. Pyrolysis of waste biomass and plastics for production of biochar and its use for removal of heavy metals from aqueous solution. Bioresour Technol 2021;320:124278. 40. El-Sayed Y, Bandosz TJ. Adsorption of valeric acid from aqueous solution onto activated carbons: role of surface basic sites. J Colloid Interface Sci 2004;273:64–72. 41. Bhatt JP, Pandit MKA. Macro-invertebrate based new biotic index to monitor river water quality. Curr Sci 2010;99:196–203.

Saheed O. Sanni*, Samson O. Akpotu, Agnes Pholosi and Vusumzi E. Pakade

10 Comparative study of the photocatalytic degradation of tetracycline under visible light irradiation using Bi24O31Br11-anchored carbonaceous and silicates catalyst support Abstract: This study compared two hydrothermally synthesized heterojunctions composites, Bi24O31Br10 – carbonaceous (activated carbon from zinc chloride [ACZ], phosphoric acid [ACH], carbonized material [CM]), and Bi24O31Br10 – silicates (SBA-15 and MCM-41), with nanosheets structure. The photocatalytic degradation of tetracycline (TC) was used to evaluate the synergistic influence of the catalyst supports for the corresponding heterojunction composites. The X-Ray diffractometry (XRD), Fourier transform infrared spectroscopy and scanning electron microscopy (SEM) confirmed the synthesis of the Bi24O31Br10 (BOB) – composites. After 120 min of visible LED light photocatalytic reactions, the degradation trend in removal efficiency of TC was BOB-ACZ > BOB > ACH > BOB-CM > BOB-MCM-41 > BOB-SBA-15 > BOB. The study reveals that Bi24O31Br11 – carbonaceous composite exhibits much better degradation efficiency than Bi24O31Br11 – silicates. Crucially, the synergistic surface interaction of ACZ with BOB, and the efficient separation of photogenerated charge carriers, from the SEM, XRD analysis, and photocurrent response, confirmed the photocatalytic enhancement of the heterojunction formation of the BOB-ACZ composite. This study further provides convincing insights on the superiority of carbonaceous nanomaterial to silica materials as efficient catalyst support in catalytic applications. Keywords: Bi24O31Br11-carbonaceous; Bi24O31Br11 silicates; nanosheet structure; tetracycline degradation.

*Corresponding author: Saheed O. Sanni, Biosorption and Water Treatment Research Laboratory, Department of Biotechnology and Chemistry, Faculty of Applied and Computer Sciences, Vaal University of Technology, Private Bag X021, Vanderbijlpark 1900, South Africa, E-mail: [email protected] Samson O. Akpotu, Agnes Pholosi and Vusumzi E. Pakade, Biosorption and Water Treatment Research Laboratory, Department of Biotechnology and Chemistry, Faculty of Applied and Computer Sciences, Vaal University of Technology, Private Bag X021, Vanderbijlpark 1900, South Africa As per De Gruyter’s policy this article has previously been published in the journal Physical Sciences Reviews. Please cite as: S. O. Sanni, S. O. Akpotu, A. Pholosi and V. E. Pakade “Comparative study of the photocatalytic degradation of tetracycline under visible light irradiation using Bi24O31Br11-anchored carbonaceous and silicates catalyst support” Physical Sciences Reviews [Online] 2023. DOI: 10.1515/psr-2022-0326 | https://doi.org/10.1515/9783111071428-010

182

10 Degradation of Tetracycline by Bi24O31Br11 catalyst support

10.1 Introduction The treatment of wastewater having pharmaceutical antibiotics is a very problematic situation worldwide, thus causing enormous strain on available fresh water, along with health challenges to the general populace [1, 2]. Tetracycline antibiotics have been exploited in combating bacterial and microbial infection in humans, as well as in animal husbandry, and aquaculture [3]. However, the TC antibiotics gradually accumulate in the environment as a result of low metabolism, which further cause antibiotics resistance genes proliferation, and ultimately will cause serious harm to human health [4]. Therefore, it is required to find an efficient and cost-effective physicochemical method to decimate the TC antibiotic complex structure, and remove its biotoxicity. Recently, photocatalytic degradation under advanced oxidation processes (AOPs) has been recognized as a promising approach for the efficient removal of TC antibiotics due to its cost-effectiveness, environmental friendly, and mineralization of this recalcitrant compound [5, 6]. So far, the conventional photocatalysts, TiO2, and ZnO, have been successfully utilized in their potential applications in the degradation of recalcitrant pollutants [7, 8]. However, their practical activities are limited by the poor utilization of solar light, in catalytic degradation. As such, the development of narrow band-gap semiconductors comprising of Cu2O, BiOX (X = Cl, Br, I), AgBr, C3N4 are been receiving attention in visible light driven photocatalytic applications [9–12]. The bismuth oxyhalides (BiOX; X = Cl, Br, I) possess distinct photocatalytic attributes because of their tetragonal structure and internal electrostatic field perpendicular to each layer, which can effectively expedite the separation of photogenerated charge carriers [13]. The BiOBr amongst BiOX catalysts are efficient due to their attributes in modulating their conduction and valence band, and thus enhance the absorptivity in the visible light region [14]. Host of studies have been conducted in enhancing the photocatalytic performance of BiOBr catalyst, such as crystal plane method, elements doping, enrichment strategy, and heterojunction formation [15, 16]. Lately, the rich bismuth oxybromide (Bi24O31Br10) with a lower Br/O ratio, along with a narrower band gap energy [17, 18], furthermore, present better degradation attributes in contrast with BiOBr catalyst. However, the performance of Bi24O31Br10 catalysts is still plagued by irregular morphologies, fast recombination of the photo-generated electron–hole pairs which are closely associated with the reactive oxidative species generated for the photocatalytic degradation efficiency [19]. Subsequently, it requires further modification to further develop its photocatalytic efficiency, to meet the need of wastewater treatment applications. Heterojunction construction with catalyst support with high surface area, and good conductivity properties has been demonstrated to be one of the several methods required for photocatalyst uniform dispersion, and enhancing charge carrier separation efficiency [16]. The exploration of carbonaceous, and silicates nanomaterials in heterojunction composites with photocatalyst for redox reactions, comprising of dye, organic pollutants

10.2 Materials and methods

183

degradation, photocatalytic water splitting, and CO2 photoreduction [12, 20, 21], have been carried out. These composites exhibited high activities, thus attributed to their excellent features comprising of visible-light transmittance, significant adsorptive properties, and charge carrier separation which makes photocatalytic degradation more efficient [22–24]. However, the comparative studies of these catalyst support niche, synergistic interaction with Bi24O31Br10 (BOB) has not been carried out before. Studying the preparation of BOB-carbonaceous/silicate photocatalyst composites is crucial to reduce the agglomeration, charge carrier pair recombination, and enhance the BOB catalyst’s photocatalytic activity as a result. This study thus compared BOB-carbonaceous (activated carbon, and biochar), and BOB-silicates (MCM-41, and SBA-15) composites constructed through a hydrothermal process to efficiently remove the TC from wastewater. The photocatalyst composites structure, morphology, optical properties, charge carrier separation efficiency, and photocatalytic activities on TC removal were analyzed by a series of characterization approaches.

10.2 Materials and methods 10.2.1 Preparation of activated carbon from zinc chloride, and phosphoric acid (ACZ, and ACH) from carbonized material (CM) In accordance with previously reported procedures [25], the ACZ, and ACK were carried out through a two-pyrolysis step and activation. Firstly, 10 g of PC biomass was subjected to pyrolysis inside a Carbolite Gero, furnace at 600 °C for 2 h, under N2 flow (50 mL/min) and a 10 °C/min heating rate. For carbonized material (CM) collection, the furnace was shut down after the pyrolysis, and cooled with N2 flow until it reached 200 °C. Consequently, 5 g of CM were combined with 25 mL each of a selected chemical activating agent (2 M ZnCl2, 99%, Merck) and phosphoric acid (2 M H3PO4, 99% Sigma Aldrich) solution under ambient conditions, continuously stirred for 24 h, and then heated at 80 °C for 12 h. Under nitrogen atmosphere flow (50 mL/min), the heated sample was further pyrolyzed for 16 min at a microwave power of 400 W [25]. The samples were then rinsed with hot distilled water and 0.1 M HCl (99% Sigma Aldrich) until a stable pH of 7 was achieved in the filtrate. The samples were then dried overnight at 105 °C, sealed, and stored in an airtight container for subsequent analysis.

10.2.2 Preparation of MCM-41 and SBA-15 The MCM-41 mesoporous silica was prepared according to the published protocol from [26]. 1.99 g of HTAB was dissolved in 120 mL of double-distilled deionized water at room temperature. After complete dissolution, 8 mL of aqueous ammonia water (32% in water, Merck) was added to the above solution, under continuous stirring. Then followed by the

184

10 Degradation of Tetracycline by Bi24O31Br11 catalyst support

addition of 10 mL of tetraethyl orthosilicate (TEOS, 99%, Aldrich) to the solution under vigorous stirring (300 rpm, 1 h). Filtration of the material was done afterward, washed with double-distilled deionized water, and allowed to dry under static air at 110 °C for 24 h. After 5 h of calcination, of the dry material at 550 °C, the MCM-41-mesoporous material was obtained. For the SBA-15 material, 2 g of Pluronic P123 template dissolved in 100 ml doubledistilled deionized water and 60 ml of 2 M hydrochloric acid solution and stirred. Simultaneously, 4.25 g TEOS was slowly added to the stirred solution form a homogeneous solution, and then stirred continually at 40 °C for 24 h. The solution was hydrothermally treated inside a Teflon-coated autoclave, at 100 °C for 24 h. The treated solids were washed several times with the help of double-distilled deionized water, dried at room temperature, and then calcined at 550 °C for 5 h to remove the triblock copolymer.

10.2.3 Preparation of BOB photocatalysts a total of 4 mmol of HTAB was dissolved into 20 mL of ethyl alcohol as solution A, while 4 mmol of Bi(NO3)3. 5H2O was also added to 30 mL ethylene glycol (EG) along with 1 g of as-prepared catalyst support (CM, ACH, ACZ, MCM-41, and SBA-15), as solution B. Then solution A was slowly added to solution B, afterward stirred for 30 min, and then adjustment of pH through the addition of 6 ml of 2 M NaOH solution. This mixture was further stirred for 30 min, then transferred into Teflon-coated autoclave and set at 160 °C for 16 h. After solvothermal reaction, the precipitate was collected by washing and centrifugation using equimolar of ethanol and distilled water several times, dried at 80 °C overnight and calcined at 300 °C for 2 h. The calcined material is represented as BOB-X (X indicated the catalyst supports). The reaction process described above was utilized for synthesis of Bi24O5Br2 (BOB) without the addition of catalyst support.

10.2.4 Materials characterization The BOB and BOB-X photocatalyst composites were measured by X-ray diffraction measurement (Shimadzu X-ray 700), Fourier transform infrared spectroscopy (Perkin Elmer spectrum 400), UV-Vis-diffuse reflectance spectroscopy (Maya 2000), and Biologic SP 240 potentiostat workstation, for their morphological attributes, structural characteristics, nature of functional groups, optical attributes and charge carrier recombination rate. The photodegradation of tetracyline (TC) antibiotic on BOB-X (X-CM, ACZ, ACH, MCM-41, and SBA-16) and BOB photocatalysts was carried out under a 36 W LED lamp’s visible light irradiation. The following were the main details: in order to achieve adsorption–desorption equilibrium 60 mg of the as-prepared photocatalyst were added to the solution in a 500 mL beaker (150 mL, 10 mg/L TC) and stirred for 60 min under dark

10.3 Result and discussion

185

condition. To remove the photocatalyst powders at predetermined intervals during the photocatalytic reaction, 3 mL of the reaction suspension was collected, centrifuged and passed through a 0.45 μm filter membrane. The residual TC concentration were determined at the characteristic absorption peak of 376 nm, using UV–vis spectrophotometer (Shimadzu UV-2450).

10.3 Result and discussion 10.3.1 Structural, morphological, and optical characteristics Figure 10.1 presented the XRD patterns of the sample prepared, the characteristic diffraction peak of carbon for CM, ACH, and ACZ appear at 27.5° which corresponds to crystal planes (002) [25], whilst the characteristic peaks for MCM-41, and SBA-15 were not visible in the composites. However, the characteristics peaks of BOB are present at 24.29°, 30.76°, and 31.85°, which correspond to the (111), (213), and (017) to the monoclinic phase Bi24O31Br10 standard comparison card (JCPDS 75–0888) [14]. Compared with other composites, BOB-ACZ diffraction peaks shifted left by 0.3°, which could be a result of a strong interface interaction between BOB, and ACZ catalyst support. The SEM images of the as-synthesized BOB catalyst and in tandem with the support material are presented in Figure 10.2. The calcined BOB catalyst (Figure 10.2a) retained its cauliflower-like irregular nanosheet morphology, which are agglomerated in structure, and similar with other studies [27]. However, upon dispersion unto the carbonaceous

Figure 10.1: XRD patterns of BOB, BOB-CM, BOB-SBA-15, BOB-MCM-41, BOB-ACH, and BOB-ACZ.

186

10 Degradation of Tetracycline by Bi24O31Br11 catalyst support

Figure 10.2: SEM images of (a) BOB catalyst, (b) BOB-CM, (c) BOB-ACH, (d) BOB-ACZ, (e) BOB-MCM-41, and (f) BOB-SBA-15.

materials (CM, ACH, and ACZ), the composites exhibited well-uniform nanosheet morphologies, and were less aggregated as presented in Figure 10.2b–d. SEM images of the composite BOB – MCM-41 and BOB-SBA-15 in Figure 10.2e and f are quite similar to that of BOB composites from carbonaceous nanomaterials, thus revealing the good distribution of nanosheet morphologies, and less agglomerated with BOB – MCM-41 (Figure 10.2e). However, the nanosheet flakes are much smaller with the BOB-ACZ composite that thus aligns very well with XRD peak shift observation in Figure 10.1, thus increasing the active sites for enhanced degradation activities. In addition, the hierarchical pores between ACZ catalyst support enable more light reflection activities, which in turn improves the utilization of photons, and thus enables the overall composites to produce photoelectron–hole pairs and improve the photocatalytic activities as further presented in Figure 10.6a. The FT-IR spectra of BOB-carbonaceous, BOB-silicates composites and BOB are shown in Figure 10.3a and b. The O–H vibrations were observed at around 3392 cm−1 for the BOB-carbonaceous composites, and broader with BOB-ACZ composite. Also, the carbonaceous features, comprising an asymmetrical C–H stretching at 2857 cm−1 and C–O stretching at 1084 cm−1 can be detected [28, 29]. Of note, that BOB-ACZ composite was right shifted as compared to BOB, and other BOB-carbonaceous composites, suggesting as strong synergistic interface interactions. The vibration modes of Bi–O bonds appeared at several bands between 1611 and 524 cm−1 [30]. For the BOB-silicate composites as presented in Figure 10.3b, the main band of silicate material is observed at the band of 1084 and 807 cm−1, which were assigned as Si–O–Si asymmetric and symmetric vibrations, respectively [26]. The BOB-MCM-41 aforementioned bands, are more intense as compared to BOB-SBA-15 composite, evidencing good synergistic interaction of MCM-41 with BOB

10.3 Result and discussion

187

Figure 10.3: FTIR spectrum of (a) BOB catalyst, BOB-CM, BOB-ACH, and BOB-ACZ, and (b) BOB catalyst, BOB-SBA-15, and BOB-MCM-41.

catalyst, and thus correlates the SEM image observations in Figure 10.2e. The characteristics peak of Bi–O bonds at 1611, and 524 cm−1 are also present in the as-prepared BOB-silicates composite. Figure 10.4a reveals that BOB catalyst has an optical absorption edge on 467 nm which does respond towards visible light irradiation. However, with the incorporation of carbonaceous support, their absorption edge slightly red-shifted to 470, 475, and 480 nm, respectively for BOB-CM, BOB-ACH, and BOB-ACZ. Figure 10.4b shows that the absorption edge is stronger and wider in the visible region with the silicate materials, between 490 and 545 nm as compared to the BOB catalyst, and also the BOB-carbonaceous composites. This intense visible light response, with the BOB-silicate composites could be attributed to the synergistic interaction between both photocatalyst and catalyst support, thus aiding the photoreduction activities through production of more photo-generated charge pairs [31].

Figure 10.4: UV-DRS spectrum of (a) BOB catalyst, BOB-CM, BOB-ACH, and BOB-ACZ; (b) BOB catalyst, BOB-SB-15, and BOB-MCM-41.

188

10 Degradation of Tetracycline by Bi24O31Br11 catalyst support

10.3.2 Charge transfer properties Figure 10.5 depicts the electrochemical impedance spectroscopy (EIS) of pure BOB and corresponding BOB composites. It is clearly shown that BOB-carbonaceous composites possessed the smallest arc radius than the BOB-silicate composites, and pure BOB, which further demonstrates that they present effective separation of photogenerated charge carriers with least amount of resistance to charge transfer [32]. Crucial, the BOB-ACZ composite has the highest efficiency in charge carrier separation at the interface between BOB and ACZ. The result of EIS were consistent with the SEM, FTIR, and XRD analysis, which further validates that the introduction of ACZ carbon material is an effective way to improve photocatalytic performance of BOB catalyst.

10.3.3 Photocatalytic activity Figure 10.6a shows the activities of BOB catalyst and other BOB composites on the photocatalytic degradation of TC antibiotic under visible LED light irradiation at room temperature. The extent to which TC has degraded following 120 min of exposure to visible light is 59.28%, 77.26%, 78.73%, 83.20%, 88.29%, and 91.63% for BOB, BOB-SBA-15, BOB-MCM-41, BOB-CM, BOB-ACH, and BOB-ACZ, respectively (Figure 10.6a). As a result of the synergistic surface interaction of ACZ with BOB, and the high separation efficiency of photogenerated charge carriers, the photocatalytic degradation of TC is able to proceed faster with the BOB-ACZ composites. The rate constant and the corresponding

Figure 10.5: Electrochemical impedance spectroscopy of BOB catalyst, and BOB with catalyst support materials.

10.4 Conclusions

189

Figure 10.6: Photodegradation efficiencies of TC over the as-synthesized photocatalysts. (a) Photocatalytic degradation of TC using BOB, BOB-CM, BOB-ACH, BOB-ACZ, BOB-MCM-41, and BOB-SB-15; (b) the corresponding degradation kinetics, and (c) the time-dependent absorption spectra of TC solution for BOB-ACZ composite.

degradation kinetics of BOB, and corresponding composites are compared in Figure 10.5b. The first-order equation fits well for the photocatalytic degradation reaction on the prepared samples. Based on the Langmuir–Hinshelwood (L–H) model, the BOB-ACZ kinetic constant (k) is 0.0069 min−1 [33], which is higher than BOB (0.0025 min−1), and other composites in this study. In line with aforementioned analysis, the addition of carbonaceous ACZ significantly enhances the catalytic properties of BOB. Moreover, the UV–Vis spectral absorption changes of TC solution degraded effectively over BOB-ACZ nanosheets as presented in Figure 10.6c. The characteristic absorption wavelength of TC solution at 376 nm thus decreased intermittently with extension of the exposure time. The observation further evidence that the TC molecules are effectively degraded after 120 min visible-light irradiation.

10.4 Conclusions In summary, nanosheet Bi24O31Br10 (BOB) composite photocatalysts dispersed on carbonaceous and silicates catalyst support were synthesized at room temperature via a hydrothermal approach. The TC degradation activities for the BOB photocatalyst species trend was as follow: BOB-ACZ > BOB-ACH > BOB-CM > BOB-MCM-41 > BOB-SBA-15 > BOB. The results indicated that the BOB-carbonaceous composites (BOB-ACZ, BOB-ACH and, BOB-CM) outperformed the BOB silicate composites in terms of photocatalytic performance for TC removal under visible LED light system. The reaction rate constant (k) of the BOB-ACZ composite (0.0069 min−1) was nearly 2.7 times higher than that of BOB (0.0025 min−1). The superior photocatalytic properties of BOB-ACZ from the carbonaceous composites were attributed to reduced agglomeration of BOB with intimate interface interaction, and thus achieving an effective lower charge carrier recombination probability. Through heterojunction interaction with carbonaceous supports, this work suggests the possibility of enhancing BOB’s photocatalytic degradation capacity for future remediation purposes.

190

10 Degradation of Tetracycline by Bi24O31Br11 catalyst support

References 1. Akpotu SO, Diagboya PN, Lawal IA, Sanni SO, Pholosi A, Peleyeju MG, et al. Designer composite of montmorillonite-reduced graphene oxide-PEG polymer for water treatment: enrofloxacin sequestration and cost analysis. Chem Eng J 2022;453:139771. 2. Gordanshekan A, Arabian S, Solaimany Nazar AR, Farhadian M, Tangestaninejad S. A comprehensive comparison of green Bi2WO6/g-C3N4 and Bi2WO6/TiO2 S-scheme heterojunctions for photocatalytic adsorption/degradation of Cefixime: artificial neural network, degradation pathway, and toxicity estimation. Chem Eng J 2023;451:139067. 3. Sanni SO, Brink HG, Viljoen EL. Influence of synthesis approach on controlled microstructures and photocatalytic properties of Ag/AgBr-activated carbon composites on visible light degradation of tetracycline. Catalysts 2021;11:1396. 4. Chu-Ya Wang XZ, Qiu HB, Huang GX, Han-Qing Y, Yu HQ. Bi24O31Br10 nanosheets with controllable thickness for visible–light–driven catalytic degradation of tetracycline. Appl Catal B Environ 2017;205:615–23. 5. Sanni S, Viljoen E, Ofomaja A. Accelerated electron transport and improved photocatalytic activity of Ag/ AgBr under visible light irradiation based on conductive carbon derived biomass. Catal Lett 2019;149: 3027–40. 6. Ma Y, Liu Y. Preparation of graphene–ZnO composite with enhanced photocatalytic performance. Carbon Lett 2022;32:1–11. 7. Onkani SP, Diagboya PN, Mtunzi FM, Klink MJ, Olu-Owolabi BI, Pakade V. Comparative study of the photocatalytic degradation of 2–chlorophenol under UV irradiation using pristine and Ag-doped species of TiO2, ZnO and ZnS photocatalysts. J Environ Manag 2020;260:110145. 8. Sanni SO, Idemudia OG. Synthesis and characterization of rhodium doped on TiO2/HCP for enhanced photocatalytic performance on pentachlorophenol. J Nanomater 2014:287493. 9. Koiki BA, Orimolade BO, Peleyeju GM, Arotiba OA. Rapid and template-free synthesis of copper(I) oxidegraphitic carbon nitride heterojunction for photocatalytic degradation of orange II dye in water. Solid State Sci 2019;97:105994. 10. Mao D, Ding S, Meng L, Dai Y, Sun C, Yang S, et al. One-pot microemulsion-mediated synthesis of Bi-rich Bi4O5Br2 with controllable morphologies and excellent visible-light photocatalytic removal of pollutants. Appl Catal B Environ 2017;207:153–65. 11. Moundzounga T, Peleyeju MG, Sanni SO, Klink MJ, Oseghe E, Viljoen E, et al. A nanocomposite of graphitic carbon nitride and carbon dots as a platform for sensitive voltammetric determination of 2-chlorophenol in water. Int J Electrochem Sci 2021;16:10560. 12. Zhang T, Yin Q, Zhang M, Zhang S, Shao Y, Fang L, et al. Enhanced photocatalytic activity of AgBr photocatalyst via constructing heterogeneous junctions with reduced graphene. J Mater Sci Mater Electron 2021;2:5331–42. 13. Wen Wu XM, Li D, Xuan Y, Meng S, Chen M. Assembly of WO3 nanosheets/Bi24O31Br10 nanosheets composites with superior photocatalytic activity for degradation of tetracycline hydrochloride. J Mater Sci 2018;53:15804–16. 14. Cao W, Chen C, Zhou H, Yang J, Jiang C, Wang Y. In situ preparation of 3D flower sphere Bi4O5Br2/Bi24O31Br10 heterojunctions by calcination for enhanced antibiotic degradation. Colloids Surf A Physicochem Eng Asp 2021;629:127373. 15. Xiao B, Zhao W, Xiang Y, Wu X, Zhang G. Vis-NIR responsive Bi24O31Br10 and corresponding composite with up-conversion phosphor towards efficient photocatalytic oxidation. Appl Surf Sci 2019;489:210–9. 16. Zhang M, Sun X, Wang C, Wang Y, Tan Z, Li J, et al. Photocatalytic degradation of rhodamine B using Bi4O5Br2-doped ZSM-5. Mater Chem Phys 2022;78:125697. 17. Wang J, Li Y. Synthesis of single-crystalline nanobelts of ternary bismuth oxide bromide with different compositions. Chem Commun 2018:2320–1.

References

191

18. Zhang X, Zhang L. Electronic and band structure tuning of ternary semiconductor photocatalysts by self doping. The case of BiOI. J Phys Chem C 2010;114:18198–206. 19. Chen Y, Jiang Z, Xu L, Liu C, Cheng Y, Zou Y, et al. Composite magnetic photocatalyst Bi24O31Br10/NiFe2O4: hydrothermal preparation, characterization and photocatalytic mechanism. Mater Sci Semicond Process 2021;126:105669. 20. Oseghe EO, Akpotu SO, Mombeshora ET, Oladipo AO, Ombaka LM, Maria BB, et al. Multi-dimensional applications of graphitic carbon nitride nanomaterials – a review. J Mol Liq 2021;344:117820. 21. Alfred MO, Moodley R, Oladoja NA, Omorogie MO, Adeyemi OG, Olorunnisola D, et al. Sunlight-active Cu/ Fe@ZnWO4-kaolinite composites for degradation of acetaminophen, ampicillin and sulfamethoxazole in water. Ceram Int 2021;47:19220–33. 22. Liu X, Liu J, Chen J, Zhong F. Degradation of benzene, toluene, and xylene with high gaseous hourly space velocity by double dielectric barrier discharge combined with Mn3O4/activated carbon fibers. J Phys D Appl Phys 2021;55:125206. 23. Abedini F, Allahyari S, Rahemi N. Oxidative desulfurization of dibenzothiophene and simultaneous adsorption of products on BiOBr-C3N4/MCM-41 visible-light-driven core–shell nano photocatalyst. Appl Surf Sci 2021;569:51086. 24. Liou TH, Liu SM, Chen GW. Utilization of e-wastes as a sustainable silica source in synthesis of ordered mesostructured Titania nanocomposites with high adsorption and photoactivity. J Environ Chem Eng 2022; 10:107283. 25. Sanni S, Viljoen E, Ofomaja A. Three-dimensional hierarchical porous carbon structure derived from pinecone as a potential catalyst support in catalytic remediation of antibiotics. RSC Adv 2020;10:8717–28. 26. Akpotu SO, Moodley B. Synthesis and characterization of citric acid grafted MCM-41 and its adsorption of cationic dyes. J Environ Chem Eng 2016;4:4503–13. 27. Xiao X, Zheng C, Lu M, Zhang L, Liu F, Zuo X, et al. Deficient Bi24O31Br10 as a highly efficient photocatalyst for selective oxidation of benzyl alcohol into benzaldehyde under blue LED irradiation. Appl Catal B Environ 2018;228:142–51. 28. Oseghe EO, Msagati TA, Mamba BB, Ofomaja AE. An efficient and stable narrow bandgap carbon dotbrookite composite over other CD-TiO2 polymorphs in rhodamine B degradation under LED light. Ceram Int 2019;45:14173–81. 29. Sanni SO, Moundzounga TH, Oseghe EO, Haneklaus NH, Viljoen EL, Brink HG. One-step green synthesis of water-soluble fluorescent carbon dots and its application in the detection of Cu2+. Nanomaterials 2022;12: 958. 30. Zhao C, Wang Z, Li X, Yi X, Chu H, Chen X, et al. Facile fabrication of BUC-21/Bi24O31Br10 composites for enhanced photocatalytic Cr (VI) reduction under white light. Chem Eng J 2020;389:123431. 31. Wang CC, Li JR, Lv XL, Zhang YQ, Guo G. Photocatalytic organic pollutants degradation in metal–organic frameworks. Energy Environ Sci 2014;7:831–67. 32. Sanni S, Viljoen E, Ofomaja A. Tailored synthesis of Ag/AgBr nanostructures coupled activated carbon with intimate interface interaction for enhanced photodegradation of tetracycline. Process Saf Environ Protect 2021;146:20–34. 33. Sanni S, Modise S, Viljoen E, Ofomaja A. Enhanced degradation of dye mixtures: physicochemical and electrochemical properties of titania dispersed on clinoptilolite, synergistic influence. SN Appl Sci 2020;2: 1–10.

Siphumelele T. Mkhondwane and Viswanadha Srirama Rajasekhar Pullabhotla*

11 Synergistic effect in bimetallic gold catalysts: recent trends and prospects Abstract: Bimetallic gold (Au) catalysts present an exceptional development trend toward enhancing the catalytic efficiency of the Au based catalysts. The aim of this review is to provide an insight into synergic effect of the bimetallic Au catalysts in enhancing the efficiency of various processes. The review covers some important aspects involving the effect of particle size, composition, metal-support interaction, morphology and the interaction between Au atom and the secondary metal on catalytic properties of the bimetallic Au catalysts. Particularly, the effect of the core–shell and faceted bimetallic Au catalysts morphologies are clearly articulated in the introduction. In the next section, various spectroscopic and microscopic characterization techniques, which often form a basis for the discussion of the synergic effect of the catalysts in enhancing the process efficiency are also discussed. Finally, we provide a summary on the progress made in catalytic exploration of bimetallic Au catalysts focusing in oxidation of hydrocarbons, fuel cell processes, oxidative transformation of the biomass derived products and photocatalysis. Keywords: bimetallic Au nanoparticles; catalytic processes; synergic effect.

11.1 Introduction The rapid advancement of nanotechnology and nanoscience over the past decades has spurred an intensive increase in the catalytic application of the gold (Au) nanoparticles [1]. Gold nanoparticles, due to their unique electronic and surface properties are indispensable catalysts for numerous processes [2]. Even though monometallic Au catalysts are promising in terms of catalytic performance, however they are often prone to intrinsic defects such as (i) high sensitivity toward moisture, which habitually reduces their catalytic activity, (ii) sintering upon heating, which limit their exploration at high temperatures and (iii) inherent inertness toward H2 and O2 [2, 3], which results to a decrease in catalytic efficiency in the absence of a support. These pitfalls impede

*Corresponding author: Viswanadha Srirama Rajasekhar Pullabhotla, Department of Chemistry, University of Zululand, Private Bag X1001, Kwa-Dlangezwa 3886, South Africa, E-mail: [email protected]. https://orcid.org/0000-0002-0093-460X Siphumelele T. Mkhondwane, Department of Chemistry, University of Zululand, Private Bag X1001, KwaDlangezwa 3886, South Africa As per De Gruyter’s policy this article has previously been published in the journal Physical Sciences Reviews. Please cite as: S. T. Mkhondwane and V. S. R. Pullabhotla “Synergistic effect in bimetallic gold catalysts: recent trends and prospects” Physical Sciences Reviews [Online] 2023. DOI: 10.1515/psr-2022-0269 | https://doi.org/10.1515/9783111071428-011

194

11 Synergistic effect in bimetallic gold catalysts

extensive applications of these catalysts for industrial benefit. Hence, the primary goal of the researchers is to design and fabricate relatively active and stable Au catalysts for sustainable development of the society. Among different strategies explored, the utilization of the bimetallic Au-based catalysts provides a versatile route that can be used to overpass these shortcomings [4–8]. The phenomenon of catalysis by bimetallic Au nanoparticles has also advanced into an effective approach toward reducing the consumption of Au due to its high cost while exploiting the tremendous synergic effect resulting from different components for enhancing catalytic activity [9]. In general, bimetallic Au catalysts can be categorised in to two types depending on the type of the secondary metal. The first type is called Au-BM type, in which the BM refers to any base metal such as Co, Cu, Ag and Ni, whereas the second class is called Au-PGM type, where PGM represents the platinum group metals such as platinum, ruthenium, iridium and palladium. These classifications provide an insight into differences in catalytic efficiencies of the bimetallic Au catalysts resulting from the synergic effect between different Au bimetallic alloys. The synergic effect between bimetallic Au alloys is generally attributed to geometric and electronic effects. For example, the bimetallic Au catalysts are subjected to changes in electronic configuration caused by electron transfer from PGM or BM metal to Au, since Au possess higher electronegativity (2.54) than the PGM and BM metals [3]. Engineering of bimetallic Au catalysts is still in its initial phase, however it is anticipated to explore the undiscovered prodigy in the near future, not only in organic catalysis but in other applications as well. The tailoring of the physical and chemical properties emanating from engineered particle size, morphology and the effect of the secondary metal have shifted the focus of the Au based catalysts research to bimetallic Au catalysts. This approach does not only improve organic catalysis activity of the Au catalysts but is also cost efficient since Au is highly expensive. Therefore, in this review, we aim in summarizing the recent advances in catalysis by bimetallic Au based catalysts. The main objectives are to evaluate the effect of particle size, composition, metal-support interaction, morphology and the interaction between Au atom and the secondary metal and align these properties with catalytic activity in organic catalysis.

11.2 Synthesis of Au bimetallic catalysts In general, bimetallic Au catalysts can be acquired by trademark bottom-up approach such as the reduction of the metal cation or top–down process such as dissembling large particles by either grinding or laser ablation often used for the preparation of the nanoparticles [10, 11]. The bottom-up approach involves the use of a highly soluble salt such as chlorides or sulphates as a source of an active metal catalyst. Subsequently, the reducing agent with a robust effect on the properties of the catalysts is added. A vast number of bottom-up catalysts’ synthesis methods using various reducing agents have been developed. Among them, the so-called polyol method presents a special case. Polyol

11.2 Synthesis of Au bimetallic catalysts

195

method encompasses the utilization of a high boiling solvent which also function as the reducing agent. In this case, the colloidal stability is usually maintained by the utilization of a suitable capping agent most commonly polymers and polyelectrolytes [12]. In addition, seed arbitrated growth method has also become one of the exceptional and efficient bottom-up methods used for the preparation of the highly stable, shape and size regulated bimetallic Au catalysts over the past years [1]. Most commonly, for bimetallic supported catalysts, hybrid bottom up method, in which the first method encompasses sequential or simultaneous reduction of the Au and secondary metal precursor to bimetallic Au nanoparticles following by the impregnation method for immobilization of the as-prepared nanoparticle on a support material is utilized [1, 2, 8]. On the other hand, the most prominent top-down method for the preparation of the bimetallic catalysts is laser ablation due to its controllable characteristics. In this case, the bimetallic alloy is subjected to a laser beam, in which highly dispersed particles can be acquired under suitable conditions. The obtained nanoparticles can be further fractionized and surface functionalized [13]. In quest to improve the catalytic activity, researchers encounter strict perpetual emission regulations and moderate operational expenses, it is highly recommended to comprehend and regulate the major factors responsible for catalytic activity. Most often, the catalytic efficiency of the Au alloys is routinely enhanced by regulating composition, particle size, morphology or by immobilization in a dispersed state on the highly stable nanostructured support material [1, 14]. In addition, it has also been well articulated that the presence of strain alters the chemical properties of metallic arrangement. In this manner, designing catalysts with high density of strain sites provides a possibility for further leverage in fabrication of highly active catalysts [15, 16]. Strains can be intrinsic or extrinsic. Most commonly, the intrinsic strains emerge from finite size [17], domain structure [18] or morphology and is caused by the nanoparticles themselves [19]. Whereas, extrinsic strains are instigated by extrinsic factors, for example, by promoting the mismatch at an interface of the core–shell lattice structure [20, 21], or Metal-support interface [22].

11.2.1 Controlling the particle size and composition In general, one of the major trends in enhancing the performance of the catalysts lies in an aptitude to confine its grain size within the nanoscale regime [23]. The small size effect of the catalysts can be visualised from the feature of the relationship between the particle size and the surface area of the nanomaterials for surface catalysed reactions. Accordingly, a decrease in particle size is often accompanied by an increase in surface area, which subsequently enhances catalytic activity for surface catalysed reactions because of the large number of active sites available for the surface reaction [24]. Commonly, small particle sizes are usually obtained by using a reducing agent in conjunction with the stabilizing agent during catalyst preparation or by immobilization of the catalysts on a

196

11 Synergistic effect in bimetallic gold catalysts

highly refractory support material [1, 3]. Nonetheless, variation in particle sizes of the same catalyst also possesses a resilient effect on the formation of the lattice strain. For a typical example, in a work by Bae et al. the size threshold of strain produced from lattice mismatch and finite size was demonstrated for bimetallic Pt-Au catalysts [23]. In this case, when Pt clusters of particles smaller than 4 nm are grown on Au substrate, finite size promoted strain was reported, suggesting an unexpected modification in surface strain in comparison to Au and Pt monolayer. In addition, when the particle size of a Pt cluster increases beyond 10 nm, the lattice mismatch promoted surface strain becomes dominant. In addition to particle size, the catalysts’ composition with tenable inter-particle distance effectively improves the stability of the bimetallic catalysts in a chemical reaction. Generally, the incorporation of the secondary metal results in synergic effect, which alters the electronic configuration of the catalysts such as inducing ligand effect as well as lattice strain [1–3]. For example, changing the composition of the catalyst results to new reaction pathway, with distinct product selectivity. However, this phenomenon requires full comprehension in other to exploit its advantages for optimum efficiency [3]. The catalytic efficiency of the bimetallic catalysts can be induced by numerous conducts such as charge transfer phenomenon, which subsequently influences the binding energy and reduces the activation energy for chemical reactions [25]. The charge transfer is associated with the interaction among particles, which in return is influenced by the particle size. This effect plays an important role in preventing the deactivation of the catalysts by poisoning and sintering [26].

11.2.2 Controlling the morphology of the catalyst The tailoring of the crystallographic morphology of the catalysts possesses resilient influence on their catalytic activity since different crystallographic architectures have diverse atomic configurations [27–30]. This approach provides a superficial method for tailoring the physicochemical properties of the bimetallic Au catalysts to manoeuvre their catalytic activities [30]. Substantial development has been made in the synthesis of tailor-made bimetallic Au catalysts. In this review, we focus on summarizing the basic principles of designing and fabrication of the core shell and faceted bimetallic Au catalysts. 11.2.2.1 Core shell bimetallic Au nanoparticles Core shell nanoparticles are the cornerstone of the modern research, due to their unprecedented high catalytic activity resulting from synergic effect between an active metal rich core and protective secondary metal rich shell, which enhances stability against sintering and coalescence under realistic chemical reactions [31]. The nature of the resulting bimetallic catalyst can be determined by the presence of the catalyst’s precursor material during reduction process [32]. In cases where both metal precursors are present

11.2 Synthesis of Au bimetallic catalysts

197

simultaneously during reduction, a statistical mixture of an alloy is usually obtained. In contrast, when the reduction process is conducted sequentially, the core shell particles are usually acquired [33, 34]. For typical example, when bimetallic Au-Ag catalyst is synthesized from sequential reduction, in which Au3+ was first reduced followed Ag3+, the core shell bimetallic catalysts with Ag rich shell and Au rich core were acquired. This phenomenon transpires because Au is nobler than Ag and the reversibility is not always feasible [35]. Supposing that the silver cation is first reduced to Ag nanoparticles, subsequently the Au3+ is added. In this case, the metallic Ag nanoparticles becomes oxidized by nobler Au3+ cations, subsequently the Au nanoparticles get deposited at the surface of the Ag nanoparticles. Such phenomena often result to an assemble of a hollow Au nanoparticles, in which the Ag rich core has been fully dissolved. In order to constrain the oxidation of the less noble Ag by the Au cations, an excess of a reducing agent in conjunction with nobler Au metal ions should be added. However, the literature suggests that controlling the chemistry in such complex chemical reaction mixtures at nanoscale presents a huge challenge [36]. A lot of the core shell bimetallic Au based catalysts have been fabricated following a similar concept and tested for various reactions. For typical example, Au-Cu core shell catalysts with Au rich core encapsulated in Cu shell were fabricated using seed mediated growth technique. In oxidation reactions the Cu species were oxidized to Cu2+, which subsequently played a profound role in providing the O2− species in the Au-Cu catalysed oxidation reactions. In contrast, when Au-Cu catalyst was utilized in selective hydrogenation of esters, the Au-Cu surface sites were proposed to be the active sites and the role of the Cu species was to enhance H2 activation and stabilize low coordinated Au atoms. In another study, the core–shell of the Au-Pd catalyst with Pd surface enriched shell and Au enriched core were also fabricated using CTAC as structure mediating agent. Here, the catalytic efficiency of the material was proposed to emanate from the Au rich core, whereas the Pd rich shell functioned as the Au promoter that inhibited over oxidation of the intermediates, poisoning by the Au catalyst and enhances stability of the catalyst by AuPd electron transfer. Nonetheless, the limited assembly of the reactants to the active metal core is the major impediment of the core shell particles for some processes. In this manner, the inorganic porous species are usually utilized as protecting shell agents. In such instances, the shell formed on the surface of the active metal core develops pores upon calcination at elevated temperatures to remove structure directing agent [37]. In a recent study, the etching of the protective surface shell was reported to offer an alternative strategy to control the degree of the shell porosity [38]. Typically, Au-Pt/SiO2 catalysts with Au-Pt core and SiO2 shell were fabricated with controlled SiO2 porosity using polyvinylpyrrolidone (PVP) and the etyltrimethylammonium bromide (CTAB) as structure directing agent and selective etching agent respectively. Here, the addition of etyltrimethylammonium bromide (CTAB) during catalyst synthesis resulted to SiO2 selective etching, generating pores. The degree of porosity was controlled by the interplay between the concentrations of the CTAB and polyvinylpyrrolidone (PVP). High CTAB concentrations resulted to an increase in degree of porosity, whereas high ascorbic acid

198

11 Synergistic effect in bimetallic gold catalysts

concentrations resulted to a decrease in porosity. In another study Yang at al, used the controlled supersaturation method to fabricate AuPd core shell octahedra, cuboctahedra, concave cubes, truncated octahedra and truncated cubes types of crystallographic morphologies. The morphology of the Au-Pd catalyst was governed by the degree of supersaturation. On one hand, the concave tubes and truncated cubes were formed at higher Pd consumption rates. Alternatively, the assembly of the Au-Pd core shell octahedra and cuboctahedra was favoured at low Pd consumption rate [39]. Even though the idea of encapsulating the Au catalyst may be prone to limitations such as restricting the full excess of the reactants to the active metal catalyst, however it also has been reported to be highly beneficial for fuel cell processes (oxidation of methanol and formic acid). In general, the Au catalyst has high adsorption aptitude for methanol and formic acid. This phenomenon results to subsequent deactivation of the Au catalysts due to active site blockage by the substrates and the reaction intermediates [40]. Recently, the catalytic activities of the Au-Cu core shell and the concave octopod nanoframe catalysts were evaluated. The Cu-Au core shell material was reported to exhibit induced catalytic activity than the concave octopod nanoframe analogous. This was attributed to the encapsulation of the Au catalysts which subsequently reduced the binding efficiency of the Au with the reaction intermediate [41]. 11.2.2.2 Faceted bimetallic Au nanoparticles Facets are flat surfaces characterized by a particular atomic coordination assembly. There are two types of facets, namely, low and high index facets, depending on the surface energy and atomic arrangement. Low index facets possess low surface energy and are denoted by miller indices with which the sum of the hkl values is small. The typical examples include (100), (110) and (111) miller indices found in cubic, rhombic dodecahedrons and octahedral polyhedrons of the face centred cubic crystallographic nanoparticles, respectively. On the other hand, high index facets possess higher surface energy than low index facets. These facets are characterized by a set of hkl values with at least one of the three indexes greater than a unity i.e. (331), (311) and (310) [24]. Usually, the crystallographic architecture of the metal nanoparticles greatly influences the nature of the exposed facets [42]. In such context, the coordination of the atoms at edges and corners as well as surface atomic assemblies become more expedient [43, 44]. Different facets possess diverse catalytic activities, which can be favourable or unfavourable for certain reactions. Generally, most thermodynamically favoured crystallographic metal facets are low index facets, however these facets possess poor catalytic activities than high index facets. The high facets have large number of low coordination with kinks, terraces, strains and steps which play an enormous role during adsorption of the substrate for catalytic processes [45, 46]. For this reason, a large number of studies has been devoted to developing an environmental benign and cost effective methods for facile fabrication of the various tailor made metal catalysts with high surface energy facets [47–55].

11.2 Synthesis of Au bimetallic catalysts

199

Nonetheless, this presents a huge challenge because high index facets are thermodynamically unstable; therefore they tend to disappear on the final crystal due to high growth rate persuaded by high surface energy. Similar to mediating particle size, this challenge can be overpassed by the utilisation of the surfactants such as organic compounds equipped with either ionic charged head functional group, neutral head group or both anionic and cationic charged groups [56, 57]. The surfactant adsorbs on the high surface energy facets and reduces their surface energy during crystal growth, which subsequently alters their chemical state and reduces growth rate. In this way, the high energy facets can be preserved on the final crystal. In general, PGM metals have face centred crystallographic architecture enclosed by (100) low index facets (Figure 11.1a). The geometric evolution of the high energy facets of either mono or bimetallic PGM catalysts can be achieved by cutting corners of a cube, with the help of a surfactant (Figure 11.1b) [57]. The facet engineering of the Au nanoparticles is relatively facile due to small differences in surface energies between low and high surface energy facets [58]. However, the fabrication of the faceted bimetallic Au alloys is comparatively convoluted due to large differences in surface energies of the Au material and the secondary metal [59]. In addition, choosing a proper surfactant also present a substantial challenge, because various metals have different adsorption potentials for surfactants. This spectacle may result to diverse effects between the two metals forming an alloy. This challenge ascends from the fact that the structure surfactants do not only affect the surface energy, but also

Figure 11.1: Evolution of different faceted Au nanoparticles from cubic shaped particles. (a) Face centred cubic crystallographic structure of the bimetallic Au-PGM based catalyst, (b) geometric evolution of the new facets from cutting 8 corners of an ordinary cube, (c) geometric illustration of the high index facet, with steps and kinks, (d) cubic crystallographic structured catalyst before cutting corners with the aid of the surfactant, (e) the role of the surfactant in geometric evolution of the new facets from an ordinary cube and (f) the role of the surfactant in mediating the morphology of the final crystal and the nature of the exposed facets.

200

11 Synergistic effect in bimetallic gold catalysts

Figure 11.2: Basic principles of photocatalysis.

influence kinetics of the Au growing crystal. In this manner, the Wulff construction rule is not the only element influencing the surface morphology and shape of the growing crystal. In fact, all the high surface bimetallic Au catalysts result from the kinetic controlled growth by the surfactant [45]. For example, the cubic crystallographic shaped AuCu3 nanoparticles were fabricated in ethanol solvent. However, the cubic shape of the Au catalysts consists of the low surface energy facets. On the other hand, when ethanol was used in conjunction with n-butylamine, the multipod AuCu3 nanoparticles dominated by high surface energy side facets were obtained [60]. This indicates that these facets are thermodynamically unstable in the absence of the n-butylamine. Despite that, a significant progress has been made in synthesis of the structure mediated bimetallic Au catalysts focusing on the type of the exposed facets, using different structure directing agents. For example, octahedral Au3Ag nanoframes consisting 3D active sites outline with high surface defects characterized by low coordinated atoms, stacking faults, vacancies, dislocation and lattice strains located on vertices and edges were synthesized using CTAB as the structure directing agent [61]. These structural surface defects facilitate the interaction between absorbates and the surface of the catalyst, owing to the improved shifted d-bands and surface electronic states [62]. The rational surface adsorption of the reaction intermediates to the surface of the catalyst is influenced by the electronic structure of the catalyst [63]. Typically, the valence p orbital of the reaction intermediate and catalyst form bonding and antibonding state with the d-bands of the catalyst [64]. When the coordination number of the metal atoms is low, the width of the d-band decreases, which subsequently induces the shift in the d-band centre toward the Fermi level. This results to an enhanced interaction between the substrate and the catalyst [65]. All these properties can be attained from the correlation between various characterization techniques. For this reason, next section is focussed on summarizing an in depth exploitation of various characterization techniques to study the physicochemical properties of the catalysts.

11.3 Catalysts characterization

201

11.2.3 The role of the support material In addition to the utilization of the reducing and stabilizing agents, the immobilization of the catalysts in a dispersed state on the highly stable nanostructured support material provide a facile method for controlling the grain size, stability and tuning activity of the catalysts. Catalysts’ supports come in extensive diversity of the highly porous accommodates varying in particle sizes, composition and shapes [66]. These encompasses refractory microporous metal oxides such as TiO2, SiO2, Al2O3, ZrO2, CeO2 etc., mesoporous molecular sieves such as SBA-15, MCM-41, FSM-16, MCM-48 and MCM-50 and non-metal oxide supports such as activated charcoal etc. [67]. The support material, by virtue of its high surface area and thermal stability provides a medium for dispersion of the active metal species at nanometre scale, enhancing surface-to-volume ratios and subsequently active site densities for surface catalysed reactions. The immobilization of the metal catalysts to the support material results to the alteration in the electronic structures of the catalysts persuaded by prolific metal-support interaction (MSI) through electron transfer between the active metal species and the support material [68, 69]. The MSI phenomenon becomes more expedient with shrinkage in particle size of the catalyst, mostly below 5 nm range. This is because the charge transfer is attenuated by the large nanoparticles caused by the reduced surface area of the metal in contact with the support [70]. The metal-support interaction leads to the formation of the interface, which possess distinct properties from the bulk of the catalysts [71]. The formation of the interface can alter the properties of both the active metal and the support. From the discussion of the supported bimetallic Au catalysts point of view, the properties of the metal-support interface are largely influenced by the nature of the secondary metal and the support material. For example, it was shown that when bimetallic Au-Pd alloy is supported on SiO2, the charge transfer from Pd to Au transpires, which certainly enhances the electron affinity of the Pd and influences catalytic and chemical properties of Au. Subsequently, the Pd atom abstract an electron from SiO2 resulting to an electron rich Au and electron deficient SiO2 [72]. However, when CeO2 is used instead of SiO2, the electron transfer from Pd to CeO2 transpires, resulting to the formation of Ce3+ from Ce4+. This sensation endorses diminution in oxygen vacancy formation energy, which successively stabilizes substantial surface oxygen vacancies of the support material that may not be stable under reducing environment. This improves the Mars van Krevelen reaction mechanisms and reverse oxygen spill over properties of the catalysts, which are highly expedient for oxidation reactions [73].

11.3 Catalysts characterization The involvement of the secondary metal and even the support material in bimetallic Au catalysts increases the complexity of the structure of the catalyst [71]. In spite of the

202

11 Synergistic effect in bimetallic gold catalysts

particle shape and size, the elemental composition, surface atomic arrangement and distribution are some of the prime factors which play a huge role in governing activity and selectivity of the catalysts [30, 36, 45]. The elemental distribution inside nanoparticles such as core–shell or an alloy and the crystallographic morphology such as the nature of the exposed facets present a huge concern. For typical example, it is often vague to state whether the obtained distribution of the elements is the representation of the frozen image of the synthesis conditions or a thermodynamic equilibrium state [65]. However, bimetallic Au catalysts seldom present a thermodynamic equilibrium state, since numerous forms of the alloys can be acquired [67]. Generally, in order to precisely correlate the relationship between the specific sites of the catalysts to physicochemical properties, advanced catalysts’ characterization techniques at nanometric to atomic scale need to be explored [71]. Spectroscopic and microscopic characterization techniques are usually explored to acquire structural information concerning atomic arrangement or configuration at the surface of the catalysts, interfaces and inside particles [15]. For example, transmission electron microscopy (TEM) and scanning electron microscopy (SEM) techniques are used to probe particle size, morphology, elemental distribution and resolution of the atoms. High angle annular dark field (HAADF) and High resolution transmission electron microscopy (HRTEM) are used to gain an insight on the atomic alignment at nanometre regime with advanced spherical deviation corrector [68]. Particularly, HRTEM spherical deviation corrected technique is usually utilized to examine comprehensive strain resulting from the synergy of the alloyed or de-alloyed metals, typically in the case of core–shell bimetallic catalysts. The lattice mismatch in the Pt rich shell of the bimetallic Au-Pt core–shell catalysts was examined using HRTEM, in which a small lattice parameter was observed, suggesting that the deposition of the Au shell induces strain formation on Pt rich core. Using TEM aberration corrected technique, in a study by Yoshida et al. it was also shown that the (100) Au facet experiences surface atomic reconstruction during CO oxidation upon its adsorption at the surface of the catalyst [74]. Numerous analytical techniques have been established to measure the particle sizes of the catalysts, based on HAADF. For example, the number of atoms in a cluster can be identified by subsequent integration of the intensity of the cluster area through blurring propagation technique [75]. In addition, the HAADF also provides a native clarification on the location of the various metals present on a single crystal, owing to its induced sensitivity toward mass thickness contracts. For example, HAADF is one of the dominant methods for characterization of the core–shell catalysts [76]. The scanning transmission electron microscopy (STEM) characterization techniques is used to examine surface adsorption and dissociation of various molecules. Long et al. observed that the presence of the Pd atom in metal organic framework-supported bimetallic Au-Pd catalyst induces oxygen adsorption at the surface of the catalyst. This was attributed to the presence of the defect sites due to surface strain formation upon Pt addition [77]. In a

11.3 Catalysts characterization

203

recent study, it has been demonstrated that the resolution of the STEM can be improved by using probe corrected STEM in couple with column silicon drift detector. In this way, core–shell structures, random alloys and segregations in individual particles can be differentiated [78]. The properties of the catalysts can be acquired from correlation between various characterization techniques. Typically, the HRTEM was used to study atomic arrangement of the Au-Pt catalysts. In this study, high structural defects were unveiled, including grain boundaries, vacancies, atomic steps and dislocations, which were suggested to have effectively modified the electronic states of the surface of the catalyst. This effect was suggested to bring about intensive difference in properties and catalytic performance of the bimetallic Au-Pt in comparison to monometallic bulk Au and Pt counterparts. On the other hand, the high resolution energy dispersive X-ray spectroscopy mapping attained under HAADF-STEM mode revealed that the Au metal is located on top of the Pt core. By subsequent comparison of the elemental mapping of the Au and Pt, it was perceived that the signal of the Au was highly intensive, indicating that the surface is the major constituent of the Au metal [79]. In another by study by Wang et al., the STEM-EDX mapping and HRTEM analysis revealed that the good alloying of the Pd and Au metals is dependent on the ratio of the Pd and Au. Typically, it was reported that good Au-Pd alloy systems could be attained from Pd:Au ratios of 9:1, 2:8 and 4:6, whereas the segregated systems were obtained from Pd:Au ratio of 0.5:9.5 and 8:2. The segregated alloy systems were suggested to be the main reason pertaining poor catalytic activities and fast catalysts deactivation for oxidation of glycerol [80]. The X-ray diffraction (XRD) is a very powerful tool for determining the particle size and phases of the catalysts. These domains can be probed by proper examination of the diffraction peaks outline [81]. Unlike electron microscopy, billions of nanoparticles can be computed simultaneously using XRD, which provide a better insight on the properties of the whole sample. This is one of the advantages of the XRD characterization technique over electron microscopy [82]. However, XRD is also associated with numerous pitfalls such as the fact that the differences between the individual particles in a sample are frequently overlooked, because its results provide an averaged data of the whole sample. In addition, the crystallite size of the twinned particles is almost always smaller than the particle size. It is only for single crystalline nanoparticles in which the particle size and crystallite size are identical. For this reason, it is often highly expedient when used in compliment with microscopic characterization techniques [83]. The XRD is very useful in probing the intermetallic alloys [84]. Typically, when two different metal components in an alloy having different lattice spaces are separated, the XRD is able to differentiate between the two or more phases present in that alloy. On the other hand, when a bimetallic alloy is formed the XRD can precisely compute the alteration in lattice spacing, which is often different compared to monometallic analogous. Oseghale et al. used XRD to study the structural differences between Au0.8Pd0.2-C and

204

11 Synergistic effect in bimetallic gold catalysts

Pd-C catalysts. Here, the increase in d-spacing of the (111) miller indices was observed in Au0.8Pd0.2-C catalyst compared to that of the Pd-C. This effect was attributed to the lower (111) peak of the Au metal caused by subsequent incorporation of the Pd atom. In addition, the reflection peaks of the Au0.8Pd0.2-C catalyst exhibited a significant shift toward lower 2Ѳ values. This phenomenon was suggested to ascend from non-dispersive incorporation of the Pd in to Au lattice, indicating that the two components of an alloy are poorly aggregated. The lattice contraction could be indexed to the differences between the lattice spacing of the Au and Pd metals. When comparing the d-spacing of the (200) plane in Au0.8Pd0.2-C catalyst with the trademark d-spacing of the (200) plane of the bulk Au metal, the deviation of 0.39% was detected. This was suggested to be caused by complete surface coverage of the Pd metal by Au metal [85]. The XRD is also widely used to determine the crystallinity of the catalysts. For example, in another study by Xing et al., the development of the crystallographic structure of the nanostructured Au-Ag catalysts was probed and compared with those of either monometallic Au or Ag counterparts. In a typical study, it was perceived that the four main diffraction peaks corresponding to (111), (200), (220) and (311) of Au and Ag planes were very close to each other, indicating that the both Au and Ag share a similar face centered cubic (fcc) crystallographic structure. Further examination of the XRD patterns indicated that the alloying of Au and Ag metals resulted to a significant decrease in crystallographic stability of both Au and Ag metals, observed by broadening of the diffraction peaks corresponding to these metals when compared to those of monometallic analogous [86]. The purification of the catalysts, for example, by filtration, centrifugation or dialysis habitually tampers with the elemental composition. In such cases, it is necessary to evaluate whether elemental composition is the same as anticipated from synthesis stoichiometry. Such evidence can be acquired from X-ray fluorescence (XRF) and atomic absorption spectroscopy (AAS). In generally, the AAS encompasses the dissolution of the catalyst, which is mostly accomplished by digestion in aqua regia or concentrated nitric acid. However, it is difficult to obtain complete digestion in bimetallic Au catalysts at ambient pressure. Subsequently, this process is usually done at high pressures. The literature report suggests that AAS does not provide accurate stoichiometry for partially dissolved catalysts. In addition, the AAS is also prone to nanoparticles’ detection limit, which is different for each particle. For example, detection limit for Au, Pt, Pd and Ru are 1 ppm, 24 ppm, 1.2 ppm and 0.8 ppm, respectively [87]. On the other hand, the XRF have the detection limit of 1 ppm, which comparably provides reasonable data for bimetallic Au catalysts [88]. In compliment with electron microscopy, wavelength dispersive X-ray spectroscopy (WDX), energy-dispersive X-ray spectroscopy (EDX) and XRF offers semi-quantitative results for microscopic samples [89]. X-ray photoelectron spectroscopy (XPS) is sensitive for surface analysis of the catalysts, which does not only provide an insight on the particle composition of the catalyst surface, but also give information on the valence state of the inner shell electrons binding

11.3 Catalysts characterization

205

energy. Maintaining the homogeneous distribution of the metal atoms in bimetallic Au catalyst from random alloys to core shell presents a substantial interest. A lot of progress have been done in probing the dissemination of the Au and the secondary metal in bimetallic Au catalysts and the interaction between the supporting material and the alloyed metals using XPS technique [90]. For example, the surface state of bimetallic PdAu catalysts supported on TiO2 was evaluated using XPS. Here, it was unveiled that surface of the catalyst is dominated by the Pd nanoparticles and the Au particles were suggested to endorse Pd surface deposition through electronic effects [91]. This was in correlation with the work reported by Herzing et al. [92]. The evaluation of the monometallic Pd/TiO2 detected that Pd exists as Pd2+, surprisingly when small amount of Au was added, the subsequent change in oxidation state from Pd2+ to Pd0 was perceived. This is nevertheless contradictory with the literature, which is due to high electronegativity Au tends to withdraw an electron from Pd in PdAu alloy systems. In this manner, it was suggested that both the preparation method and the supporting material play a profound effect on the electronic behaviour of the catalyst. However, when the oxidation state of TiO2 was monitored for pure TiO2, Pd/TiO2, Au/TiO2 and Pd-Au/TiO2 catalysts with XPS, no apparent change was observed for both O1S and Ti2P spectra. Subsequently, Auger electron spectroscopy was also utilized to monitor the surface reconstruction of the Pd-Au/TiO2 compared to Pd/TiO2 catalyst. Similarly, no surface alterations were observed. Alternatively, Raman spectroscopy was used as an alternative approach to monitor the surface TiO2 in pure TiO2 support, Pd/TiO2, Au/TiO2 and PdAu/TiO2 catalysts. In this case, the significant weakening of the B1g and shift of low frequency E.g. Raman peaks upon Pd-Au alloying was perceived, indicating that there is a change in Ti-O-Ti symmetry and Ti surface coordination. The unsuccessful detection of the surface reconstruction of the TiO2 upon PdAu alloying by XPS and AES analysis was indexed to long escaping depth of the electron having more than the outermost atomic layer, which rendered these techniques as the trustworthy surface analysis in this case. This is typical for TiO2 with particle size less than 10 nm size [93]. X-ray absorption near edge structure (XANES) and extended X-ray absorption fine structure (EXAFS) spectroscopies are utilized to examine absorption coefficient, average oxidation state and defect sites of the catalysts as a function of surface energy. As already outlined in this review, the defects sites comprise of low coordinated atoms with higher energy at the surface. In principle, the decay of the X-ray photons transmitted through the catalyst’s sample is induced by defect sites due to their high surface energy, which also increases the absorption coefficient. The XANES spectroscopy is also highly sensitive to surface coordination of the absorbing atoms of the catalysts. In this manner, XANES spectroscopy provides an insight on the structural and electronic properties of the catalyst [94]. The structural information such as bond distance, coordination environment and structural disorder can be acquired by fitting the X-ray absorption spectra. In bimetallic catalysts, the absorption edge of both metals gives information on the coordination environment and the homo and hetero-metallic bonding. These techniques have

206

11 Synergistic effect in bimetallic gold catalysts

been highly expedient on elucidating structural models of the core–shell, alloyed and layered bimetallic Au catalysts. In addition, EXAFS is a powerful probe for interface studies, since it encompasses the contribution of the heterojunction regions of the catalysts [64]. For example, Baudelet et al. studied the bimetallic Au-Co super lattice epitaxial strain using EXAFS technique [95]. In this case it was observed that increasing the CO layer thickness resulted to an increase in the in-plane strain. This is contradictory with the literature. In general, increasing the thickness of the Co layers results to a decrease in Au-Co strain. This technique is very useful in elucidating the number of the empty d-state in a catalyst by L absorption edge measurement. The acquired value is the slight modification of the d-band occupancy in comparison to that of the bulk metal [96]. Photoluminescence (PL) spectroscopy is one of the most expedient techniques used in photocatalysis to monitor the separation the photo generated electrons and holes. Ideally, defects sites, vacancies, atomic steps and dislocations often traps photons with equivalent or higher energy from the light source. Afterwards, the photons excitates the electrons from LUMO orbitals to the HOMO orbitals leaving the holes in the LUMO orbitals. Subsequently, the electron-hole pair recombines through fluorescence, in a phenomenon that produces photoluminescence emission spectra [97]. In this manner, the higher recombination of the electron–hole pair produces intensity photoluminescence emission spectrum. However, PL cannot provide an insight on the ability of the catalysts to generate radical species, which are the most dominant highly reactive oxygen species for photocatalysis. In this manner, PL is usually coupled with electron paramagnetic resonance (EPR) spectroscopy. The EPR spectroscopy can quench the radical species present in the reaction solution through electron spin-trapping phenomenon using appropriated quencher. In regard to bimetallic Au catalysts, the aptitude of the core– shell, faceted and supported bimetallic Au catalysts have been explored to a great extent using both photoluminescence and electron paramagnetic resonance spectroscopy and reported to possess tremendous photogenerated charge carriers separation aptitude [98]. For example, Su et al. [99] used EPR and PL to study the photocatalytic properties of the bimetallic Pd-Au/TiO2 catalysts. In a typical study, spin-trapping electron spin resonance was used to study the efficiency of the Pd-Au/TiO2 catalysts to generate the radical species using UV light. Here, it was unveiled that the Pd-Au/TiO2 catalyst plays an enormous role in promoting the generation of the •OH radical species. However, the mechanistic insight of this phenomenon was not elucidated. Alternatively, photoluminescence spectroscopy was used to monitor the ability of the catalyst to separate the photogenerated excitons. The PL spectroscopy revealed that the TiO2 support material effectively enhance the migration of the photogenerated electrons, which renders the recombination of the electron–hole pair. Typically, the photo excited electrons from Pd-Au alloy were transferred to TiO2 support, leaving the hole on the Pd-Au catalyst. This phenomenon prolonged the life time of both electrons and holes.

11.4 Applications

207

11.4 Applications Bimetallic Au catalysts have served many catalytic applications over the past decades, including selective oxidation of hydrocarbons [77], oxidation of biomass derived products [100], photocatalysis [99] and fuel cells processes [101–104]. This section entails at providing an overview on the application of bimetallic Au catalysts in various processes.

11.4.1 Oxidation of hydrocarbons The oxidation of hydrocarbons to value added products has attracted interest in both research field and chemical industries [105–111]. This is because these processes are used for the production of the value added oxygenated compounds, such as alcohols, aldehydes [105, 106], ketones [107, 109] and acids [109]. Accordingly, the selective oxidation of alkanes has been the hotspot of research over the past years owing to the gas shale rebellion and petroleum apprehensions. Efficiency of these processes, however, is restricted thus far regardless of the remarkable effort. This is due to high stability of the alkanes due to absence of a pi bonding, which easily react with other molecules [112]. In general, the oxidation of hydrocarbons encompasses the C–H bond activation. In case of higher alkanes, harsh reaction constitutes such as high temperature and pressure are required for optimum efficiency in terms of percentage conversion. Nonetheless, such environmental conditions endorse the production of the undesirable by-products, which compromises the selectivity toward the desired products at high conversion rate. As a result, in industries there is a compromise between percentage selectivity and percentage conversion [113, 114]. For example, the commercial oxidation of cyclohexane is kept at 3–4% conversion to obtain 70–83% selectivity toward cyclohexanol and cyclohexanone. However, the bimetallic Au nanoparticles are promising catalysts that can be used to optimize the efficiency of these processes [115–119]. In general, the oxidation of hydrocarbons encompasses H-abstraction, which results to the formation of the alkyl radical. This process transpires through numerous conduits already outlined in our previous report [109]. Subsequently, the alkyl radical reacts with the oxygen (oxidant) to form alkyl peroxide radical. Due to its high reactive potential, alkyl peroxide radical abstracts the hydrogen atom from hydrocarbon to form alkyl hydrogen peroxide and alkyl radical. The H-abstraction is the rate limiting step of this process. The bimetallic Au catalysts have been reported to play a major role in enhancing the rate of the H-abstraction in oxidation of hydrocarbons through numerous mechanisms. Beside direct H-abstraction by bimetallic Au metal centre already outlined above, the oxidation of the hydrocarbons can also be indirectly enhanced through dissociation of the oxidant at the surface of the bimetallic Au catalyst to form highly reactive radical species. In this manner, the rate of the H-abstraction can be improved and subsequently percentage conversion. In addition, the bimetallic Au catalysts have been reported to

208

11 Synergistic effect in bimetallic gold catalysts

control the percentage selectivity by regulating dissociation mechanism alkyl hydrogen peroxide. For typical example, Liu et al. [120] recently studied selective oxidation of cyclohexane over Au-Pd/MgO catalysts spanning a wide range of Au:Pd molar ratio using molecular oxygen as an oxidant. The role of the Au-Pd/MgO catalysts was monitored with EPR spin trapping approach and the results were compared to those acquired from Au/MgO, Pd/MgO and commercially utilized cobalt naphthenate catalysed reactions under similar condition. The Au-Pd/MgO catalysts exhibited higher catalytic activity (percentage conversion and selectivity) and stability with high turnover frequency than all the used catalysts. The high catalytic activity of the Au-Pd/MgO catalysts was indexed to synergic effect between Au, Pd and MgO support. On one hand, the MgO support was suggested to play an enormous role in stabilizing and increasing the surface area of the catalysts. On the other hand, the Au-Pd alloy was suggested to enhance the homolytic cleavage of O-O bond in O2 and dissociation of cyclohexyl hydrogen peroxide. The homolytic cleavage of the O-O bond resulted to the formation of the highly reactive singlet oxygen radical specie, which played a massive role in reinforcing the initiation of the trademark radical chain pathway of the cyclohexane oxidation process. Whereas, the decomposition of the cyclohexyl hydrogen peroxide resulted to the formation of the cyclohexyl oxy radical and hydroxide radical, which both play an enormous role of a radical initiator. In such context, the cyclohexyl oxy radical abstract hydrogen atom from cyclohexane, which results to the formation of the cyclohexyl radical and cyclohexanol. Similarly, hydroxide radical also abstracts hydrogen atom from cyclohexane by forming cyclohexyl radical and H2O. In order to rationalize the high catalytic activity of the Au-Pd/MgO catalysts, the surface electronic state of the catalyst was considered as the massive influencer for strong intrinsic chemical affinity for cyclohexyl hydrogen peroxide. Here, Au-Pd alloy was suggested to promote the shift in the Pd electron density to Au atoms, whereas maintaining the high electronegativity on Au. This is also in correlation with the literature [121]. In this manner, the surface adsorption of the cyclohexyl hydrogen peroxide involved neighbouring Pd and Au atoms, by virtue of the differences in induced charge between the two alloyed atoms selectively resulted to the dissociation of the cyclohexyl hydrogen peroxide O-O bond. Therefore, it can be proposed that the synergic effect between Pd and Au in Pd-Au/MgO emerge from the electronic basic in Pd-Au alloy. The morphology of the Au bimetallic catalysts has a large effect on the oxidation of hydrocarbons. For example, the morphology of the support can largely influence the interaction between the support and the Au catalyst this was reported for the steam reforming of methanol. Furthermore, the defects sites at an interface of the bimetallic Au catalysts in which the secondary metal is the reducible metal oxide (RMO) can be tailored by controlling the morphology of the RMO. The defects sites play a profound role in activating reactants in organic reactions.

11.4 Applications

209

11.4.2 Fuel cell processes Bimetallic Au catalysts have also been utilized in energy conversions such as direct alcohol electro-oxidation (ethanol and methanol), formic acid oxidation and CO oxidation processes in fuel cell technology [122–124]. The fuel cell technology encompasses the direct production electrical energy by coveting chemical energy. Therefore, it presents a cost effective and environmental benign sustainable approach for the generation of power from chemicals in comparison to a combustion Carnot engine analogous [122]. These processes possess high energy efficiency, for typical example, they produce up to 45% in electrical energy and 90% in total energy (heat and electrical energy) [123]. However, fuel cells are subjected to several pitfalls such as, the incomplete and complex oxidation of the starting materials, which results to low process efficiency and the methanol crossover, particularly through proton exchange membrane. These shortcomings have become the main attention of the active fuel cell technology research over the past years [122]. However, bimetallic Au nanoparticles have shown potential of being the future catalysts for optimizing the efficiency of the fuel cell processes [124]. For example, in a study by Sandoval et al., the catalytic efficiency of the Au-Ag/TiO2 catalysts was evaluated for CO oxidation. To examine the synergic effect of the Au-Ag alloy in catalytic activity, the results obtained were compared with those acquired from Au/TiO2, Ag/TiO2 and Au-Ag nanoparticles. The Au-Ag/TiO2 catalyst exhibited high catalytic activity than its analogous. Furthermore, there was no observable difference between the catalytic activities of Au-Ag and Au-Ag/TiO2 catalysts. However, a substantial difference in catalytic activities between Au/TiO2 and Au-Ag/TiO2 catalysts was perceived. This was attributed to the synergic effect between Au and Ag atoms. The mechanism of this effect was not clearly articulated [125]. In another study [61], it was shown that the CO has colossal influence on the catalytic properties of the faceted Au3Ag nanoparticles in the oxidation of methanol. Here, the superior catalytic activity of the Au3Ag catalysts could be indexed to the open 3D framework active sites and the synergic effect of the Au-Ag interaction of defects sites residing on vertices and edges capped by low coordinated atoms, stacking faults and lattice strains. As outlined earlier, such structural defects effectively improve the interaction between the catalyst surface and the absorbents. In a typical study, the shift in the d-band toward the Femi level can be enhanced by CO surface adsorption. In such context, CO adsorption promoted the adsorption of the OH− specie at the surface of the catalyst, which subsequently endorses methanol beta hydrogen elimination. In addition, the structural defects were also reported to play an important part in enhancing the OH− adsorption aptitude on the catalyst surface. Of particular interest, positively polarized Auδ+ sites were reportedly highly proficient on reinforcing the oxidation activity of the catalyst. As the evidence of the synergy of the numerous factors outlined here, the electro kinetics of the methanol oxidation is enormously improved upon arbitration of intermediate binding and corresponding activation barriers.

210

11 Synergistic effect in bimetallic gold catalysts

11.4.3 Oxidation of biomass derived products With the depletion of the fossil fuel stashes and the growing concern on the climate change which results to global warming, the research field has shifted the focus toward establishing new sustainable processes for the production of chemicals and fuels [100]. Among numerous developments in motion, the utilization of the biomass as carbon feedstock has attracted enormous attention for the past two decades. By definition, biomass is a material usually produced from animal or plant growth. However, in catalysis the term biomass refers to lignocellulose based supplies most often herbaceous crops and forestry residues [126]. Lignocellulose is by far the most abundant biomass reserve which comprises of lignin (20–30%), hemicellulose (20–40%) and cellulose (40–50%) [127]. Even though numerous biomass reserves have been utilized for production of fuel and chemical commodities, the oxidation of glucose, glycerol and 5-hydroxymethylfurfural to glyconic acid, glyceric acid and furan dicarboxylic acid respectively, have been the most investigated conduits owing to widespread application of these products. The various oxidation products of the glucose, glycerol and 5-hydroxymethylfurfural oxidation processes are illustrated in Schemes 1, 2 and 3. Gluconic acid is often produced from transformation of the biochemical, even though the catalytic selective oxidation of glucose has proved to be the most versatile route for industrial scale production process [128]. Recent reports, underline bimetallic Au catalysts as one of the tremendous catalysts for these processes due to their low temperature catalytic activities and selectivities in comparison to other traditional oxidation catalysts analogous [129]. Zhang et al. investigated the catalytic activities of a series of bimetallic and tri-metallic Au catalysts in selective oxidation of glucose. In a first report, the Au-Ag core shell catalysts, with Au core and Ag shell covering a wide range of Ag:Au ratio fabricated by simultaneous reduction protocol using PVP, were report to significantly enhance the percentage conversion and selectivity of glucose to gluconic acid [130]. The

Scheme 1: The main products for glucose oxidation process.

Scheme 2: The main products for 5-Hydroxymethylfurfural oxidation process.

11.4 Applications

211

Scheme 3: The main products for glycerol oxidation process.

enhanced catalytic activity was attributed to electron transfer from Ag to Au atom. This phenomenon was subsequently corroborated by DFT studies [131]. Later, it was shown that the activity of the Au-Ag catalyst can be improved by incorporation of the Pt atom in to low coordinated Pd face, top and edge sites of the Au-Ag catalyst to form Au-Ag-Pt trimetallic catalyst [132]. In this manner, the catalytic activity could be improved up to 30 times higher than that of the monometallic Au based catalysts counterparts and 8 times higher than that of the Au-Ag catalyst. In another study by Gao et al., the catalytic activity of the Au-Pd supported La-doped CaMgAl layered double hydroxide (LDH) catalyst was investigated for selective oxidation of the cellulose derived 5-hydroxymethylfurfural to 2,5-furandicarboxylic acid. The obtained results revealed that both surface basic sites of the La-doped CaMgAl layered double hydroxide (LDH) support and the synergy between Pd and Au had a massive role in enhancing 5-hydroxymethylfurfural conversion and 2,5-furandicarboxylic acid product selectivity. Typically, the catalyst exhibited high catalytic activity with almost 100% 5-hydroxymethylfurfural conversion. Furthermore, aldehyde specie of the 5-hydroxymethylfurfural was selectively oxidised to form 5-hydroxymethyl-2- furancarboxylic acid as the main reaction intermediate instead of 2,5-diformylfuran over the Au-Pd supported La-doped CaMgAl layered double hydroxide (LDH) catalyst. The doping of the CaMgAl layered double hydroxide with La2O3 effectively stabilized the

212

11 Synergistic effect in bimetallic gold catalysts

support by forming carboxylic acids, which prevented deterioration of the support material [133]. In general, the oxidation of glycerol over Au catalysts requires basic reaction conditions for optimum efficiency. This is the major drawback of this process which renders its industrial exploration. Over the past years, the bimetallic Au catalysts have been extensively studied to bridge this pitfall. Typically, the bimetallic supported Au-Pd catalysts were shown to enhance the selective oxidation of the OH− of the terminal carbon. Furthermore, the utilization of the Pt atom as the co-catalyst was reported to enhance the selectivity toward glyconic acid. In a study by Ketchie et al. carbon supported bimetallic Au-Pd catalysts [134] and tested for oxidation of glycerol. The Au particles were preferentially deposited on the surface of the catalyst, and in this manner Au and Pd were segregated. The carbon supported Au catalysts exhibited poor catalytic activity compared to carbon supported monometallic Pd and bimetallic Au-Pd catalysts. Furthermore, the carbon supported bimetallic Au-Pd catalysts exhibited high catalytic selectivity toward glyceric acid than carbon supported monometallic Au catalysts. This elucidation resulted to the rational that Pd catalyses H2O2 decomposition, which is often formed as a by-product in glycerol oxidation that renders product selectivity toward glyceric acid.

11.4.4 Photocatalytic oxidation Photocatalytic oxidation entails the phenomena of catalysis and photochemistry, denoting that catalysts and light play a massive role in accelerating redox reaction. In such context, the charge carriers (electrons and holes) are generated by the excitation of electrons through light irradiation. Subsequently, the photogenerated electrons and holes are trapped at surface cation sites and surficial M−OH group, respectively. The surficial M−OH sites instigate oxidative pathways, whereas surface cation sites instigate reductive pathways [135]. Photocatalytic oxidation processes find potential applications in soil and water remediation processes, such as oxidative degradation of chlorinated organic compounds and pharmaceuticals. Although, variety of tremendous photocatalysts such as CdS, CeO2, ZnO, BiOCl and BiVO4 have been well studied focusing in shape such as crystallinity and anisotropy [30] size and composition dependent catalytic performance, however the photocatalytic efficiency of these materials is far from the currently required merit. For example, CeO2 has a wide band gap (3.2 eV) therefore, its solar energy harvesting abilities and carrier conductivities are relatively low [136]. Another enormously explored classical semiconductor with narrow band gap (2.4 eV) is CdS, due to leakage of the Cd2+ which causes damage to the environment and vulnerability to light corrosion, its widespread photocatalytic practical application is limited [137]. These stumbling blocks have shifted the focus toward other photocatalysts. Among others, titanium dioxide (TiO2) nanoparticles, an exceptional photocatalyst with salient catalytic efficiency, non-toxicity and high stability is one of the promising photocatalysts [138]. Nonetheless, due to large

11.5 Conclusion and outlook

213

band gap (3.2 eV), TiO2 can only be sufficiently effective when utilized under UV irradiation (λ < 387 nm) [139]. In addition, one of the massive complications of the photocatalytic processes is the recombination of the electrons and holes, which subsequently compromises the efficiency of both oxidative and reductive pathways [140]. Nonetheless, bimetallic Au catalysts have shown promising features in both light harvesting aptitudes even under visible light irradiation and separation of the photogenerated charge carriers [99, 141]. Recently, it has been shown that the high catalytic activity of the Pd-Au alloy supported in surface tailored TiO2 (101) catalyst in photocatalysis results from the oxygen induced de-alloying of Pd-Au, in which the Pd atom becomes deposited to Pd-Au/TiO2 (101) interface perimeter. The result of the de-alloying ascends from the differences in oxygen affinity between Pd and Au metals. Typically, Pd has high affinity for oxygen than Au. This phenomenon results to preferential segregation of these atoms in which Pd is deposited to the interface of the Pd-Au/TiO2 (101) catalyst, which result to formation of the unique morphology resembling Au rich core and Pd rich shell structure in the vicinity of the Pd-Au/TiO2 (101) interface. This preferential segregation has substantial influence on the position of the fermi level and subsequently the photocatalytic activity. In addition, the Au atom does not only play a massive role on improving the photocatlytic activity, but also have a colossal influence on photocatalytic selective oxidation reactions. Typically, it prevents the in situ oxidation the Pd atom, which limit the oxidizing power of the photo excitons, thus inhibiting further oxidation of the reaction products [141].

11.5 Conclusion and outlook The bimetallic Au catalysts provide a versatile approach toward enhancing the catalytic activity of the Au catalysts. The enhanced catalytic activity emerges from synergic effect of numerous components such particle size, composition, morphology, alloyed metalssupport interaction and interaction between the two alloyed metals, which is not expedient in monometallic Au catalyst. As a result, an extensive research on bimetallic Au catalysts has been significantly explored, mostly focusing on probing the origin of catalytic activity by correlation of different characterization techniques. In this review, a critical overview on fabrication, characterization and application of bimetallic Au catalysts for oxidation processes has been summarized, particularly focusing on oxidation of hydrocarbons, fuel cell processes, oxidative transformation of the biomass derived products and photocatalysis. Most prominently, core–shell, faceted and supported bimetallic Au catalysts have shown to possess prolific catalytic properties than most of the bimetallic Au analogous. These catalysts provide an extraordinary interaction, which results to construction of the interface heterojunction between Au and the secondary atom or between Au alloy and support material and the different facets in a single crystal. Such interaction is relatively associated with the oxidation states of the different components of the catalyst and is mostly probed by XPS, Raman and Auger spectroscopy. The

214

11 Synergistic effect in bimetallic gold catalysts

interface plays a profound role in separating the photogenerated charge carriers by interfacial charge transfer phenomenon in photocatalytic processes. The effectiveness of the interfacial charge transfer phenomenon is largely influenced by (i) the surface states of the two alloyed atoms and different facet sites, which often controls the interfacial energy band gap alignment and (ii) interfacial state and the electronic connections dependent on contacts of the facets and the build-up of photogenerated charge carriers on interface forming atoms and facets. In addition to interface heterojunction, the surface defects often observed in high energy faceted bimetallic Au catalysts have shown tremendous catalytic properties on fuel cell and photocatalytic processes. The enhanced catalytic activity originates from low density of high coordinated atoms. Even though, promising advances have been achieved in enhancing the catalytic activity of the bimetallic Au catalysts, however, controlling the interface of the catalysts in a multi-component hybrid state presents a substantial challenge, thus far. This challenge does not only emerge from the lack of synthetic approach, but also necessitate innovative characterization techniques to monitor the interface charge kinetics. Even though the reaction parameters have an important role in the catalytic efficiency of the various catalysts, however, to our best knowledge, the role of the reaction parameters in the catalytic activity of the Au based catalysts has not yet been established. For this reason, it would be of high interest to study the effect of the reaction parameters in the catalytic performance of the bimetallic Au catalysts.

References 1. Louis C. Chemical preparation of supported bimetallic catalysts. Gold-based bimetallic, a case study. Catalysts 2016;346:110. 2. Ma A, Yang W, Gao K, Tang J. Concave gold nano-arrows (AuCNAs) for efficient catalytic reduction of 4-nitrophenol. Chemosphere 2023;310:136800. 3. Wang A, Liu XY, Mou CY, Zhang T. Understanding the synergistic effects of gold bimetallic catalysts. J Catal 2013;308:258–71. 4. Sankar M, He Q, Morad M, Pritchard J, Freakley SJ, Edwards JK, et al. Synthesis of stable ligand-free gold– palladium nanoparticles using a simple excess anion method. ACS Nano 2012;6:6600–13. 5. Hao Y, Hao GP, Guo DC, Guo CZ, Li WC, Li MR, et al. Bimetallic Au-Pd nanoparticles confined in tubular mesoporous carbon as highly selective and reusable benzyl alcohol oxidation catalysts. ChemCatChem 2012;4:1595–602. 6. Yin M, Huang Y, Lv Q, Liang L, Liao LJ, Liu C, et al. Improved direct electrooxidation of formic acid by increasing Au fraction on the surface of PtAu alloy catalyst with heat treatment. Electrochim Acta 2011;58: 6–11. 7. Albonetti S, Bonelli R, Delaigle R, Femoni C, Gaigneaux EM, Morandi V, et al. Catalytic combustion of toluene over cluster-derived gold/iron catalysts. Appl Catal 2010;372:138–46. 8. An Q, Yu M, Zhang Y, Ma W, Guo J, Wang C. Fe3O4@Carbon microsphere supported Ag–Au bimetallic nanocrystals with the enhanced catalytic activity and selectivity for the reduction of nitroaromatic compounds. J Phys Chem C 2012;116:22432–40.

References

215

9. Bhardwaj S, Pal B. Photodeposition of Ag and Cu binary co-catalyst onto TiO2 for improved optical and photocatalytic degradation properties. Adv Powder Technol 2018;29:2119–28. 10. Xia Y, Xia X, Wang Y, Xie S. Shape-controlled synthesis of metal nanocrystals. MRS Bull 2013;38:335. 11. Pedone D, Moglianetti M, De Luca E, Bardi G, Pompa PP. Platinum nanoparticles in nanobiomedicine. Chem Soc Rev 2017;46:4951–73. 12. Guisbiers G, Mendoza-Cruz R, Bazan-Diaz L, Velazquez-Salazar JJ, Mendoza-Perez R, Robledo-Torres JA, et al. Electrum, the gold–silver alloy, from the bulk scale to the nanoscale: synthesis, properties, and segregation rules. ACS Nano 2016;10:188–203. 13. Intartaglia R, Das G, Bagga K, Gopalakrishnan A, Genovese A, Povia M, et al. Laser synthesis of ligand-free bimetallic nanoparticles for plasmonic applications. Phys Chem Chem Phys 2013;15:3075. 14. Liu J. Advanced electron microscopy of metal-support interactions in supported metal catalysts. ChemCatChem 2011;3:934–48. 15. Blomberg S, Zetterberg J, Zhou J, Merte LR, Gustafson J, Shipilin M, et al. Strain dependent light-off temperature in catalysis revealed by planar laser-induced fluorescence. ACS Catal 2017;7:110–4. 16. Pingel TN, Jørgensen M, Yankovich AB, Grönbeck H, Olsson E. Influence of atomic site-specific strain on catalytic activity of supported nanoparticles. Nat Commun 2018;9:2722. 17. Wang XX, Swihart MT, Wu G. Achievements, challenges and perspectives on cathode catalysts in proton exchange membrane fuel cells for transportation. Nat Catal 2019;2:578–89. 18. Adzic RR. Platinum monolayer electrocatalysts: tunable activity, stability, and self-healing properties. Electrocatalysis 2012;3:163–9. 19. Walsh MJ, Yoshida K, Kuwabara A, Pay ML, Gai PL, Boyes ED. On the structural origin of the catalytic properties of inherently strained ultrasmall decahedral gold nanoparticles. Nano Lett 2012;12:2027–31. 20. Leonardi A, Leoni M, Siboni S, Scardi P. Common volume functions and diffraction line profiles of polyhedral domains. J Appl Crystallogr 2012;45:1162–72. 21. Strasser P, Koh S, Anniyev T, Greeley J, Yu KM, Liu Z, et al. Lattice-strain control of the activity in dealloyed core–shell fuel cell catalysts. Nat Chem 2010;2:454–60. 22. Wu J, Li P, Pan YT, Warren S, Yin X, Yang H. Surface lattice-engineered bimetallic nanoparticles and their catalytic properties. Chem Soc Rev 2012;41:8066–74. 23. Harder R, Pfeifer MA, Williams GJ, Vartaniants IA, Robinson IK. Orientation variation of surface strain. Phys Rev B 2011;76:115425. 24. Bae SE, Gokcen D, Liu P, Mohammadi P, Brankovic S. Size effects in monolayer catalysis—model study: Pt submonolayers on Au(111). Eletcrocatalysis 2012;3:1–8. 25. Pal J, Pal T. Faceted metal and metal oxide nanoparticles: design, fabrication and catalysis. Nanoscale 2015;7:14159–90. 26. Liu JC, Luo L, Xiao H, Zhu J, He Y, Li J. Binary Au–Cu reaction sites decorated ZnO for selective methane oxidation to C1 oxygenates withnNearly 100% selectivity at room temperature. J Am Chem Soc 2022;144: 20601–9. 27. Lisowski P, Colmenares JD, Lomot D, Chernyayeva O, Lisovytskiy D. Preparation by sonophotodeposition method of bimetallic photocatalysts Pd–Cu/TiO2 for sustainable gaseous selective oxidation of methanol to methyl formate. J Mol Catal Chem 2016;41:247–56. 28. Yu Y, Wang X, Lim KH. A DFT study on the adsorption of formic acid and its oxidized intermediates on (100) facets of Pt, Au, monolayer and decorated Pt@Au surfaces. Catal Lett 2011;141:1872–82. 29. Huang L, Zahng X, Wang Q, Han Y, Fang Y, Dong S. Shape-control of Pt–Ru nanocrystals: tuning surface structure for enhanced electrocatalytic methanol oxidation. J Am Chem Soc 2018;140:1142–7. 30. Liu MT, Chen LX, Li DN, Wang AJ, Zhang QL, Feng JJ. One-pot controlled synthesis of AuPd@Pd core-shell nanocrystals with enhanced electrocatalytic performances for formic acid oxidation and glycerol oxidation. J Colloid Interface Sci 2017;508:551–8. 31. Peng YK, Tsang SCE. Facet-dependent photocatalysis of nanosize semiconductive metal oxides and progress of their characterization. Nano Today 2018;18:15–34.

216

11 Synergistic effect in bimetallic gold catalysts

32. Zhang Q, Lee I, Joo JB, Zaera F, Yin YD. Core–shell nanostructured catalysts. Acc Chem Res 2013;46: 1816–24. 33. Xia Y, Gilroy KD, Peng H, Xia CX. Keimvermitteltes Wachstum kolloidaler Metallnanokristalle. Angew Chem, Int Ed 2017;129:60–72. 34. Cobley CM, Chen JY, Cho EC, Wang LV, Xia YN. Gold nanostructures: a class of multifunctional materials for biomedical applications. Chem Soc Rev 2011;40:44–56. 35. Krishnan S, Estevez-González M, Perez R, Esparza R, Meyyappan M. A general seed-mediated approach to the synthesis of AgM (M = Au, Pt, and Pd) core–shell nanoplates and their SERS properties. RSC Adv 2017;7: 27170. 36. Fu T, Fang J, Wang C, Zhao J. Hollow porous nanoparticles with Pt skin on a Au–Pt alloy structure as a highly active electrocatalyst for the oxygen reduction reaction. J Mater Chem 2016;4:8803–14. 37. Loza K, Heggen M, Epple M. Synthesis, structure, properties, and applications of bimetallic nanoparticles of noble metals. Adv Funct Mater 2020;30:1909260. 38. Lyu Z, Xie M, Aldama E, Zhao M, Qiu J, Zhou S, et al. Au@Cu core–shell nanocubes with controllable sizes in the range of 20–30 nm for applications in catalysis and plasmonics. ACS Appl Nano Mater 2019;2:1533–40. 39. Byoun W, Yoo H. Peapod assemblies of Au and Au/Pt nanoparticles encapsulated within hollow silica nanotubes. Chem Eur J 2017;2:213–9. 40. Yang CW, Chanda K, Lin PH, Wang YN, Liao CW, Huang MH. Fabrication of Au–Pd core–shell heterostructures with systematic shape evolution using octahedral nanocrystal cores and their catalytic activity. J Am Chem Soc 2011;133:19993–20000. 41. Al-Akraa IM, Mohammad AM, El-Deab MS, El-Anadouli BE. Electrocatalysis by design: synergistic catalytic enhancement of formic acid electro-oxidation at core–shell Pd/Pt nanocatalysts. Int J Hydrogen Energy 2015;40:1789–94. 42. Bauer JC, Mullins D, Li M, Wu Z, Payzant EA, Overbury SH, et al. Synthesis of silica supported AuCu nanoparticle catalysts and the effects of pretreatment conditions for the CO oxidation reaction. Phys Chem Chem Phys 2011;13:2571–81. 43. Dozzi MV, Selli E. Specific facets-dominated anatase TiO2: fluorine-mediated synthesis and photoactivity. Catalysts 2013;3:455–85. 44. Lee K, Kim M, Kim H. Catalytic nanoparticles being facet-controlled. J Mater Chem 2010;20:3791–8. 45. Sha AB, Sivapalan ST, DeVetter BM, Yang TK, Wen J, Bhargava R, et al. High-index facets in gold nanocrystals elucidated by coherent electron diffraction. Nano Lett 2013;13:1840–6. 46. Xia BY, Wu HB, Wang X, Lou XW. One-Pot synthesis of cubic PtCu3 nanocages with enhanced electrocatalytic activity for the methanol oxidation reaction. J Am Chem Soc 2012;134:13934–7. 47. Luan Y, Jing L, Xie Y, Sun X, Feng Y, Fu H. Exceptional photocatalytic activity of 001-facet-exposed TiO2 mainly depending on enhanced adsorbed oxygen by residual hydrogen fluoride. ACS Catal 2013;3: 1378–85. 48. Chen Q, Zhang J, Jia Y, Jiang Z, Xie Z, Zheng L. Wet chemical synthesis of intermetallic Pt3Zn nanocrystals via weak reduction reaction together with UPD process and their excellent electrocatalytic performances. Nanoscale 2014;6:7019–24. 49. Zhang JW, Zhang L, Jia YY, Chen GX, Wang X, Kuang Q, et al. Synthesis of spatially uniform metal alloys nanocrystals via a diffusion controlled growth strategy: the case of Au-Pd alloy trisoctahedral nanocrystals with tunable composition. Nano Res 2012;5:618–29. 50. Liu J, Yang TY, Wang DW, Lu GQM, Zhao DY, Qiao SZ. A facile soft-template synthesis of mesoporous polymeric and carbonaceous nanospheres. Nat Commun 2013;4:2798. 51. Boles MA, Ling D, Hyeon T, Talapin DV. The surface science of nanocrystals. Nat Mater 2016;15:141–53. 52. Liang HF, Jiang XD, Qi ZB, Chen W, Wu ZT, Xu BB, et al. Hematite concave nanocubes and their superior catalytic activity for low temperature CO oxidation. Nanoscale 2014;6:7199–203. 53. Wu HB, Chen JS, Lou XW, Hng HH. Asymmetric anatase TiO₂ nanocrystals with exposed high-index facets and their excellent lithium storage properties. Nanoscale 2011;3:4082–4.

References

217

54. Hu JQ, He HC, Li L, Zhou X, Li ZS, Shen Q, et al. Highly symmetrical, 24-faceted, concave BiVO4 polyhedron bounded by multiple high-index facets for prominent photocatalytic O2 evolution under visible light. Chem Commun 2019;55:4777–80. 55. Deng C, Wu P, Li H, Zhu H, Chao Y, Tao D, et al. Engineering polyhedral high entropy oxide with high-index facets via mechanochemistry-assisted strategy for efficient oxidative desulfurization. J Colloid Interface Sci 2023;629:569–80. 56. Ahmed S, Kazmi WW, Butt FN, Irshad M, Sher F, Kazmi SMA, et al. Fabrication of nanocage structured based electrocatalyst for oxygen evolution reactions. Mater Lett 2023;331:133416. 57. Qi X, Balankura T, Zhou Y, Fichthorn KA. How structure-directing agents control nanocrystal shape: polyvinylpyrrolidone-mediated growth of Ag nanocubes. Nano Lett 2015;15:7711–7. 58. Sun S, Zhang X, Cui J, Yang Q, Liang S. High-index faceted metal oxide micro-/nanostructures: a review on their characterization, synthesis and applications. Nanoscale 2019;1:15739. 59. Wang SS, Wu CW, Chen PY, Lee CL. Preferential deposition of gold and platinum atom on palladium nanocube as catalysts for oxidizing glucose in the phosphate-buffered solution. J Electroanal Chem 2023; 7:117142. 60. Kelly CHW, Benedetti TM, Alinezhad A, Schuhmann W, Gooding JJ, Tilley RD. Understanding the effect of Au in Au–Pd bimetallic nanocrystals on the electrocatalysis of the methanol oxidation reaction. J Phys Chem C 2018;122:21718–23. 61. Zhang N, Yan H, Chen X, An L, Xia Z, Xia D. Origins for the synergetic effects of AuCu3 in catalysis for oxygen reduction reaction. J Phys Chem C 2014;119:907–12. 62. Xiong L, Sun Z, Zhang X, Zhao L, Huang P, Chen X, et al. Octahedral gold-silver nanoframes with rich crystalline defects for efficient methanol oxidation manifesting a CO-promoting effect. Nat Commun 2019;10:3782. 63. Pedireddy S, Lee HK, Tiju WW, Phang IY, Ru H, Tan H, et al. One-step synthesis of zero-dimensional hollow nanoporous gold nanoparticles with enhanced methanol electrooxidation performance. Nat Commun 2014;5:4947. 64. Ahn J, Wang D, Ding Y, Zhang J, Qin D. Site-selective carving and Co-deposition: transformation of Ag nanocubes into concave nanocrystals encased by Au–Ag alloy frames. ACS Nano 2018;12:298–307. 65. Villa A, Thaw CEC, Hammond Veith GM, Wang D, Manzoli M, et al. Characterisation of gold catalysts. Chem Soc Rev 2016;45:4953–94. 66. Van Deelen TW, Meija CH, de Jong KP. Control of metal-support interactions in heterogeneous catalysts to enhance activity and selectivity. Nat Catal 2019;2:955–70. 67. Gao W, Tang X, Yi H, Jiang S, Yu Q, Xie X, et al. Mesoporous molecular sieve-based materials for catalytic oxidation of VOC: a review. J Environ Sci 2023;125:112–34. 68. Ewbank JL, Kovarik L, Diallo FZ, Sievers C. Effect of metal–support interactions in Ni/Al2O3 catalysts with low metal loading for methane dry reforming. Appl Catal 2015;494:57–67. 69. Lokteva ES, Golubina EV. Metal-support interactions in the design of heterogeneous catalysts for redox processes. Pure Appl Chem 2019;91:609–31. 70. Lykhach Y. Counting electrons on supported nanoparticles. Nat Mater 2016;15:284–8. 71. Villa A, Wang D, Su DS, Prati L. New challenges in gold catalysis: bimetallic systems. Catal Sci Technol 2015; 5:55–68. 72. Sarkany A, Horvath A, Beck A. Hydrogenation of acetylene over low loaded Pd and Pd-Au/SiO2 catalysts. Appl Catal, A 2012;229:117–25. 73. Edwards JK, Pritchard J, Lu L, Piccinini M, Shaw G, Carley AF, et al. The direct synthesis of hydrogen peroxide using platinum-promoted gold-palladium catalysts. Angew Chem, Int Ed 2014;53:2381–4. 74. Yoshida H, Kuwauchi Y, Jinschek JR, Sun K, Tanaka S, Kohyama M, et al. Visualizing gas molecules interacting with supported nanoparticulate catalysts at reaction conditions. Science 2012;335:317–9.

218

11 Synergistic effect in bimetallic gold catalysts

75. Gonzalez J, Henanderz J, Haro ML, Rio ED, Delgado J, Hungria A, et al. 3 D Characterization of gold nanoparticles supported on heavy metal oxide catalysts by HAADF-STEM electron tomography. Angew Chem 2009;121:5417–9. 76. Silva TAG, Neto ET, Lopez N, Rossi LM. Volcano-like behavior of Au-Pd core-shell nanoparticles in the selective oxidation of alcohols. Sci Rep 2014;4:1–5. 77. Long J, Liu H, Wu S, Liao S, Li Y. Selective oxidation of saturated hydrocarbons using Au–Pd alloy nanoparticles supported on metal–organic frameworks. ACS Catal 2013;3:667–54. 78. Torrente-Murciano L, Villager T, Chadwick D. Selective oxidation of salicylic alcohol to aldehyde with O2/H2using Au-Pd on titanate nanotubes catalysts. ChemCatChem 2015;7:925–7. 79. Tiruvalam RC, Pritchard JC, Dimitratos N, Lopez-Sanchez JA, Edwards JK, Carley AF, et al. Aberration corrected analytical electron microscopy studies of sol-immobilized Au + Pd, Au{Pd} and Pd{Au} catalysts used for benzyl alcohol oxidation and hydrogen peroxide production. Faraday Discuss 2011;63:152–64. 80. Wang D, Villa A, Porta F, Prati L, Su D. Bimetallic gold/palladium catalysts: correlation between nanostructure and synergistic effects. J Phys Chem C 2008;112:8617. 81. Prymak O, Jakobi J, Rehbock C, Epple V, Barcikowski S. Crystallographic characterization of lasergenerated, polymer-stabilized 4 nm silver-gold alloyed nanoparticles. Mater Chem Phys 2018;207:442–56. 82. Rostek A, Breisch M, Loza K, Garcia PRAF, Oliveira CLP, Prymak O, et al. Wet-chemical synthesis of Pd-Au core-shell nanoparticles (8 nm): from nanostructure to biological properties. ChemistrySelect 2018;3: 4994–509. 83. Alizabeth SR, Biparva P, Goli HR, Khan BA, Ebrahimzadeh MA. Green synthesis of AuNPs by crocus caspius —investigation of catalytic degradation of organic pollutants, their cytotoxicity, and antimicrobial activity. Catalysts 2023;13:63. 84. Ristig S, Prymak O, Loza K, Gocyla M, Meyer-Zaika W, Heggen M, et al. Nanostructure of wet-chemically prepared, polymer-stabilized silver–gold nanoalloys (6 nm) over the entire composition range. J Mater Chem B 2015;3:465–674. 85. Oseghale CI, Abdalla AH, Uddin MKH, Hall PJ. Gold-based carbon-supported bimetallic catalysts for energy storage and biomedical applications. Microchem J 2019;149:103917. 86. Xing S, Feng Y, Tay YY, Chen T, Xu J, Pan M, et al. Reducing the symmetry of bimetallic Au@Ag nanoparticles by exploiting eccentric polymer shells. J Am Chem Soc 2010;132:9537–9. 87. Loza K, Epple M. Silver nanoparticles in complex media: an easy procedure to discriminate between metallic silver nanoparticles, reprecipitated silver chloride, and dissolved silver species. RSC Adv 2018;8: 24386–9. 88. Suzuki S, Suzuki T, Tomita Y, Hirano M, Okazaki KI, Kuwabata S, et al. Compositional control of AuPt nanoparticles synthesized in ionic liquids by the sputter deposition technique. CrystEngComm 2012;14: 4922–8. 89. Newbury DE, Ritchie NWM. Is scanning electron microscopy/energy dispersive X-ray spectrometry (SEM/ EDS) quantitative? Scanning Microsc 2013;35:141–8. 90. Alshammari A, Kalevaru VN, Martin A. Bimetallic catalysts containing gold and palladium for environmentally important reactions. Catalysts 2016;6:97. 91. Dimitratos N, Villa A, Wang D, Porta F, Su D, Prati L. Pd and Pt catalysts modified by alloying with Au in the selective oxidation of alcohols. J Catal 2006;244:113. 92. Herzing AA, Watanabe M, Edwards JK, Conte M, Tang Z-R, Hutchings GJ, et al. Energy dispersive X-ray spectroscopy of bimetallic nanoparticles in an aberration corrected scanning transmission electron microscope. Faraday Discuss 2008;138:337–51. 93. Peng YK, Hu Y, Chou HL, Fu Y, Teixeira IF, Zhang L, et al. Mapping surface-modified titania nanoparticles with implications for activity and facet control. Nat Commun 2017;8:675–81. 94. Barrabes N, Ostolaza J, Reindl S, Mahr M, Schrenk F, Drexler H, et al. Doped metal clusters as bimetallic AuCo nanocatalysts: insights into structural dynamics and correlation with catalytic activity by in situ spectroscopy. Faraday Discuss 2023;19:1052–61.

References

219

95. Clarke R, Lamelas FJ, Hui HD, Baudelet F, Dartyge E, Fontaine A. X-ray scattering and absorption studies of epitaxial strains in Co-Au superlattices. J Magn Magn Mater 1991;93:53–7. 96. Fang YL, Zhao Z, Heck KN, Pretzer LA, Guo N, Wu T, et al. Thermal annealing effects on palladiumdecorated gold nanoparticle catalysts. J Catal 2022;410:246–55. 97. Mkhondwane ST, Matshitse R, Nyokong T. Porphyrin-graphitic carbon nitride quantum dots decorated on titanium dioxide electrospun nanofibers for photocatalytic degradation of organic pollutants. J Coord Chem 2022;75:15–6. 98. Turcsanyi A, Ungor D, Wojnicki M, Csapo E. Protein-stabilized bimetallic Au/Ag nanoclusters as fluorescent reporters: synthesis, characterization and their interactions with biocolloids. J Mol Liq 2023;370:121002. 99. Su R, Tiruvalam R, Logsdail AJ, He Q, Downing CA, Jensen MT, et al. Designer titania-supported Au–Pd nanoparticles for efficient photocatalytic hydrogen production. ACS Nano 2014;8:3490–7. 100. Cattaneo S, Stucchi M, Villa A, Prati L. Gold catalysts for the selective oxidation of biomass‐derived products. ChemCatChem 2019;21:309–23. 101. Sandoval A, Aguilar A, Louis C, Traverse A, Zanella R. Bimetallic Au–Ag/TiO2 catalyst prepared by deposition–precipitation: high activity and stability in CO oxidation. J Catal 2011;281:40–9. 102. Davis SE, Ide MS, Davis RJ. Selective oxidation of alcohols and aldehydes over supported metal nanoparticles. Green Chem 2013;15:17–45. 103. Yan S, Gao L, Zhang S, Gao L, Zhang W, Li Y. Investigation of AuNi/C anode catalyst for direct methanol fuel cells. Int J Hydrogen Energy 2013;38:12838–46. 104. Oseghe EO, Maddila SM, Ndungu PG, Jonnaladda SB. Effect of surfactant concentration on active species generation and photocatalytic properties of TiO2. Appl Catal, B 2015;176:288–97. 105. Guo X, Xu M, She M, Zhu Y, Shi T, Chen Z, et al. Angew Chem 2020;132:2633–28. 106. Gopi T, Swetha G, Shekar SC, Gupta AK. Ozone assisted vapor phase selective catalytic oxidation of cyclohexane on the 13X zeolite supported cobalt oxides. J Environ Chem Eng 2017;5:4031–40. 107. Liu L, Tsunoyama Y, Akita T, Xie S, Tsukuda T. Aerobic oxidation of cyclohexane catalyzed by size-controlled Au clusters on hydroxyapatite: size effect in the sub-2 nm regime. ACS Catal 2011;1:2–6. 108. Wang K, Xue B, Wang JL, He ZH, Li SS, Wang D, et al. Construction of indium oxide/N-doped titanium dioxide hybrid photocatalysts for efficient and selective oxidation of cyclohexane to cyclohexanone. J Phys Chem C 2021;125:19791–801. 109. Shiraishi Y, Shiota S, Hirakawa H, Tanaka S, Ichikawa S, Hirai T. Titanium dioxide/reduced graphene oxide hybrid photocatalysts for efficient and selective partial oxidation of cyclohexane. ACS Catal 2017;7: 293–300. 110. Wang TC, Qu G, Li J, Lu N. Transport characteristics of gas phase ozone in soil during soil remediation by pulsed discharge plasma. Vacuum 2014;101:91–86. 111. Mkhondwane ST, Pullabhotla VSRR. Ozone initiated pH dependent oxidation of cyclohexane over Fe supported SiO2 and γ-Al2O3 catalysts. Top Catal 2022;7:1–16. 112. Seitz KW, Dombrowski N, Eme L, Spang A, Lombard J, Sieber JR, et al. Asgard archaea capable of anaerobic hydrocarbon cycling. Nat Commun 2019;10:1822. 113. Conte M, Liu X, Murphy DM, Whiston K, Hutchings GJ. Cyclohexane oxidation using Au/MgO: an investigation of the reaction mechanism. Phys Chem Chem Phys 2012;14:16279–85. 114. Schneider J, Matsuoka M, Takeuchi M, Zhang J, Horiuchi Y, Anpo M, et al. Understanding TiO2 photocatalysis: mechanisms and materials. Chem Rev 2014;114:9919–86. 115. Ye M, Zhang C, Liu Z, Li H, Fu Z, Zhang H, et al. E-waste derived CuAu bimetallic catalysts supported on carbon cloth enabling effective degradation of bisphenol A via an electro-Fenton process. Separ Purif Technol 2023;305:122507. 116. Xu C, Jin L, Wang X, Chen Y, Dai L. Honeycomb-like porous Ce–Au oxide/N-doped carbon nanostructure: achieving high catalytic performance for the selective oxidation of cyclohexane to KA oil. Carbon 2020;160: 287–97.

220

11 Synergistic effect in bimetallic gold catalysts

117. Pokutsa A, Bloniarz P, Fliun O, Kubaj Y, Zaborovskyi A, Paczeŝniak T. Sustainable oxidation of cyclohexane catalyzed by a VO(acac)2-oxalic acid tandem: the electrochemical motive of the process efficiency. RSC Adv 2020;10:10959–71. 118. Zhang T, Hu YQ, Han T, Zhai YQ, Zheng YZ. Redox-active cobalt(II/III) metal–organic framework for selective oxidation of cyclohexene. ACS Appl Mater Interfaces 2018;10:15786–92. 119. Wang L, Zhao S, Liu C, Li C, Li X, Li H, et al. Aerobic oxidation of cyclohexane on catalysts based on twinned and single-crystal Au75Pd25 bimetallic nanocrystals. Nano Lett 2015;15:2875–80. 120. Liu X, Conte M, Meenakshisundaram S, He Q, Murphy DM, Morgan D, et al. Liquid phase oxidation of cyclohexane using bimetallic Au–Pd/MgO catalysts. Appl Catal 2015;504:373–80. 121. Li Z, Gao F, Wang Y, Calaza F, Burkholder L, Tysoe WT. Formation and characterization of Au/Pd surface alloys on Pd(111). Surf Sci 2007;60:1898–908. 122. Sekhar YC, Raghavendra P, Chandana PS, Maiyalagan T, Sarma LS. Effects of gold nanoparticles (Au-NPs) on the electrical properties of reduced graphene oxide: an experimental and DFT study. J Phys Chem Solid 2023;174:111133. 123. Mierczynski P, Vasilev K, Mierczynska A, Maniukiewicz W, Ciesielski R, Rogowski J, et al. The effect of gold on modern bimetallic Au–Cu/MWCNT catalysts for the oxy-steam reforming of methanol. RSC Adv 2016;6: 4168–83. 124. Kelly CHW, Benedetti TM, Alinezhad A, Schuhmann W, Gooding JJ, Tilley RD. Understanding the effect of Au in Au–Pd bimetallic nanocrystals on the electrocatalysis of the methanol oxidation reaction. J Phys Chem C 2018;122:21718–23. 125. Sandoval A, Aguilar A, Louis C, Traverse A, Zanella R. Bimetallic Au–Ag/TiO2 catalyst prepared by deposition–precipitation: high activity and stability in CO oxidation. J Catal 2011;281:40–9. 126. Zhou CH, Xia X, Lin CX, Tong DS, Beltramini J. Catalytic conversion of lignocellulosic biomass to fine chemicals and fuels. Chem Soc Rev 2011;40:5588–97. 127. Wang Y, Akbarzadeh A, Chong L, Du J, Tahir N, Awasthi MK. Catalytic pyrolysis of lignocellulosic biomass for bio-oil production: a review. Chemosphere 2022;17:134181. 128. Gao J, Wang Z, Guo R, Hu Y, Dong XY, Shi Q, et al. Efficient cascade conversion of starch to gluconic acid by a chemoenzymatic system with co-immobilized Au nanoparticles and enzymes. Catal Sci Technol 2023;74: 264–73. 129. Della Pina C, Falletta E, Rossi M. Update on selective oxidation using gold. Chem Soc Rev 2012;41:350–69. 130. Zhang H, Okuni J, Toshima N. One-pot synthesis of Ag–Au bimetallic nanoparticles with Au shell and their high catalytic activity for aerobic glucose oxidation. J Colloid Interface Sci 2011;354:131–8. 131. Zhang H, Haba M, Okumura M, Akita T, Hashimoto S, Toshima N. Novel Formation of Ag/Au bimetallic nanoparticles by physical mixture of monometallic nanoparticles in dispersions and their application to catalysts for aerobic glucose oxidation. Langmuir 2013;29:10330–9. 132. Zhang H, Toshima N. Preparation of novel Au/Pt/Ag trimetallic nanoparticles and their high catalytic activity for aerobic glucose oxidation. Appl Catal 2011;400:9–13. 133. Gao Z, Xie R, Fan G, Yang L, Li F. Highly efficient and stable bimetallic AuPd over La-doped Ca–Mg–Al layered double hydroxide for base-free aerobic oxidation of 5-hydroxymethylfurfural in water. ACS Sustainable Chem Eng 2017;5:5852–61. 134. Ketchie WC, Murayama M, Davis RJ. Selective oxidation of glycerol over carbon-supported AuPd catalysts. J Catal 2007;250:264–73. 135. Mkhondwane ST, Pullabhotla VSRR. Cyclohexane oxidation using advanced oxidation processes with metals and metal oxides as catalysts: a review. Phys Sci Rev 2022. https://doi.org/10.1515/psr-2021-0146. 136. Magdalane CM, Kaviyarasu K, Vijaya JJ, Siddhardha B, Jeyaraj B. Elucidation of photocatalysis, photoluminescence and antibacterial studies of ZnO thin films by spin coating method. J Photochem Photobiol, B 2017;173:23–34. 137. Cheng L, Xiang Q, Liao Y, Zhang H. CdS-Based photocatalysts. Energy Environ Sci 2018;11:1362–91.

References

221

138. Kumar SG, Devi LG. Review on modified TiO2 photocatalysis under UV/visible light: selected results and related mechanisms on interfacial charge carrier transfer dynamics. J Phys Chem A 2011;115:13211–41. 139. Dai G, Liu S, Liang Y, Liu H, Zhong Z. A simple preparation of carbon and nitrogen co-doped nanoscaled TiO2 with exposed {001} facets for enhanced visible-light photocatalytic activity. J Mol Catal 2013;368: 38–42. 140. Peng YK, Keeling B, Li Y, Zheng J, Chen T, Chou HL, et al. Unravelling the key role of surface features behind facet-dependent photocatalysis of anatase TiO2. Chem Commun 2019;55:4415–8. 141. Czelej K, Wieka K, Colmenares JC, Kurzydlowski KJ, Xu Y. Toward a comprehensive understanding of enhanced photocatalytic activity of the bimetallic PdAu/TiO2 catalyst for selective oxidation of methanol to methyl formate. ACS Appl Mater Interfaces 2017;37:31825–33.

Ntandokazi Mabungela*, Ntaote David Shooto*, Fanyana Mtunzi and Eliazer Bobby Naidoo

12 Simultaneous removal of methylene blue, copper Cu(II), and cadmium Cd(II) from synthetic wastewater using fennel-based adsorbents Abstract: This work looked into viability of using fennel-based adsorbents to simultaneously eliminate cadmium, methylene blue, and copper from water solution. Phosphoric acid (H3PO4) and calcium hydroxide (Ca(OH)2) solutions were applied to the untreated fennel seeds (PFS) to yield H3FS and CaFS, respectively. The presence of –OH, –C–O–C–, and –C=O functional groups on the surface of the adsorbents was confirmed by FTIR results. XRD and UV–Vis results established hydrolysis of cellulose from fennel seeds. According to studies on time and kinetics, the adsorption process was relatively quick in the first 60 min. Furthermore, isotherm models showed that the results fit Langmuir model more closely. This finding indicated that uptake takes place on uniform active sites on adsorbent surfaces. Studies on thermodynamics showed that the adsorption procedure was advantageous and practicable. PFS, H3FS, and CaFS had maximum Cu(II) adsorption capacities of 7.208, 5.504, and 5.791 mg/g. It was 2.274, 5.021, and 12.3 mg/g for Cd(II) by PFS, H3FS, and CaFS. PFS, H3FS, and CaFS could adsorb MB to a maximum of 11.114, 4.071, and 18.468 mg/g. Reusability studies of the adsorbents were also evaluated and the results suggested that the adsorbents can be recycled a number of times. Keywords: adsorption; cadmium; copper; fennel seeds; methylene blue.

12.1 Introduction Water contamination with pollutants like heavy metal ions and methylene blue dyes (MB) is a significant problem. The primary sources of these pollutants are mainly nontreated industrial influents [1]. These pollutants are not easily degraded, carcinogenic and toxic to living organisms and the ecosystem [2]. The goal of this research is to

*Corresponding authors: Ntandokazi Mabungela and Ntaote David Shooto, Department of Chemistry, Applied Chemistry and Nano Science Laboratory, Vaal University of Technology, P.O. Box X021, 1900, Vanderbijlpark, South Africa, E-mail: [email protected] (N. Mabungela), [email protected] (N.D. Shooto) Fanyana Mtunzi and Eliazer Bobby Naidoo, Department of Chemistry, Applied Chemistry and Nano Science Laboratory, Vaal University of Technology, P.O. Box X021, 1900, Vanderbijlpark, South Africa As per De Gruyter’s policy this article has previously been published in the journal Physical Sciences Reviews. Please cite as: N. Mabungela, N. D. Shooto, F. Mtunzi and E. B. Naidoo “Simultaneous removal of methylene blue, copper Cu(II), and cadmium Cd(II) from synthetic wastewater using fennel-based adsorbents” Physical Sciences Reviews [Online] 2023. DOI: 10.1515/psr-2022-0329 | https://doi.org/10.1515/9783111071428-012

224

12 Simultaneous removal of methylene blue, Cu(II) and Cd(II)

simultaneously remove methylene blue, copper, and cadmium from synthetic wastewater. Drinking water contaminated with Cu(II) and Cd(II) causes increased blood pressure, destruction of testicular tissue, kidney damage, cancer, metabolic disorders, destruction of red blood cells, and osteoporosis [3, 4]. Exposure to water contaminated with MB dye leads to induced tissue cells, painful urination, mental confusion and eye injury [5, 6]. Heavy metals have been removed from wastewater using a variety of processes, such as chemical precipitation, adsorption, membrane filtration, reverse osmosis, ion exchange, and coagulation [7–10]. The most favored technique for pollutants removal is adsorption. The method is simple, inexpensive, practical, does not produce dangerous derivatives, and is unselective [11, 12]. The utilization of plant biomass materials as potential biosorbents for removing pollutants from wastewater has gained popularity. These natural materials are inexpensive, accessible, and renewable, making them environmentally friendly [13]. The main goal of current research is to modify agricultural materials chemically or physically in order to increase their capacity for adsorption [14, 15]. Physically treated biomaterials are less efficient for adsorption [16]. As a result, adding acids and bases to biomaterials increases the amount of oxygen-containing functional groups (–COOH and –C=O) on the surfaces of the materials. These functional groups are advantageous for removing toxic heavy metal ions and dyes [17, 18]. This study used fennel seeds (Foeniculum vulgare) as a biosorbent for methylene blue, copper Cu(II) and cadmium Cd(II). Fennel is biodegradable, renewable, and non-toxic, making it eco-friendly. It is also inexpensive and readily available [19]. Fennel has a high affinity for heavy metals leading to faster mass transfer due to carboxylic acids, phenolic acids and lactonic acids [20]. Few studies have reported the use of fennel seeds as an adsorbent for pollutants. These include Cd(II) [21], Zn(II) [20] and dyes (methylene blue and crystal violet) [22], Congo red [23], and ethidium bromide [19]. Modified fennel seeds have not been utilized to simultaneously remove pollutants. This work aimed to remove methylene blue dye, Cd(II), and Cu(II) simultaneously using virgin fennel seeds (PFS) and modified fennel seeds. This was done by chemically treating PFS with phosphoric acid (H3PO4) and Ca(OH)2 and characterize using UV–Vis, SEM, FTIR, and XRD. Concentration effect, pH, temperature, and contact time were investigated. The mechanism of the adsorption process was also determine.

12.2 Resources and procedures 12.2.1 Resources Virgin fennel seeds were bought at Dischem in Vanderbijlpark, South Africa. Calcium hydroxide (Ca(OH)2)-95%, potassium nitrate (KNO3), sodium hydroxide pellets-(NaOH)-

12.2 Resources and procedures

225

98.5%, hydrochloric acid (HCl)-32%, phosphoric acid (H3PO4)-75%, copper nitrate hydrate (Cu(NO3)2)·(2H2O)-99.95%, cadmium acetate (Cd(CH3CO2)2)-99.95%, and methylene blue (C16H18ClN3S)-95% were purchased from Sigma-Aldrich, Johannesburg, South Africa LTD.

12.2.2 Method used to produce the adsorbents 12.2.2.1 Virgin fennel seed (PFS) Fennel seeds were cleaned, blended, and labeled (PFS). The other adsorbents were made using the PFS. 12.2.2.2 Acid treated adsorbent (H3FS) Twenty grams of the PFS were placed in 1000 mL 0.1 M H3PO4 solution. After stirring the mixture for 120 min, it was given another 120 min to settle. The sediment was labeled H3FS after drying in an oven for three days at 50 °C. 12.2.2.3 Base treated adsorbent (CaFS) preparation Twenty grams of the PFS were placed in 1000 mL 0.1 M Ca(OH)2 solution. After stirring the mixture for 120 min, it was given another 120 min to settle. The sediment was labeled CaFS after drying in an oven for three days at 50 °C.

12.2.3 Methods of adsorption preparation Using Cu(NO3)2, Cd(CH3CO2)2, and (C16H18ClN3S) salts, a 100 mg/L stock solution was created by dissolving 1 g of each salt in a 1 L volumetric flask. On the standard working solutions (20–100 mg/L), the solution᾽s initial concentration was measured at 298 K for 120 min. At 298 K, contact time was measured 5–120 min apart using a 100 mg/L working solution. A working solution of 100 mg/L was used to test the effects of pH at 298 K for 2 h at various pH levels starting at 1–8. On a standard working solution of 100 mg/L, the temperature effect was assessed at 288, 298, and 308 K for 2 h. For each parameter, precisely 0.1 g of the adsorbent was added to 20 mL of the designated standard working solution in capped vials. The mixture was then stirred utilizing orbital shaker at 200 rpm for 2 h. Duplicate samples were prepared to confirm the repeatability of the results. Centrifugation was used to separate the solid from the solution for 5 min at a speed of 4000 rpm. AAS and a UV–Vis spectrometer were used to analyse the supernatant [24].

226

12 Simultaneous removal of methylene blue, Cu(II) and Cd(II)

12.2.4 Point zero charge process Separately, 0.1 g of PFS, CaFS, and H3FS adsorbents were added to a centrifuge flask. Using 0.1 M HCl and 0.1 M NaOH, a 0.1 M KNO3 solution was created and pH levels ranging from 1 to 12 were desired. The centrifuge flasks containing the adsorbents were filled with 20 mL of the KNO3 solution that had been adjusted. After that, the solution was shaken for 24 h. pH levels were checked both before and after agitation [25].

12.2.5 Reusability procedure Cd, Cu, and MB were prepared as a 100 mg/L solution by dissolving 0.1 g of each metal salt in 1 L of ultrapure water. In a 500 mL volumetric flask, a 0.05 M HCl solution was made. Each adsorbent was weighed precisely 0.1 g in a 50 mL centrifuge flask. Each flask received exactly 20 mL of the Cd/Cu/MB solution before being shaken for 2 h and centrifuged for 5 min. The results following adsorption were examined by AAS, and this was regarded as the initial or first step. The same centrifuge flask that contained the used adsorbent received 20 mL of 0.05 M HCl solution, which was then shaken for 60 min and centrifuged for 5 min [24].

12.2.6 Adsorption data management The following equation was used to calculate the adsorption capacity (qe) of Cd(II), Cu(II), and MB ions at equilibrium: qe =

(C o – C e ) V W

(12.1)

Isotherms models Langmuir and Freundlich models were calculated using the equations. Qo bC e 1 +bC e

(12.2)

qm = k f C e1/n

(12.3)

qe =

Pseudo-first-order, pseudo-second-order, and intraparticle diffusion models were calculated using the following nonlinear equations: qe = qt (1 − e−k1 t ) qe =

1 + k 2 qe t k 2 q2e t

qt = k i (t1/2 ) + C

(12.4) (12.5) (12.6)

12.4 Results and discussion

227

The following equations were used to estimate the thermodynamic parameters: enthalpy change (ΔH⁰), Gibbs free energy (ΔG⁰), and entropy change (ΔS⁰): In K c = −

ΔH o ΔS o − RT R

ΔGo = −RT In K c

(12.7) (12.8)

12.3 Characterization of the adsorbents The functional groups and surface morphology of the PFS, CaFS, and H3FS adsorbents were identified using FTIR and SEM. A Nicolet iS50 FTIR spectrometer was used to determine the functional groups. A Joel-JSMIT 500 was used to capture SEM images. The concentration of MB following adsorption was determined using an EVOLUTION 220 UV-Visible spectrometer, which was also used to characterize the adsorbents. Cu(II) and Cd(II) sample concentrations were determined using an AAS Shimdzu SAC 7000 autosampler.

12.4 Results and discussion 12.4.1 Ultraviolet–Visible spectroscopy results The UV–Vis findings for the PFS, H3FS, and CaFS are shown in Figure 12.1. The findings showed that the PFS showed a peak at 669 nm (0.575). Upon modification, this peak became broad, blue shifted to a wavelength and its intensity reduced to 680 nm (0.480)

Figure 12.1: UV–Vis results for PFS, CaFS, and H3FS.

228

12 Simultaneous removal of methylene blue, Cu(II) and Cd(II)

and 675 nm (0.238) for CaFS and H3FS, respectively. The peak shifts could suggest the removal and hydrolysis of material after being chemically modified with phosphoric acid and calcium hydroxide [10, 26].

12.4.2 X-ray crystallography results Figure 12.2 displays the XRD results for PFS, CaFS, and H3FS. The diffraction peak at 2θ = 19.46° for PFS is ascribed to the presence of cellulose [1, 27]. This peak showed decreased intensity for CaFS and H3FS, with diffraction peaks at 20.10° for CaFS and 19.75° for H3FS, respectively. The decrease in intensity was ascribed to the hydrolysis of cellulose during chemical treatment with Ca(OH)2 and H3PO4. Other characteristic peaks for CaFS were found at 2θ = 14.31, 15.01, 24.35, 30.01, 32.20, 36.01, and 38.19°. The noncrystalline nature of the adsorbent may have caused these peaks [28].

12.4.3 Scanning electron microscope and energy dispersive X-ray analysis results The surface morphologies and chemical composition of PFS, CaFS, and H3FS were determined using SEM and EDX, as shown in Figure 12.2(a–c). The high magnification SEM images showed that PFS, CaFS, and H3FS had rough surface morphologies with cavities. It is expected that adsorbents with uneven surfaces and cavities would favor adsorption by providing access to active binding sites [29]. EDX showed that the main components of the adsorbents are carbon (C) and oxygen (O). Trace amounts of Na, K, Mg, Ca, Al, P, and Pb elements were observed on the PFS surface. Some of these substances might be helpful for the cation exchange process that adsorbs metal ions [30].

Figure 12.2: XRD results for PFS, CaFS, and H3FS.

12.4 Results and discussion

229

12.4.4 Fourier transform infrared spectroscopy results Figure 12.3 displays the adsorbents᾽ FTIR spectra. Protruding peaks at 3287.86 cm−1 in the spectrum were ascribed to the –OH group from phenolic and carboxylic groups. This –OH peak widened, lost intensity, and shifted to 3284.74 and 3270.06 cm−1 for CaFS and H3PFS,

(a)

(b)

(c)

Element

Mass %

Atom %

C

54.37±0.07

63.93±0.08

O

38.75±0.16

34.21±0.14

Na

0.40±0.02

0.24±0.01

Mg

0.75±0.02

0.44±0.01

Al

0.16±0.01

0.09±0.01

K

0.38±0.02

0.14±0.01

Ca

2.09±0.03

0.74±0.01

Pb

3.09±0.07

0.21±0.00

Total

100 %

100 %

Element

Mass %

Atom %

C

54.42±0.51

79.06±0.74

O

16.86±0.62

18.39±0.67

Au

28.71±2.06

2.54±0.18

Total

100 %

100 %

Element

Mass %

Atom %

C

73.95±0.94

79.86±1.02

O

23.56±1.56

19.10±1.27

P

2.49±0.31

1.04±0.13

Total

100 %

100 %

Figure 12.3: Scanning electron microscope and energy dispersive X-ray analysis results for (a) PFS, (b) CaFS, and (c) H3FS.

230

12 Simultaneous removal of methylene blue, Cu(II) and Cd(II)

respectively. At 3005 cm−1, a –HC=CH– group peak was seen. The two distinct peaks at 2918 and 2849 cm−1 were attributed to –CH3 and –CH2. Due to the presence of the –COOH group, the peaks at 1742.18 and 1597.18 cm−1 are ascribed to –C=O, and this peak at 1597.18 cm−1 has been moved to 1619 and 1645 cm−1 for CaFS and H3PFS, respectively. The peak at 1027.43 cm−1 was attributed to –C–O–C– because cellulose was present. This peak shifted and decreased in intensity for CaFS and H3PFS to wavenumbers of 1030.07 and 981.04 cm−1 [22]. The changes in intensities and peaks observed after chemical modification indicate that the material was hydrolysed (Figure 12.4).

12.4.5 Point zero charge (pH(pzc)) The pH at which the net charge on the adsorbent᾽s surface equals zero is known as point zero charge (pHpzc) (Table 12.1). The pzc results for PFS, H3FS, and CaFS are displayed in Table 12.2. PFS, H3FS, and CaFS had pH(pzc)s of 7.5, 3.2, and 6, respectively. This suggests that the surface functional groups of the adsorbent were protonated below their pzc, whereas they were deprotonated above the pH(pzc). At pH 8, all adsorbents had their highest adsorption capacity [24].

Figure 12.4: FTIR spectrum for PFS, H3FS, and CaFS. Table .: Point zero charge of PFS, HFS, and CaFS. Adsorbents

PFS

HFS

CaFS

pH(pzc)

.

.

.

12.4 Results and discussion

231

Table .: Isotherms results. HFS

PFS Langmuir Qe B R

CaFS

Cu(II) . . .

Cd(II) . . .

MB . . .

Cu(II) . . .

Cd(II) . . .

MB . . .

Cu(II) . . .

Cd(II) . . .

MB . . .

. . . .

. . . .

. . . .

. . . .

. . . .

. . . .

. . . .

. . . .

. . . .

Fruendlich K N R Exp. qe

12.4.6 Effect of concentration The results of the investigation into the influence of concentration on the removal of MB, Cu(II), and Cd(II) in the range of 20–100 mg/L are displayed in Figure 12.5(a–c). The outcome demonstrated that the uptake of all adsorbents increased as the concentration of

Figure 12.5: Concentration effect for Cu(II), Cd(II), and MB adsorption by (a) PFS, (b) H3FS, and (c) CaFS.

232

12 Simultaneous removal of methylene blue, Cu(II) and Cd(II)

the solution was raised from 20 to 100 mg/L. When compared to the initial concentration of 100 mg/L, the uptake was modest at 20 mg/L. This is attributed to low mass transfer because at low initial concentrations there is less interaction between the metal ionsMB solution and the adsorbents surface [31]. Moreover, at greater concentrations (100 mg/L), more interactions between the surfaces of the adsorbents and the adsorbate solution occurred, resulting in higher mass transfer and high adsorption capacity [1, 32]. Higher adsorption capacity for MB on CaFS and PFS was observed and this showed that these adsorbents had a stronger attraction for MB dye than the metal ions [24]. When the solution concentration was 100 mg/L, the high adsorption capacities through PFS for MB, Cd(II), and Cu(II) were 14.066, 12.274, and 5.504 mg/g, respectively. Cu(II), Cd(II), and MB had the highest H3FS adsorption rates at 7.208, 5.021, and 4.071 mg/g, respectively. Cu(II), Cd(II), and M.B. were 5.791, 12.300, and 18.468 mg/g for CaFS, respectively.

12.4.7 Isotherm studies Adsorption isotherm models provide critical information about the adsorbent and metal solution interaction. In this study, the adsorption behavior and nature of Cu(II), Cd(II), and MB on the surface of PFS, H3FS, and CaFS were assessed using the Langmuir and Freundlich isotherm models. The accuracy of the fit of experimental results to isotherms is assessed using the correlation coefficient (R 2). An (R 2) closer to 1 indicates a better fit [33]. With R2 greater than the Freundlich model, Table 12.2 demonstrates that the adsorption process better suited the Langmuir model. This indicates that the adsorption process occurred on the binding sites of the adsorbents homogenously [34, 35]. Therefore, the adsorption process could not be described by the Freundlich model.

12.4.8 Effect of time The ability of PFS, H3FS, and CaFS to adsorb Cu(II), Cd(II), and MB dye over time intervals of 5, 10, 15, 20, 30, 45, 60, and 120 min is shown in Figure 12.6. As the time passed, the adsorption rate accelerated. The prolonged exposure time between the adsorbate and MB and the adsorption sites on the adsorbents may be the cause of the enhanced adsorption [36]. The best adsorption rate was observed in the first 5–60 min for Cd(II) on all the adsorbents, MB by H3FS and Cu(II) by CaFS. Rapid adsorption occurred in the first 30 min for MB by CaFS and 10 min for MB by PFS and Cu(II) by H3FS. However, after 60 min, the adsorption rate slowed down, especially for MB and Cu(II) by PFS, H3FS, and CaFS. This slower rate may be attributed to the overload of the active sites [37] (Table 12.3).

12.4 Results and discussion

233

Figure 12.6: Time effect for Cu(II), Cd(II), and MB adsorption by (a) PFS, (b) H3FS, and CaFS.

Table .: Kinetics studies. Model

PFO

PSO

IPD

EPA ESA Exp.

PFS

qe k R qe k R C kf R

qe

HFS

CaFS

Cu(II)

Cd(II)

MB

Cu(II)

Cd(II)

MB

Cu(II)

Cd(II)

MB

. . . . . . . . . . . .

. . . . . . . . . . . .

. . . . . . . . . . . .

. . . . . . . . . . . .

. . . . . . . . . . . .

. . . . . . . . . . . .

. . . . . . . . . . . .

. . . . . . . . . . . .

. . . . . . . . . . . .

234

12 Simultaneous removal of methylene blue, Cu(II) and Cd(II)

12.4.9 Kinetic model studies Kinetic models such as pseudo-first-order (PFO) and pseudo-second-order (PSO) were evaluated for the uptake of MB, Cu(II), and Cd(II) by PFS, H3PO4, and CaFS. The data show that PSO was the preferred model for metal ions and MB adsorption. This was deduced by linking the coefficient of determination value of PFO and PSO. The coefficient of determination values of PSO were closer to 1 for all the adsorbents ranging from 0.952–0.998 whereas that of PFO ranged from 0.510–0.996. Therefore, PSO described the kinetic data suggesting that adsorption involved a chemical process and the adsorbents and the adsorbate were drawn together electrostatically [2, 38]. Furthermore, the nature of adsorption—whether it took place on the surface or through the pores of the adsorbents —was assessed using the intraparticle diffusion (IPD) model. According to the data, surface adsorption predominated [39].

12.4.10 Effect of temperature Figure 12.7a–c displays the results for the effect of temperature on the uptake of the adsorbate by PFS, H3FS, and CaFS. The adsorption increased as the system’s temperature increased. This surge indicated that the adsorption capacities followed an endothermic process [40]. The adsorbent’s surface becomes activated by the rise in temperature, which also causes the pore size to increase. The collision between the adsorbent’s surface and the adsorbates becomes more intense as the temperature rises [41].

12.4.11 Thermodynamics studies At temperatures of 288, 298, and 308 K, the thermodynamic variables like the change in entropy (ΔS o), enthalpy (ΔH o), and Gibbs free energy (ΔGo) were calculated. The calculated (ΔS o) values are positive, indicating that there are more collisions taking place between the solutions of metal ions and the adsorbents’ surfaces during adsorption process. The adsorption process was endothermic and that was supported by the positive ΔH o values. The favorable and practicable adsorption process was indicated by the calculated positive ΔGo values [42] (Table 12.4).

12.4.12 Effect of pH Figure 12.8 illustrates the results of an investigation into how pH affects adsorption processes. With an increase in solution pH, it was found that Cu(II), Cd(II), and MB adsorption uptake increased. Due to the presence of hydrogen ions, which results in a net positive charge on the adsorbent’s surface and electrostatic repulsion against adsorbate, the adsorption capacity was low at low pH, and prevented the adsorption of pollutants

12.4 Results and discussion

235

Figure 12.7: Temperature effect on (a) PFS, (b) H3FS, and (c) CaFS for Cu(II), Cd(II), and MB. Table .: Thermodynamic studies. Parameters

HFS

PFS Cu(II)

Cd(II)

MB

Cu(II)

Cd(II)

CaFS MB

Cu(II)

Cd(II)

MB

ΔS°(KJ, mol−) . . . . . . . . . ΔH°(KJ, mol−) ,  . .  . .  . ΔG°(KJ, mol−) °K −. −. −. −. −. −. −. −. −. °K −. −. −. −. −. −. −. −. −. °K −. −. −. −. −. −. −. −. −.

onto the adsorbent [43]. However, a decrease in the repulsive forces between the adsorbate and hydrogen ions is responsible for an increase in adsorption capacity. By dominating the available H3O+ ions at this point, the adsorbate ions create more readily available negatively charged binding sites, which improve the removal of metal ions and MB by the adsorbents. The capacity for adsorption decreases when metal ions precipitate at pH levels higher than 7, which prevents the movement of the metal ions [48].

236

12 Simultaneous removal of methylene blue, Cu(II) and Cd(II)

Figure 12.8: pH effect on (a) PFS, (b) H3FS, and (c) CaFS for MB, Cu(II), and Cd(II).

12.4.13 Proposed mechanism reaction The surface of the adsorbents had hydroxyl (–O–H) and carbonyl (–C=O, –COOH) functional groups, which improved the adsorption process through hydrogen bond and electrostatic attraction interactions, and that was confirmed by FTIR spectra of PFS, H3FS, and CaFS adsorbents before and after adsorption [2, 44] (Scheme 12.1).

12.4.14 Reusability studies Figure 12.9 shows the results of the recycling and recovery of the fennel seeds’ adsorbents. The findings revealed that after cycle 3, the adsorption capacity decreased. However, results showed that the adsorbents are stable and can be reused.

12.4 Results and discussion

Scheme 12.1: Adsorption mechanism of Cu(II), Cd(II), and MB on PFS, H3FS, and CaFS.

Figure 12.9: Recycling results of (a) PFS, (b) H3FS, and (c) CaFS for Cu(II), Cd(II), and MB.

237

238

12 Simultaneous removal of methylene blue, Cu(II) and Cd(II)

12.4.15 Comparison studies of the qemax of the current adsorbents with previous studies For MB, Cu(II), Cd(II), and MB, the maximum biosorption capacities of the modified fennel seeds used in this study are compared to those of other biomaterials (Table 12.5). The modified fennel seeds performed better than some of the other biomaterials. It is critical to indicate that modified fennel seeds can remove toxic heavy metals simultaneously, with an average qe higher than the other materials.

12.5 Post adsorption results 12.5.1 FTIR results after adsorption After adsorption, the FTIR spectra for PFS, H3FS, and H3FS were compared to examine the functional groups involved in adsorption. After adsorption, the broad –OH peak decreased in intensity and shifted from 3287.86 to 3323.63 cm−1. Following the adsorption of the metal ions and MB, the –C=O group vanished. The intensity of the –C–O– group at wavenumber 1027.43 cm−1 dropped and shifted to 1057.75 cm−1. This suggests that the functional groups –OH, –C=O, and –C–O– were involved in the adsorption process. Therefore, the adsorption is pi-bonding complexation, electrostatic attraction, hydrogen bond, and cation exchange adsorption (Figure 12.10) [25].

Table .: Comparison studies. Adsorbents

Pollutants

Qe (mg/g)

pH

Conc. (mg/L)

Time (min)

Black cumin seeds Chlorella vulgaris biomass Litchi chinensis peel biomass Waste fennel seeds HPO treated fennel seeds Ca(OH) treated fennel seeds Fennel seeds Oak seeds HPO treated fennel seeds Ca(OH) treated fennel seeds Spent coffee grounds Wood powder peel HPO treated fennel seeds Ca(OH) treated fennel seeds

Cd(II) Cd(II) Cd(II) Cd(II) Cd(II) Cd(II) MB MB MB MB Cu(II) Cu(II) Cu(II) Cu(II)

. . . . . . .  . . . . . .

   – . .   . . – . . .

  –     –   – –  

   –    –   –   

Reference [] [] [] [] Current study Current study [] [] Current study Current study [] [] Current study Current study

12.6 Conclusions

239

Figure 12.10: FTIR results of PFS, H3FS, and H3FS after adsorption.

12.6 Conclusions To obtain the desired H3FS and CaFS adsorbents for the ternary adsorption of Cu(II), Cd(II), and MB from an aqueous solution, pure fennel seeds (PFS) were chemically treated using H3PO4 and Ca(OH)2. The UV–Vis and XRD results revealed that cellulose was hydrolysed on H3FS and CaFS. The surface of CaFS became more extensive than that of PFS and H3FS. The FTIR results demonstrated carboxyl, carbonyl, and hydroxyl groups, which enhanced the adsorption process. As the initial concentration rose from 20 to 100 mg/L, the initial concentration-effect revealed an increased adsorption capacity. Because there were fewer metal ions and MB in the solution, there was less of a magnetic attraction between the metal ions and MB, which may have contributed to the poor uptake at the lower initial concentration. The presence of more metal ions and MB, however, results in a strong repulsion effect at high initial concentrations. The maximum uptake for Cu(II), Cd(II), and MB obtained by PFS at 100 mg/L was 5.504, 12.274, and 14.066 mg/g, respectively. Cu(II), Cd(II), and MB uptake by H3FS was 7.208, 5.021, and 4.071 mg/g, respectively. For Cu(II), Cd(II), and MB, the adsorption by CaFS was 5.791, 12.300, and 18.468 mg/g, respectively. The time effect revealed a rapid uptake for the first 10 min for MB by PFS and Cu(II) by H3FS. On the other hand, the rapid uptake of MB by CaFS was seen in the first 30 min. All of the adsorbents quickly absorbed Cd(II) over the course of the first 60 min. This also occurred for MB by H3FS and Cu(II) by CaFS. The accumulation of active species and cavities on the adsorbents’ surface, however, caused the adsorption to decline after 60 min. The kinetic experiment showed that the surface of the adsorbent and the adsorbate were electrostatically attracted to one another. The adsorption process was endothermic, beneficial, and practicable, as shown by the

240

12 Simultaneous removal of methylene blue, Cu(II) and Cd(II)

temperature effect and thermodynamics. Because there were more collisions between the metal ions, MB, and the adsorbent’s surface, the S0 values demonstrated that the adsorption was advantageous. For all of the adsorbents, an improvement in uptake occurred as the initial pH was raised. The lower uptake at low pH could be explained by the adsorbents’ surfaces becoming protonated, which creates a force of repulsion between the surface and the metal ion and MB. However, the surface became deprotonated as the pH increased, causing significant interaction between the surface and adsorbate. Thus greater adsorption capacity was obtained. Notably, H3FS showed less attraction for the metal and MB solutions than PFS and CaFS. This could be explained by its smooth surface, which hindered internal adsorption. Acknowledgement: The authors acknowledge the support of the Department of Chemistry, Vaal University of Technology, Vanderbijlpark, South Africa for granting facilities.

References 1. Thabede PM, Shooto ND, Naidoo EB. Removal of methylene blue dye and lead ions from aqueous solution using activated carbon from black cumin seeds. S Afr J Chem Eng 2020;33:39–50. 2. Shooto ND, Naidoo EB, Maubane M. Sorption studies of toxic cations on ginger root adsorbent. J Ind Eng Chem 2019;76:133–40. 3. Mahmud HNME, Haq AKO, Yahya RB. Removal of heavy metal ions from wastewater aqueous solution by polypyrrole-based adsorbent: a review. R Soc Chem 2015;6:1–13. 4. Ayangbenro AS, Babalola OO. New strategy for heavy metal polluted environments: a review of microbial biosorbents. Int J Environ Res Publ Health 2017;1494:1–16. 5. Duman O, Tunc S, Polat TG, Bozoglan BC. Synthesis of magnetic oxidized multiwalled carbon nanotube– carrageenan-Fe3O4 nanocomposite adsorbent and its application in cationic methylene blue dye adsorption. Carbohydr Polym 2016;147:79–88. 6. Pathania D, Sharma S, Singh P. Removal of methylene blue by adsorption onto activated carbon developed Ficus carica bast. Arab J Chem 2017;10:S1445–51. 7. Carolin CF, Kumar PS, Saravanan A, Joshiba GJ, Naushad M. Efficient techniques for the removal of toxic heavy metals from aquatic environment: a review. J Environ Chem Eng 2017;5:2782–99. 8. Akhter M, Habib G, Qumar SU. Application of electro-dialysis in wastewater treatment and impact of fouling on process performance. J Membr Sci Technol 2018;8:1–8. 9. Chaemiso TD, Nefo T. Removal methods of heavy metals from laboratory wastewater. J Nat Sci Res 2019;9: 36–42. 10. Nkutha CS, Shooto ND, Naidoo EB. Adsorption studies of methylene blue and lead ions from aqueous solution by using mesoporous coral limestones. S Afr J Chem Eng 2020;34:151–7. 11. Zhang L, Zeng Y, Cheng Z. Removal of heavy metal ions using chitosan and modified chitosan: a review. J Mol Liq 2016;214:175–91. 12. Shooto ND, Thabede PM, Naidoo EB. Simultaneous adsorptive study of toxic metal ions in quaternary system from aqueous solution using low cost black cumin seeds (Nigella sativa) adsorbents. S Afr J Chem Eng 2019;30:15–27. 13. Dai Y, Sun Q, Wang W, Lu L, Liu M, Li J, et al. Utilizations of agricultural waste as adsorbent for the removal of contaminants: a review. Chemosphere 2018;211:235–53.

References

241

14. Elhafez SEA, Hamad HA, Zaatout AA, Malash GF. Management of agricultural waste for removal of heavy metals from aqueous solution: adsorption behaviours, adsorption mechanisms, environmental protection, and techno-economic analysis. Environ Sci Pollut Res 2017;24:137–45. 15. Acharya J, Kumar U, Rafi M. Removal of heavy metals ions from wastewater by chemical modified agricultural waste material as potential adsorbent –a review. Int J Curr Eng Technol 2018;8:526–30. 16. Asim N, Amin MH, Samsudin NA, Badiei M, Razali H, Akhtaruzzaman M, et al. Development of effective and sustainable adsorbent biomaterial from an agricultural waste material: Cu(II) removal. Mater Chem Phys 2020;249:1–11. 17. Thabede PM, Shooto ND. Application of black cumin (Nigella sativa L.) seeds for the removal of metal ions and methylene blue from aqueous solutions. Cogent Eng 2022;9:1. 18. Choi SS, Choi TR, Choi H. Surface Modification of phosphoric acid–activated carbon in spent coffee grounds to enhance Cu(II) adsorption from aqueous solutions. Appl Chem Eng 2021;32:589–98. 19. Sulthana Z, Taqui SN, Zameer F, Syed UT, Syed AA. Adsorption of ethidium bromide from aqueous solution onto nutraceutical industrial fennel seed spent: kinetics and thermodynamics modelling studies. Int J Phytoremediation 2018;20:1075–86. 20. Laskar MA, Ali SK, Siddiqui S. Characterization of the kinetics and thermodynamics for the adsorption of zinc(II) on fennel seeds. Anal Lett 2016;49:1537–47. 21. Rao RAK, Khan MA, Rehman F. Utilization of Fennel biomass (Foeniculum vulgari) a medicinal herb for the biosorption of Cd(II) from aqueous phase. Chem Eng J 2010;156:106–13. 22. Husein TK, Jasim NA. Removal of crystal violet and methylene blue from industrial wastewater using fennel seed as an adsorbent. J Eng Sci Technol 2019;14:2947–63. 23. Taqui SN, Yahya R, Hassan A, Nayak N, Syed AA. Development of sustainable dye adsorption system using nutraceutical industrial fennel seed spent—studies using Congo red dye. Int J Phytoremediation 2017;19: 686–94. 24. Mabungela N, Shooto ND, Mtunzi F, Naidoo EB. The adsorption of copper, lead metal ions, and methylene blue dye from aqueous solution by pure and treated fennel seeds. Adsorpt Sci Technol 2022:1–16. https://doi.org/10.1155/2022/5787690. 25. Mabungela N, Shooto ND, Mtunzi F, Naidoo EB. Binary adsorption studies of toxic metal ions of lead and copper from aqueous solution by modified Foeniculum vulgaris seeds (Fennel seeds). Asian J Chem 2021; 33:1–9. 26. Shooto ND. Removal of toxic hexavalent chromium (Cr(VI)) and divalent lead (Pb(II)) ions from aqueous solution by modified rhizomes of Acorus calamus. Surface Interfac 2020;20:1–9. 27. Kebede TG, Mengistie AA, Dube S, Nkambule TTI, Nindi MM. Study on adsorption of some common metal ions present in industrial effluents by Moringa stenopetala seed powder. J Environ Chem Eng 2018;6: 1378–89. 28. Shooto ND, Thabede PM, Bhila B, Moloto H, Naidoo EB. Lead and methylene blue removal form aqueous solution by mucuna beans (velvet beans) adsorbents. J Environ Chem Eng 2020;8:1–11. 29. Yang L, Weia Z, Zhonga W, Jing Cuia J, Weia W. Modifying hydroxyapatite nanoparticles with humic acid for highly efficient removal of Cu(II) from aqueous solution. Colloids Surf A Physicochem Eng Asp 2016;490: 9–21. 30. Koohzad E, Jafari D, Esmaeili H. Adsorption of lead and arsenic ions from aqueous solution by activated carbon prepared from Tamarix leaves. ChemistrySelect 2019;4:12356–67. 31. Nkutha CS, Naidoo EB, Shooto ND. Adsorption studies of metal ions of Cr(VI) and Pb(II) from synthetic wastewater by pristine and calcined coral limestones. S Afr J Chem Eng 2021;36:43–57. 32. Shooto ND, Thabede PM. Binary adsorption of chromium and cadmium metal ions by hemp (Cannabis sativa) based adsorbents. Environ Nanotechnol Monit Manag 2022;18:1–15. 33. Ayub S, Mohammadi AA, Yousefi M, Changani F. Performance evaluation of agro-based adsorbents for the removal of cadmium from wastewater. Desalination Water Treat 2019;142:293–9.

242

12 Simultaneous removal of methylene blue, Cu(II) and Cd(II)

34. Esmaeili H, Tamjidi S. Ultrasonic-assisted synthesis of natural clay/Fe3O4/graphene oxide for enhance removal of Cr(VI) from aqueous media. Environ Sci Pollut Res 2020;27:31652–64. 35. Ahmad A, Khan N, Giri BS, Chawdhary P, Chaturvedi P. Removal of methylene blue dye using rice husk, cow dung and sludge biochar: characterization, application, and kinetic studies. Bioresour Technol 2020;306:1–5. 36. Foroutan R, Peighambardous SJ, Hosseini SS, Akbari A, Ramavandi B. Hydroxyapatite biomaterial production from chicken (femur and beak) and fishbone waste through a chemical less method for Cd2+ removal from shipbuilding wastewater. J Hazard Mater 2021;413:1–13. 37. Mabungela N, Shooto ND, Dikio ED, Modise SJ, Monaphathi ME, Mtunzi FM, et al. Multi-application fennelbased composites for the adsorption of Cr(VI) ions from water and control of Escherichia coli and Staphylococcus aureus. Environ Chem Ecotoxicol 2022;4:171–95. 38. Amin MT, Alazba AA, Shafiq M. Removal of copper and lead using banana biochar in batch adsorption systems: isotherms and kinetic studies. Arabian J Sci Eng 2018;43:5711–22. 39. Ramli MRM, Shoparwe NF, Amadi MA. Methylene blue removal using activated carbon adsorbent from Jengkol peel: kinetic and mass transfer studies. Arabian J Sci Eng 2022:1–10. https://doi.org/10.1007/ s13369-022-07141-5. 40. Takmil F, Esmaeili H, Mousavi SM, Hashemi SA. Nano-magnetically modified activated carbon prepared by oak shell for treatment of wastewater containing fluoride ion. Adv Powder Technol 2020;31:3236–45. 41. Pandey S, Fosso-Kanheu E, Spiro MJ, Waanders F, Kumar N, Ray SS, et al. Equilibrium, kinetics and thermodynamics studies of lead ion adsorption from mine wastewater onto MoS2-clinoptilolite composite. Mater Today Chem 2020;18:1–11. 42. Abshirini Y, Esmaeili H, Foroutan R. Enhancement removal of Cr (VI) ion using magnetically modified MgO nanoparticles. Mater Res Express 2019;6:1255131–15. 43. Fakhre NA, Ibrahim BM. The use of new chemically modified for heavy metal ion adsorption. J Hazard Mater 2018;343:324–31. 44. Wakkel M, Khiari B, Zagrouba F. Textile wastewater treatment by agro-industrial waste: equilibrium modelling, thermodynamics and mass transfer mechanisms of cationic dyes adsorption onto low-cost lignocellulosic adsorbent. J Taiwan Inst Chem Eng 2019;96:439–52. 45. El-Sheekh M, El Sabagh S, El-Souod GA, Elbeltagy A. Biosorption of cadmium from aqueous solution by free and immobilized dry biomass of Chlorella vulgaris. Int J Environ Res 2019;13:511–21. 46. Ansari TM, Shaheen S, Manzoor S, Naz S, Hanif MS. Litchi chinensis peel biomass as green adsorbent for cadmium (Cd) ions removal from aqueous solutions. Desalination Water Treat 2020;173:343–50. 47. Borghei SA, Zare MH, Ahmadi M, Sadeghi MH, Marjani A, Shirazian S, et al. Synthesis of multi-application activated carbon from oak seeds by KOH activation for methylene blue adsorption and electrochemical supercapacitor electrode. Arab J Chem 2021;14:1–16. 48. Ben-Ali Jaouali I, Souissi-Najar S, Ouederni A. Characterization and adsorption capacity of raw pomegranate peel biosorbent for copper removal. J Clean Prod 2017;142:3809–21.

Uche Eunice Ekpunobi*, Uzochukwu Abraham Onuigbo*, Ifeyinwa Tabugbo, Emma Amalu, Christopher Ihueze, Caius Onu, Philomena Igbokwe, Azubike Ekpunobi, Sunday Agbo and Happiness Obiora-Ilouno

13 The investigation of the physical properties of an electrical porcelain insulator manufactured from locally sourced materials Abstract: The work aims to evaluate the effect of temperature and composition on the physical properties of ceramic electrical porcelain insulators, produced from locally sourced materials in Nigeria. The basic raw materials of triaxial porcelain (Kaolin, feldspar, and quartz) were pulverized, milled for 22 h, and sieved using a 200 μm mesh size. The chemical composition and characterization of the raw materials were obtained using X-ray diffraction (XRD) and X-ray fluorescence (XRF) analysis. The mixtures were formulated using sodium silicate as a deflocculant to help produce the ceramic porcelain samples. The green samples were weighed and fired at temperatures of 1200 °C and 1250 °C. The samples were subjected to 1 h of boiling plus 2 h of soaking. The slip casting technique was used in the production of porcelain insulators. The linear shrinkage, water absorbance, apparent porosity, and bulk density were measured and studied as a function of firing temperature. The apparent porosity and water absorption decreased as the firing temperature increased. The bulk density increased gradually from 1200 °C to 1250 °C and the percentage of moisture remained fairly unaffected by the temperature increase. The linear shrinkage was also found to increase as the firing temperature increased. Despite having the same composition, the average physical properties of the locally manufactured insulators revealed that those manufactured at higher temperatures provided a better insulating effect than those manufactured at

*Corresponding authors: Uche Eunice Ekpunobi and Uzochukwu Abraham Onuigbo, Department of Pure and Industrial Chemistry, Nnamdi Azikiwe University, Awka, Anambra State, Nigeria, E-mail: [email protected] (U.E. Ekpunobi) and [email protected] (U.A. Onuigbo) Ifeyinwa Tabugbo and Emma Amalu, Department of Pure and Industrial Chemistry, Nnamdi Azikiwe University, Awka, Anambra State, Nigeria Christopher Ihueze, Department of Industrial and Production Engineering, Nnamdi Azikiwe University, Awka, Anambra State, Nigeria Caius Onu and Philomena Igbokwe, Department of Chemical Engineering, Nnamdi Azikiwe University, Awka, Anambra State, Nigeria Azubike Ekpunobi, Department of Physics and Industrial Physics, Nnamdi Azikiwe University, Awka, Anambra State, Nigeria Sunday Agbo, Project Development Institute, Independent Layout, Enugu, Nigeria Happiness Obiora-Ilouno, Department of Statistics, Nnamdi Azikiwe University, Awka, Anambra State, Nigeria As per De Gruyter’s policy this article has previously been published in the journal Physical Sciences Reviews. Please cite as: U. E. Ekpunobi, U. A. Onuigbo, I. Tabugbo, E. Amalu, C. Ihueze, C. Onu, P. Igbokwe, A. Ekpunobi, S. Agbo and H. Obiora-Ilouno “The investigation of the physical properties of an electrical porcelain insulator manufactured from locally sourced materials” Physical Sciences Reviews [Online] 2023. DOI: 10.1515/psr-2022-0236 | https://doi.org/10.1515/9783111071428-013

244

13 Analysis on the properties of locally produced electrical porcelain insulator

lower temperatures. In other words, it shows that excellent ceramic porcelain insulators can be manufactured from locally sourced materials using the appropriate composition and firing temperature. Keywords: insulators; kaolin; porcelain.

13.1 Introduction The use of locally sourced materials in the production of essential industrial materials, especially those materials that serve as the bedrock for national development, is the major goal of any developing nation. The word “porcelain” is used to describe a collection of ceramic materials that are fired at elevated temperatures to produce vitreous and glassy materials that have opaque or translucent visibility. Due to their extremely high density, industrially quick fire cycles, palpable mechanical strength, and wear resistance, porcelains are thought to be true stoneware [1]. Porcelain’s composition varies greatly, however the typical porcelain is made up of 50% clay, 25% feldspar, and 25% quartz [2]. Because of how these three elements combine to affect porcelain’s oxide concentration, the term “triaxial porcelain” is employed [3]. Flint or quartz [SiO2] retains the shape of the created object during fire, clay [Al2Si2O5(OH)4] gives the ceramic mixture plasticity, and feldspar [KxNai = x(AlSi3)O8] works as a flux that is used to reduce the firing temperature to conserve fuel or energy [3]. The creation of glass and the mineral mullite inside the fired body at high temperatures is the primary cause of porcelain’s hardness, strength, and translucence [3]. Generally, porcelains are fine-grained and vitrified ceramic white wares, used either glazed or unglazed. Insulators are materials that do not permit the passage of electric current. They serve to separate and support electrical conductors in electrical transmission lines while blocking the flow of electricity [4]. In an electrical system, an insulator plays a vital role by offering a maximum resistivity path which is resistant to current passage. Most suitable high voltage insulators are made from porcelain due to their high mechanical strength and ability to withstand stress in harsh weather conditions. Porcelain insulators are utilized in electrical transmission lines, high voltage switches, and transformer insulation requirements. For both low-tension and high-tension insulation, porcelain insulators make up a sizable portion of the most often used ceramic insulator. The most extensively researched ceramic system and one of the most complicated ceramic materials are electrical porcelain insulators [5]. Most developing nations are channeling their energy and resources towards the development and promotion of locally manufactured products, especially those products whose raw materials are locally abundant. It is known that Nigeria has access to the raw resources needed to produce heated ceramic products and electrical porcelain insulators [6]. In the face of these abundant raw materials, Nigeria still imports the majority of the high voltage porcelain insulators she consumes, relegating locally manufactured ones to

13.2 Materials

245

low voltage shackles. Nigeria, still battling with the need to provide constant electricity to its constantly growing population, needs to channel its resources to the development of locally manufactured electrical porcelain insulators. This will reduce the cost of maintenance of transmission lines, boost the dwindling economy, and reduce unemployment. The investigation and comparison of the physical characteristics of electrical porcelain insulators made in Nigeria and fired at two different temperatures is the primary aim of this study. (1200 °C and 1250 °C).

13.2 Materials The raw materials used for this analysis include Nafuta clay, quartz, feldspar, sodium silicate, and water. Equipment used are Mixer, digital weighing balance, Electric dryer, magnetic bar, crusher, mesh (200 μm), tap water, Kiln, and mold.

13.2.1 Characterization Two major techniques were used to characterize the Nafuta clay samples. X-ray fluorescence spectroscopy analysis was employed to find out the major content of the clay sample while the X-ray diffraction analysis was used to analyze the phase composition of the sample.

13.2.2 Method The several stages in the methods of production of electrical porcelain insulators are shown in the flow chart below in Figure 13.1-1: The excavated lumps of clay samples collected from Nafuta community in BarkinLadi local government area in Plateau state in Nigeria were washed and dried to remove unwanted materials. The lumps were crushed using a hammer to reduce them into tiny lumps. The sample was then ground into powder using a pulverizer, and the balls were milled for 22 h at 350 revolutions per minute (rpm) for 15 min using a 500 g ball. The same procedure was applied to feldspar, nsu, and quartz. The powders were screened for metal content using magnetic bars and sieved through a 200 μm sieve. The bulk samples were weighed and proportioned using an algorithm that varied the different concentrations of the raw materials and the required water content. Up to 20 different compositions were formulated. These samples were duplicated for the two temperatures (1200 °C and 1250 °C). The samples were mixed manually but thoroughly. Sodium silicate was added as a deflocculant. The samples were molded using the slip casting method. The slip cast mold was produced with plaster of Paris (POP), which is an effective absorption material. They

246

13 Analysis on the properties of locally produced electrical porcelain insulator

Figure 13.1-1: Flow diagram for porcelain production.

were allowed to air dry for 48 h after being removed from the mold, before bisque firing using a kiln. The samples were all fired to temperatures of 1200 °C and 1250 °C, respectively, using the identical heating sequence at a rate of 1200 °C/h. Before the essential testing could begin, the samples were sorted and labeled based on their individual compositions and firing temperature after air drying them.

13.2.3 Water absorption Boiling technique was employed for this experiment at 100 °C for 2 h. The samples were boiled for 2 h, and soak for an additional 1 h. The difference between the sample weights before and after submersion was used to calculate the amount of water absorbed. The water absorption was computed using equation (13.1) [7].

13.2 Materials

Water Absorption =

Ws − Wd × 100% Wd

247

(13.1)

where Ws = soaked weight boiling at 100 °C for 2 h and Wd = dry weight.

13.2.4 Linear shrinkage After firing at 1200 °C and 1250 °C, the dimensional changes in length were measured, and the data were utilized to calculate the linear shrinkage. A Vernier caliper was used to measure the fired and green porcelain insulator. The equation below was used to calculate the linear shrinkages of each sample as a percentage of the initial green readings. (13.2) [8]. Linear Shrinkage =

Lgreen – Lfired × 100% Lgreen

(13.2)

where Lgreen is the length of the green sample while Lfired is the length of the fired sample.

13.2.5 Apparent porosity The samples were boiled for 2 h, then soaked in water for an additional 4 h before being weighed, Ws. The wet piece was then suspended from a balance’s beam in the water-filled vessel such that it was entirely submerged in the liquid without hitting the side of the container. The suspended sample in water weighed as Wsp. The sample’s weight difference between soaked weight and dry weight as well as the sample’s weight difference between soaked weight and suspended immersed weight were used to calculate the sample’s porosity. These findings were obtained using an equation (13.3) [9]. Porosity =

Ws − Wd × 100% W s − W sp

(13.3)

13.2.6 Bulk density Direct volume method was used for the calculation of bulk density. To calculate the necessary bulk density, this approach multiplies a substance’s relative density by the density of water. Equation (13.4) was used to obtain the bulk density in g/cm3 [10]. Bulk Density =

W d × 100% W s − W sp

(13.4)

where Ws = soaked weight, Wd = dry weight and Wsp = suspended immersed weight.

248

13 Analysis on the properties of locally produced electrical porcelain insulator

13.3 Result and discussion 13.3.1 X-ray fluorescence and X-ray diffraction analysis of the clay The result of the mineralogical analysis of Nafuta clay is shown Figure 13.2-1. The phases significant as shown above are kaolinite and quartz with other smaller phases such as montmorillonite and illite. The result agrees well with that obtained in the XRF result shown in table below. The result of chemical analysis of the Nafuta clay with XRF is shown in Table 13.1. It is observed that Nafuta clay is dominant with silica (60.01%) and alumina (23.30%). Nafuta clay silica content was found to be high and it satisfies the requirements for the manufacture of porcelain ceramics (>60.5%), refractories (>51.7%) and melting clays (53–73%) [11]. Alkali and transitional oxides in Nafuta clay are low and that makes the clay suitable for high temperature ceramics applications such as porcelain insulators and high refractory ceramics as the oxides are low temperature fluxes and could affect the structure and aesthetics of ceramic products formed [12]. The high SiO2 and Al2O3 content of the clay with very low iron oxide (Fe2O3) suggests that the clay is kaolinite clay.

Figure 13.2-1: X-ray diffractogram of Nafuta clay.

Table .: X-ray fluorescence result of Nafuta clay. Compound SiO AlO KO NaO CaO MgO TiO FeO

Oxide composition (%) . . . . . . . .

13.3 Result and discussion

249

13.3.2 Apparent porosity The porosity of the electrical insulator is a vital factor since it affects the insulating characteristics of the porcelain. The apparent porosity decreased as the temperatures were increased in all the samples. At samples 1, 3, 7, 11, and 16, the temperature difference has little effect on the apparent porosities. The apparent porosity decreases with increasing fire temperature. High temperatures produce a liquid or glass phase, which will ultimately fill the vacuum and cause the insulator to become denser [10]. The apparent porosity decreases as the temperature increases due to shrinkage in size [4]. The best sample for the fabrication of electrical porcelain insulators is Sample 15, which provided the most optimal apparent porosity. Also, its composition of clay, quartz, and silica (60%, 15%, and 25%) is similar to that recommended by ASTM C373 industrial standard ceramic materials procedure. Figure 13.3-1, shows the apparent porosity of the samples compared at two separate temperatures (1200 °C and 1250 °C). From Figure 13.3-1, we can observe that the apparent porosity is higher at lower temperatures while the value at higher temperatures is within the recommended value for a good electrical porcelain insulator [10]. Electrical porcelain needs porosity to function, however too much porosity increases the water absorptivity, which is not always good for their insulating characteristics because of current leakages [13]. Porosity is also vital for

Figure 13.3-1: Effect of firing temperature on the apparent porosity of porcelain bodies.

250

13 Analysis on the properties of locally produced electrical porcelain insulator

insulating refractories (electrical porcelains) because it increases electrical resistivity due to the presence of pores (air), which serves as an insulator [14, 15]. Electrical porcelains are typically glazed to achieve the greatest porosity for electrical resistance with the least amount of water absorption capacity.

13.3.3 Water absorption Figure 13.4-1 shows the water absorption capacity of the porcelain bodies fired at different temperatures. The graph illustrates decreasing water absorption as the temperature increases during firing. A porcelain insulator’s insulating characteristics improve with a decrease in water absorption. The quantity of water a material absorbs while it is in use will impact its service life and maybe even lower its resistivity [16]. Sample 18 has the highest water content at a lower temperature. This is attributed to the low content of plastic materials in the sample (feldspar & quartz) when compared with the plastic materials [9]. Sample 16 gave the least water absorption (0.717%) at high and low temperatures. This is because it is composed of high plastic material. The majority of the samples, in general, are consistent with the values advised for typical electrical porcelain insulator’s water absorption [13]. Low water absorption indicates better vitrification [16]. Since water affects a product’s electrical resistivity, a good porcelain insulator should have a low water absorption capacity [13].

Figure 13.4-1: Effect of firing temperature on the water absorption of porcelain insulator.

13.3 Result and discussion

251

13.3.4 Linear shrinkage The linear shrinkage of a sample is the level of reduction in a porcelain dimension after firing. Figure 13.5-1, shows that the total linear shrinkage, the graph demonstrates that linear shrinkage declined as firing temperature increased. Samples 1 and 19 have the least linear shrinkage, attributed to the low content of plastic materials while samples 9 and 20 have high linear shrinkage due to high plastic content. Sample with low linear shrinkage (less than 6%) will probably warp during firing while those with high linear shrinkage (greater than 9%) will come out fine but with crack after firing [3]. Figure 13.4 shows that, aside from the samples with either a high percentage of non-plastic elements or a higher amount of plastic materials, the bulk of the samples are within the established international standard (7–10%). Since the porosity plus the particle size in the initial state are crucial to the sintering qualities of fine powders and lower the firing shrinkage, the linear shrinkage may also be related to particle size [17].

13.3.5 Bulk density Bulk density is a vital feature in porcelain insulator. Figure 13.6-1, shows that the bulk density of the samples increases as the temperature was increased. This is correlated to vitrification, which decreases the quantity of open pores and results in a denser insulation. The bulk density of all the sample fall within the accepted standard of 1.7–2.3 g/cm3 for fired porcelain (10). As the temperature of the system increases the bulk density increases was also reported in previous research [4].

Figure 13.5-1: Effect of firing temperature on the linear shrinkage of the porcelain insulator.

252

13 Analysis on the properties of locally produced electrical porcelain insulator

Figure 13.6-1: Effect of firing temperature on the bulk density of porcelain insulator.

13.4 Conclusion In summary, the XRF analysis shows that the Nafuta clay has the required amount of silica and alumina for electrical porcelain insulator production. The XRD analysis reveals that the Nafuta clay has a high content of kaolinite and quartz, which is in line with international standards for the manufacture of electrical porcelain insulators. The apparent porosities, water adsorption, linear shrinkages, and bulk density values are all well within the internationally accepted range for electrical porcelain insulators. Investigation into the physical properties shows that Nafuta clay can be effectively used for the production of porcelain insulators for different voltages on a transmission line. Most of the samples, even though unglazed, had excellent physical properties. Glazing these samples will intensify these qualities and make them excellent electrical porcelain insulators that are competitive anywhere in the world. The need to investigate other parameters of Nafuta clay will reduce the cost incurred from importation, provide a standard porcelain insulator, and will be a source of employment for Nigeria’s teeming population. Acknowledgement: I want to say a special thank you to Tertiary Education Trust Fund (TETFund NRF) for their unwavering support throughout this work, and most importantly for believing in the work we are doing. I also want to acknowledge my academic mentor Associate Professor Mrs. Ekpunobi U.E for her guidance. Finally, I am grateful to everybody that contribute to this paper.

References

253

References 1. Tucci A, Esposito L, Malmusi L, Rambaldi E. New body mixes for porcelain stoneware tiles with improved mechanical characteristics. J Eur Ceram Soc 2007;27:1875–81. 2. Kitouni S, Harabi A. Sintering and mechanical properties of porcelains prepared from Algerian raw materials. Cerâmica 2011;57:453–60. 3. Nwachukwu V, Lawal S. Investigating the production quality of electrical porcelain insulators from local materials. In: IOP conference series: materials science and engineering; 2018:413012076 p. 4. Ezenwabude E, Madueme T. Evaluation of mixed local materials for low voltage insulators. Int J Multidiscip Sci Eng 2015;6:37. 5. Dana K, Das S, Das K. Effect of substitution of fly ash for silica in triaxial kaolin-silica-feldspar system. J Eur Ceram Soc 2004;24:3169–75. 6. Federal Ministry of Science and Technology (FMST). Profile on selected 6 commercialisable research and development results [Online]; 2004. Available from: http://www.First.Gov.Ng/Docs/Profile.Technology. Selected.RD.2004. 7. ASTM C356-17. Standard test method for linear shrinkage of preformed high temperature thermal insulation subjected to soaking heat. West Conshohocken: ASTM International; 2017. [Accessed 13 Aug 2010]. 8. Atanda PO, Oluwole OO, Oladeji TA. Electrical Porcelain production from selected Kaolin deposit in South Western Nigeria using slip casting. Int J Mater Chem 2010;2:86–89. 9. Density of Materials. Material-powder, ore, solids, etc [online]; 2009. Available from: http://www.simetric. co.uk/si_materials.htm [Accessed 6 Aug 2009]. 10. Temitope P. Process and development of electrical porcelain insulator using Edo state, Nigerian raw materials. Int J Multidiscip Sci Eng 2020;43–55. https://doi.org/10.5815/ijem.2020.03.04. 11. Agbo S, Ekpunobi U, Onu C, Oriaku E. Mineralogical and physicochemical characterization of enugu ivapottery silica- rich deposit for ceramics applications. Iran J Chem Chem Eng 2022. https://doi.org/10.30492/ ijcce.2022.541893.5001. 12. Ekpunobi E, Agbo S, Ajiwe V. Evaluation of the mixtures of clay, diatomite, and sawdust for production of ceramic pot filters for water treatment interventions using locally sourced materials. J En Chem Eng 2019;7: 2213–3437. 13. Onuoha C, Ovri J, Mark U. Characterization of Ibere clay for the production of electrical porcelain insulators. Int Res J Eng Sci Technol 2014;11:47–55. 14. Krivandin V, Markov B. Metallurgical furnaces (translated from the Russian by V. V. AFANASYEV). Moscow: Mir Publishers; 1980:229–61 pp. 15. Nwobodo C, Davies T. The effect of apparent porosity on the modulus of rupture of alumina-chromia refractory matrix a paper presented at FUTO 2000, annual conference of NAMMES; 2000:8 p. 16. Amutha K, Sivakumar G. Densification behaviour of bioceramic tiles from bioresidue. In: ICANMEET: IEEE; 2013:6609257 p. 17. Glasscock J, Esposito V, Foghmoes PV, Stegk T, Matuschek D, Ley MWH, et al. The effect of forming stresses on the sintering of ultra-fine Ce0.9Gd0.1O2−δ powders. J Eur Ceram Soc 2012;33:1289–96.

Edwige Anagued Haman, Valéry Paul Moumbon, spce Abdourahman Fadimatou, Jean Momeni* and Bathelemy Ngameni

14 A new sphingoid derivative from Acacia hockii De Wild (Fabaceae) with antimicrobial and insecticidal properties Abstract: In Cameroon, several species of the genus Acacia are traditionally used for protection in granaries of stored foodstuffs such as cowpeas, maize and millet. The literature review on Acacia hockii made it possible to detect that few studies were carried out on this plant which would traditionally have the properties we seek. The objective of this work is to extract and isolate the active principles and then evaluate the insecticidal and antifungal activities of the extracts and compounds isolated from the fruits of A. hockii. The maceration method was used to obtain hexane, acetone and methanol extracts of A. hockii fruits which were used against adults of Callosobruchus maculatus (Coleoptera: Bruchidae), a pest of cowpea (Vigna unguiculata). The most active extract was fractionated by column chromatography and the compounds were elucidated by 1D and 2D NMR spectroscopy. The well-scattering method was used for evaluating the antifungal activity. The results show that all extracts were active against adults of C. maculatus and that the acetone extract was the most active with a 100% mortality rate at the concentration of 0.500 mg/mL and an LD50 of 0.06 g per gram of cowpea. The antifungal activity test of the acetone extract showed sensitivity against all tested strains Fusarium solani, Aspergillus flavus and Penicillium citrinum with MIC of 0.0625; 0.500 mg/ mL, respectively. Fractionation of this extract led to the isolation of four compounds, among β-stigmasterol and β-sitosterol, β-stigmasterol-3β-O-D-glucopyranoside and N-((2S,3S,4R,14E)-1,3,4-trihydroxyicos-14-en-2-yl)palmitamide, a new sphingolipid with

*Corresponding author: Jean Momeni, Laboratory of Organic Chemistry and Applications, Department of Chemistry, Faculty of Science, University of Ngaoundere, PO Box: 454, Ngaoundere, Cameroon, E-mail: [email protected]. https://orcid.org/0000-0002-2772-3159 Edwige Anagued Haman and spce Abdourahman Fadimatou, Laboratory of Organic Chemistry and Applications, Department of Chemistry, Faculty of Science, University of Ngaoundere, PO Box: 454, Ngaoundere, Cameroon, E-mail: [email protected] (E. Anagued Haman), [email protected] (s.A. Fadimatou) Valéry Paul Moumbon, National Advanced School of Agro-Industrial Sciences, University of Ngaoundere, PO Box: 455, Ngaoundere, Cameroon, E-mail: [email protected] Bathelemy Ngameni, Department of Pharmacognosy and Pharmaceutical Chemistry, Faculty of Medicine and Biomedical Sciences, University of Yaounde I, PO Box: 1364, Yaounde, Cameroon, E-mail: [email protected] As per De Gruyter’s policy this article has previously been published in the journal Physical Sciences Reviews. Please cite as: E. A. Haman, V. P. Moumbon, S. A. Fadimatou, J. Momeni and B. Ngameni “A new sphingoid derivative from Acacia hockii De Wild (Fabaceae) with antimicrobial and insecticidal properties” Physical Sciences Reviews [Online] 2023. DOI: 10.1515/psr-2022-0267 | https://doi.org/10.1515/9783111071428-014

256

14 A new sphingoid derivative from Acacia hockii

insecticidal and moderate antibacterial activities. A. hockii fruits can be considered a potential source for the production of biopesticides. Keywords: Acacia hockii; fruits; insecticidal and antifungal activities; sphingoid derivative.

14.1 Introduction In the world as a whole, 795 million people are food insecure [1]. In the Far North of Cameroon, there are several weather phenomena such as storms, tornadoes, and hurricanes as well as climate variations that cause poverty and consequently famine in this part of the country [2]. To solve this problem, agricultural diversity in Cameroon is a great potential that is necessary to exploit. In addition, it should be noted that the main diet of the population in the Far North of Cameroon consists of cereals and legumes [3]. Among these, we can mention millet, maize, groundnut and cowpea (Vigna unguiculata (L.)) which is in particular a legume that possesses proteins, carbohydrates, and vitamins B and E for good human health. Furthermore, the economic importance and the ability to fix atmospheric nitrogen in the soil make cowpea a crop of great importance in Africa [4]. However, the latter cowpea (V. unguiculata (L.)) stock is attacked by insect pests Callosobruchus maculatus which are the major pests of cowpea and which can destroy up to 35% of the world‘s production [5]. The development of these insects is favoured by climatic conditions namely a long dry season and high temperatures [6]. In addition, these climatic conditions favoured the development of fungi of which the most widespread are Fusarium solani, Aspergillus flavus and Penicillium citrinum in stored foodstuffs. The infiltration of bacteria both in the food consumed and in the fields also causes a lot of damage [7–9]. To end this several insecticides and various synthetic pesticides are used to remedy the situation. But these have several disadvantages namely the induction of serious diseases in humans, loss of biodiversity and appearance of resistance in pests [10]. This situation prompted researchers to find alternative methods to protect stored foodstuffs from insect pests [11]. The problem that arises is finding alternative methods to overcome this limitation of synthetic pesticides that have several disadvantages to protecting stored foodstuffs (cowpea) from the destruction of insects and fungi. Research has shown that plant extracts can destroy insects [12]. Thus, plant extracts are increasingly used by farmers for the protection of these stored crops without harming human health [13]. Thus, Acacia hockii has demonstrated to the rural population its effectiveness against insects [14]. Bibliographic surveys show that few phytochemical works have been carried out on some Acacia species, and some compounds belonging to the family of phenolic compounds, flavonoids, triterpenoids and steroids have been isolated [15, 16]. The objective of the present research work is to extract, isolate the active principles and evaluate the insecticidal and antifungal activities of the extracts and active compounds isolated from the fruits of A. hockii.

14.2 Material and methods

257

14.2 Material and methods 14.2.1 General experimental procedure Column chromatography (CC) was performed on 230–400 mesh silica gel (Merck) and thin layer chromatography (TLC) was performed on 60 F254 aluminium percolated silica gel (Merck), the spots of the compounds were visualized under UV light (254 and 365 nm) and by spraying with dilute sulphuric acid followed by heating at about 100 °C for 5–10 min. Low-resolution mass spectra were obtained with a QTOF Compact spectrometer (Bruker). The spectrometer was operated in positive (mass range: 50–1500, with a scan rate of 1.00 Hz) with automatic gain control to provide high-accuracy mass measurements within 0.4 ppm using Na formate as a calibrator. 1H and 13C NMR spectra were recorded on Avance 600 MHz and 150 MHz spectrometers in deuterated solvents. Chemical shifts were reported in δ (ppm) using tetramethylsilane (TMS) as an internal standard, while coupling constants (J ) were measured in Hz.

14.2.2 Plant material The fruits of A. hockii were collected in April 2020 in Gaban-Lara near the city of Kaele, Mayo-Kani Division, in the Far North region of Cameroon. These plant samples were authenticated with the help of Mr. Binwe Jean-Baptiste, a botanist in agroforestry.

14.2.3 Extraction and isolation The fruits of A. hockii were shade-dried and then ground in the mortars. The powder of 1 kg was obtained and extracted by maceration using hexane, acetone and methanol by soaking for 72 h in these different solvents, successively repeating this operation three times at room temperature. After filtration and evaporation, the TLC of the acetone extract was done, and 35 g of this extract was fractionated by column chromatography (CC) using the n-hexane/ethyl acetate on a silica gel system. Indeed, the column was filled with 350 g of silica gel prepared in 2 L of hexane. This mixture called silica gel slurry is poured into the column where a layer of about 1 cm of cotton wool was previously adjusted in the lower part of the column to retain the silica gel and then allow only the solute to pass through. The column is then evenly packed as the silica gel slurry is poured into it until it is full. In addition, 35 g of the acetone extract was fixed onto 70 g of silica gel. After collecting 10 fractions of 300 mL, they were pooled and purified. Compounds 2 and 3 (10 mg) were obtained in the n-hexane/EtOAc (95/5) system as well as compound 4 (5 mg) in the hexane/EtOAc (85/15) system. Continuing to collect the fractions up to the 115th fraction, compound 1 crystallized in the 100% methanol elution system. These fractions

258

14 A new sphingoid derivative from Acacia hockii

were allowed to dry at room temperature and treated with methanol to give 22 mg of compound 1. After dissolving in DMSO, phytochemical screening of this compound was carried out in the 95% methanol/water solvent system and revealed with 50% sulphuric acid to give a spot on a TLC plate.

14.2.4 Evaluation of the antimicrobial activity The extracts and the isolated compounds were tested for their antimicrobial activity against bacterial and fungal strains. The strains of microorganisms used for this study were obtained from isolates in the Laboratory of Microbiology of the National Advanced School of Agro-Industrial Sciences (ENSAI). Two Gram (−) bacteria Escherichia coli and Salmonella enterica and a Gram (+) bacteria Staphylococcus aureus were used for the antimicrobial test. Three (3) strains of fungi, namely Penicillilium citrinium, A. flavus and F. solani, were used for the antifungal test. 14.2.4.1 Preparation of solutions and antimicrobial test To determine the MIC, a concentration range of four different concentrations of each plant extract was prepared according to the method of double dilution in a liquid medium with a geometric progression of the concentrations of A. hockii extracts of reason ½. These concentrations varied from 0.0625, 0.125, 0.250 and 0.500 mg/mL. The Mueller Hinton broth for bacteria was prepared and mixed with the different extracts according to the concentrations used and then spread on empty Petri dishes. This mixture was left to incubate for a few minutes and then, the sterilized discs were taken with flaming forceps on which the inoculum was carefully deposited in the middle of each petri dish. The mixture was placed in an oven for 18–24 h at 37 °C. This operation was repeated three times to obtain more reliable results. Fluconazole and ciprofloxacin using 10% DMSO were used, respectively, as reference drugs for fungi and bacteria. 14.2.4.2 MIC determination Only extracts/compounds showing inhibition diameter were considered. Specifically, 50 mL of Sabauraud dextrose or Mueller Hinton broth was introduced into a 96-well microplate for fungi and bacteria, respectively. Fifty (50 mL) of concentrated extract/ compound at 1000 μg/mL was added to the first line wells, and a sterile two-fold dilution was performed by transferring 50 mL of the mixture from the first wells into the subsequent wells until the last well. Thus the final concentrations varied from 0.0625, 0.125, 0.250 and 0.500 mg/mL.

14.3 Results and discussion

259

14.2.5 Insecticidal test of the hexane, acetone, methanol extracts and compound 1 Each extract having a mass of 2.812 g is in turn dissolved in 5.625 mL of acetone to constitute the stock solution. Thus, to obtain the four different concentrations (0.063; 0.125; 0.250 and 0.500 mg/mL) used for this manipulation, the solution of concentration 0.5 mg/mL required 3 mL of the stock solution which was directly taken for the three trials. The preparation of the 0.250 mg/mL concentration solution required 1.5 mL of the stock solution which was taken and then made up to 3 mL with acetone. The solution of concentration 0.125 mg/mL required 0.75 mL of the stock solution which was completed to 3 mL with acetone. Finally, for the concentration of 0.0625 mg/mL, 0.375 mL of the stock solution was taken and then made up to 3 mL with acetone. Then, using a micropipette, 1 mL of each of these solutions was mixed with 40 g of cowpea and corn seeds contained in 500 mL glass jars. The seed and extract mixture was manually shaken for 5 min so that all seeds were evenly coated, and then left at room temperature for 1 h to allow complete evaporation of the acetone. A batch of 10 bruchids up to two days old was introduced into each jar and closed with perforated lids. For the test with compound 1, the concentration of 5 mg/mL was used. The latter was dissolved in acetone and the dissolved compound was poured onto cowpea seeds in a jar. A batch of 10 bruchids up to two days old was introduced into this mixture in the same manner as with the extracts. The insecticidal test of the extracts and compounds isolated from the fruits of A. hockii against the adults of C. maculatus was carried out for 7 days and counting of the dead insects was done every 24 h. For the negative control, the test was done only with acetone which was poured on cowpea seeds then this mixture received 10 insects and Protect DP is one of the reference insecticides which is used by the same process as the positive control. For each dose as well as the controls, three replications were performed [17].

14.3 Results and discussion 14.3.1 Identification of compound 1 Compound 1 (22 mg) obtained in 100% methanol is a grey solid soluble in DMSO. The structure of this compound (1) was elucidated using 1H NMR, 13C NMR and Ms spectroscopy. Its TOF-MS-EI+ showed the molecular ion peak [M]+ at m/z 602.3 presenting the fragment ions at m/z 367.2 ((M-C21H40NO4) + 2H)+, 211 ((M-C15H31) + 2H)+, 253 ((M-C17H33O) + H)+, 339.2 ((M-C20H40NO3) + 2H)+, 95[(M-C7H13) + 2H]+ and 44 (M-CH2N0)+ which correspond to the molecular formula C36H71NO4 with 2 degrees of unsaturation. The 1H NMR spectrum reveals a signal at δH 8.60 (1H, d, J = 9.5 Hz) attributable to the proton of a secondary amide function –CONH–; At δH 4.74 (m, H-3) and δH 4.49 (H-4) the

260

14 A new sphingoid derivative from Acacia hockii

signals of two oxygenated methines. In addition, signals from two olefinic protons are observed at δH 5.51 (1H, m, H-14) and 5.06 (1H, m, H-15), an intense signal between δH 1.30–1.68 ppm suggesting the presence of a long aliphatic chain and at δH 0.90 (6H, t, J = 6.9, H-20/16′) (δC 14.1) the presence of two terminal methyl groups at δH 0.90 (6H, t, J = 6.9, H-20/16′) (δC 14.1). All these 1H NMR spectral data suggest that compound 1 is a sphingolipid [18]. On the 13C NMR spectrum, the appearance of two signals at δC 51.0 (C–N) and 173.0 (C=O) suggests the presence of an amide group. At δC 76.5 (C-3), 71.0 (C-4) appears signals confirming the presence of two hydroxyl groups [19]. At δC 63.0 (C-1) appears signal of methylene attributed to the osidic unit [20]. In addition, on the 13C NMR spectrum of compound 1, we observe two carbon signals at δC 130.2 (C-14) and 129.7 (C-15) corresponding to a carbon–carbon double bond with the trans configuration evidenced by the chemical shift of C-13 at δC 33.8 and C-16 at δC 31.9. In general, the stereochemistry of the olefinic functional group is assigned from the 13C chemical shift values of the allylic carbons, δC 32–33 for the trans (E ) configuration and δC 27–28 for the cis (Z ) configuration [21]. Several correlations are observed in the 1H–1H COSY spectrum of compound 1 (Figure 14.2). A correlation between the proton located is observed at 4.74 (m, H-3) and δH 4.49 (H-4) and another correlation between the proton 4.74 (m, H-3) and the proton H-1 located at 8.60 ppm. A correlation at the level of olefinic protons located at δH 5.51 (1H, m, H-14) and 5.06 (1H, m, H-15) with their respective methylene protons located at 1.96 (H-13) and 2.08 (H-16) ppm.

Figure 14.1: Structure of N-((2S,3S,4R,14E)-1,3,4-trihydroxyicos-14-en-2-yl)palmitamide or (Figure 14.1) hockiamide (1).

Figure 14.2: 1H–1H COSY (

) and keys HMBC correlates (

) of hockiamide (1).

14.3 Results and discussion

261

Figure 14.3: EI-MS fragmentations pattern of hockiamide (1).

The HMBC spectrum shows several correlations between the terminal protons appearing at 0.90 ppm and the methylene groups directly linked to the carbons bearing these protons (Figure 14.2). All these data in the 1H NMR and 13C NMR spectrum indicate that compound 1 is the sphingoid base is a phytosphingosine having the name N-((14Z,3S,4R)-1,3,4-trihydroxyicos14-en-2-yl)palmitamide (Figure 14.1) isolated for the first time from A. hockii, to which we have given the trivial name hockiamide (1). Figure 14.3 presents the different fragmentations observed for compound 1. Spectroscopic data of the newly isolated ceramide hockiamide (1) are given below. Hockiamide (1): TOF-MS-EI+ m/z = 582 (M+Na)+ calc. 602.3 for C36H71NO4. 1H NMR (600 MHz, Pyr) δ 8.60 (s, 2H), 5.74 (s, 1H, H-14), 5.49 (m, 6H, H-15), 4.74 (s, 1H, H-3), 4.49 (s,1, H-4), 2.18 (d, J = 314.7 Hz, 2H, H-2′), 3.65 (m, 2H, H-1), 1.96 (s, 4H, H-13/H-16), 1.33 (d, J = 166.2 Hz, 2H, H-15′), 3.23 (m, 1H, H-2), 1.30 (s, 12H, H-6/H-11), 1.30 (s, 22H, H-4’/H-14′), 0.90 (6H, t, J = 6.9, H-20/16′). 13C NMR (130 MHz, Pyr) δ 173.0 (C-1′), 35.6 (C-2′), 76.5 (C-3), 71.0 (C-4), 130.2 (C-14), 129.7 (C-15), 27.4–31.8 (C-4′-C-16′), 22.6 (C-20), 51.0 (C-2), 63.0 (C-1), 14.1 (C-16′/C-20), 31.7 (C-5), 23.8 (C-6), 30.0 (C-7), 29.0 (C-8/C-11), 25.7 (C-3′), 28.7 (C-4′). Compounds 2 and 3 obtained in the Hex/AcOEt (95/10, v/v) solvent system is a white powder soluble in chloroform. These are a mixture of two compounds of molecular formula C29H48O and C29H50O with degrees of unsaturation of 6 and 5 respectively. The 1H NMR spectrum at 3.55 ppm shows the signal of the (H-3) proton as a multiplet linked to an OH (δ = 2.10) group. At 4.98 and 5.15 ppm, appear as a multiplet the signals of the protons characteristic of the olefinic protons of β-stigmasterol and β-sitosterol let (1H, m, H-22) and δ (1H, m, H-23). At 5.30 ppm the signal appears as a multiplet of β-stigmasterol and β-sitosterol (1H, m, H-6). On the 13C at δC 129.2 (C-22) and δC 138.3 (C-23) NMR spectrum, signals of the characteristic carbons of β-stigmasterol and β-sitosterol. At 121.7 (C-6) and 140.7 (C-5) signals of the characteristic of the β-stigmasterol and β-sitosterol. Compounds 2 and 3 were identified by comparing their NMR spectroscopic data with literature as a mixture of stigmasterol (C29H48O) and sitosterol (C29H50O) (Figure 14.4) [22].

262

14 A new sphingoid derivative from Acacia hockii

Figure 14.4: Structures of other compounds isolated from the fruits of Acacia hockii (2) (β-stigmasterol); (3) (β-sitosterol; (4) β-stigmasterol-3β-O-D-glucopyranoside).

Compound 4 responds positively to the Molish test suggesting that this compound is a steroidal glucoside in the form of grey powder soluble in DMSO. The NMR 1H spectrum of this compound shows in addition to the signals from two olefinic protons at δH 5.15 (1H, dd, J = 15.1; 8.7 Hz) and 5.02 (1H, dd, J = 15.2; 8.8 Hz), generally attributable to the protons H-22 and H-23 respectively of a stigmastane skeleton. This hypothesis is confirmed by the presence on the 13C NMR spectrum of signals at 138.0 (C-22) and 128.0 (C-23). All these data in addition to the integrations of the protons whose peaks resonate at δH 5.33 (5H, m, H-6); 4.22 (5H, d, J = 7.8 Hz, H-22) and 3.47 (5H, m, H-3) as well the data compared to the literature review helped to identify compound 4 as the 3β-O-Dglucopyranosyl-β-stigmasterol [23] (Figure 14.4).

14.3.2 Result of the insecticide test on C. maculatus The results of the insecticide test of the hexane extract of the fruits of A. hockii on adults of C. maculatus showed high efficacy in killing 100% of the insects from the third day after the contamination of cowpea grains at the concentration of 0.500 mg/mL. The acetone extract also led to the death of 100% of the adults of C. maculatus on the second day of the test exactly as the synthetic insecticide (Protect DP). The methanol extract in the case of the insect C. maculatus led to a mortality of 80% of these insects at the concentration of 0.500 mg/g of cowpea. The mortality rate of the extracts can be explained by the presence in these extracts, different classes of compounds such as terpenoids as well as tannins

14.3 Results and discussion

263

which have the particularity of being repulsive towards insects, alkaloids and terpenes which destroy the oviposition and the different stages of development of insects, saponins as for them induce inhibitory effects of the growth and the ovogenesis towards insects [24]. The difference in mortality percentages may therefore be due to the amount of these different classes of compounds, which are variable from one extract to another (Figures 14.5–14.7). From the analysis of variance, it can be seen that there was no significant difference at the 95% confidence level (p ≤ 0.05) for the hexane and acetone extracts against C. maculatus. Cowpea grains coated with the different extracts of A. hockii fruits were kept under observation for six months. Results indicated that no new insect outbreaks were observed, meaning that A. hockii extracts have the ability not only to destroy insects but also to prevent the development of new insects. Table 14.1 shows the statistical analysis of insecticidal tests of hexane, acetone and methanol extracts of A. hockii fruits against C. maculatus.

14.3.3 Results of the antimicrobial test The antimicrobial test showed a positive action of all extracts on the different bacterial strains tested. A greater inhibition diameter is observed on S. aureus with all extracts: 39.33 for the acetone extract and 24.67 for the hexane extract.

Figure 14.5: Evolution of the mortality of C. maculatus for the hexane extract and hockiamide (1).

264

14 A new sphingoid derivative from Acacia hockii

Figure 14.6: Evolution of the mortality of C. maculatus for the acetone extract and hockiamide (1).

Figure 14.7: Evolution of the mortality of C. maculatus for the acetone methanol and hockiamide (1).

14.3 Results and discussion

265

Table .: Statistical analysis of the hexane, acetone and methanol extracts against Callosobruchus maculatus. Days

Hexane extract

Acetone extract

Methanol extract

D D D D D D D D D D D D D D D D D D D D D

Concentration (mg/mL)

Positive control

.

.

.

.

Protect DP (.%)

. ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .

 ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .

. ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .  ± . . ± . . ± . . ± . . ± . . ± . . ± .

. ± . . ± . . ± . . ± . . ± . . ± .  ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .

 ± .  ± .  ± .  ± .  ± .  ± .  ± . . ± . . ± .  ± .  ± .  ± .  ± .  ± . . ± .  ± .  ± .  ± .  ± .  ± .  ± .

This result is justified and explained by the fact that S. aureus being a Gram-positive bacterium with only one plasma membrane is generally more sensitive to antibiotics than Gram-negative bacteria with two plasma membranes such as S. enterica and E. coli which present the smallest inhibition diameter of 28.67 and 15.33, respectively, for the acetone extract against S. enterica and E. coli. The membrane wall of Gram-positive bacteria organized in this way offers them less protection against foreign bodies (antibiotics), making these bacteria more vulnerable to destruction. These results are also close to the work of Yala [25], which shows that the acetone extract has a higher antibacterial activity than the other extracts. Moreover, phytochemical screening of A. hockii extracts revealed that they possess different families of compounds such as triterpenes, steroids, alkaloids, phenolic derivatives and flavonoids known for their antimicrobial properties. These results also show that the methanol extract registers the smallest inhibition diameters compared to the other two extracts: the hexane and acetone extracts tested on the three bacterial strains S. enterica, E. coli and S. aureus. Table 14.2 below shows the result of the antifungal test. These results reveal that the acetone extract inhibited all three fungal strains tested F. solani, A. flavus and P. citrinum

266

Table .: Statistical analysis of the antimicrobial test of Acacia hockii extracts. Extracts

Bacteria

Staphyloc-cocus aureus

Escherichia coli

Salmonella enterica

Fungi

Penicillium citrinum Aspergillus flavus Fusarium solani

Acetone

Concentrations (mg/mL)

Inhibition diameter in mm

MIC (mg/mL)

Inhibition diameter in mm

. . . . . . . . . . . . a a a

. ± .  ±  . ± .  ±  . ± . ±  ±  . ± . . ± .  ±   ±   ±  a a a

.

. ± .  ±  . ± . . ± . ± ± . ± . . ± . . ± . . ± . . ± . . ± . b b b

a: Not determined. b: No inhibition observed.

.

.

b . b

Methanol MIC (mg/mL) .

.

.

. . .

Inhibition diameter in mm

MIC (mg/mL)

 ± . ±  ± .  ±  . ± .  ± .  ± .  ± . . ± . ± ±  ± . b b b

.

.

.

b . b

14 A new sphingoid derivative from Acacia hockii

Hexane

References

267

with MIC ranging from 0.0625 to 0.500 mg/mL. As for the hexane and methanol extracts, an inhibition against the strain A. flavus alone is observed with a MIC equal to 0.500 mg/ mL. This inhibition can be explained by the high presence of compounds such as triterpenes, alkaloids, phenolic derivatives, steroids and flavonoids as well as other terpenoids in the different extracts and much more in the acetone extract.

14.4 Conclusion Investigations carried out on the fruits of A. hockii led to the isolation of a new compound of the sphingolipid family N-((2S,3S,4R,14E)-1,3,4-trihydroxyicos-14-en-2-yl)palmitamide or hockiamide. Its hexane, acetone and methanol extracts as well as the newly isolated ceramide have both insecticidal and antimicrobial activities. The fruits of A. hockii can be considered a bioinsecticide and can then be used by local populations to protect stored foodstuffs against some pests and fungi during storage. Acknowledgements: The authors thank the head of the Laboratory of Microbiology and Food Biotechnology of the National Advanced School of Agro-Industrial Sciences of the University of Ngaoundere and his team for providing the microbial strains and assisting in the evaluation of the antimicrobial tests.

References 1. Mbuli SC, Fonjong NL, Fletcher JA. Climate change and small farmers’ vulnerability to food insecurity in Cameroon. Sustainability 2021;13:1–16. 2. Awazi PN, Tchamba MN, Temgoua FL, Avana TML. Appraisal of smallholder farmers’ vulnerability to climatic variations and changes in the western highlands of Cameroon. Sci Afr 2020;10:00637. 3. Daka A, Wang Y, Hu L. Public agrarian policies and food security in Cameroon: problems and challenges towards agricultural development. EJ-Social 2021;1:2736–5522. 4. Saric MM, Ljubicic M, Lapcic I, Guine FPR. Contribution of fruit, vegetables, whole cereals, and legumes to total fibre intake in adult Croatian Dalmatian population. Arh Hig Rada Toksikol 2020;71:138–45. 5. Sanon A, Zakaria I, Clémentine LDB, Niango BM, Honora CRN. Potential of botanicals to control Callosobruchus maculatus (Col: Chrysomelidae, Bruchinae), a major pest of stored cowpeas in Burkina Faso. Int J Insect Sci 2018;10:1–8. 6. Muthoni J, Nyamongo DON, Mbiyu M. Climatic change, its likely impact on potato (Solanum tuberosum L.) production in Kenya and plausible coping measures. Int J Hortic 2019;7:115–23. 7. Yadav NA, Verma P, Kumar V, Sangwan P, Mishra S, Panjiar N, et al. Biodiversity of the genus Penicillium in different habitats. In: Gupta VK, editor. In New and Future Developments in Microbial Biotechnology and Bioengineering: Penicillium System Properties and Applications 2017; 3-18. 1st Elsevier in Scotland. 8. Guèye TM, Seck D, Wathelet JP, Lognay G. Lutte contre les ravageurs des stocks de céréales et de légumineuses au Sénégal et en Afrique occidentale: synthèse bibliographique. Biotechnol Agron Soc Environ 2011;15:183–94. 9. Lakhoua NM. Analysis and management of a grain storage system. Int J Inf Technol Electr Eng 2019;8:13–6.

268

14 A new sphingoid derivative from Acacia hockii

10. Isman MB. Bridging the gap: moving botanical insecticides from the laboratory to the farm. Ind Crop Prod 2017;110:10–4. 11. Bokobana EM, Koba K, Poutouli WP, Akantetou PK, Nadio AN, Laba B, et al. Evaluation du potentiel insecticide et répulsif de l’huile essentielle de Cymbopogon schoenanthus (l.) Spreng. sur Aphis gossypii Glover (Homoptera: Aphididae), ravageur du cotonnier au Togo. Rev Cames 2014;2:48–55. 12. Kouninki H, Matakone RR, Nukenine NE. Insecticidal properties and persistence of Berlinia grandifolia (J. Vahl) and Securidaca Iongepedunculata Fres, two aromatic plants from the Far-North region of Cameroon alone or in combination against Sitophilus oryzae L. (Coleoptera: Curculionidae). Int J Farm Allied Sci 2015;4: 635–42. 13. Acheuk F, Lakhdari W, Abdellaoui K, Belaid M, Allouane R, Halouane F. Phytochemical study and bioinsecticidal effect of the crude ethanolic extract of the Algerian plant Artemisia judaica L (Asteraceae) against the black bean aphid, Aphis fabae Scop. Agric For 2017;63:95–104. 14. Batiha GES, Akhtar N, Alsayegh AA, Abusudah FW, Almohmadi HN, Shaheen MS, et al. Bioactive compounds, pharmacological actions, and pharmacokinetics of genus Acacia. Molecules 2022;27:7340. 15. Magnini RD, Hilou A, Millogo-Kone H, Compaore S, Pagès JM, Davin RA, et al. A review of ethnobotanical uses, biological activities and phytochemical aspects of Acacia senegal (L.) Willd. and Acacia seyal Delile. (Fabaceae). Int J Plant Sci Hort 2020;2:32–55. 16. Elmi A, Spina R, Risler A, Philippot S, Mérito A, Duval ER, et al. Evaluation of antioxidant and antibacterial activities, cytotoxicity of Acacia seyal Del bark extracts and isolated compounds. Molecules 2020;25:2392. 17. Ndomo FA, Tapondjou AL, Tendonkeng F, Tchouanguep MF. Evaluation des propriétés insecticides des feuilles de Callistemon viminalis (Myrtaceae) contre les adultes d’Aconthoscelides obtectus (Say) (Coleoptera; Bruchidae). Tropicultura 2009;27:137–43. 18. Abdou JP, Nyemb JN, Momeni J, Fadimatou A, Gbaweng YJA, Tsopmejio JP, et al. A new cytotoxic ceramide from Dalbergia boehmii Taub. stem bark. Pharmacogn Res 2022;14:442–5. 19. Nyemb JN, Tchinda TA, Talla E, Nanga BE, Ngoudjou TD, Henoumont C, et al. Vitellaroside, a new cerebroside from Vitellaria paradoxa (Sapotaceae) and its bioactivities. Nat Prod Chem Res 2018;6:306. 20. Tao WW, Yang NY, Liu L, Duan JA, Wu DK, Tang YP, et al. Two new cerebrosides from the pollen of Typha angustifolia. Fitoterapia 2010;81:196–9. 21. Muralidhar P, Radhika P, Krishna N, Venkata Rao D, Rao CB. Sphingolipids from marine organisms: a review. Nat Prod Sci 2003;9:117–42. 22. Lemilemu F, Girmay S, Shenkute K, Endale M. Antibacterial steroids from roots of Bersama abyssinica. Ethiop J Sci Sust Dev 2020;7(1):27–34. 23. Sheng Z, Dai H, Pan S, Wang H, Hu Y, Ma W. Isolation and characterization of an α-glucosidase inhibitor from Musa spp. (Baxijiao) flowers. Molecules 2014;19:10563–73. 24. Ngamo LST, Hance TH. Diversité des ravageurs des denrées et méthodes alternatives de lutte en milieu tropical. Tropicultura 2007;25:215–20. 25. Yala JF, Ntsameso-Mve-Mba V, Issembe YA, Lepengue NA, Souka A. Évaluation in vitro de l’activité antimicrobienne de l’extrait aqueux d’Eryngium foetidium récolté dans la ville de Franceville. J Appl Biosci 2016;103:9886–93.

Kazeem A. Alabi*, Ibrahim O. Abdulsalami, Kazeem O. Ajibola, Nusirat A. Sadiku, Mariam D. Adeoye, Abdul Azeez T. Lawal and Rasheed A. Adigun

15 Protection of wood against bio-attack and research of new effective and environmental friendly fungicides Abstract: This research investigated the design, chemical modification, characterization and biocidal evaluation of waxes. Tallow (animal fat), bee-wax (insect) and shea butter (plant fat) were first converted to carboxylates by metathesis and later transformed into urea and thiourea complexes. The transformation was monitored using UV–visible, FT-IR and scanning electron microscopy (SEM) coupled with energy dispersive X-ray spectroscopy. They were also screened for biocidal activities using two white rots (Pleurotus sajor-cajor and Pleurotus oestratus), two brown rots (Sclerotium rolfsii and Rhizotonia solanii) and a soft rot (Cheatomium globosum). The UV–visible absorption peaks shifted to a longer wavelength for the complexes in relation to the carboxylates signifying lower energy and higher activities. Carboxylates showed very sharp peaks around 1700 cm−1 attributable to the carbonyl functional group (C=O) (Scheme 15.1), the carbonyl (C=O) peaks in the carboxylates were replaced by the appearance of another peaks in the urea and thiourea complexes at around 1600 cm−1 attributable to azomethine (C=N) (Scheme 15.2 and 15.3). None of the surface morphologies of the samples (crystalline) is identical. This result further confirmed the formation of the products. The result of fungi assay showed that tallow based carboxylate, urea and thiourea complexes greatly inhibited the growth of all the fungi species used. However, bees wax based carboxylate and its complexes as well as plantfat based carboxylate and its complexes could not inhibit the growth of Sclerotium rolfsii. For insect and plant-based urea complexes, there were tiny growths (pin head) seen on the plates inoculated with P. sajor-cajor and P. oestratus, respectively. The findings of this work showed that urea and thiourea complexes performed better than carboxylates in fungi inhibition. Tallow-based products (carboxylates, urea and thiourea) showed the greatest anti-fungi properties.

*Corresponding author: Kazeem A. Alabi, Industrial and Environmental Unit, Department of Chemical Sciences, College of Natural and Applied Sciences, Fountain University, P.M.B 4491 Osogbo, Nigeria, E-mail: [email protected] Ibrahim O. Abdulsalami, Kazeem O. Ajibola, Mariam D. Adeoye, Abdul Azeez T. Lawal and Rasheed A. Adigun, Industrial and Environmental Unit, Department of Chemical Sciences, College of Natural and Applied Sciences, Fountain University, P.M.B 4491 Osogbo, Nigeria Nusirat A. Sadiku, Department of Forest Resources Management, University of Ilorin, Ilorin, Kwara State, Nigeria As per De Gruyter’s policy this article has previously been published in the journal Physical Sciences Reviews. Please cite as: K. A. Alabi, I. O. Abdulsalami, K. O. Ajibola, N. A. Sadiku, M. D. Adeoye, A. A. T. Lawal and R. A. Adigun “Protection of wood against bio-attack and research of new effective and environmental friendly fungicides” Physical Sciences Reviews [Online] 2023. DOI: 10.1515/ psr-2022-0283 | https://doi.org/10.1515/9783111071428-015

270

15 Protection of wood against bio-attack

Keywords: Brown rot; carboxylate; thiourea; urea; soft rot; white rot.

15.1 Introduction Wood is among the most valuable natural resources for mankind [1]. It is utilized all over the world for various tasks, ranging from simple to a highly finished structural applications, ornate decoration and it is a dominant industrial raw material in the country [2]. Wood has been in use for more than four centuries and it is still the most common known raw material for house construction including framing [3]. Despite the usefulness, it is highly degradable by microorganisms and as a result of climatic conditions. When exposed to atmosphere, fungi and insects consume the lignocellulose in wood and hence leads to decay [4]. Wood-decay fungi digest moist wood, causing it to rot. Some types of fungi attack dead wood, like Armillaria (honey fungus), while others are parasitic and attack living trees. Fungal only colonize a wood with excessive moisture well above the fibre saturation [5]. Lignicolous fungi grow and penetrate the wood fibre to cause decay. This process helps the nature in enriching the soil by breaking down the complex molecules in the wood [6]. These lignicolous fungi consume wood differently; some attack the carbohydrates and others lignin. The rate at which the wooden materials decay in climates can be evaluated empirically [6]. Preservative system is chemical-treatment given to the wood that elongate their lifespan as a result of protection from fungi and insects attack. The prominent method always involves the use chemical preservatives [7]. The protective-chemicals could be oil-borne (oil soluble), water-borne (water soluble), and organic solvent-borne (organic soluble) preservatives [8]. Generally, oil-borne preservatives can provide long-term protection because they are thermal and chemical stabile and also highly resistant to leaching because of their insolubility in water [9]. Wood is the basic material from which paper and paper-related products are produced. Well above 50 percent of the average world paper product is derived from virgin wood-based pulp [10]. For thousands of years, wood has been the major source of paper making materials [11]. Paper and paperboard production worldwide is well above four hundred million tons [12]. Although, larger percentage of paper are from virgin pulp, about 40 percent are from recycling of the used paper based materials while less than 10 are from non-wood sources [12]. The use of metal to improve the activity of the ligand by complexation reaction has increased the awareness of its usefulness in the treatment of microorganisms [13]. Most of the potential antimicrobial agents are organic-based compounds and the activity is enhanced when complexed with metals especially the transition metals [14]. The biological activity of the compounds depends largely on the nature of the ligands and metal used [15].

15.2 Materials and methods

271

Nitrogen containing ligands, despite being the most common; still have been found to be most effective against enzymes, bacteria and fungi [16]. Numerous number of compounds containing nitrogen and sulphur as hetero-atoms in the heterocyclic compounds shows different types of bio-activities [17]. Carbamides (ureas) and thiocarbamides (thioureas) are rich sources of nitrogen. Urea is widely used in agriculture, industry, medicine and automobile systems. Thiourea, an organosulphur compound, is a well-known reagent in organic synthesis for various applications. Thiol compounds form various complexes with transition meats because of their exceptional ligating properties, and many of them are applicable in different areas [18]. Wood-decay fungi are better described regarding to the nature of decay that they cause to the wood. The best-known types are brown rot, soft rot, and white rot [19]. Each type rot produces enzymes that can degrade different part of the plant materials in different environment [20]. Brown-rot breaks down both hemicellulose and cellulose from the wood. The hydrogen peroxide (H2O2) produced during the breaking down of the hemicellulose later breaks down the cellulose [19]. Soft-rot fungi survive the climatic conditions that are too harsh for both brown and white-rots. Soft-rot can also decompose woods with high levels of compounds that are resistant to biological attack like tannin as contain in the bark of woody plants [21]. Some white-rot fungi break down only lignin in wood, leaving cellulose untouched; while some break down both lignin and cellulose [22]. As a result, the wood becomes white or yellow and texture also changes, becoming moist, soft, spongy, or stringy. There are wide range of enzymes that are involved in the decomposition of the wood by white-rot, some directly oxidize lignin [23]. Honey mushroom (Armillaria spp.) is a good example of white-rot fungus notorious for attacking living trees. Pleurotus ostreatus and other oyster mushrooms are commonly cultivated white-rot fungi, but P. ostreatus is not parasitic and will not grow on a living tree, unless it is already dying from other causes. Other white-rot fungi include the turkey tail, artist’s conk, and tinder fungus [24]. In this research, copper soaps, their corresponding urea and thiourea complexes were synthesized using three waxes (shea butter, bee wax and tallow) as starting materials. The synthesized compounds were characterized using UV, FT-IR and SEM coupled with EDX for elemental analysis. The antifungal activities of the synthesized compounds were tested against two each of white and brown-rots and one soft-rot fungi.

15.2 Materials and methods Animal fat, Tallow (beef fat) was obtained at Ibro abattoir, Lameco round about, Ilobu road, Osogbo; insect wax (bee wax) was collected at bee farm, behind Independent

272

15 Protection of wood against bio-attack

National Electoral Commission (INEC) Office, Osogbo Local Government while vegetable fat, shea Butter was bought at Oja-Oba Market Osogbo, Osun state. All the chemicals used were procured from sigma Aldrich, United Kingdom as analytical grade chemicals and were used as received. The fungi isolates (brown and white rot) were supplied by the Pathology Department of Forest Research Institute of Nigeria, Jericho, Ibadan, while the soft rot isolate (Cheatomium globosum) was obtained from the Department of Plant Biology, University of Ilorin, Ilorin, Kwara state.

15.3 Preparation of soluble soap Ten (10) ml of the tallow oil was saponified with 30 ml of 20% NaOH in a beaker, the mixture was stirred vigorously using glass rod. The beaker became warm which indicated the exothermic nature of the reaction. The PH of the mixture was tested using moist litmus papers. The product (soap) was salted out with addition of common salt (NaCl). The same process was repeated for shea butter and bee wax to obtain their soluble soaps [25] and [26].

15.4 Production of metallic soap [copper (II) soap] The chloride salt of copper metal (CuCl2·7H2O) (O.5 M) was prepared and kept in an amber bottle for future use. Ten (10 cm3) of 0.5 M HCl was added to 20 cm3 of 10% soluble soap solution, and the mixture was titrated against the copper chloride salt solution with constant stirring until complete precipitation was observed. The mixture was then filtered. The residue (metallic soap) was washed with warm water, air-dried followed by cleaning with petroleum ether and re-crystallized in benzene [25] and [26] (Scheme 15.1).

15.5 Synthesis of urea complexes from metallic soap The complexes were prepared by reacting the insoluble soap (derived from three different types of oil) with urea in ratio 1:1 [13]. The insoluble soap (1 g) and 1 g of urea were dissolved into 10 ml of benzene and the resulting mixture was refluxed for 30 min with

Scheme 15.1: Production of copper (II) metallic soap.

15.7 Physicochemical parameters of the synthesized compounds

273

continuous stirring, the crystal obtained from the reaction was filtered, washed using hot water and alcohol and later air dried (Scheme 15.2).

15.6 Synthesis of thiourea complexes from metallic soap The complexes were prepared by reacting the copper soap with thiourea in ratio 1:1 [13]. Metallic soap (1 g) and 1 g of thiourea were dissolved into 10 ml of benzene and the resulting mixture was refluxed for 30 min with continuous stirring, the solid obtained (complex) was filtered, washed with hot water and alcohol and air dried (Scheme 15.3).

15.7 Physicochemical parameters of the synthesized compounds 15.7.1 Melting point A capillary tube was sealed at one end in a Bunsen burner. A small quantity of the sample was introduced into the tube up to 1 mm with gentle tapping on the table for tight packing. The tube was stuck to a thermometer using a fine thread such that the bottom and the thermometer bulb were at the same level. Then, the thermometer was dipped into about 50 cm3 liquid paraffin in a 100 cm3 beaker up to the sample level.

Scheme 15.2: Synthesis of urea complex.

Scheme 15.3: Synthesis of thiourea complex.

274

15 Protection of wood against bio-attack

The beaker was gently heated so that the temperature rose gradually and with constant stirring for even temperature distribution. The temperature at which melting starts and ends were recorded.

15.7.2 Moisture content Each sample (1 g) was weighed into a pre-weighed crucible and heated in oven at 105 °C. The crucible and its contents were weighed at regular intervals until constant weights were obtained after cooling in desiccator. Percentage moisture content was calculated using the formula below: WO − Wf MC = ( )100 WO where: MC = Percentage moisture content WO = Initial weight (wet sample) Wf = Weight of the sample after drying

15.7.3 Determination of ash content The moisture free samples were weighed into pre-weighed crucibles and placed in a muffle furnace at the temperature of 650 °C for 8 h. The furnace was then allowed to cool after which the crucibles were transferred into desiccator for further cooling before the contents and the crucibles were weighed. Percentage ash content was calculated using this formula: % Ash = (

y−x )100 a

where: x = Weight of empty crucible y = Weight of crucible + ash a = Weight of sample before heating

15.7.4 Determination of sulphated ash contents The samples were accurately weighed into crucibles that had been previously ignited, cooled and weighed. Concentrated sulphuric acid (1 ml) was added to each sample in the crucible and charred at low temperature without causing ignition of the samples until there were no more white fumes given off. The crucibles with the charred samples were then transferred into muffle furnace regulated at 650 °C and heated for 8 h. The

15.7 Physicochemical parameters of the synthesized compounds

275

furnace was then allowed to cool and crucibles were transferred into desiccator for further cooling before the contents and the crucibles were re-weighed. The above formula used in calculating ash contents was also employed to found sulphated ash content.

15.7.5 Solubility test Solubility of the samples in various solvents was carried out by introducing small amount of each sample into test tubes containing the solvents and visual observations were recorded.

15.7.6 Colour Colour of each sample was observed with naked eye and the there was no colour change for long period of time.

15.7.7 Absorption spectral analysis Ultralviolet–visible spectroscopic analysis of the dilute solution of the synthesized compounds were performed in each of the solvent, in the concentration of 10−4 M using Jenway 6405 UV–visible spectrophotometer. One of the pair of matched quartz cuvettes contained in the reference compartment of the spectrophotometer and the other in the sample compartment contained dilute solution of known concentration. The optical excitation was done with deuterium lamp as source of light in the UV region and tungsten lamp in the visible region. The spectra were scanned at 25 °C in the range 200–800 nm for both solution and solvent whose absorption serves as blanks or baselines for the solution absorption bands. The data obtained were used for further analysis.

15.7.8 Infrared spectroscopy analysis Frontier Transform Infrared spectrum of the synthesized compounds were determined between 350 and 4400 cm−1 using Potassium Bromate (KBr) disc.

15.7.9 Scanning electron microscope coupled with energydispersive X-ray spectroscopy (SEM/EDS) The scan was carried out using an FEI model FEG-ESEM Quanta 200 field-emission environmental scanning electron microscope equipped with an energy dispersive

276

15 Protection of wood against bio-attack

X-ray microanalysis [Si(Li) EDAX Genesis 7000 Super Ultra-Thin Window (SUTW)]. Analyses were performed at 20 kV (beam voltage) in the high vacuum mode (∼10−6 mbar). Acquisition time of the spectrums was 300 s, and the detector dead time was of about 30%.

15.8 Antifungal assay The antifungal activity of the synthesized compounds were evaluated using two each of white and brown-rots and a soft-rot fungi by the growth rate of poison medium culture method following the method of [27]. Fungi species used are two white rots: Pleurotus sajor-cajor (W1), Pleurotus oestratus (W2); two brown rots: Sclerotium rolfsii (BR1), Rhizotonia solanii (BR2); and one soft rot: C. globosum (SR) while Ct is the control which is non-inoculated. Known quantities of the hot filtered medium (potato dextrose agar) were poured into agar bottles. Two and half percent (2.5%) concentration of the synthesized compounds was prepared and kept in another bottles. This concentration was based on the actual weight of the SM in the final medium-SM mixture. The bottles containing the proper amounts of preservative and medium are sealed and sterilized. The preservative and medium were sterilized separately to avoid contamination. After sterilization, the synthesized compounds were interfused into the PDA liquid medium under sterile conditions and the contents were homogenized. Streptomycin was added to inhibit the growth of bacteria. Just before the mixture solidifies, it was poured into sterile Petri-dishes of 90 mm in diameter and 15 mm deep. The total volume of the PDA, streptomycin, synthesized compounds and the solvent used in dissolving the synthesized compounds was 10 ml. The control plates were without the synthesized compounds. After the medium has cooled, a small piece of each of the fungi hyphae cut from a vigorously growing Petri-dish culture was inoculated at the centre of each dish including the control plates. The inoculated dishes were then placed in an incubator in the dark at 28 °C ± 2 °C and 60%–65% relative humidity for 10 days, and held at a constant temperature for 10 days. Frequent growth observations were made 2 days interval until the control plates were filled to the brim, the radial growth of each of the fungus were measured in mm with the aid of a veneer caliper. The inhibitory/antifungal indices (%) was calculated as: Δdo − Δd % inhibition = ( ) × 100 Δdo where Δdo and Δd are the average diameter of the fugal colonies in the control and poisoned plates respectively.

15.9 Results and discussions

277

15.9 Results and discussions 15.9.1 Physical properties of synthesized compounds Complexes of urea and thiourea with relatively high melting points (around 226–231 °C) as presented in Table 15.1 is an indication that these compounds would be thermally stable, also all the synthesized compounds are not soluble in many solvents which shows that they will have stability against many chemicals. Furthermore, the synthesized compounds are not soluble in water; therefore, they are highly resistance to leaching. In short, the synthesized compound can provide long term protection against fungus that cause wood decay due to the fact that they are thermally and chemically stable and are highly resistance to leaching which is in conformity with the report of [9] on oil borne wood preservatives.

Table .: Physical properties of synthesized compounds. Compounds (origin)

Colour

Melting point

Ash content (%)

Sulphated ash contents (%)

Copper soap (tallow)

Light blue

–

.

.

Copper soap (beeswax)

Deep green

–

.

.

Copper soap (shea butter)

Turquoise blue

–

.

.

Urea complex (tallow)

Sky blue

–

.

.

Urea complex (beeswax)

Dark green

–

.

.

Urea complex (shea butter)

Leaf green



.

.

Thiourea complex (tallow) Thiourea complex (beeswax) Thiourea complex (shea butter)

Carton colour Deep ash

–

.

.

–

.

.

–

.

.

Light brown

Percentage Solubility yield (%) (room temp.) . Benzene Partly in DMSO  Benzene Partly in DMSO  Benzene Partly in DMSO . Acetone Partly in benzene . DMSO Partly in acetone . Acetone Partly in DMSO . DMSO Benzene . DMSO Benzene  DMSO Benzene

278

15 Protection of wood against bio-attack

15.10 UV–visible spectra of synthesized compounds UV spectrum of copper soap from tallow with λmax of 281.50 nm and intensity of absorption 1.872 while its urea complex shows a bathochromic shift in lamder max to 307 nm with intensity of 4.00; the reason for the shift is not far fetch because of the conjugation in the proposed structure of the complex (Scheme 15.2). Conjugation raises the energy of the HOMO and lowers the energy of the LUMO, therefore, less energy was required for an electronic transition and generally, energy is inversely proportional to wavelength, that is decrease in energy leads to longer wavelength, therefore, conjugation in the complex was responsible for the red shift which strongly suggests that the urea complex had been formed. Also in thiourea complex (Scheme 15.3), there was bathochromic shift due to conjugation as well but the λmax was not up to that of urea complex, this is because oxygen had been substituted with sulphur and substituent is another factor that influences the relative energy of molecular orbital. Oxygen as an auxochrome with higher electronegativity than sulphur will cause higher bathochromic shift compare to that of sulphur. Therefore, the λmax of the complex formed with thiourea was 289 nm with intensity of 3.10; this indicates that the oxygen had been replaced with sulphur which suggests that thiourea complex had been produced.

15.11 Infra-red spectra of syntthesized compounds The characteristic absorption frequencies obtained from infrared spectra of copper soap of tallow origin and its urea and thiourea complexes are presented below. In carboxylates (soaps) the absorption bands at 1741 cm−1 and 1717 cm−1 indicate the presence of carbonyl group (C=O) while the intense peaks at 2919 cm−1 and 2850 cm−1 are attributed to the stretching of C–H bond and the bend at 719 cm−1 further shows that the compound is a long chain hydrocarbon. The absorption peak at 1600 cm−1 is also ascribed to the presence of C=C, which shows that the compound contains unsaturated hydrocarbon. Next is the IR spectrum of urea complexes. Though, there is still an absorption peak around 1714 cm−1 which indicates the presence of carbonyl functional group, the intensity is weak compare to the former. This shows that the carbonyl groups are in different environments. The carbonyl group in urea complex has less double bond due to electron delocalization, therefore absorbs at lower frequency with weak intensity compare to that of copper carboxylates which have localized electrons. Also observed to reappear here are absorption bands at 2918 cm−1 and 2850 cm−1 and bending at 719 cm−1 which indicate presence of aliphatic C–H bond and long chain hydrocarbon respectively. There is now an intense peak at 1654 cm−1 which suggest the presence of imine (C=N) group which normally appears at lower wavelength compare to carbonyl functional group because oxygen is more electronegative than nitrogen. Likewise, the

15.11 Infra-red spectra of syntthesized compounds

279

stretching vibration of the C=N bond (1654 cm−1) appeared at higher wavelength compare to that of C=C (around 1585 cm−1) because nitrogen is more electronegative than carbon. Also the peaks in the region around 3400 cm−1 (3425 cm−1 and 3375 cm−1) are attributed to stretching of the N–H bond. Finally, the IR spectrum of thiourea complex shows that the peaks around 1700 cm−1 which appeared in carboxyletes and urea complexes have totally disappeared in thiourea complexes which are an indication that the carbonyl functional group is absent. The intense absorption peak at 1616 cm−1 now shows the presence of C=S which absorbs at lower frequency than that of C=N. This is an indication that thiourea complex has been produced.

15.11.1 Energy-dispersive X-ray analysis (EDX) with Scanning electron microscope (SEM) Presented in Figures 15.1–15.3 are the SEM images with EDX analyses of copper soap, urea and thiourea complexes from tallow origin. These images revealed changes in the surface morphology of the compounds. The change in the surface morphology indicates that reaction had taken place. Crystalline structure with conspicuous pores was observed in copper soap and this changed to wool-like particles upon complexation with urea, and thread-like morphology with thiourea. Also shown in Figures 15.4–15.6 are SEM images with EDX peaks profile of synthesized compounds of bees wax origin, Figure 15.4 which revealed SEM image of copper soap is cloudy in nature with wide pore sizes while Figure 15.5 is crystalline in nature with smaller pore sizes compare to that of Figures 15.4 and 15.6 shows that of thiourea complex which is sheath-like in nature. Figures 15.7–15.9 are SEM images with EDX peaks profile of copper soap, urea and thiourea complexes of shea butter origin, respectively. Spherical shaped particles were

Figure 15.1: SEM/EDX images of copper soap from tallow.

280

15 Protection of wood against bio-attack

Figure 15.2: SEM/EDX images of urea complex from tallow.

Figure 15.3: SEM/EDX images of thiourea complex from tallow.

Figure 15.4: SEM/EDX images of copper soap from bee wax.

15.11 Infra-red spectra of syntthesized compounds

Figure 15.5: SEM/EDX images of urea complex from bee wax.

Figure 15.6: SEM/EDX images of thiourea complex from bee wax.

Figure 15.7: SEM/EDX images of copper soap from shea butter.

281

282

15 Protection of wood against bio-attack

Figure 15.8: SEM/EDX images of urea complex from shea butter.

Figure 15.9: SEM/EDX images of thiourea complex from shea butter.

observed in copper soap and this changed to cloudy particles upon complexation with urea, and wool-like surface morphology with thiourea. All the SEM images clearly indicate that there were changes in surface morphology upon complexation of copper soap with urea and thiourea. The result of morphology change on the surface ligands after complexing was also reported by [28] and [29]. The peaks of EDX profile in Figure 15.1 shows presence of copper, carbon and oxygen while upon complexation with urea, Figure 15.2 (urea complex) indicates copper, nitrogen, oxygen and carbon which are elements that constitute the molecules of urea complex as shown in the chemical structure. Also Figure 15.3 which is the EDX peaks of thiourea complex shows sulphur and nitrogen alongside with elements that constitute molecules of copper soap, indeed, the SEM coupled with EDX has confirmed formation of the complexes. This is in conformity with the report of [29].

15.12 Anti fungi assay

283

15.12 Anti fungi assay No fungi was able to colonize any of the plates poisoned with copper soap, urea and thiourea complexes of tallow origin which shows that all the synthesized compounds of tallow origin totally inhibit the growth of all the fungi employed. Copper soaps and thiourea complexes of bee wax and shea butter origin showed inhibitory properties to all the fungi except Sclerotium rolfsii. In other words, sclerotium rolfsii was able to colonise plates poisoned with them while other fungi could not. For urea complexes of bee wax and shea butter origin, similar results were observed as recorded for copper soaps and thiourea complexes of the same origin except that tiny growth (pin head) were seen on the plates of urea complex of bee wax origin inoculated with Pleurotus sajour cajour while thiourea complex from bee wax had tiny growth on the plate inoculated with P. oestratus. In general, the synthesized compounds are highly effective in inhibiting the growth of all the fungi employed in this research except sclerotium rolfsii which showed resistance against compounds from bee wax and shea butter (Table 15.2).

Table .: Radial growth of the fungi on the poisoned and control plates. S/N

SM code

Radial growth (mm) 

WR

BR

BR

SR

    , .     , 

     ,     , 

 .,  ,   ,  ,   .,  .,  , 

         , 

         , 

WR          

Copper soap (tallow) Copper soap (beeswax) Copper soap (shea butter) Urea complex (tallow) Urea complex (beeswax) Urea complex (shea butter) Thiourea complex (tallow) Thiourea complex (beeswax) Thiourea complex (shea butter) Control

15.13 Conclusions In conclusion, the synthesized compounds (copper soaps, urea and thiourea complexes) are potential wood protector agents especially against wood-decay fungi. Tallow based products have the highest potency by inhibiting the fungi hundred percent (100%) and also give better yield. Both bees wax and shea butter-based products are closely related

284

15 Protection of wood against bio-attack

as the two are poor inhibitors of a brown-rot (BR1) and were able to inhibit others in various degrees.

References 1. Teck-Jin T, Mohamad NM, Kumar S, Anna N, Zaihan J, Eng-Poh N, et al. Conventional technology and nanotechnology in wood preservation. Nanoprotection of wood: review. Bioresources 2018;13:9220–52. 2. Fuwape JA. Wood utilization from cradle to the grave. Federal University of technology Akure. Inaugural Lecture 2000;25:1–33. 3. Adedokun MO, Ojo TM, Dairo GS. Economic importance and marketing of timber species in Oyo town. Int J Sci Eng Res 2018;8:263–72. 4. Wong LJ, H`ng PS, Wong SY, Lee SH, Lum WC, Chai EW, et al. Termite digestomes as a potential source of symbiotic microbiota for lignocelluloses degradation: a review. Pakistan J Biol Sci 2014;17:956–63. 5. Harris WV. Termites, their recognition and control, 3rd ed. London, UK: Longmans; 1961. 6. Viitanen H, Toratti T, Makkonen L, Peuhkuri R, Ojanen T, Ruokoloainen L, et al. Towards modelling of decay risk of wooden materials. Eur J Wood Prod 2010;68:303–13. 7. Okorski A, Pszczółkowska A, Oszako T, Nowakowska JA. Current possibilities and prospects of using fungicides in forestry. For Res Pap 2015;76:191–206. 8. Ozdemir T, Temiz A, Aydin I. Effect of wood preservatives on surface properties of coated wood. Adv Mater Sci Eng 2015;2015:1–6. 9. Kitchens SC, Amburgey TL. Oil-borne encapsulation treatments combined with borate treated non-seasoned crossties. In: 111th Annual Meeting of the American Wood Protection Association, Sturgis, Mississippi; 2015:140–6 pp. 10. Dillen JR, Dillén S, Hamza MF. Pulp and paper: wood sources. Reference module in materials science and materials engineering. Oxford: Elsevier; 2016:1–6 pp. 11. Nilsson T, Rowell R. Historical wood − structure and properties. J Cult Herit 2012;13:S5–9. 12. Sutherland K. Pulp and paper: filtration prospects in wood products and paper making. Filtrat Separ 2014; 51:24–7. 13. Sharma AR, Sharma R, Saxenn M. Biomedical and antifungal application of Cu (II) soaps and its urea complexes derived from various oils. Open Access J Transl Med Res 2018;2:38–42. 14. Khan S, Sharma R, Sharma AK. A pharmaceutical approach & antifungal activities of copper soaps with their N & S donor complexes derived from mustard and soya bean oils. Glob J Pharmaceu Sci 2017;3:555–619. 15. Sharma AK, Sharma S, Sharma R. Thermal degradation of Cu (II) metallic soaps and their characterizations: a pharmaceutical application. Chronicles Phar Sci 2017;1:312. 16. Tank P, Sharma R, Sharma AK. A pharmaceutical approach & antifungal activities of copper soaps with their N & S donor complexes derived from mustard and soyabean oils. Glob J Pharmaceu Sci 2017;3:555–619. 17. Ranjan KM, Pradeep KDK, Pradhan MMED, Halima FS. Recent advances in urea- and thiourea-based metal complexes: biological, sensor, optical, and corroson inhibition studies. J Crit Discuss Curr Lit 2019;39: 127–87. 18. Ekhuemelo DO, Agbidye FS, Anyam JV, Ekhuemelo C, Igoli JO. Antifungal activity of compounds obtained from sawdust and stem bark of sasswood tree (Erythrophleum suaveolens) on wood rot fungi. J Appl Sci Environ Manag 2019;23:1685–90. 19. Christopher HV, Trevor CD, Colin ES, Michael HS, Clinton F. Decay of cultivated apricot wood (Prunus armeniaca) by the ascomycete Hypocrea sulphurea, using solid state 13C NMR and off-line TMAH thermochemolysis with GC–MS. Int Biodeterior Biodegrad 2005;55:175–85.

References

285

20. Christopher H, Vane TC, Drage C, Snape E. Bark decay by the white-rot fungus Lentinula edodes: polysaccharide loss, lignin resistance and the unmasking of suberin. Int Biodegrad Bioteriorat 2006:57: 14–23. 21. Robert G, David B, Armin W. Fire safety challenges of tall wood buildings [Technical report]. The Fire Research Protection Foundation; 2013:201–162 pp. 22. Vane CH. The molecular composition of lignin in spruce decayed by white-rot fungi (Phanerochaete chrosporium and Trametes versicolor) using pyrolysis GC-SM and thermochemolysis with tetramethylammonium hydroxide. Int Biodegrad Bioteriorat 2003;51:67–75. 23. Stamets P. Mycelium running: how mushrooms can help save the world. Berkley: Ten Speed Press; 2005. 24. Owolabi JB, Alabi KA, Lajide L. Synthesis and characterization of copper metal soaps from Thevetia peruviana and Hura crepitans seed oils. Acad J 2015;10:649–54. 25. Alabi KA. Characterization and termite repellence activity of copper carboxylates from Mangifera indica L. And Sesamum indica L. seed oils. J Chem Soc Nigeria 2019;44:145–52. 26. Bajpai D, Vankar PS. Phyto-remediation of chrome-VI of tannery effluent by Trichoderma species. In: Conference on Desalination and the Environment, European Desalination Society and Center for Reasearch and Technology Hellas (CERTH), Sani Resort, Greece; 2008, vol. 222:255–62 pp. 27. Prakash P, Gnanaprakasam P, Emmanuel R, Arokiyaraj S, Saravanan M. Green synthesis of silver nanoparticles from leaf extract of Mimusops elengi, Linn. for enhanced antibacterial activity against multi drug resistant clinical isolates. Colloids Surf B Biointerfaces 2013;108:255–9. 28. Kangkana S, Kamalika S, Sen K. Polyvinyl alcohol based hydrogels for urea release and Fe(III) uptake from soil medium. J Environ Chem Eng 2018;6:736–44. 29. Omar BI. Complexes of urea with Mn(II), Fe(III), Co(II), and Cu(II) metal ions. Adv Appl Sci Res 2012;3: 3522–39.

Cheriyan Ebenezer and Rajadurai Vijay Solomon*

16 Exploring the solvation of water molecules around radioactive elements in nuclear waste water treatment Abstract: Nuclear waste water contains many actinides which coordinate with water molecules to form complexes. The hydration of water molecules with varying coordination numbers and modes makes it interesting and intriguing in understanding the extraction process of these radioactive ions. In order to separate these complexes from the nuclear waste water, many organic ligands are being used. However, prior knowledge on the nature of electronic environment of these hydration patterns will help us to understand the extraction mechanism. Therefore, a series of complexes such as [Np(H2O)9]4+, [Cm(H2O)9]3+, [Am(H2O)9]3+, [Pu(H2O)9]4+, [Pu(H2O)9]3+, [U(H2O)9]3+, [NpO2(H2O)5]+, [UO2(H2O)5]2+ and [PuO2(H2O)5]2+ have been calculated by means of relativistic DFT. Bond length analysis and energy decomposition analysis are executed with the intention to comprehend the bonding situation of these complexes. To account for the stabilizing interactions amid the radioactive ion and the water molecules, a detailed QTAIM investigation is done. It is seen that the metals having higher oxidation state readily complex with water molecules. Energy decomposition analysis throws light on the significant orbital interactions in the [M(H2O)9]n complexes, whereas in the metal oxide complexes significant contribution is resulted from electrostatic interactions. In summary, this investigation brings out the nuances of coordination modes of solvation in nuclear waste water which will help us to explore and design novel extraction techniques in near future. Keywords: nuclear waste water; QTAIM; radioactive recovery; relativistic DFT; solvation.

16.1 Introduction Isotope research laboratories, nuclear research centers and reactors, uranium ore processing units and laboratories where radioactive substances are used for therapeutic purposes are the primary sources of radioactive waste water [1–3]. In general, water is used as coolant in nuclear reactors and these reactors have two types of circulating water systems [4]. The first one deals with the direct cooling of radioactive material using

*Corresponding author: Rajadurai Vijay Solomon, Department of Chemistry, Madras Christian College (Autonomous) [Affiliated to the University of Madras], Chennai, Tamil Nadu, 600 059, India, E-mail: [email protected] Cheriyan Ebenezer, Department of Chemistry, Madras Christian College (Autonomous) [Affiliated to the University of Madras], Chennai, Tamil Nadu, 600 059, India As per De Gruyter’s policy this article has previously been published in the journal Physical Sciences Reviews. Please cite as: C. Ebenezer and R. V. Solomon “Exploring the solvation of water molecules around radioactive elements in nuclear waste water treatment” Physical Sciences Reviews [Online] 2023. DOI: 10.1515/psr-2022-0262 | https://doi.org/10.1515/9783111071428-016

288

16 Solvation around radioactive metal ions in nuclear waste water treatment

demineralized water. The heat from this water is transferred into the second, indirect cooling system. This is done because, the cooling water in the first system is somewhat contaminated with radioactive by corroded pipe [5]. This activity is lowered by continually passing a small amount of the water via ion exchangers, where dissociated radioactive chemicals are maintained and quasi radioactive components are continuously removed in suspension [6]. It is evident from this description that now the second cooling system is safe from radioactive contamination. However, risks such as corrosion and damage to heat exchangers, water seepage and so on might occur when the system is open. Nonetheless, the radiation inside the water of the second system ought to be negligible. Nuclear waste water with significant radioactivity is produced during the treatment of nuclear fuel and is frequently very acidic. In the area of the mines, contaminated water is produced from the mining of natural uranium [7]. Wash water, sand, as well as other solid wastes are the principal causes of pollution. This water is hazardous because it contains radioisotopes with extremely long half-lives, including radium 226 [8]. The wastewater discharge from nuclear research facilities and that from cleaning protective garments fluctuate significantly depending on the isotope used and how it is used [9]. Whenever a nuclear reactor is closed down and also the nuclear fission reaction has stopped, a large quantity of heat is still generated owing to fission product beta decay [9]. As a result, assuming the reactor has maintained a long and consistent power history, decay heat would be around 7% of the prior core power at the stage of reactor shutdown [10]. The heat decay would be around 11/2% of the earlier core power 1 h subsequent to shut down. After 1 day, the decay heat is 0.4%, while after a week, it is 0.2%. The rate of decay heat generation will gradually decrease with time [10]. Spent nuclear fuel that is scrubbed off from a reactor is typically held inside a water-filled spent nuclear fuel pool for one year or more (in some cases 10–20 years) to cool it and provide radiation shielding [11]. Practical used fuel pool designs need not depend on passive cooling and instead require water to be actively circulated through heat exchangers [12]. Due to this, the radioactive material seeps into water and forms complexes with water. Although much work has been done on the extraction of these radioactive material using organic ligands and other techniques, hardly any computational investigations have been conducted to comprehend the complexation functioning of these radioactive materials inside the water environment. By understanding this complexation behavior, it would be easier to design ligands appropriate for the extraction of these radioactive ions. As a result, an attempt has been made in this study to comprehend the nature of radioactive complexes generated from water and radioactive materials. Here, we have considered [Np(H2O)9]4+, [Cm(H2O)9]3+, [Am(H2O)9]3+, [Pu(H2O)9]4+, [Pu(H2O)9]3+, [U(H2O)9]3+, [UO2(H2O)5]2+, [PuO2(H2O)5]2+ and [NpO2(H2O)5]+ complexes and their electronic structures in solvation environment have been studied using relativistic DFT calculations. We focused our attention only to actinides having a charge ranging from +3 to +6. Moreover the coordinating water molecules are usually nine in Mn+ ions whereas five coordinating atoms are observed in MO2n+ ions.

16.3 Results and discussion

289

16.2 Computational details Density Functional Theory (DFT) is employed to optimize the complexes [Np(H2O)9]4+, [Cm(H2O)9]3+, [Am(H2O)9]3+, [Pu(H2O)9]4+, [Pu(H2O)9]3+, [U(H2O)9]3+, [UO2(H2O)5]2+, [PuO2(H2O)5]2+ and [NpO2(H2O)5]+ in the solvent phase utilizing DFT method. In all computations, ZORA (Zero Order Regular Approximation) was applied [13–17] as given in ADF2019.105 program [18]. Triple-ζ Slater-type orbital (STO) with one polarization function has been utilized for entire calculations. Analytical frequencies [19] have been calculated utilizing the universal gradient approximation (GGA) functional encompassing the Becke and Perdew 86 (BP86) [20, 21] exchange and correlation after the optimization of all the complexes. From earlier done works, it is well established that the ZORA/BP86/TZP approach has been widely accepted to optimize and deduce the properties of complexes involving f-orbitals [22–26]. Therefore the same approach has been used in this study. It is commonly anticipated that the presence of spin-orbital coupling resulted in a nonzero first-order transformation in the perturbed density. Yet, it is generally quite low and therefore the same is ignored in ZORA calculations [27–30]. This work uses (2S + 1) spin state for entire complexes. The ADF/ZORA/TZP database includes the basis set for entire atoms in the complexes [26]. In these complexes, the frozen-core approximation has been utilized, wherein four-component Dirac-Slater calculations present the core density for the atoms present in the complexes. In the case of carbon C[1s] and nitrogen N[1s] atoms the 1s orbital is frozen. Where as in heavy elements, Ln [4d] and An [5d] 1s-4d and 1s to 5d orbitals are frozen. Since the radioactive elements form complexes in water, COSMO continuum solvation model is used keeping water as solvent [31].

16.3 Results and discussion 16.3.1 Coordination environment Complexes [Np(H2O)9]4+, [Cm(H2O)9]3+, [Am(H2O)9]3+, [Pu(H2O)9]4+, [Pu(H2O)9]3+, [U(H2O)9]3+, [UO2(H2O)5]2+, [PuO2(H2O)5]2+ and [NpO2(H2O)5]+ are optimized (Figure 16.1) at BP86 level in water medium [22–25, 32, 33]. It is important to note that there are two types of complexes considered here. One is the metal oxides (UO2, NpO2 and PuO2) form complexes with five water molecules and second is the metals (Np, Cm, Am, Pu and U) form complexes with nine water molecules. The oxidation states vary from +3 to +6 in these complexes. Table 16.1 shows the calculated bond parameters of the optimized geometries. Looking into the optimized geometries, it is observed that [Np(H2O)9]4+, [Cm(H2O)9]3+, [Am(H2O)9]3+, [Pu(H2O)9]4+, [Pu(H2O)9]3+ and [U(H2O)9]3+ complexes have

290

16 Solvation around radioactive metal ions in nuclear waste water treatment

Figure 16.1: The optimized geometries of all the complexes having different oxidation states considered for this study.

nine coordinate bonds amid the metal and oxygen of nine water molecules. In [UO2(H2O)5]2+, [NpO2(H2O)5]+ and [PuO2(H2O)5]2+ complexes there are five coordinate bonds amid the metal and the oxygen of water molecules.

Table .: The computed bond distance between the metal and oxygen atoms involved in the coordination of the studied complexes (in Å). Complexes

M–O

M–O

M–O

M–O

M–O

M–O

M–O

M–O

M–O

M=O

M=O

[Np(HO)]+ [Cm(HO)]+ [Am(HO)]+ [Pu(HO)]+ [Pu(HO)]+ [U(HO)]+ [UO(HO)]+ [NpO(HO)]+ [PuO(HO)]+

. . . . . . . . .

. . . . . . . . .

. . . . . . . . .

. . . . . . . . .

. . . . . . . . .

. . . . . . – – –

. . . . . . – – –

. . . . . . – – –

. . . . . . – – –

– – – – – – . . .

– – – – – – . . .

16.3 Results and discussion

291

From the table, it is evident that the M–O bond lengths between the metal and water molecules are in the range of 2.38–2.57 Å. The M–O largest bond length is observed in the case of [Pu(H2O)9]3+ (∼2.5 Å) whereas the smallest M–O bond length is observed in [Np(H2O)9]4+(∼2.3 Å). This proposes that the strength of M–O bonds is more in [Np(H2O)9]4+ and therefore it is expected that the Np4+ ion will bind strongly to the water molecules than rest of the metal ions considered here. On the other hand, M–O bonds are the weakest in [Pu(H2O)9]3+ complex and therefore the Pu3+ ion has weaker bonds with water molecules than that of the other metal ions. When we look into the metal oxide with water molecules, it is realized that the bond length of M–O bonds is ∼2.4–2.5 Å. It is presumed that shorter the bond length, stronger the bond and greater will be the stability. From the table, it can be implied that the binding of the metal ions to the water molecules deceases in the order [Pu(H2O)9]4+ > [Np(H2O)9]4+ > [U(H2O)9]3+ > [Cm(H2O)9]3+ > [Am(H2O)9]3+ > [Pu(H2O)9]3+. This means that metal with higher oxidation number binds better to the metal complexes. Also in the metal oxide–water complexes the binding of metal oxides to the water molecules decreases in the order [PuO2(H2O)5]2+ > [UO2(H2O)5]2+ > [NpO2(H2O)5]+. Here also, it is observed that metal oxides in which metals having higher oxidation state tend to show stronger bonds with the water molecules with relatively shorter bond lengths. In addition to the water-metal ion bonds, the M=O bond lengths in metal oxides can be seen in Table 16.1. It can be perceived that the highest M=O bond length is observed in [NpO2(H2O)5]+ (1.83 Å) and the lowest M=O bond length is observed in [PuO2(H2O)5]2+ (1.75 Å). It is worth noting that when metal oxide complexes with water, the length of the M=O bond increases (∼0.05 Å). Such as the M=O bond lengths before complexation in [UO2]2+ is 1.73 Å, whereas after it coordinates with water molecules the M=O bond length surges to 1.78 Å. This is reiterating the aforementioned points. Moreover all the M=O bond lengths lie in the double bond range (1.16–1.83 Å). According to this analysis, the stronger the oxidation state, the greater is the capacity of metal ions to establish coordination bonds with molecules of water.

16.3.2 Interactions between water molecules and metals/metal oxides Energy Decomposition Analysis aids in calculating the binding energy amongst any two interacting atoms/molecules. Here the total energy is classified into four energy components namely orbital, electrostatic, Pauli’s repulsion, solvation energy and electrostatic interaction when the complex is considered as two different fragments. As a result, EDA has been performed on optimized geometries where the metal ion serves as one fragments as well as the water molecules are the other fragment. In the case of metal oxide complexes, the metal oxide (MO2) serves as one fragment and the water molecules as the other fragment. The metal/metal oxides are given respective charges while the water clusters (five/nine molecules of water) are regarded as neutral systems throughout the EDA calculations.

292

16 Solvation around radioactive metal ions in nuclear waste water treatment

Table .: Energy constituents perceived from EDA amongst the fragments determined at ZORA/BP level (units in eV). Complexes [Np(HO)]+ [Cm(HO)]+ [Am(HO)]+ [Pu(HO)]+ [Pu(HO)]+ [U(HO)]+ [UO(HO)]+ [NpO(HO)]+ [PuO(HO)]+

Paulis repulsion

Electrostatic interactions

Orbital interactions

Solvation energy

Total binding energy

. . . . . . . . .

−. −. −. −. −. −. −. −. −.

−. −. −. −. −. −. −. −. −.

−. −. −. −. −. −. −. −. −.

−. −. −. −. −. −. −. −. −.

Table 16.2 summarizes the various energy components obtained from EDA for all compounds. The reaction M3+ + 9H2O → [M(H2O)9]3+ applies to [Cm(H2O)9]3+, [U(H2O)9]3+, [Am(H2O)9]3+ and [Pu(H2O)9]3+ complexes. For [Pu(H2O)9]4+ and [Np(H2O)9]4+ the M4+ + 9H2O → [M(H2O)9]4+ reaction is used. For [PuO2(H2O)5]2+ and [UO2(H2O)5]2+ complexes the MO22+ + 9H2O → [MO2(H2O)5]2+ reaction is considered. Finally for [NpO2(H2O)5]+ complex, the MO2+ + 9H2O → [MO2(H2O)5]+ reaction is applied. The negative sign of total binding energy tells that all these complexes are stable and these ions (metal/metal oxides) readily form complexes with water molecules as expected. The overall binding energy ranges from ∼10 to ∼73 eV. The table shows that the orbital interactions stabilize the [M(H2O)9]n complexes more compared to the electrostatic interactions. However, the metal oxide complexes are greatly stabilized by the electrostatic interactions compared to the orbital interactions. For instance, in [UO2(H2O)5]2+, [NpO2(H2O)5]+ and [PuO2(H2O)5]2+ complexes, the electrostatic interactions are observed to be −10.21, −7.01 and −9.94 eV respectively whereas the orbital interactions are −8.28, −5.46 and −9.44 eV respectively. Also it is fascinating to note that the Paulis interactions, Electrostatic interactions, Orbital energy, Solvation energy and Total Binding Energy values in metal oxide complexes are way lower than the values in other complexes. Moreover the total binding energy deceases in the order [Pu(H2O)9]4+ > [Np(H2O)9]4+ > [U(H2O)9]3+ > [Cm(H2O)9]3+ > [Am(H2O)9]3+ > [Pu(H2O)9]3+ in the [M(H2O)9]n complexes. In the metal oxide complexes, the total binding energy deceases in the order [PuO2(H2O)5]2+ > [UO2(H2O)5]2+ > [NpO2(H2O)5]+. The above mentioned sequences are in line with the bond length analysis and reiterates that Pu4+ ion binds strongly to the water molecules than the other metal ions among the [M(H2O)9]n complexes. For example the total binding energy of Pu4+ complex is −72.94 eV, whereas it is −70.85 eV in Np4+ complex. Also Pu4+ complex has greater total binding energy than U3+ (−68.80 eV), Cm3+ (−47.24 eV), Am3+ (−45.16 eV) and Pu3+ (−44.59 eV) complexes. It also retells that the metal with greater oxidation number have a better binding. Since the total binding

16.3 Results and discussion

293

energy of Pu3+ complex is −72.94 eV followed by Pu3+ which is −44.56 eV and [PuO2]2+ having a total binding energy of −20.71 eV. Furthermore, the solvation energy is a significant aspect in defining the binding energy. The total binding energy shows that metals with higher oxidation numbers have higher solvation energy. The solvation energy reduces as the quantity of water molecules decreases. For instance in [Np(H2O)9]4+ the solvation energy is −29.00 eV, whereas in [NpO2(H2O)5]+ the same is −3.18 eV.

16.3.3 Strength of interactions among water molecules and metal/ metal oxides Over the years, the Quantum Theory of Atoms in Molecules (QTAIM) has been utilised to detect interactions in coordination compounds [34–37]. QTAIM analysis is done on the optimised geometries to investigate the strength of bonding interactions among water molecules and metal/metal oxide ions. QTAIM’s critical points are parameters that explain the bonding behaviour of molecules. Four different kinds of critical points are observed in QTAIM: bond critical points (BCP), nuclear critical points, cage critical points (CCP) and ring critical points (RCP). Each critical point has various topological descriptors for-instance Laplacian of electron density (▽2ρ), electron density (ρ) and energy density (H ) that throws light on the properties of the bonding and non-bonding interactions. Based on the different descriptors (ρ, ▽2ρ and H), the interactions can be categorized under different categories [38–42]. The ρ value is one such descriptor. If the magnitude of ρ is higher than 0.2 au then the interactions are classified as covalent where if it is less than 0.1 au then those interactions are ionic [38–42]. Table 16.3 lists the various characteristics of the M–O bond critical points observed in all the complexes and Figure 16.2 shows the molecular graphs. According to the molecular graphs (Figure 16.2), the water molecules have a bond critical point linking the oxygen atom and the metal ion. These bond critical points are linked via bond paths. It is interesting to note that though there are many water molecules surrounding the metal ion, there is no inter-water interaction and this is evident from the absence of critical points between the water molecules. The table clearly shows that all of the electron density values at the bond critical points of the metal-oxygen bonds are lower than 0.1 au. Therefore, the M–O bonds have greater ionic character. All the ρ values exist in ∼0.258–0.042 au range. Moreover the M=O bonds in the metal oxide complexes have electron density values more than 0.2 au and is in the range of 0.25–0.30 au. This suggests that the bonds are covalent and strong as expected. The Laplacian of electron density at the BCP provides important details on the spike or drop in charge of bonds, which aids in understanding the interactions. Since the magnitude of the laplacian of electron density values are observed to be in the range of −0.050 to −0.060 au there is a drop in charge of the O atoms. In all complexes ∇2ρ, ellipticity and energy density distribution of M–O bonds are computed. The electron

294

16 Solvation around radioactive metal ions in nuclear waste water treatment

Table .: Various descriptors (∇ρ, V, H, ellipticity and G) depicting bond critical points of metal oxygen bonds computed from QTAIM investigation (in a.u.).

[Np(HO)]

M–O

M–O

M–O

M–O

M–O

M–O

M–O

M–O

M–O

. −. . −. −. .

. −. . −. −. .

. −. . −. −. .

. −. . −. −. .

. −. . −. −. .

. −. . −. −. .

. −. . −. −. .

. −. . −. −. .

. −. . −. −. .

. −. . −. −. .

. −. . −. . .

. −. . −. −. .

. −. . −. −. .

. −. . −. −. .

. −. . −. −. .

. −. . −. −. .

. −. . −. . .

. −. . −. −. .

. −. . −. −. .

. −. . −. −. .

. −. . −. −. .

. −. . −. −. .

. −. . −. −. .

. −. . −. −. .

. −. −. −. . .

. −. . −. −. .

. −. . −. −. .

. −. . −. −. .

. −. . −. −. .

. −. . −. −. .

. −. . −. −. .

. −. . −. −. .

. −. . −. −. .

. −. . −. −. .

. −. . −. −. .

. −. . −. . .

. −. . −. −. .

. −. . −. −. .

. −. . −. −. .

. −. . −. −. .

. −. . −. . .

. −. . −. −. .

. −. . −. . .

. −. . −. −. .

+

ρ ∇ρ G V H Ellipticity

[Cm(HO)]+ ρ ∇ρ G V H Ellipticity

. −. . −. −. .

[Am(HO)]+ ρ ∇ρ G V H Ellipticity [Pu(HO)]+ Ρ ∇ρ G V H Ellipticity [Pu(HO)]+ Ρ ∇ρ G V H Ellipticity

16.3 Results and discussion

295

Table .: (continued) M–O

M–O

M–O

M–O

M–O

M–O

M–O

M–O

M–O

. −. . −. −. .

. −. . −. −. .

. −. . −. −. .

. −. . −. −. .

. −. . −. −. .

. −. . −. −. .

. −. . −. −. .

. −. . −. −. .

. −. . −. −. .

+

[U(HO)] Ρ ∇ρ G V H Ellipticity

M–O

M–O

M–O

M–O

M–O

M=O

M=O

. −. . −. −. .

. −. . −. −. .

. −. . −. −. .

. −. . −. −. .

. −. . −. −. .

. −. . −. −. .

. −. . −. −. .

. −. . −. −. .

. −. . −. −. .

. −. . −. −. .

. −. . −. −. .

. −. . −. −. .

. −. . −. −. .

. −. . −. −. .

. −. . −. −. .

. −. . −. −. .

. −. . −. −. .

. −. . −. −. .

. −. . −. −. .

. −. . −. −. .

. −. . −. −. .

+

[UO(HO)] Ρ ∇ρ G V H Ellipticity

[NpO(HO)]+ Ρ ∇ρ G V H Ellipticity [PuO(HO)]+ Ρ ∇ρ G V H Ellipticity

density’s Laplacian, ∇2ρ, is negative, demonstrating that electron density is concentrated between the linked atoms. The charges on oxygen atom of the water molecules (from −0.685 to −0.528) are depleted subsequently the Laplacian values (∇2ρ) are low (∼−0.04 au) whereas the charges on oxygen atoms having a covalent bond with the metal ions are not depleted so much due to higher Laplacian values. The potential energy density V is found to be in the range of −0.04–0.08 au for the M–O bonds. For M–O bonds, the kinetic energy density G ranges between 0.04 and 0.08 au. Hence the

296

16 Solvation around radioactive metal ions in nuclear waste water treatment

Figure 16.2: Molecular graphs of the metal complexes in which red dots are the bond critical point and green dots are the ring cirtical point.

total charge density which is a sum of V and G is almost in the range of −0.006 to 0.000 au. Ellipticity, is used comprehend how much electron density is built up along the bond path between the interacting atoms. It is known that high values of ellipticity signifies anisotropy, whereas low values signify isotropy in the electron density over the bonded region [43]. Low ellipiticity is observed in M–O bonds, which signifies isotropy whereas in M=O bonds the ellipiticity values are high which signifies anisotropy. Thus QTAIM suggests that M–O bonds have a predominantly ionic character whereas M=O are covalent in nature.

16.4 Conclusions The complexes [Np(H2O)9]4+, [Cm(H2O)9]3+, [Am(H2O)9]3+, [Pu(H2O)9]4+, [Pu(H2O)9]3+, [U(H2O)9]3+, [UO2(H2O)5]2+, [PuO2(H2O)5]2+ and [NpO2(H2O)5]+ are optimized and all the metal ions show great binding towards the water molecules. EDA exhibits that the [M(H2O)9]n complexes have greater orbital interactions than electrostatic interactions whereas in metal oxide complexes it is just the opposite. QTAIM tells that there are no

References

297

interactions between the water molecules involved in the solvation. Bond length analysis suggests that the metal ions which greater oxidation number have greater binding. It has also been discovered that the larger the charge on the metal, the greater its complexation capacity. Pu4+ shows the best complexation ability among the metals and [PuO2]2+ shows the best complexation among the metal oxides and is due to their higher orbital interactions. Overall, this work contributes to enhanced knowledge of the nature of interaction among metal ions and water molecules and this information will be used to further elucidate the mechanism of extraction processes. Acknowledgment: RVS is thankful to the Department of Science and Technology – Science and Engineering Research Board (DST-SERB) for the Early Career Research grant (Ref. No. ECR/2017/001147), and CE is grateful to DST-SERB for financial assistance from RVS’s ECR project.

References 1. Zakrzewska-Trznadel G. Advances in membrane technologies for the treatment of liquid radioactive waste. Desalination 2013;321:119. 2. Wisnubroto D, Zamroni H, Sumarbagiono R, Nurliati G. Challenges of implementing the policy and strategy for management of radioactive waste and nuclear spent fuel in Indonesia. Nucl Eng Technol 2021;53:549. 3. Darda SA, Gabbar HA, Damideh V, Aboughaly M, Hassen I. A comprehensive review on radioactive waste cycle from generation to disposal. J Radioanal Nucl Chem 2021;329:15. 4. Choi J-H, Cleveland J, Aksan N. Improvement in understanding of natural circulation phenomena in water cooled nuclear power plants. Nucl Eng Des 2011;241:4504. 5. Kusuma MH, Putra N, Antariksawan AR, Imawan FA. Investigation of the thermal performance of a vertical two-phase closed thermosyphon as a passive cooling system for a nuclear reactor spent fuel storage pool. Nucl Eng Technol 2017;49:476. 6. Dietrich JR, Zinn WH. Solid fuel reactors. Dallas: Addison-Wesley Publishing Company; 1958. 7. Mehta K, Kumar V, Pandey B, Tamrakar P. Bioleaching-an alternate uranium ore processing technology for India. Energy Proc 2011;7:158. 8. Rich AL, Crosby EC. Analysis of reserve pit sludge from unconventional natural gas hydraulic fracturing and drilling operations for the presence of technologically enhanced naturally occurring radioactive material (TENORM). New Solutions. J Environ Occup Health Policy 2013;23:117. 9. Hwang I-K, Kang H-H, Lee I-S, Oh J-E. Assessment of characteristic distribution of PCDD/Fs and BFRs in sludge generated at municipal and industrial wastewater treatment plants. Chemosphere 2012;88:888. 10. Elsheikh BM. Safety assessment of molten salt reactors in comparison with light water reactors. J Radiat Res Appl Sci2013;6:63. 11. Forsström H. Spent fuel and radioactive waste management in nuclear power programmes. In: Infrastructure and methodologies for the justification of nuclear power programmes. Sweden: Elsevier; 2012:465 p. 12. Banks D, Athresh A, Al-Habaibeh A, Burnside N. Water from abandoned mines as a heat source: practical experiences of open-and closed-loop strategies, United Kingdom. Sustain Water Resour Manag 2019;5:29. 13. Lenthe E, Baerends E-J, Snijders JG. Relativistic regular two‐component Hamiltonians. J Chem Phys 1993;99: 4597.

298

16 Solvation around radioactive metal ions in nuclear waste water treatment

14. Te Velde G, Bickelhaupt FM, Baerends EJ, Fonseca Guerra C, van Gisbergen SJ, et al. Chemistry with ADF. J Comput Chem 2001;22:931. 15. van Lenthe E, Baerends E-J, Snijders JG. Relativistic total energy using regular approximations. J Chem Phys 1994;101:9783. 16. Van Lenthe E, Ehlers A, Baerends E-J. Geometry optimizations in the zero order regular approximation for relativistic effects. J Chem Phys 1999;110:8943. 17. Van Lenthe E, Van Leeuwen R, Baerends E, Snijders J. Relativistic regular two‐component Hamiltonians. Int J Quant Chem 1996;57:281. 18. Bickelhaupt F, Baerends E, Guerra CF, Van Gisbergen S, Snijders J, Ziegler T, et al. Chemistry with ADF. J Comput Chem 2001;22:931. 19. Fan L, Ziegler T. Application of density functional theory to infrared absorption intensity calculations on transition-metal carbonyls. J Phys Chem A 1992;96:6937. 20. Van Lenthe E, Baerends EJ. Optimized Slater‐type basis sets for the elements 1–118. J Comput Chem 2003; 24:1142. 21. Wang C-Z, Lan J-H, Wu Q-Y, Chai Z-F, Shi W-Q. Theoretical insights on the complexation of Am(III) and Cm(III) with amide-type ligands. J Radioanal Nucl Chem 2019;1:14810–20. 22. Bhattacharyya A, Ghanty TK, Mohapatra PK, Manchanda VK. Selective americium (III) complexation by dithiophosphinates: a density functional theoretical validation for covalent interactions responsible for unusual separation behavior from trivalent lanthanides. Inorg Chem 2011;50:3913. 23. Zaiter A, Amine B, Bouzidi Y, Belkhiri L, Boucekkine A, Ephritikhine M. Selectivity of azine ligands toward lanthanide (III)/actinide (III) differentiation: a relativistic DFT based rationalization. Inorg Chem 2014;53: 4687. 24. Wu Q-Y, Cheng Z-P, Lan J-H, Wang C-Z, Chai Z-F, Gibson JK, et al. Insight into the nature of M–C bonding in the lanthanide/actinide-biscarbene complexes: a theoretical perspective. Dalton Trans 2018;47: 12718. 25. Meskaldji S, Belkhiri L, Arliguie T, Fourmigué M, Ephritikhine M, Boucekkine A. Density functional theory investigations of the Homoleptic Tris (dithiolene) complexes [M (dddt) 3]− q (q= 3, 2; M= Nd3+ and U3+/4+) related to lanthanide (III)/actinide (III) differentiation. Inorg Chem 2010;49:3192. 26. Roger M, Belkhiri L, Thuéry P, Arliguie T, Fourmigué M, Boucekkine A, et al. Lanthanide (III)/actinide (III) differentiation in mixed cyclopentadienyl/dithiolene compounds from X-ray diffraction and density functional theory analysis. Organometallics 2005;24:4940. 27. Krykunov M, Ziegler T, van Lenthe E. Implementation of a hybrid DFT method for calculating NMR shieldings using slater-type orbitals with Spin−orbital coupling included. Applications to 187Os, 195Pt, and 13C in heavy-metal complexes. J Phys Chem A 2009;113:11495. 28. Bouten R, Baerends E, Van Lenthe E, Visscher L, Schreckenbach G, Ziegler T. Relativistic effects for NMR shielding constants in transition metal oxides using the zeroth-order regular approximation. J Phys Chem A 2000;104:5600. 29. Smith AR, Burn PL, Powell BJ. Spin–orbit coupling in phosphorescent iridium (III) complexes. ChemPhysChem 2011;12:2429. 30. Neese F. Metal and ligand hyperfine couplings in transition metal complexes: the effect of spin–orbit coupling as studied by coupled perturbed Kohn–Sham theory. J Chem Phys 2003;118:3939. 31. Sadhu B, Sundararajan M, Bandyopadhyay T. Efficient separation of europium over americium using cucurbit-[5]-uril supramolecule: a relativistic DFT based investigation. Inorg Chem 2016;55:598. 32. Wu Q-Y, Song Y-T, Ji L, Wang C-Z, Chai Z-F, Shi W-Q. Theoretically unraveling the separation of Am (III)/Eu (III): insights from mixed N, O-donor ligands with variations of central heterocyclic moieties. Phys Chem Chem Phys 2017;19:26969. 33. Li X-B, Wu Q-Y, Wang C-Z, Lan J-H, Ning S-Y, Wei Y-Z. Theoretical study on structures of Am (III) carbonate complexes. J Radioanal Nucl Chem 2020;325:527. 34. Gall M, Breza M. QTAIM study of transition metal complexes with cyclophosphazene-based multisite ligands I: zinc (II) and nickel (II) complexes. Polyhedron 2009;28:521.

References

299

35. Hosseinnejad T, Dehghanpour S, Hosseinjani A. Quantum chemical investigation on the structural and electronic properties of zinc–salphen complex: DFT and QTAIM analysis. Comput Theor Chem 2013;1004:31. 36. Karaush NN, Baryshnikov GV, Minaeva VA, Minaev BF. A DFT and QTAIM study of the novel d-block metal complexes with tetraoxa [8] circulene-based ligands. New J Chem 2015;39:7815. 37. Behrle AC, Myers AJ, Kerridge A, Walensky JR. Coordination chemistry and QTAIM analysis of homoleptic dithiocarbamate complexes, M (S2CNiPr2) 4 (M= Ti, Zr, Hf, Th, U, Np). Inorg Chem 2018;57:10518. 38. Mountain AR, Kaltsoyannis N. Do QTAIM metrics correlate with the strength of heavy element–ligand bonds? Dalton Trans 2013;42: 13477. 39. Schnaars DD, Gaunt AJ, Hayton TW, Jones MB, Kirker I, Kaltsoyannis N, et al. Bonding trends traversing the tetravalent actinide series: synthesis, structural, and computational analysis of AnIV (Aracnac) 4 complexes (An= Th, U, Np, Pu; Aracnac= Ar NC (Ph) CHC (Ph) O; Ar= 3, 5-t Bu2C6H3). Inorg Chem 2012;51:8557. 40. Arnold PL, Turner ZR, Kaltsoyannis N, Pelekanaki P, Bellabarba RM, Tooze RP. Covalency in CeIV and UIV halide and N‐heterocyclic carbene bonds. Chem Eur J 2010;16:9623. 41. Vlaisavljevich B, Miró P, Cramer CJ, Gagliardi L, Infante I, Liddle ST. On the nature of actinide–and lanthanide–metal bonds in heterobimetallic compounds. Chem Eur J 2011;17:8424. 42. Jones MB, Gaunt AJ, Gordon JC, Kaltsoyannis N, Neu MP, Scott BL. Uncovering f-element bonding differences and electronic structure in a series of 1: 3 and 1: 4 complexes with a diselenophosphinate ligand. Chem Sci 2013;4:1189. 43. Stephen AD, Srinivasan P, Kumaradhas P. Bond charge depletion, bond strength and the impact sensitivity of high energetic 1, 3, 5-triamino 2, 4, 6-trinitrobenzene (TATB) molecule: a theoretical charge density analysis. Comput Theor Chem 2011;967:250.

Supplementary Material: This article contains supplementary material (https://doi.org/10.1515/PSR-20220262).

Nitish Sookool and Marie Chan Sun*

17 Changing our outlook towards vulnerable women for societal resilience Abstract: Background: The vulnerabilities and risks of women injecting drug users (WIDUs) are different compared to their male counterparts. In light of scant literature in this area, we carried out this qualitative study with the aim to explore the lived experiences of WIDUs in the North of Mauritius. Its objectives were (1) To get an insight into the risks taken during the injection practices of WIDUs and (2) To obtain an in-depth understanding of the gender-specific vulnerability of the sexual behaviours of WIDUs. Methods: A qualitative phenomenological approach was used for the research work. Study participants were recruited from a specific needle exchange site, by purposive sampling until saturation of data was reached. In-depth interviews conducted were transcribed for thematic analysis. Ethical clearance was obtained from the relevant authorities. Findings: The principal theme which emerged from the data collected was “Drug Injection Scenario”, with the following sub-themes: settings for drug injection; preinjection rituals; third party assistance; sharing and recycling of injecting materials. The second theme which came out was “Sex Work and Drug Use Interplay” with either sex work preceding drug injection or drug injection preceding sex work. Finally, the third theme was “Sexual Behaviours Screenplay” with casual encounters and unprotected sex. Conclusions: This study filled the gap with respect to the absence of qualitative studies among WIDUs in Mauritius. It revealed their risky drug injecting practices and sexual behaviours. Their psychological and physical dependence on drug injection contributed to their stay within this vulnerable circle. Their inability to access adequate support hinders them from coming out of the clutches of their risky drug injection and sexual practices. For resilience of the society, there is need to address the needs of this vulnerable group of women. Keywords: lived experiences; Mauritius; resilience; society; women injecting drug users.

17.1 Introduction In the paradise island Mauritius, which lies in the Indian Ocean, around 2000 km off the South East Coast of the African continent, drug addiction is a major public health issue

*Corresponding author: Marie Chan Sun, Department of Medicine, Faculty of Medicine and Health Sciences, University of Mauritius, Réduit, 80837, Mauritius, E-mail: [email protected]. https://orcid.org/0000-00027504-8995 Nitish Sookool, Harm Reduction Unit, Ministry of Health and Wellness, Port Louis, Mauritius As per De Gruyter’s policy this article has previously been published in the journal Physical Sciences Reviews. Please cite as: N. Sookool and M. C. Sun “Changing our outlook towards vulnerable women for societal resilience” Physical Sciences Reviews [Online] 2023. DOI: 10.1515/psr-2022-0274 | https://doi.org/10.1515/9783111071428-017

302

17 Outlook towards vulnerable women

with economic, legal, medical and social impact. Considering the history of drugs in Mauritius, substance abuse dates back to the 1970s when heroin was first introduced on the island [1]. This socio-economic, legal and health problem escalated to the point where Mauritius reached the highest prevalence of opioid use in Africa [2]. The health authorities of Mauritius have been adopting a holistic public health approach with the implementation of harm-reduction strategies, opioid substitution treatment, needle and syringe exchange service, as well as drug awareness programmes with the view to reduce the incidence and the prevalence of drug use. Considering the prevalence of persons who inject drugs (PWIDs) in Mauritius, the Integrated Biological and Behavioural Surveillance (IBBS) survey conducted by the Ministry of Health and Quality of Life (MOH & QL) revealed that in 2017, there were 6000 PWIDs in Mauritius and 15.3 % of them were women injecting drug users (WIDUs) [3]. It is to be highlighted that WIDUs constitute the minority of PWIDS be it in Mauritius or in the world [4]. Moreover, there is concern about the population size of WIDUS which has more than doubled compared to 2011, when WIDUs represented only 7 % of all PWIDs [3]. Indeed, the prevalence of human immunodeficiency virus (HIV) in 2017 was 33.2 % among male PWIDs and 28.5 % among WIDUs whilst that of hepatitis C was 92 % among male PWIDs and 73.4 % among WIDUs [3]. The prevalence of syphilis in WIDUs reached 17 % in 2017 [3]. Referring to gender differences, the United Nations [5] recommends interventions customized to women to empower them to access the services they need without stigma or discrimination [5]. This is because WIDUs faced significantly higher rates of death linked to drug injection. They were also more likely to face all types of injection related problems including, unsafe risky drug injection and risky sex work [2]. Indeed, there is established evidence of important differences in the experience and vulnerability of women and men who use drugs [6]. Important gender differences were shown in the psychology [7], biology, epidemiology, aetiology and sociology of substance abuse [8]. Women’s specific drug use patterns and their sex-related practices which involve risk behaviours create an environment in which women are more vulnerable than men to HIV [8]. Differences in the understanding of health harm and risk behaviour which exist among men and women are likely to depend on the social, contextual, and behavioural components of the environment in which Injecting Drug Use (IDU) occurs [9]. Unsafe IDU was reported as a significant factor in the HIV transmission dynamics of WIDUs [9]. Indeed, WIDUs are more likely than their male counterparts to face multiple barriers affecting access and entry to substance abuse treatment [8] [Tuchman, 2010]. WIDUS compared to male counterparts are more prone to be infected by HIV/AIDS [10] and thus have higher risks of diseases and mortality. The rather scarce literature on WIDUs in Mauritius led to the identification of the gap with respect to the vulnerabilities and risks of Mauritian WIDUs, who constitute an extremely difficult population to reach in society. We thus carried out this qualitative study with the aim to explore the lived experiences of WIDUs in the North of Mauritius. Its objectives were as follows: (1) To get an insight into the risks taken during the injection practices of WIDUs, and (2) To obtain an in-depth understanding of the gender-specific vulnerability of the sexual behaviours of WIDUs.

17.2 Methods

303

17.2 Methods 17.2.1 Study design overview This qualitative study was carried out in the North of Mauritius during the months of July and August 2019. The study participants were WIDUs who were willing to participate in the study. They were recruited by purposive sampling. Face-to-face, in-depth interviews were carried out with 10 WIDUs.

17.2.2 Inclusion and exclusion criteria The inclusion criteria were as follows: Any WIDUs with current or past experience of IDU, of age above 18, residing in the North of Mauritius was eligible to participate in the study. In case of WIDUs less than 18 years old, consent from her parents or legal guardian would have to be sought. On the other hand, the exclusion criteria were as follows: Male; Transgender and Women who used drugs other than by the injecting route; WIDUs who never utilized used needle, syringe or other drug-preparing equipment; WIDUs who had never had unprotected sex.

17.2.3 Consent and confidentiality The purpose of the study and the details contained in the information sheet was clearly explained to each participant. They were also explained that full confidentiality would be preserved for each and every participant. Only after this procedure, written and signed consent was sought from each participant. Also at any stage during the conduct of the research they could withdraw from the study for whatsoever reason, if they wished.

17.2.4 Topic guide for interview The two lead questions, in line with the two main objectives of the study, asked the participants to describe their drug injection practice and their sexual behaviour. Probing questions addressed the following domains of drug injection: Context of drug injection, drug preparation for injection, drug injecting equipment used, injectors involved, the injection process itself, detail on whether drug preparing equipment, needle and syringes were shared, and fate of the used drug preparing equipment, needle and syringe. Probing questions addressed the following domains of sexual practice: Background of their sexual partners (whether they were PWIDS or not, of the same sex, both sexes or heterosexual); were they regular or casual partners; whether sexual relationship was protected with the use of condoms or not; whether there was sex in exchange of money; in case of sex work did it come first or whether drug injection practice preceded it.

304

17 Outlook towards vulnerable women

Interview was conducted by the first author NS who is a male medical doctor who has experience with PWID, the second author MCS being a female doctor with doctoral degree in public health. Data collection was undertaken in a place and at time convenient to both interviewer and interviewee after established rapport and scheduled appointment. The reasons and modalities for the research work were made clear to the participants. The anonymous and confidential aspect of the work was highlighted. Interviewer respected the fact that interviewees refused the recording of their interview. Nevertheless, they did not object to written notes being taken during the interview which lasted for around 1 h participants were given a stipend of 200 Mauritian Rupees (for a meal ± transport) at the end of their interview.t

17.2.5 Data analysis Every interview was totally and precisely transcribed. After conducting the interviews, on the same evening each interview was diligently gone through on one more occasion in order to ascertain that no information has been left out inadvertently. All the data collected were analyzed manually with identification of themes and sub-themes. The second author verified the themes and sub-themes which were derived from the data collected and ensured the consistency between the data presented and the findings.

17.3 Results The youngest WIDU in the study was aged 27 years and the eldest was 59 years old. All the participants had a drug injection history of a more than 10 years. They were all from low socio-economic status and had achieved low level of education. Table 17.1 shows the level of education and socioeconomic status of the WIDU, with the duration of their drug injection practice and any history of violence/sexual abuse prior to start of drug injection. Table .: Profile of WIDU. WIDU F F F F F F F F F F

Experience (years)          

Education level

Economic status

Violence/sexual abuse

Low Low Low Low Low Low Low Low Low Low

Low Low Low Low Low Low Low Low Low Low

Physical Physical No No No Sexual No Physical No No

17.3 Results

305

Table .: Themes and Sub-themes emerging from the study. Theme

Sub-theme



Drug injection scenario



Sex work and drug use interplay



Sexual behaviours screenplay

Settings for drug injection Pre-injection rituals Third party assistance for drug injection Needle and syringe sharing Reuse of injecting materials Sex work leading to drug injection Drug injection leading to sex work Sexual encounters Unprotected sexual practice

Table 17.2 depicts the themes and sub-themes developed from the data collection and analysis. The principal theme which emerged from the data collected was “Drug Injection Scenario”. The second theme which came out was “Sex Work and Drug Use Interplay” with either sex work preceding drug injection or drug injection preceding sex work. Finally, the third theme was “Sexual Behaviours Screenplay” with casual encounters and unprotected sex.

17.3.1 Theme 1: drug injection scenario 17.3.1.1 Settings for drug injection The settings for drug injection were basically wooden and corrugated iron sheet houses or rooms, very often without proper sanitation and hygiene. There were generally no water and electricity. Overall, this study found that almost all settings were the rooms and houses of the poor and underprivileged people and in one case it was in a room of an abandoned house near a well-known public garden in Port Louis. Below are the quotes from the coded participants illustrate the themes: As a commercial sex worker (CSW) every night I would work at the ‘Jardin Compagnie’. I had two friends who were both CSW and WIDU. I needed my first dose before starting work. We all met in a small corrugated iron sheet roofed, wooden room annexed to a small building that was abandoned and which was found just beside the river near ‘Jardin Compagnie’ in Port Louis. There was no electricity, and during rainy weather the roof would leak. There was a mattress and two wooden cases which would serve as table and in the hollow part of which we could keep our injection material and candle. We all had torches as well and this would give us the light we needed for injection purpose. We would all often come later to inject another dose (F 01). In those years we were five close friends who would live in the same wooden house with corrugated iron sheet roof. We would share a mattress among three persons sleeping on it. There was electricity

306

17 Outlook towards vulnerable women

and water. The toilet and kitchen were very old but still usable but they were in poor hygienic condition. It was rented by the pimp and drug dealer who made us work for him and buy drugs from him. He was living there with his male partner as well, both MSM and an IDU (F 02).

17.3.1.2 Pre-injection rituals Below is an extract of the description of the ritual prior to drug injection: Deep bottle caps, from ‘Rhum or wine bottles’ were used in-order to put the drug powder in it. Vinegar was added to dissolve it and make a solution. During this process, a lighted candle was used and more rarely a lighter. This flame was placed at the bottom of the caps and the heat it would generate would do dissolve the powder. Then, a filter from a cigarette would be used or a piece of cotton wool to act as a filter when drawing the solution from the bottle cap and into the syringe. The needle used was the same which had been used for the past weeks and the syringe as well. Till the needle tip is still sharp there would be no need to change it. Besides it would cost to go and buy new needles and syringes. It would also be reckless to expose one’s identity of being WIDUs to people and eventually to the Police (F 06).

All the WIDUs used almost the same material with few variations, for example, using a spoon, which was large instead of a bottle cap which was deep. 17.3.1.3 Third party assistance for drug injection All 10 participants had been dependent over the years for drug injection on a third party and these individuals carrying out drug injection for the WIDUs kept on changing throughout their WIDU life. During the first years of drug injection, a WIDU could have more than one injector. One WIDU had a drug dealer who would act as ‘doctor’, injecting her doses for her and for other members of the group as well. Of the eight male injectors: one was a drug dealer, four were sex partners and three were male friends. Of the two female persons to provide assistance for drug injection, one was a commercial sex worker. Only two WIDUs had a female drug injector. Below are extracts shared by WIDUs: My sexual partner who was an IDU for many years finally influenced me and I started to inject drugs. He always injected me. I finally started sex work to sustain expenses of the household and for the drug injection. He was reluctant to let me inject drug myself. Finally, I had to depend on him for drug injection (F 04). My friend who was also my sexual partner injected me in the beginning and we were only the two of us. However, we had other friends and on and off we would all meet and sort of have a bash and a party. Then there would be six of us who will need injection and there would be two injectors and each injector would use the syringe and needle for three persons (F 06). My husband was the first person who carried out my first drug injection and this had continued for many years. When he got jailed, I had another male sexual partner with whom I lived, he carried out my drug injections (F 10). For many years, four male friends with whom I used to hang around would do my drug injections. Any of the four guys would do my injections depending on who was around and available when I needed them (F 05).

17.3 Results

307

I used to pay a friend of mine to do my injections with a needle and syringe which belonged to me. However, when my friend does not have money and badly needs a dose then we share a dose or two with my injecting material (F 08). I used to have a group of female friends and one of them was the leader of the group and she used to inject each one of us daily for many years. Now the group is no more, many friends are no more as well (F 09).

17.3.1.4 Needle and syringe sharing The WIDUS in the study have been sharing all materials involved in the preparation of the drug till its injection during the first years of drug injection. The number of persons with whom the injecting material, equipment and drug has been shared is at least two. The maximum number is not known. The individual who will carry out the drug injection for us would put us in a line. He would put the needle of the syringe in the bottle top containing the prepared drug. He would then draw the plunger of the syringe upwards and aspirate all the volume of the drug. If there are four doses, he would share it among four of us. He would inject himself first. Then he would look for the vein of the first IDU. He would clearly force the needle into the flesh and look for the veins. If it was difficult to find, then he would have to pierce the skin and other vessels several times, aspirating blood by drawing the plunger of the syringe upwards several times. He would get difficulty for WIDUS more than for men except if the men have been injecting for a long time. Even before injecting drugs in our veins we could see blood on the needles. At all times when the needle was in the vein he would aspirate blood into the syringe, thus ensuring that he needle was really in the blood vessel, then he would inject the dose. For the one to be injected the next dose, the blood of previous IDUS would already be already mixed with the drug and get injected into the vein of the recipient IDU (F 02).

Other WIDUS as well, have clearly described undergoing similar drug injection practice. The drug dealer would himself act as ‘Doctor’. We would wait in line and he would inject a dose for each of us, one after the other (F 02). I considered him a friend; he would come and inject both me and himself, only the two of us at my place. He later forced me into commercial sex work so that I can buy drugs for him as well and he would continue to inject me. I never learned how to inject and finally became dependent on him (F 03). One of my fellow commercial sex worker (CSW) would inject me and the others before we would start to work at night. The day she would not come to work or even be late, we would start to fret at the perspective of not having the usual dose. We would have to pay a pimp then in-order to get injected, and his price would be a dose of the drug (F 01).

17.3.1.5 Post-injection recycling use The needle and syringes are usually washed with running water and then stored in a hidden place for reuse. This is what all the study participants have experienced in their early years of their WIDU life.

308

17 Outlook towards vulnerable women

The injecting material and equipment used for preparation of drugs are taken care of in similar way. However, they are washed well by the injectors. This was done only after weaning off of the drug effect. The washing was carried out with water only in all cases. The hiding places varied a lot. For some injectors, the injecting materials were put in small tin boxes and kept in the cupboard. Others kept on a plate or another recipient and were put on the table. In some cases, these materials were simply put under the bed. In fact, the whole practice of drug injection is carried out away from the open in order to keep away from public and police’s sight. If an individual is caught by the Police with drugs and or drug injecting material, the former can be liable to prosecution and even imprisonment. After injection, the needle and syringe would just lie anywhere. It could lie on the floor or on a table or elsewhere. It would however be put in a safe place as soon as the effect of the drug would wean away and I would be in my senses. I would usually wash the needle and the syringe inside out. I would aspirate water into the syringe with sufficient pressure and push down the plunger to evacuate it with force. I would repeat this process two or three times until the needle and the syringe would appear clean to me. I would then place both the needle and the syringe above the wooden structure that form the entrance of the door. This wooden structure was 10–12 cm in width and it was on this structure that the needle and syringe were kept (F 04).

17.3.2 Theme 2: sex work interplay 17.3.2.1 Sex work leading to drug injection Four of the study participants first started sex work to earn a living and afterwards started injecting drugs. The heavy psychological burden of commercial sex work (CSW) led them to start drug injection amid the promiscuity and peer pressure of other sex workers who were WIDUs. Below are the quotes from two participants to illustrate the findings: I was 18-year old when I started sex work for subsistence. My mother, who was unwed, passed away when I was 9-year old. My grandmother did not take care of me and I had to start sex work in-order to earn a living as I was uneducated. I could not get any decent work and wherever I worked, I worked like a slave and got peanuts. Sex work paid well. However, I did it against my will. My heart was against it and after few weeks, it was traumatizing and psychologically I was drained out. It was a burden. Fellow sex workers who were WIDUs encouraged me and I would spend my free time with them. One day I accepted to be injected by a fellow sex worker (F 01). I was physically abused by my boyfriend and was forced to sleep with a seaman from Philippines when I was around 13-year old in one hotel of the capital which was more of a brothel. I could not refuse and flee or go back home. I had to comply and keep on doing sex work for them till the time I became stronger physically and psychologically and could fight them. I started to smoke ‘brown’ inorder to forget my demise and carry on with life. Then I got addicted to it. Once there was a lack of ‘brown’ in the market and then a friend who got some told me that if I wanted to get the same effect, I needed to inject. From that day onwards, I started to inject (F 02).

17.3 Results

309

17.3.2.2 Drug injection leading to sex work Four of the study participants started injecting drugs first and then got engaged in sex work. All four WIDUS got engaged in sex work because of two reasons. The first and foremost one was that they needed money for buying their doses of drugs for injection. They also had to earn money for subsistence. Below is the quotation from Participant F10 to illustrate the findings: My partner was an injecting drug user and he had no work. He had introduced me to injecting drug use. Then he went to prison for having committed a theft. I was helped for some days by a male friend who would share his dose with me. Then due to lack of money, I had to indulge in sex work, helped by that friend who would act as a pimp. Finally, I had to do sex work in-order to earn money for buying my daily doses of drugs and to buy food and other stuff (F 10).

17.3.3 Theme 3: sexual behaviour screenplay 17.3.3.1 Sexual encounters The WIDUs in this study have all had as sexual partners a male IDU at some point in time. All have also had as sex partners non IDUs as well. All have been having partners within and outside of partnerships. For WIDUs carrying out commercial sex work, their clients included both IDUs and non IDUs. It had happened to me that I had to go with a client on a motorcycle and on the way back, he dropped me in the dark of the night at a place unknown to me, on the side of the road. I had to stop cars to be able to return to Port Louis or nearby so that I could eventually return home. A car stopped to give me a lift. There were three men in the car and I agreed to take a lift. I was brought to an unknown place where there were only sugar cane field and under threat I had to have sex will all three without condom and they later dropped me at the ‘Jardin Compagnie’ without any remuneration (F 02).

17.3.3.2 Unprotected sexual practice All 10 WIDUS have been having sexual activity for years, without using condoms. All the sex workers have proposed condoms to their clients in the past. However, there were clients who refused to use condoms. The participants all had sex with their clients even if they refused to use condoms. The WIDUS were reluctant to give details on the sexual practices like anal, oral or exclusively vaginal and whether they had sex with more than one individual at the same time. They all said they had sex in any way we could imagine it. These guys were not all IDUs. Some were HIV infected; some were infected with Hepatitis C. They were not agreeable to putting a condom before having sex. They deliberately wanted to infect us it seemed (F 03). I never had sex for money. But the friend whom I used to pay to inject me, at times after injection, we would sleep in the same room and when we woke up on a couple of occasions we had sex without condom (F 08).

310

17 Outlook towards vulnerable women

17.3.4 Additional data The WIDUS in the study had never in their first years of their injecting career heard about HIV, Hepatitis and Syphilis. However, they all knew that sharing of needles and unprotected sex represented health hazards. The craving, referred to as ‘yen’ in the local context, they had for drugs was too strong and overpowered them. The effect drugs had so far psychological dependence was concerned on them; compelled them to indulge in sharing of needles and or unprotected sex with people who might be infected by the above-named blood borne or sexually transmitted infections. In addition, the peer pressure for both indulging in the sharing of needle and getting involved in sex work for those involved in it was also too strong they could not resist. I was really afraid of needles. The sight of it made me shudder. But the effect I experienced after being injected was so great that my fear for the needle flew away. It was only the super effect of the drug that counted (F 05). There was no consideration whatsoever when the craving for the drug ‘yen’ was present. There was no consideration for life and death. What mattered was only the drug and how to get money to obtain it and get it injected. The means to achieve it did not count whether it was by the sharing of needles and syringes or blood itself or by having sex with an HIV infected client. Not having the dose of drug was equivalent to death. This was the only thing that matters (F 03).

17.4 Discussion This study which involved an in-depth exploration on the drug injecting practices and the sexual behaviours of WIDUs in Mauritius is the first of its kind in the local context and reveals poignant testimonies which were homogenous.

17.4.1 Background and setting All study participants hailed from families of low socio-economic background with low level of education and lived in poverty. This finding is in line with the literature whereby various studies showed that social disadvantage is one of the factors that influences drug injection among females in various countries [11–13]. Violence and sexual abuse were reported in this study as pre-existing factors prior to engaging in IDU, in line with Neaigus [12] who has shown that violence is a proven factor that influences a woman to start drug injection [12] while sexual abuse is also a trigger factor for women to indulge in drug injection [14]. The settings where drug injection was carried out were hygiene-deprived places where either their sexual partners or theirs friends of the same group could be present. Bryant [15] and Roy [16] have come up with similar findings [15, 16]. The reported lack of hygiene in these settings adds further to the burden of health hazards faced by WIDUs.

17.4 Discussion

311

17.4.2 Injection practices of WIDUs In the current study, all WIDUs were injected by someone else, often a male sexual partner at initiation and for several years afterwards. This finding is in line with the study by Kermode [11] and Vidal-Trecan [17]. The possible reasons for this situation are the fact that a large number of WIDUS depend on male IDU partners for the procurement of drugs and also for the injection, as put forward by Crofts [18]. In addition to being injected by another injector, this study showed that the injecting material and equipment used by the injector were likely to have been previously used materials. The studies of Doherty [19], Diaz [20] and Tuchman [9] have come up with findings supporting our claims [9, 19, 20]. The fact that only 52.4 % of PWIDs were following the needle-exchange programme in Mauritius in 2017 [3] supports the findings of this study. There is thus the urgent need to address this particular issue of using and sharing used syringe and needle for drug injection among WIDUs.

17.4.3 Sexual behaviours of WIDUs This study revealed that all the WIDUs interviewed have at one point in time been in a relationship of variable duration with male IDUs. Indeed, numerous studies have shown that WIDUs tend to have male IDUs as partners [21–25] The problem that arises in the Mauritian context is that they not only share needles and syringes among themselves, but they also indulge in sex without protection. Unfortunately, needle and syringe sharing along with unprotected sex are also common practice in many countries [9, 26, 27] All the WIDUs in the current study, had been engaged in sex work or unprotected sex. There is a very strong relationship between WIDUs and commercial sex work: Either drug injection precedes sex work or vice versa, as reported in the literature [28, 29]. All WIDUs in this study engaged in street sex work which exposed them to clients at high risks of having HIV, Hepatitis and other sexually transmitted diseases [30]. Indeed, indulging in commercial sex has been associated these diseases through with inconsistent condom use and syringe sharing [31]. WIDUS engage in sex work to find money to procure drugs for themselves and often for their male IDU partners as well [28, 32]. The use of condoms by women drug users, both with clients and partners, was low in our study. Risky sexual behaviour is a stronger determinant for acquiring HIV than risky injection practices [33–35].

17.4.4 Participants’ insight Participants revealed their risky sexual behaviour of inconsistent and rare condom use as well as their risky drug injection practices with sharing of needles. WIDUs did have

312

17 Outlook towards vulnerable women

insight on the fact that their risky drug injection and sexual practice exposed them to health hazards, but they were driven by their psychological and physical dependence on drug injection which overwhelmed them. Their dependence on drug use was so powerful that they could not come out of its clutches and thus continued to engage in risky behaviours. In order to bring sustainable and positive changes to the situation of WIDUs in the local context, tailor-made policies need to be adopted within a well-defined legal framework to deliver, with inter-disciplinary stakeholders, the required psychological support, medical treatment and harm reduction programmes which have to be easily accessible to WIDUs.

17.4.5 Strengths and limitations Although the inconsistent and rare use of condoms was explicitly put forward by the WIDUs, there was reluctance to provide more details on the type of sexual practices and the number of partners. This reluctance was considered with due respect by the interviewer. On the other hand, this study among WIDUs, considered since long as an extremely difficult population to reach [4], has been completed with the required sample size, determined through saturation of data. Findings with respect to drug injection practice and sexual behaviour have shed light on the specific risks WIDUs face. The specific needs of WIDUS participating in this study have been put forward, thus filling the highlighted gap with respect to studies on WIDUs in the literature.

17.4.6 Recommendations Based on the findings of the study, we make the following recommendations: 1. Constitution of a team comprising of physicians, nurses, psychologists and peer leaders so as to empower the vulnerable and underprivileged WIDUs population to get out of the clutches of IDU through motivational intervention and follow-up. 2. Enhanced access by WIDUs to the drop-in points for condom distribution, needle exchange and methadone substitution therapy through WIDU-friendly services customized to their needs. 3. Active collaboration between Government and NGO’s for more accessible outreach programs through peer leaders in order to provide anonymous WIDUs with needle, syringes and drug injection materials.

17.5 Conclusions This study, in line with its objectives, provided an insight into the risks taken by WIDUs, and reveals their gender-specific vulnerability. The principal theme which emerged from

References

313

this study was “Drug Injection Scenario”, the second theme was “Sex Work and Drug Use Interplay” with either sex work preceding drug injection or drug injection preceding sex work and finally, the third theme was “Sexual Behaviours Screenplay” with casual encounters and unprotected sex. This qualitative study showed that all study participants were from families of low socio-economic background with low level of education and lived in poverty. All the WIDUs were injected by someone else, often a male sexual partner at initiation and for several years afterwards. All of them have at one point in time been in a relationship of variable duration with male IDUs. In addition, all the WIDUs in the current study had engaged in sex work or unprotected sex. All WIDUs in this study partake in risky drug injection practices and unprotected sexual behaviours which expose them to risk of infections like HIV, hepatitis, syphilis and other drug injection related hazards. Nonetheless, WIDUs have insight into their complex situation which they attribute to their psychological and physical dependence on drug use. This dependence on drug injection contributed to their stay within this vulnerable circle. Their inability to access adequate support hinders them from coming out of the clutches of their risky drug injection and sexual practices. For resilience of the society, there is need to address the needs of this vulnerable group of women and develop the required framework for counselling, motivating and empowering them towards resilience. Customized interventions with well-trained healthcare providers and peer leaders are mandatory to address the specific needs of WIDUs and to empower them with a new life. We need a change in our outlook towards these vulnerable women with strengthening of community work for societal resilience. Acknowledgments: Mrs Anne Marie Cupidon Guilliani, at “Collectif Urgence Toxida”, Non-Governmental Organisation for granting permission to have access to WIDUS. Mrs Winda Moutou, at the Needle Exchange Site for arranging appointments with WIDUs. We are grateful to all the women who participated in this research work.

References 1. Chelin R. Drug trafficking/synthetic drugs on the rise despite Mauritius’s best efforts. ENACT project, Institute of Security Studies; 2020. Available from: https://enactafrica.org/enact-observer/synthetic-drugson-the-rise-despite-mauritiuss-best-efforts. 2. United Nations. World drug report; 2010. Available from: https://www.unodc.org/unodc/en/data-andanalysis/WDR-2010.html [Accessed 28 Oct 2022]. 3. Ministry of Health and Quality of Life. Integrated biological and behavioural surveillance: a respondent driven survey among PWIDS in the Island of Mauritius. Port Louis: Government Printing Press; 2007. 4. United Nations. Women and drug use: the problem in India. New Delhi: United Nations Office of Drugs Crime; 2002. 5. United Nations. World drug report; 2022. Available from: https://www.unodc.org/res/wdr2022/MS/ WDR22_Special_Points.pdf [Accessed 28 Oct 2022].

314

17 Outlook towards vulnerable women

6. Tweed EJ, Miller RG, Schofield J, Barnsdale L, Matheson C. Why are drug-related deaths among women increasing in Scotland? A mixed-methods analysis of possible explanations. Drugs Educ Prev Pol 2022;29: 62–75. 7. Collins LH. Alcohol and drug addiction in women: phenomenology and prevention. In: Ballou M, Brown LS, editors. Rethinking mental health and disorder: feminist perspectives. New York: The Guilford Press; 2002: 198–230 pp. 8. Tuchman E. Women and addiction: the importance of gender issues in substance abuse research. J Addict Dis 2010;29:127–38. 9. Tuchman E. Women’s injection drug practices in their own words: a qualitative study. Harm Reduct J 2015; 12:1–8. 10. Johnston L, Saumtally A, Corceal S, Mahadoo I, Oodally F. High HIV and hepatitis C prevalence amongst injecting drug users in Mauritius: findings from a population size estimation and respondent driven sampling survey. Int J Drug Pol 2011;22:252–8. 11. Kermode M, Longleng V, Singh BC, Bowen K, Rintoul A. Killing time with enjoyment: a qualitative study of initiation into injecting drug use in north-east India. Subst Use Misuse 2009;44:1070–89. 12. Neaigus A, Gyarmathy VA, Miller M, Frajzyngier VM, Friedman SR, Des Jarlais DC. Transitions to injecting drug use among noninjecting heroin users: social network influence and individual susceptibility. J Acquir Immune Defic Syndr 2006;41:493–503. 13. Fuller CM, Borrell LN, Latkin CA, Galea S, Ompad DC, Strathdee SA, et al. Effects of race, neighborhood, and social network on age at initiation of injection drug use. Am J Publ Health 2005;95:689–95. 14. Hien DA, Nunes E, Levin FR, Fraser D. Posttraumatic stress disorder and short-term outcome in early methadone treatment. J Subst Abuse Treat 2000;19:31–7. 15. Bryant J, Treloar C. The gendered context of initiation to injecting drug use: evidence for women as active initiates. Drug Alcohol Rev 2007;26:287–93. 16. Roy E, Haley N, Leclerc P, Cédras L, Boivin JF. Drug injection among street youth: the first time. Addiction 2002;97:1003–9. 17. Vidal-Trecan GM, Varescon-Pousson I, Boissonnas A. Injection risk behaviors at the first and at the most recent injections among drug users. Drug Alcohol Depend 2002;66:107–9. 18. Crofts N, Louie R, Rosenthal D, Jolley D. The first hit: circumstances surrounding initiation into injecting. Addiction 1996;91:1187–96. 19. Doherty MC, Garfein RS, Monterroso E, Latkin C, Vlahov D. Gender differences in the initiation of injection drug use among young adults. J Urban Health 2000;77:396–414. 20. Diaz T, Vlahov D, Edwards V, Conover S, Monterroso E. Sex-specific differences in circumstances of initiation into injecting-drug use among young adult Latinos in Harlem, New York City. AIDS Behav 2002;6:117–22. 21. Strathdee SA, Philbin MM, Semple SJ, Pu M, Orozovich P, Martinez G, et al. Correlates of injection drug use among female sex workers in two Mexico–US border cities. Drug Alcohol Depend 2008;92:132–40. 22. MacRae R, Aalto E. Gendered power dynamics and HIV risk in drug‐using sexual relationships. AIDS Care 2000;12:505–15. 23. Absalon J, Fuller CM, Ompad DC, Blaney S, Koblin B, Galea S, et al. Gender differences in sexual behaviors, sexual partnerships, and HIV among drug users in New York City. AIDS Behav 2006;10:707–15. 24. Shah NG, Galai N, Celentano DD, Vlahov D, Strathdee SA. Longitudinal predictors of injection cessation and subsequent relapse among a cohort of injection drug users in Baltimore, MD, 1988–2000. Drug Alcohol Depend 2006;83:147–56. 25. Mburu G, Limmer M, Holland P. Role of boyfriends and intimate sexual partners in the initiation and maintenance of injecting drug use among women in coastal Kenya. Addict Behav 2019;93:20–8. 26. Hearn KD, O’Sullivan LF, El-Bassel N, Gilbert L. Intimate partner violence and monogamy among women in methadone treatment. AIDS Behav 2005;9:177–86. 27. Loxley W, Ovenden C. Friends and lovers: needle sharing in young people in Western Australia. AIDS Care 1995;7:337–52.

References

315

28. Burns K. Women, harm reduction, and HIV: key findings from Azerbaijan, Georgia, Kyrgyzstan, Russia, and Ukraine. Open Society Institute; 2009. Available from: https://idpc.net/publications/2009/11/osi-reportwomen-harm-reduction-hiv. 29. Bruneau J, Lamothe F, Franco E, Lachance N, Désy M, Soto J, et al. High rates of HIV infection among injection drug users participating in needle exchange programs in Montreal: results of a cohort study. Am J Epidemiol 1997;146:994–1002. 30. Harcourt C, van Beek I, Heslop J, McMahon M, Donovan B. The health and welfare needs of female and transgender street sex workers in New South Wales. Aust N Z J Publ Health 2001;25:84–9. 31. Benotsch EG, Somlai AM, Pinkerton SD, Kelly JA, Ostrovski D, Gore-Felton C, et al. Drug use and sexual risk behaviours among female Russian IDUs who exchange sex for money or drugs. Int J STD AIDS 2004;15: 343–7. 32. Smith FM, Marshall LA. Barriers to effective drug addiction treatment for women involved in street‐level prostitution: a qualitative investigation. Crim Behav Ment Health 2007;17:163–70. 33. Fuller CM, Vlahov D, Latkin CA, Ompad DC, Celentano DD, Strathdee SA. Social circumstances of initiation of injection drug use and early shooting gallery attendance: implications for HIV intervention among adolescent and young adult injection drug users. J Acquir Immune Defic Syndr 2003;32:86–93. 34. Estebanez PE, Russell NK, Aguilar MD, Béland F, Zunzunegui MV. Women, drugs and HIV/AIDS: results of a multicentre European study. Int J Epidemiol 2000;29:734–43. 35. Semaan S, Des Jarlais DC, Sogolow E, Johnson WD, Hedges LV, Ramirez G, et al. A meta-analysis of the effect of HIV prevention interventions on the sex behaviors of drug users in the United States. In: Database of Abstracts of Reviews of Effects (DARE): quality-assessed reviews [Internet], Centre for Reviews and Dissemination, UK. Alphen aan den Rijn, the Netherlands: Wolters Kluwer Health, Inc; 2002.

Index 13C NMR 24 1H NMR 26 AAS 204 absorption 246 acacia hockii 256 active sites 232 ADF 289 ADMET 51, 70, 71 adsorbate 173, 178 adsorbent 170 adsorption 160, 161, 170, 224 adsorption capacity 226 adsorption 101 advanced oxidation process 123 Ag nanoparticles 197 agglomeration 189 agricultural 224 alkyl peroxide radical 207 alloys 202 ample 178 anaerobic digestion 122 analysis 64, 70, 80 anambra 137, 139, 153 antifungal 265, 276 antifungal activity 94, 95 antifungal agents 82 apparent 249 application 191 aqueous 178 aromaticity 126 ash content 274 ashed 161 assembly 198 atomic arrangement 202 atrazine persistence 102 atrazine side effect 118 atrazine sorption 116 atrazine 100 Au alloy 199, 213 Au and Ag metals 204 Au based catalysts 194 Au bimetallic 194 Au catalysts 194 Au nanoparticles 197, 199 Au-Ag catalyst 197, 211 Au-Ag core shell 210

https://doi.org/10.1515/9783111071428-018

Au-Ag interaction 209 Au-Ag/TiO2 209 Au-Ag/TiO2 catalyst 209 Au-Co 206 Au-Cu catalysed 197 Au-Cu core shell catalysts 197 Au-Pd catalyst 197, 198, 202, 212 Au-Pd/MgO 208 Au-Pt 203 Au-Pt catalysts 203 Au-Pt core–shell catalysts 202 Au/TiO2 205 autoclave 184 autodock vina 53 auxochrome 278 available P 116 B3LYP 37 bacterial 258 BCP 293 Bi24O31Br10 190 bimetallic 193 bimetallic alloy 195 bimetallic Au 193–196, 200, 207, 210, 213, 214 bimetallic Au-Pd alloy 201 bimetallic Pt-Au 196 biochar pretreatment 107 biochar 101 biodegradability enhancement 123 biodegradability of WAS 123 biodegradable intermediates 129 biodegradation 101 biogas production 122 biomass 210 bismuth oxybromide 182 bismuth oxyhalides 182 BOD5:COD ratio 127 bond critical point 293 bond distance 39, 41 BP86 289 bruchids 259 cadmium 223 CaFS 223 calcination 184, 197 (Callosobruchus maculatus) 256 cancer ratio 137, 143, 150 carbon 183

318

Index

carbonaceous 182 carbonyl functionality 57, 58, 60–63, 66, 67 carboxylates 278 carboxylic 116 carcinogenic 138, 142, 143, 150, 154 catalysis 2, 13 catalyst 6, 181 catalytic activity 213 catalytic properties 193 CATOFIN 7 central composite 9 CeO2 201, 212 ceramics 248 ceramide 267 C-H bond 207 charge 43, 47 charge carrier 184, 212 charge separation 44 charge transfer 188 chemical bonding 48 chemical composition 228 chemical fertilizer 104 chemical oxygen demand 123 chemical-treatment 270 chitosan 164 chromatography 65 chronic daily intake 142 clay 244 CO oxidation 202 cocoa 162 collectif urgence toxida 313 color intensity 129 column 54–59, 61, 64 column chromatography 257 commercial sex worker 307 compared 238 comparison 190 complex situation 313 complexes 272 composition 175, 190, 201 compound 259 computational chemistry 17, 30 computational tasks 28 concentration 170, 173, 225, 259 condom distribution 312 constraints 10 contact time 225 contaminants 167 contamination 262

conventionally 175 conversion 4, 207 cooling 162 coordination bond 291 core shell nanoparticles 196 core–shell, 213 coronavirus 70, 80 correlation 260 correlation coefficient (R2) 232 COSMO 289 cowpea 256 cracking 4 crystallographic 198 CTAB 197, 200 Cu4 core 43 Cu4X4 37 customized interventions 313 cycles 177 cyclohexane oxidation 208 cyclohexanol 207 cyclohexanone 207 cyclohexyl hydrogen peroxide 208 cyclohexyl radical 208 D. mespiliformis 82, 83, 95 D. senecioides 82, 83, 95 dechlorinating 166 deflocculant 245 degeneracy 22 degradation 181 degradation kinetics 189 degraded 189 dehydrogenation 2 delocalization 278 density 251 depropanizer 3 deproteinized 163, 230 dermatophytes 85 dermatophytosis 81 design 52, 69 destroy 263 DFT 35, 288 DFT approach 47 DFT studies 211 diameter 265 diffraction peaks 204 dihedral angle 40, 41 disc diffusion assay 94 disinfectants 160 dispersion correction 37

Index

distillery wastewater 121 docking 51, 53, 69, 70, 80 dosage 172 drop-in points 312 drug 51, 52, 69, 70, 80 drug addiction 301 drug dealer 306 drug injection practice 303 drug injection scenario 305 drug likeness 51, 70 dry weight 247 drying 165 d-spacing 204 ecotoxicity 116 EDA 296 EDX 282 effect of temperature 234 electrical conductivity 93 electrochemical 191 electron density 293 electron paramagnetic resonance 206 electrons 212 electrostatic interaction 291 electrostatic repulsion 234 encapsulation 198 energy 51, 70, 80 energy decomposition analysis 287 energy density 293 enol-ether 55–58, 60–63, 65–67, 69 enthalpy 234 entropy 234 enzyme 51, 69 epimer 56, 58, 59 epoxide 65, 69 equilibrium 172 experimental data 30 extracellular polymeric substances 127 extract 54, 57, 58, 61 extracted 257 extraction 54, 70 face centred cubic 198 FCC 2 feed purity 12 feedstock 105 feedstock purification 13 feldspar 244 fennel-based 223 flash chromatography 61 foodstuffs 256

functional group 116, 230 fungi 258, 272 furnace 161, 274 gambari soil series 102 Gaussian 16, 19, 38 GaussView 5.0 19 GaussView 5.0/Gaussian programs 31 gender differences 302 gender-specific vulnerability 302 GGA 289 Gibb reactor 4 Gibbs free energy 234 Gibbs minimization 3 glazed 250 grains 263 H2O2 decomposition 212 H-abstraction 207 halogen 39, 42 halogen substituent 43 hazardous 160 health effects 138, 147, 148, 151 health risk 137, 139, 141, 142, 148, 150, 154 herbicide 100 heteroatoms 168 heterogeneous junctions 190 heterojunction 181 hierarchical pores 186 high energy facets 199 high molecular weight 122 high resolution energy dispersive X-ray spectroscopy 203 HIV transmission 302 holes 212 HOMO 42, 206, 278 HOMO/LUMO gap 43 HRTEM 202, 203 hydration pattern 287 hydrocarbon 207, 278 hydrolysis 228 hydrolysis of cellulose 228 hydrothermal 183 hydroxyl 116 hydroxyl radicals 132 impregnation method 195 improved biodegradability 132 impurities 12 incubation 103, 165 incubator 276 industries 168

319

320

Index

influent 174 infrared spectroscopy 18 inhibition 267 inhibition zones 94 inhibitors 284 inhibitory 263 injecting material 307 inorganic complexes 31 inorganic curriculum 17 insecticidal 267 insoluble 272 instructional manual 20 instructional video 23 interaction 69, 177, 232 interface interaction 185 intermediates 127 investigation 252 ionic 296 isotherm 177 isotherm models 232 kaolinite 252 kiln 246 kinetic models 234 Langmuir and Freundlich 232 Langmuir-Hinshelwood 189 laplacian of electron density 293 lattice strain 196, 200 layered double hydroxide 211 LDH 211 leachates 167 leaching 277 leaching column 103 less interaction 232 ligand 48, 51, 69, 70, 71, 80 lignin 271 linear 247 locally 244 low level of education 304 low mass transfer 232 low socio-economic status 304 LUMO 42, 206, 278 macronutrient 102 maize seedling 104 materials 244 maximum resident limits 141 melanoidin 123 melting 274 melting point 62 MEP plot 45

mesoporous 184 metal cluster 36 metal oxide 291, 292 metal-organic framework 35, 47 metal-support interaction 201 methadone substitution therapy 312 methylene blue 223 MgO suppor 208 microorganisms 258 microscope 165 microstructures 190 miller indices 198, 204 mineralogical 248 minimum inhibitory concentrations 95 minimum inhibitory concentrations (MIC) 82 mixture 272 MO 42 M–O bond 291 M=O bond length 291 modification 161, 172 molecular graphs 293 molecular orbitals 22 monometallic Au catalyst 213 monometallic Au catalysts 193 morphology 195, 202, 208, 279 morphopolgies 165 mortality 262 motivational intervention 312 multipod AuCu3 nanoparticles 200 nafuta 245 nanomaterial 181 nanosheet morphology 185 natural bond orbital 38 needle and syringe sharing 307 needle exchange 312 needle exchange site 313 negative electrostatic potential 45 Nigeria 137–139, 143, 146–148, 152, 153, 243 nitrogen 271 non-carcinogenic 142, 154 non-covalent interaction 48 nuclear magnetic resonance 18 nuclear reactor 287 nuclear waste water 287 nucleation 175 objective functions 10 OFAT 4 •OH radical species 206 olefins 2

Index

OLEFLEX 7 on-purpose method 2 optimum 10, 172 orbital energy 292 organic carbon 106 organic ligand 36 organic loading rate 125 organic molecular linker 36 organochlorine 137, 139, 140, 141–143, 146–150, 151, 153 organometallic complexes 18 organophosphate 137, 139, 141–143, 146–151, 153 ormic acid oxidation 209 outreach program 312 oxidation activity 209 oxidation of hydrocarbons 213 oxidation processes 210 oxidation state 289 oxidative transformation 213 oxygen 168 ozonation of activated sludge 126 ozonation pre-treatment 127 ozone dosage 131 ozone mass transfer 131 ozone reactor 124 ozone transfer 126 ozonolysis 123 ozonolysis post-treatment 130 ozonolysis pre-treatment 128 particle size 195, 199, 201 Pauli’s repulsion 291 Pd-Au/TiO2 205, 213 Pd catalyses 212 Pd/TiO2 205 peer leader 312 Peng–Robinson 4 periwinkle 163 persistence 102 pesticide 137, 138, 140–143, 146–148, 150, 153 petrochemical 2 pH 162 pH affects 234 pharmaceutical antibiotics 182 photoactivity 191 photocatalyst 184 photocatalytic activity 213 photocatalytic degradation 182 Photocatalytic oxidation 212 photocatalytic processes 213

321

photocatalytic selective oxidation reactions 213 photoreduction 187 physically treated biomaterials 224 phytochemicals 83 PL 206 PL spectroscopy 206 plant 256 pneumonia 52 point zero charge (pHpzc) 230 poison 13 poisoning 196 pollutants 160 pollution 138 polyethylene 160 porcelain 243 porosity 197, 247 post-injection recycling use 307 post-treatment 121 potential accumulation 45 poultry manure 102 precursor 37, 41, 42, 44, 48 prediction models 9 presence of cellulose 228 preservatives 270 pressure 7 pre-treatment 121 process integration 133 process optimization 9, 13 process simulation 3 process simulator 12 propane 5 propane purity 12 propene 2 properties 191 protease 51, 53, 69, 70, 80 protein 52, 69, 70, 71, 80 proton exchange membrane 209 pseudo-first-order 234 pseudo-second-order 234 pulverizer 245 purity 13 purposive sampling 303 pyrolysis 105 QTAIM 293, 296 qualitative study 302 quartz 244 quasi radioactive 288 QuEChERS 137, 140 radiation shielding 288

322

Index

Raman and Auger spectroscopy 213 rare condom use 311 reaction thermodynamics 13 reactor model 5 receptor 52, 70, 71 recycling and recovery 236 reducing agents 194 refluxed 273 remediation 101, 189 re-polymerised 129 representational competency skills 19 residue 163 resistivity 250 reusability 167 risk assessment 139, 142, 143, 150, 154 risky drug injection 312 risky sexual behaviour 311 rots 271 rough surface morphologies with cavities 228 ruthenocene 24 SARS-CoV-2 51–53, 69–71, 79, 80 sawdust 102 scanning 175 secondary building unit 36 selective oxidation of hydrocarbons 207 selectivity 4, 6, 207 SEM 279 semiconductor 191 separation 54–58, 61, 64 sex work 306 sex work and drug use interplay 305 sexual behaviour 303 shapes 201 shea 283 shrinkage 247 signals 261 silica 183 silicates 182 singlet 39 sintering 196 SiO2 201 sludge solubilisation 121, 123 societal resilience 313 software 53, 69–71 soil organic carbon 101 soil organic matter 115 soil pH 104 solubilised matter 128 solubility 275

solvation energy 291 sorption 101 species 53 specific needs 312 spectrophotomete 166, 167 spectroscopic 275 spectrum 275 spent nuclear fuel 288 sphingolipid 260 stabilizing agents 201 STEM 202 stereochemistry 260 steroidal glucoside 262 stigmastane 262 storage 267 strengthening of community work 313 streptomycin 276 stretching 170 structure 38, 41, 42, 47, 191 structure directing agent 197 surface area 195 surface energy facets 198 surface morphologies 228 surface morphology 200 surfactant 199 synergic effect 193, 194 synergistic 191 synergistic interaction 186 synthesis 190 synthesized 271 tallow 283 target 51, 70, 71 TDDFT gap 43 teaching tool 30 TEM 202 temperature 7, 162, 168, 250 temperature effect 225 TETFund 252 tetracycline 181 texture 168 the cancer benchmark concentration 143 thermodynamic stability 46 thermodynamic variables 234 thiourea 277 time intervals 232 tinea capitis 82, 94 TiO2 205, 213 TiO2 support 205, 206 total binding energy 292

Index

toxic 160 toxicity 70 toxicity quotient 141, 148 transition metal 37 triaxial 243 T.rubrum 94 UASB reactor 125, 126, 129 unprotected sex 305, 310 urea 277 UV absorbance 63 valence angle 41 value added oxygenated compounds 207 vanillin 55–59, 61–63, 65, 66, 68 violence/sexual abuse 304 virus 51, 52 visible light 184 visible region 187 vitrification 250

voltages 252 waste activated sludge 121 wastewater 224 wastewater discharge 288 water 160, 191 water-metal ion bond 291 weed 100 women injecting drug users 302 wood 270 XPS 205, 205, 213 X-ray absorption 205 X-ray fluorescence (XRF) 204 X-ray photoelectron spectroscopy 204 X-ray photons 205 X-ray spectroscopy 204 XRD 203, 243 yield 4, 8 ZORA 289

323