Fundamentals and Sensing Applications of 2D Materials 9780081025772, 9780081025789, 2052052062, 0081025777

Table of contents :
Front Cover......Page 1
Fundamentals and Sensing Applications of 2D Materials......Page 4
Copyright Page......Page 5
Contents......Page 6
List of Contributors......Page 10
Preface......Page 12
1 Introduction......Page 14
References......Page 16
2.1 Introduction......Page 18
2.2 Surface and Interface Physics......Page 19
2.3 Band Alignment......Page 20
2.4 Preparation of 2D Materials......Page 21
2.5 Parameters of Sensor Performance......Page 23
2.6 2D Materials in Chemical and Physical Sensing......Page 26
2.7 Conclusion......Page 35
References......Page 36
3 Synthesis, Properties, and Applications of Graphene......Page 38
3.1.1 Micromechanical Cleavage......Page 40
3.1.2 Liquid-Phase Exfoliation......Page 41
3.1.4 Chemical Methods......Page 45
3.2.1 Raman Spectroscopy......Page 47
3.2.2.2 Atomic Force Microscopy......Page 49
3.3 Properties of Graphene......Page 51
3.3.1 Electrical Properties......Page 52
3.3.2 Magnetic Properties......Page 55
3.3.3 Surface Area......Page 58
3.3.4 Mechanical Properties......Page 60
3.4.1 2D and 3D Printing......Page 62
3.4.2 Detection of Volatile Organic Compounds......Page 69
3.4.3 Graphene for Gas Sensors......Page 71
3.4.4.1 Working of Lithium Ion Batteries......Page 74
3.4.4.2 Graphene in Lithium Ion Battery Anodes......Page 76
3.4.4.3 Graphene in Lithium Ion Battery Cathodes......Page 77
3.4.4.4 Graphene as Current Collector......Page 79
3.4.5.1 Introduction and Working of Supercapacitors......Page 80
3.4.5.2 Graphene Nanocomposites With Various Materials......Page 82
3.4.5.3 Doping and Surface Modifications......Page 83
3.4.6 Graphene Aerogels......Page 84
3.5 Challenges and Outlook......Page 87
References......Page 88
4.1 Introduction......Page 104
4.2 Graphene Analogs......Page 105
4.2.1.1 Hexagonal-Boron Nitride......Page 106
4.2.2 Transition-Metal Oxides......Page 107
4.2.2.2 Vanadium Oxide......Page 108
4.2.2.3 Titanium Oxide......Page 110
4.2.3.1 Lead Iodide......Page 112
4.2.4.1 Gallium Sulfide......Page 113
4.2.4.2 Gallium Selenide......Page 114
4.2.5 Layered Metal Dichalcogenides......Page 115
4.2.5.2 Tin Diselenide......Page 117
4.2.6 Transition-Metal Dichalcogenides......Page 119
4.2.6.1 Molybdenum Disulfide......Page 120
4.2.6.2 Tungsten Disulfide......Page 121
4.2.7.2 V4C3Tx......Page 122
4.2.7.3 Nb1.33C......Page 123
Germanene......Page 124
Silicene......Page 125
Stanene......Page 127
Phosphorene......Page 129
Arsenene......Page 131
Tellurene......Page 133
4.3.2 Energy Storage......Page 134
4.3.3 Transistor......Page 135
4.3.4 Supercapacitor......Page 136
4.4 Summary and Perspective......Page 139
References......Page 140
5.1 Importance of Theoretical Modeling and Simulations for Sensing Applications......Page 158
5.2 Introduction to Quantum Simulations......Page 159
5.3.1 The Many-Body Hamiltonian......Page 160
5.3.2 The Born–Oppenheimer Approximation......Page 161
5.3.3 Hartree–Fock Method......Page 162
5.3.4.1 The Hohenberg and Kohn Theorems......Page 163
5.3.4.2 The Kohn–Sham Equations......Page 164
5.3.4.3 Approximations to the Exchange-Correlation Functional......Page 165
5.4.2 Hybrid Functionals......Page 167
5.5 Sensitivity of the Simulation Results......Page 169
5.5.1 Energy Cutoff......Page 170
5.5.2 Convergence With Respect to k-Point Grid......Page 172
5.6 Modeling of Graphene-Based 2D Materials for Gas Sensing......Page 173
5.6.1 Gas-Sensing Mechanism......Page 174
5.6.3 Modeling of Graphene......Page 175
5.6.4 Modeling of Graphene Oxides and Reduced Graphene Oxides......Page 177
5.6.5.1 Influence of Acetylene Linkage......Page 180
5.7.1 Silicene, Germanene, and Stanene......Page 182
5.7.2 Phosphorene, Arsenene, and Antimonene......Page 185
5.7.3.1 MoS2......Page 187
5.8 Interaction Mechanism of Gases on 2D Materials......Page 189
5.8.2 Adsorption Energy......Page 190
5.8.3 Charge Transfer and Orbital Interactions......Page 191
5.8.4.1 Grimme’s Density Functional Theory-D2......Page 193
5.8.4.4 optPBE–van der Waals......Page 194
5.9.1 Electronic Properties......Page 195
GGA+U Method......Page 196
Hybrid Functional......Page 197
GW Corrections......Page 198
5.9.2 Optical Properties......Page 199
5.10.1 Simulations of Glucose Molecule......Page 202
5.10.2 Simulations of 2D Materials......Page 203
5.10.3 Interaction of Glucose Molecule on 2D Materials......Page 204
5.10.4 Metal-Doped Transition Metals Oxides......Page 206
5.11 Conclusions and Future Directions......Page 209
References......Page 210
6.1 Introduction......Page 218
6.2.1 Important Parameters of a Gas Sensor......Page 219
6.2.2 Various Types of Device Configurations......Page 220
6.2.2.1 Chemiresistive Sensors......Page 221
6.2.2.3 Conductometric Sensors......Page 222
6.2.2.4 Impedance Sensor......Page 223
6.2.2.5 Surface Acoustic Wave Sensors......Page 224
6.2.3 Influential Parameters of a Gas Sensor......Page 226
6.3.1 Gas-Sensing Mechanism in Graphene......Page 228
6.3.2 Gas-Sensing Mechanism in Two-Dimensional Transition Metal Dichalcogenides......Page 230
6.3.3 Gas-Sensing Mechanism in Metal Oxide–Based Two-Dimensional Material......Page 232
6.4.1 Pristine Graphene-Based Gas Sensors......Page 233
6.4.2 Defective and Functionalized Graphene-Based Gas Sensors......Page 237
6.4.3 Graphene/Polymer-Based Gas Sensors......Page 242
6.4.4 Graphene/Metal Nanocomposites–Based Gas Sensors......Page 243
6.4.5 Graphene/Metal Oxide Nanocomposite–Based Gas Sensors......Page 247
6.5 Graphene Analogous Two-Dimensional Materials......Page 250
6.5.1 Transition Metal Di-chalcogenide–Based Gas Sensors......Page 251
6.5.2 Layered III–VI Group Materials–Based Sensors......Page 256
6.5.3 Layered Metal Oxide–Based Sensors......Page 257
6.5.4 Black Phosphorous–Based Two-Dimensional Material for Sensors......Page 259
6.6 Conclusion and Future Perspectives......Page 262
References......Page 263
7.1.1 Biosensors......Page 272
7.1.2 History of Biosensors......Page 273
7.2.1 Enzymatic Biosensors......Page 275
7.2.2 Nonenzymatic Electrochemical Sensors......Page 276
7.3 Current Status......Page 277
7.5 Nanomaterials for Nonenzymatic Sensing......Page 278
7.6 Two-Dimensional Materials-Based Electrochemical Biosensors......Page 281
7.6.1 Synthesis of Graphene Sheets......Page 282
7.6.2 Nongraphene 2D Materials for Biosensors......Page 286
7.7 MoS2-Based Electrochemical Sensors......Page 287
7.7.1 Tungsten Disulfide- (WS2-) Based Materials......Page 296
7.7.2 Tin (IV) Sulfide- (SnS2-) Based Materials......Page 302
7.8 Summary and Future Perspectives......Page 306
References......Page 307
8.1.1 Importance of Detection of Metal Ion Contaminants......Page 314
8.1.2.1 Optical Sensor......Page 316
8.1.2.3 Electrochemical Sensor......Page 318
8.2 Materials for Electrochemical Sensing of Metal Ion Contaminants......Page 319
8.3 Graphene-Based Materials for Metal Contaminate Sensing: An Overview......Page 322
8.3.1 Graphene for Sensing of Metal Contaminants......Page 324
8.3.2 Graphene-Metal Hybrids for Sensing of Metal Contaminants......Page 326
8.3.3 Graphene and Metal Oxide Hybrids for Sensing of Metal Contaminants......Page 330
8.4 Conclusion and Future Perspective......Page 331
8.5 Acknowledgment......Page 332
References......Page 333
9.1 Introduction......Page 342
9.2.1 Field-Effect Transistor-Based Biosensor......Page 343
9.2.1.1 Note on Transduction Mechanism and Sensor Performance......Page 346
9.2.1.2 pH Sensing......Page 347
9.2.1.3 Specific Detection of Biomolecules......Page 349
9.2.1.4 Scalability and Single Molecule Detection Analysis......Page 350
9.2.1.5 Comparison With Graphene......Page 353
9.2.2 Work-Function Modulated Gas Sensor......Page 356
9.3 Fundamental Limitation of Electrical Sensors and the Solutions......Page 358
9.3.1 Tunnel Field-Effect Transistor–Based Biosensor......Page 359
9.3.2 Impact-Ionization-MOS-Based Biosensor......Page 366
9.3.3 Tunnel Field-Effect Transistor–Based Gas Sensor......Page 374
9.3.4 2D Materials for Steep Transistors......Page 383
9.4 Summary......Page 386
References......Page 387
Further Reading......Page 390
10.1 Introduction......Page 392
10.2 Biochemical Optical Sensing Properties of 2D Materials......Page 393
10.3 Fabrication of 2D Materials Optical Sensors......Page 394
10.3.1 Transferring CVD-Grown 2D Materials Onto Optical Sensors......Page 395
10.3.2 Solution Method of Coating 2D Materials Onto Optical Sensors......Page 396
10.3.3 Direct CVD-Grown 2D Materials Onto Optical Sensors......Page 397
10.4.1 Single Cell Detection......Page 398
10.4.2 Deoxyribonucleic Acid Sensing......Page 400
10.5.1 Gas Sensing......Page 402
10.5.2 Humidity Sensing......Page 405
10.5.3 Heavy Metal Ion Sensing......Page 407
10.6.1 Photothermal and Chemotherapy for Cancer Diagnosis......Page 409
10.6.2 Optogenetics......Page 412
10.6.3 Ophthalmology......Page 414
References......Page 416
11.1 Introduction......Page 420
11.2 Graphene and Two Dimensional Transition Metal Dichalcogenides......Page 423
11.3 Fabrication of Heterostructures From Two Dimensional Crystals......Page 424
11.3.1 Mechanical Exfoliation......Page 425
11.3.2 Hydrothermal Synthesis......Page 426
11.3.4 Chemical Vapor Deposition......Page 427
11.4.1 Humidity Sensor......Page 430
11.4.2 Gas Sensor......Page 431
11.4.2.1 Nitrogen Dioxide Sensor......Page 432
11.4.2.3 Hydrogen Sulfide Sensor......Page 433
11.4.3 Surface Plasmon Resonance Sensor......Page 435
11.4.4 Nitrite Sensor......Page 438
11.5 Conclusion......Page 440
References......Page 441
12.1 Introduction......Page 450
12.2 Commendable Considerations for Wearable and Flexible Sensor......Page 452
12.2.1 Materials for Wearable and Flexible Sensors......Page 453
12.3.1.1 Thermally Sensitive Resistor (Thermistor)......Page 454
12.3.1.2 Resistance Temperature Detector......Page 455
12.3.1.4 Semiconductor-Based Sensors......Page 458
12.4.1 Sensing Mechanisms......Page 459
12.4.1.1 Piezoresistive-Based Wearable Strain Sensor......Page 460
12.4.1.2 Piezocapacitive-Based Wearable Strain Sensor......Page 461
12.4.1.3 Piezoelectric-Based Wearable Strain Sensors......Page 462
12.5.1 Glucose Detection......Page 463
12.5.2 pH Detection......Page 465
12.5.3 Electrolytes/Ions and Metabolite Detection......Page 466
12.6 Wearable Sensors for Volatile Biomarkers Detection......Page 467
12.7 Conclusion and Promising Outlook......Page 469
References......Page 470
Further Reading......Page 476
13.1 Introduction......Page 478
13.2 Characteristics of Photosensors Based on the 2D Materials......Page 479
13.3 Photovoltaic Effect......Page 480
13.6 Molybednum Disulfide-Based Photo-Sensing Devices......Page 481
13.7 Molybdenum Diselenide-Based Photosensor......Page 485
13.8 Tungsten Disulfide-Based Photosensor......Page 486
13.10 2D Heterostructures......Page 488
13.11 Recent Development and Applications......Page 489
References......Page 491
14 Future Prospects of 2D Materials for Sensing Applications......Page 494
Index......Page 496
Back Cover......Page 514

Citation preview

FUNDAMENTALS AND SENSING APPLICATIONS OF 2D MATERIALS

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Woodhead Publishing Series in Electronic and Optical Materials

FUNDAMENTALS AND SENSING APPLICATIONS OF 2D MATERIALS

Edited by

HYWEL MORGAN CHANDRA SEKHAR ROUT DATTATRAY J. LATE

Woodhead Publishing is an imprint of Elsevier The Officers’ Mess Business Centre, Royston Road, Duxford, CB22 4QH, United Kingdom 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States The Boulevard, Langford Lane, Kidlington, OX5 1GB, United Kingdom Copyright © 2019 Elsevier Ltd. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress ISBN: 978-0-08-102577-2 (print) ISBN: 978-0-08-102578-9 (online) For information on all Woodhead Publishing publications visit our website at https://www.elsevier.com/books-and-journals

Publisher: Matthew Deans Acquisition Editor: Kayla Dos Santos Editorial Project Manager: Peter Adamson Production Project Manager: Debasish Ghosh Cover Designer: Christian J. Bilbow Typeset by MPS Limited, Chennai, India

Contents

List of Contributors Preface

ix xi

1. Introduction

1

HYWEL MORGAN, CHANDRA S. ROUT AND DATTATRAY J. LATE

References

3

2. Fundamentals and Properties of 2D Materials in General and Sensing Applications

5

DATTATRAY J. LATE, ANHA BHAT AND CHANDRA SEKHAR ROUT

2.1 Introduction 2.2 Surface and Interface Physics 2.3 Band Alignment 2.4 Preparation of 2D Materials 2.5 Parameters of Sensor Performance 2.6 2D Materials in Chemical and Physical Sensing 2.7 Conclusion References

5 6 7 8 10 13 22 23

3. Synthesis, Properties, and Applications of Graphene

25

SHIVAM TRIVEDI, KENNETH LOBO AND H.S.S. RAMAKRISHNA MATTE

3.1 Synthesis 3.2 Characterization 3.3 Properties of Graphene 3.4 Applications of Graphene 3.5 Challenges and Outlook References

27 34 38 49 74 75

4. Synthesis, Characterization, and Properties of Graphene Analogs of 2D Material

91

PRATIK V. SHINDE AND MANOJ KUMAR SINGH

4.1 Introduction 4.2 Graphene Analogs 4.3 Applications of Graphene Analogs 4.4 Summary and Perspective References

91 92 121 126 127

v

vi

CONTENTS

5. Electronic Structure and Theoretical Aspects on Sensing Application of 2D Materials

145

BRAHMANANDA CHAKRABORTY

5.1 Importance of Theoretical Modeling and Simulations for Sensing Applications 5.2 Introduction to Quantum Simulations 5.3 Overview of Quantum Simulation Methods 5.4 Corrections to Density Functional Theory 5.5 Sensitivity of the Simulation Results 5.6 Modeling of Graphene-Based 2D Materials for Gas Sensing 5.7 Modeling of 2D Materials Beyond Graphene for Gas Sensing 5.8 Interaction Mechanism of Gases on 2D Materials 5.9 Change in Properties Due to Adsorption of Sensing Elements 5.10 Modeling of Glucose Sensing in 2D Materials 5.11 Conclusions and Future Directions Acknowledgments References

6. Gas Sensors Based on Two-Dimensional Materials and Its Mechanisms

145 146 147 154 156 160 169 176 182 189 196 197 197

205

K. RAJKUMAR AND R.T. RAJENDRA KUMAR

6.1 Introduction 6.2 Gas Sensing: Fundamentals 6.3 Gas-Sensing Mechanisms 6.4 Graphene-Based Materials 6.5 Graphene Analogous Two-Dimensional Materials 6.6 Conclusion and Future Perspectives Acknowledgment References

7. Enzymatic and Nonenzymatic Electrochemical Biosensors

205 206 215 220 237 249 250 250

259

C. REVATHI AND R.T. RAJENDRA KUMAR

7.1 Sensors 7.2 Electrochemical Biosensor 7.3 Current Status 7.4 Parameters Involved in Electrochemical Sensing and Their Sensing Mechanism 7.5 Nanomaterials for Nonenzymatic Sensing 7.6 Two-Dimensional Materials-Based Electrochemical Biosensors 7.7 MoS2-Based Electrochemical Sensors 7.8 Summary and Future Perspectives Acknowledgments References

259 262 264 265 265 268 274 293 294 294

CONTENTS

8. Electrochemical Sensing Platform Based on Graphene-Metal/Metal Oxide Hybrids for Detection of Metal Ions Contaminants

vii

301

SWAGATIKA KAMILA, BISHNUPAD MOHANTY, SUSHANTA K. DAS, SATYAPRIYA SAHOO AND BIKASH KUMAR JENA

8.1 Introduction 8.2 Materials for Electrochemical Sensing of Metal Ion Contaminants 8.3 Graphene-Based Materials for Metal Contaminate Sensing: An Overview 8.4 Conclusion and Future Perspective 8.5 Acknowledgment References

9. 2D Materials for Field-Effect Transistor
Based Biosensors

301 306 309 318 319 320

329

DEBALINA SARKAR

9.1 Introduction 9.2 2D Material for Sensing 9.3 Fundamental Limitation of Electrical Sensors and the Solutions 9.4 Summary References Further Reading

329 330 345 373 374 377

10. Optical Biochemical Sensors Based on 2D Materials

379

B.N. SHIVANANJU, HUI YING HOH, WENZHI YU AND QIAOLIANG BAO

10.1 Introduction 10.2 Biochemical Optical Sensing Properties of 2D Materials 10.3 Fabrication of 2D Materials Optical Sensors 10.4 Biomolecules Sensing Application 10.5 Chemical Sensing Applications 10.6 Health-Care Applications Acknowledgment References

11. Recent Developments in Graphene-Based Two-Dimensional Heterostructures for Sensing Applications

379 380 381 385 389 396 403 403

407

PRATIK V. SHINDE, MANAV SAXENA AND MANOJ KUMAR SINGH

11.1 Introduction 11.2 Graphene and Two Dimensional Transition Metal Dichalcogenides 11.3 Fabrication of Heterostructures From Two Dimensional Crystals 11.4 Two Dimensional Crystal-Based Heterostructures Sensors 11.5 Conclusion References

407 410 411 417 427 428

viii

CONTENTS

12. Wearable and Flexible Sensors Based on 2D and Nanomaterials

437

RUTUPARNA SAMAL AND CHANDRA SEKHAR ROUT

12.1 Introduction 12.2 Commendable Considerations for Wearable and Flexible Sensor 12.3 Wearable and Flexible Temperature Sensor 12.4 Wearable Strain/Pressure Sensors 12.5 Wearable Sensors Analyzing the Sweat Metabolites 12.6 Wearable Sensors for Volatile Biomarkers Detection 12.7 Conclusion and Promising Outlook References Further Reading

13. Photo Sensor Based on 2D Materials

437 439 441 446 450 454 456 457 463

465

DATTATRAY J. LATE, ANHA BHAT AND CHANDRA SEKHAR ROUT

13.1 Introduction 13.2 Characteristics of Photosensors Based on the 2D Materials 13.3 Photovoltaic Effect 13.4 Photoconductive Effect 13.5 Photo-Thermoelectric Effect 13.6 Molybednum Disulfide-Based Photo-Sensing Devices 13.7 Molybdenum Diselenide-Based Photosensor 13.8 Tungsten Disulfide-Based Photosensor 13.9 Black Phosphorous-Based Photosensor 13.10 2D Heterostructures 13.11 Recent Development and Applications References

14. Future Prospects of 2D Materials for Sensing Applications

465 466 467 468 468 468 472 473 475 475 476 478

481

HYWEL MORGAN, CHANDRA S. ROUT AND DATTATRAY J. LATE

Index

483

List of Contributors Qiaoliang Bao Department of Materials Science and Engineering, ARC Centre of Excellence in Future Low-Energy Electronics Technologies (FLEET), Monash University, Clayton, VIC, Australia Anha Bhat Physical and Materials Chemistry Division, CSIR-National Chemical Laboratory, Pune, India; Department of Metallurgical and Materials Engineering, National Institute of Technology, Srinagar, India Brahmananda Chakraborty High Pressure and Synchrotron Radiation Physics Division, Bhabha Atomic Research Centre, Mumbai, India Sushanta K. Das Colloids and Materials Chemistry Department, CSIRInstitute of Minerals and Materials Technology, Bhubaneswar, India Hui Ying Hoh College of Electronic Science and Technology and Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, Shenzhen University, Shenzhen, P.R. China Bikash Kumar Jena Colloids and Materials Chemistry Department, CSIRInstitute of Minerals and Materials Technology, Bhubaneswar, India Swagatika Kamila Colloids and Materials Chemistry Department, CSIRInstitute of Minerals and Materials Technology, Bhubaneswar, India R.T. Rajendra Kumar Department of Nanoscience and Technology, Bharathiar University, Coimbatore, India Dattatray J. Late Physical and Materials Chemistry Division, CSIR-National Chemical Laboratory, Pune, India Kenneth Lobo

Centre for Nano and Soft Matter Sciences, Bengaluru, India

Bishnupad Mohanty Colloids and Materials Chemistry Department, CSIRInstitute of Minerals and Materials Technology, Bhubaneswar, India Hywel Morgan School of Electronics and Computer Science, University of Southampton, Southampton, United Kingdom K. Rajkumar Department of Physics, Bharathiar University, Coimbatore, India; Department of Physics, Indian Institute of Technology, Chennai, India H.S.S. Ramakrishna Matte Bengaluru, India

Centre for Nano and Soft Matter Sciences,

C. Revathi Department of Physics, Bharathiar University, Coimbatore, India; Department of Nano Biotechnology, PSG Institute of Advanced Studies, Coimbatore, India Chandra Sekhar Rout Centre for Nano and Material Sciences, Jain University, Jain Global Campus, Bengaluru, India

ix

x

LIST OF CONTRIBUTORS

Satyapriya Sahoo Colloids and Materials Chemistry Department, CSIRInstitute of Minerals and Materials Technology, Bhubaneswar, India Rutuparna Samal Centre for Nano and Material Sciences, Jain University, Jain Global Campus, Bengaluru, India Debalina Sarkar Massachusetts Institute of Technology (MIT), Cambridge, MA, United States Manav Saxena Centre for Nano and Material Sciences, Jain University, Jain Global Campus, Bengaluru, India Pratik V. Shinde Centre for Nano and Material Sciences, Jain University, Jain Global Campus, Bengaluru, India B.N. Shivananju State Key Laboratory of Applied Optics, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, Jilin, P.R. China; Department of Materials Science and Engineering, ARC Centre of Excellence in Future Low-Energy Electronics Technologies (FLEET), Monash University, Clayton, VIC, Australia Manoj Kumar Singh Centre for Nano and Material Sciences, Jain University, Jain Global Campus, Bengaluru, India; Solid State and Structural Chemistry Unit, Indian Institute of Science, Bangalore, India Shivam Trivedi Centre for Nano and Soft Matter Sciences, Bengaluru, India Wenzhi Yu Department of Materials Science and Engineering, ARC Centre of Excellence in Future Low-Energy Electronics Technologies (FLEET), Monash University, Clayton, VIC, Australia

Preface Graphene is an allotrope of carbon with a one-atom-monolayer thin sheet of carbon atoms arranged in a hexagonal arrangement. These carbon atoms are tightly packed into a two-dimensional (2D) honeycomb lattice is the “new wonder material” predicted to be a part of almost all aspects of potential upcoming technologies. This material has attracted significant attention, especially in the last decade, owing to its unique physical and chemical properties, which should play a part in technological advances of next-generation devices. Graphene sensitivity may be precise enough to accurately sense or detect one molecule of gas, which leads to new possibilities for making devices. Being a monolayer in thickness, graphene is an enormous conductor of electricity, even though it doesn’t have a bandgap, which limits the problem of switching off the device. Graphene analogous materials, such as metal chalcogenides , group III, group IV chalgogenides such as MoS2, WS2, MoSe2, InSe, SnS2, SnSe2, SnSe, PtSe2, MoTe2, WTe2, HfTe2, Phosphorene, Borophene, HfSe2, ZrSe2, ReS2, ReSe2, GaS, GaSe , GeTe, GaTe, TiS3 etc. have thereby emerged as noteworthy materials for technological applications due to their semiconducting nature and tunable thickness dependent bandgap. Their applications have been realized for practical devices such as field-effect transistor, gas sensors, photo catalysts, supercapacitors, battery materials, etc. The most significant technology of 21st century has been stamped by “sensor device technology.” Among the impressive enhancements in sensor research and development and their practical applications over the past 25 years, sensors are definitely on the brink of a revolution similar to that in microcomputers in the 1980s. There are a number of demands for sensor technology in various fields, as in modern automobiles, including the driverless vehicle. Even as the demand for sensing technology grows, the sensing device technologies used are just as varied as the applications. After the invention of graphene and 2D layered inorganic materials, incredible advances were made in sensor device technology, and many more are in development. In this book, we attempt to summarize the depth and breadth of recent advances and developments in graphene and graphene-like 2D inorganic materials such as MoS2, WS2, SnS2, SnSe2, black phosphorus, etc. for gas and biosensing, photo sensor technology applications in an up-to-date resource. We focus on the synthesis of these modern

xi

xii

PREFACE

materials, with perceptive sensor design and a working principal operation. Researchers typically have an interdisciplinary background in physics, chemistry, biology, and engineering. The cited references provide the most essential information to those who wish to study and design sensor technologies based on atomically thin nanosheets of various materials and its practical applications with all types of sensors, written by leading experts working on 2D materials-based sensors from mostly academia and industry. We attempted to present as broad a range of 2D materials-based sensors and applications as possible. The recent advances in 2D materials-based sensor device technologies were also discussed in detail. The text as a whole provides detailed information on the background of 2D materials, its synthesis, properties, design, layer control growth, functionalization, interfacing engineering, and stability of the sensor for each sensor material. The book primarily is organized for the recent updates on 2D materials-based sensor device technologies and their related applications; thus the book will be invaluable for the undergraduate, graduate, PhD student, teacher, or industrialist who works on 2D materials-based sensor device technology. Hywel Morgan, Chandra Sekhar Rout, Dattatray J. Late February 2019

C H A P T E R

1 Introduction Hywel Morgan1, Chandra S. Rout2 and Dattatray J. Late3 1

School of Electronics and Computer Science, University of Southampton, Southampton, United Kingdom 2Centre for Nano and Material Sciences, Jain University, Bangalore, India 3Physical and Materials Chemistry Division, CSIR-National Chemical Laboratory, Pune, India

A fundamental understanding of the working principles of chemical and biosensors and their development for large-scale applications is an important area of research. Chemical, gas, and biosensors have been widely used in industries, indoor air quality control, environmental monitoring, spacecraft launching, agriculture, and health-care applications. The increasing demand for highly sensitive, selective, costeffective, low-power-consumption, stable, and portable sensors has gained tremendous attention worldwide, and novel nanomaterials are being explored for this purpose. Researchers strongly prefer nanomaterials of zero, one, and two dimensions (0D, 1D, and 2D) with tunable properties and a high surface to volume ratio for the design of high-performance sensor devices. By choosing a proper combination of hybrid materials and sensor devices with high sensitivity down to parts per billion (ppb) levels, the researcher can achieve fast response, good selectivity, high stability along with low power consumption, and miniaturization. 2D materials gained a huge interest among the research community after the first report of the preparation of graphene by a simple micromechanical exfoliation of highly oriented pyrolytic graphite in 2004 [1]. Understanding the different unusual physical properties and applications of graphene provided researchers with the path to explore the vibrant area of 2D layered materials over the past decade [2]. The zero

Fundamentals and Sensing Applications of 2D Materials DOI: https://doi.org/10.1016/B978-0-08-102577-2.00001-4

1

Copyright © 2019 Elsevier Ltd. All rights reserved.

2

1. INTRODUCTION

bandgap of graphene has limited its application, which led to a search for alternate materials. Transition metal dichalcogenides are among the heavily studied 2D materials that differ from the semimetallic characteristic of graphene. They show novel, diverse, and tunable layer-dependent properties that can be controlled by their crystalline structure, number of stacking layers, and defect concentration [3]. Several hundred different 2D materials with extraordinary physical and chemical properties have been identified, including silicone [4], germanene [5], h-BN [6], graphene [7], black phosphorous [8], MXenes [9], etc. The 2D materials can be semiconductor [10], metal [11], semimetal [12], or superconductors [13]. Also, the indirect bandgap to the direct bandgap transition of the 2D material is possible by tuning the number of layers. By controlling the direct bandgap of the material it is possible to achieve enhanced photoluminescence, optoelectronic properties, and controllable electrical properties suitable for fabrication of high-performance transistors [14]. Similarly, hybridization with different 2D materials provides a very effective and powerful way not only to control the physiochemical properties of hybridized species but also to explore nanocomposites with novel functionalization [15]. Therefore these 2D materials show promise for a range of novel applications that are not possible with bulk materials. To exploit the potential of 2D materials, researchers need to advance the synthesis, properties, characterization, and applications of the material. Early experiments on 2D materials were carried out on single layers, which were exfoliated from bulk crystals using adhesive tape, which limits the size of the devices to be in the range of tens of micrometer [10,16]. Recent advances in 2D materials have now enabled the industrial requirement of wafer-scale growth for many different materials, opening new ways to direct industrial applications in various devices such as sensors, solar cells, catalyst, and others [16]. Along with these improved materials comes the need for synthesis/growth, characterization, and sensing application that can directly probe their electronic and optical properties over a wide range of energy and spatial scales. Considering the versatile applications and economic aspect, a thorough overview of the focus areas involving 2D materials is relevant and necessary. This book describes advances in the synthesis/growth, characterization, and sensing applications of 2D materials starting from the basics of graphene and its synthesis. New methods that increase the functionality of 2D materials are also presented. These include creating heterostructures with multiple active layers, novel approaches to gas-sensing device fabrication, and chemical modification during chemical vapor deposition growth. We cover fundamentals and working principles of chemical and biosensors based on 2D materials, electronic structure, and theoretical investigations relevant to

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REFERENCES

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sensing applications, synthesis, and properties of graphene and its analog 2D materials, heterostructures, and arrays of 2D materials for high-performance sensor design, electrochemical, optical, and Surfaceenhanced Raman spectroscopy (SERS)-based chemical sensors, photodetectors, and flexible sensor devices. Book chapters are organized to span these topics, starting with a general overview of fundamental material properties of 2D materials, their synthesis and applications in various sensor devices, concluding with a future prospective. We believe this book will provide an overview of most of the important fundamental properties, synthesis, and sensing applications of 2D materials in a single reference. This will be a useful textbook for undergraduates, graduate students, and researchers working in the 2D materials field. The chapters are contributed by eminent researchers from around the world, and we hope they will be of interest to a major portion of the academic community working on 2D materials and its derivatives.

References [1] K.S. Novoselov, A.K. Geim, S.V. Morozov, D. Jiang, Y. Zhang, S.V. Dubonos, et al., Science 306 (2004) 666. [2] K.S. Novoselov, D. Jiang, F. Schedin, T.J. Booth, V.V. Khotkevich, S.V. Morozov, et al., Proc. Natl. Acad. Sci. 102 (2005) 10451. [3] D.J. Late, B. Liu, H.S.S.R. Matte, C.N.R. Rao, V.P. Dravid, Adv. Funct. Mater. 22 (2012) 1894. [4] H. Nakano, T. Mitsuoka, M. Harada, K. Horibuchi, H. Nozaki, N. Takahashi, et al., Angew. Chem. Int. Ed. 45 (2006) 6303
6306. [5] L. Li, S. Lu, J. Pan, Z. Qin, Y.Q. Wang, Y. Wang, et al., Adv. Mater. 26 (2014) 4820
4824. ¨ . Girit, A. Zettl, Appl. Phys. Lett. 92 (2008) 133107. [6] D. Pacile´, J.C. Meyer, C ¸ .O [7] G. Li, Y. Li, X. Qian, H. Liu, H. Lin, N. Chen, et al., J. Phys. Chem. C 115 (2011) 2611
2615. [8] D.J. Late, ACS Appl. Mater. Interfaces 7 (2015) 5857
5862. [9] X. Wang, S. Kajiyama, H. Iinuma, E. Hosono, S. Oro, I. Moriguchi, et al., Nat. Commun. 6 (2015) 6544. [10] D.J. Late, Y. Huang, B. Liu, J. Luo, J. Acharya, S.N. Shirodkar, et al., ACS Nano 7 (2013) 4879. [11] C.S. Rout, R. Khare, R.V. Kashid, D.S. Joag, M.A. More, N.A. Lanzillo, et al., Eur. J. Inorg. Chem. 2014 (2014) 5331
5336. [12] C. Lee, E.C. Silva, L. Calderin, M.A.T. Nguyen, M.J. Hollander, B. Bersch, et al., Sci. Rep. 5 (2015) 10013. [13] Y. Saito, T. Nojima, Y. Iwasa, Nat. Rev. Mater. 2 (2016) 16094. [14] D.J. Late, B. Liu, H.S.S.R. Matte, V.P. Dravid, C.N.R. Rao, ACS Nano 6 (2012) 5635. [15] S. Ratha, A.J. Simbeck, D.J. Late, S.K. Nayak, C.S. Rout, Appl. Phys. Lett. 105 (2014) 243502. [16] D.J. Late, B. Liu, J. Luo, A. Yan, H.S.S.R. Matte, M. Grayson, et al., Adv. Mater. 24 (2012) 3549.

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C H A P T E R

2 Fundamentals and Properties of 2D Materials in General and Sensing Applications Dattatray J. Late1, Anha Bhat1,2 and Chandra Sekhar Rout3 1

Physical and Materials Chemistry Division, CSIR-National Chemical Laboratory, Pune India 2Department of Metallurgical and Materials Engineering, National Institute of Technology, Srinagar, India 3Centre for Nano and Material Sciences, Jain University, Bangalore, India

2.1 INTRODUCTION Following the development of graphene, there was a huge investment in scientific and industrial research of analogues of the 2D material family with structures similar to graphene, particularly with an impact in energy sciences and novel electronic device applications. Most of these analogues fall into transition metal dichalcogenides (TMDs) whose monolayers obtained from various routes yield layered structures similar to the planar lattice of graphene. These include 2D TMDs molybdenum disulfide (MoS2), molybdenum diselenide (MoSe2), tungsten disulfide (WS2), tungsten diselenide (WSe2), and hexagonal boron nitride (h-BN) [1]. The 2D materials fall into the class of metallic, semimetallic, semiconducting, insulating, or superconducting materials depending on their chemical composition and structural configuration. Besides having high surface to volume ratio and high surface activities, the advantages of 2D material is that they are semiconducting in nature and possess a huge potential to be made into ultra-small and low power transistors that are more efficient than silicon-based transistors in terms

Fundamentals and Sensing Applications of 2D Materials DOI: https://doi.org/10.1016/B978-0-08-102577-2.00002-6

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Copyright © 2019 Elsevier Ltd. All rights reserved.

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2. FUNDAMENTALS AND PROPERTIES OF 2D MATERIALS IN GENERAL AND SENSING

of size reduction and efficiency [2]. Besides sharing similarities of bandgap in the visible-near infrared (IR) range, high carrier mobility, and on/off ratio, TMDs can be deposited onto flexible substrates and have good stress and strain-resistant/compliance properties. In this chapter we briefly discuss their surface properties and bandgap arrangement, which is modulated by doping and external stresses, and how these differ compared to bulk counterparts. The progress and application of 2D materials in physical and chemical sensing is also discussed with respect to gas, humidity, and biosensing along with their mechanism. A general comparison is made concerning their activity and response parameters to various sensing moieties.

2.2 SURFACE AND INTERFACE PHYSICS Graphene is pivotal in the family of 2D materials and has been leading the research when it comes to sensor technologies, novel electronics, or experimental realization of the condensed matter physics problems. It comprises a honeycomb lattice of single atom thickness carbon, which gives it a membrane-like appearance. Thus electrons in graphene are called massless Dirac fermions. The band structure of massless Dirac fermions has a unique character, since it has linear energy dispersion in the vicinity of two nonequivalent symmetric points, called K1 and K2 points, in the Brillouin zone (BZ), where the conduction and valence bands conically touch as shown in Fig. 2.1. This structure is called a Dirac cone. This structure has low dimensionality, which leads to quantum fluctuations [4,5]. Since the unit cell of the honeycomb lattice contains two nonequivalent sites that form two sublattices A and B, the low-energy electronic states of graphene near the Fermi energy are (A)

(B) 6

y x

Energy (eV)

3

b1 L

0

K M K⬘

b2

–3

z y

–6 L

M

K

L

FIGURE 2.1 (A) Atomic structure of graphene and (B) energy bands in graphene calculated by the first principle method. Source: Printed with permission from J. Wang, et al., The rare two-dimensional materials with Dirac cones. Nat. Sci. Rev. 2 (1) (2015) 22
39.

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2.3 BAND ALIGNMENT

7

described by a 2 3 2 matrix form, which is equivalent to the massless Dirac equation [3]. However, the mechanical and electrical properties of 2D materials show strong anisotropy when they crystallize into structures similar to that of graphite [6]. The elements falling in the group IV
VII TMDs are generally layered, whereas some of group VIII
X TMDs fall in the category of nonlayered structures. The thin layered structures of 2D materials have extraordinary properties including a direct bandgap in the visible frequency range, spin-orbit coupling, and ultra-strong Coulomb interaction, which is associated with the valley degree of freedom [7]. The TMD monolayer is in the configuration of X-M-X covalently bonded, hexagonal quasi-2D, similar to graphene where the conduction and valence bands have degenerate extrema known as valleys located at the K and 2 K points of the hexagonal BZ respectively. In particular, monolayers are found to have a direct bandgap at these 6K valleys. The direct bandgap of the monolayer TMDs have made it a potential candidate for different device applications [8]. The large binding energy and the electrostatic tunability of excitons in 2D led to the study of excitons and their subsequent role in strong Coulomb interaction and many body phenomena because of the reduced dielectric screening in the geometry [9].

2.3 BAND ALIGNMENT To better optimize the electronic and optical properties, bulk materials are thinned down to a monolayer to tune their bandgap. This was made possible in 2D materials by controlling the external factors rather than changing synthetic routes which increases the risk of impurities. This approach manifests the operation and performance of the devices based on 2D materials. The ultrathin lattice and interplanar interaction makes it possible to open the bandgap by tuning the electric field which cannot be done in the case of bulk materials. In the case of bilayer graphene, researchers theoretically predicted that an approximately direct bandgap of the order of 250 meV could be opened by variations in electric field or by introducing dopants. However, on comparing theoretical and experimental results, this did not quite fit prediction. The optical bandgap was close to 250 meV while the electrical bandgap was found to be around 130 meV [10]. In case of TMDs the most common configurations are either in 1 T or 2 H phases which are complementary to the structure of the lattice. The 1T-MX2 phase possesses the octahedral (Oh) lattice while for the 2H-MX2 phase it is in a trigonal prismatic (D3h) arrangement. The bandgap is direct in monolayer MX2 structures (M 5 Mo, W). Their

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2. FUNDAMENTALS AND PROPERTIES OF 2D MATERIALS IN GENERAL AND SENSING

lattice arrangement is like hexagonal honeycomb that is similar to that of graphene, where their hexagonal BZ lags in the inversion symmetry [11]. Density functional theory calculations have first pointed to a direct bandgap in monolayer WSe2 and MoS2 located at the corners of the hexagonal BZ (i.e., the 6 K points). The critical difference between bulk crystals and monolayer is the symmetry. The 2 H bulk is inversion symmetric, which is broken when the bulk is reduced to monolayers. The crystal symmetry reduces from the space group D4 6 h for the 2 H bulk to the group D1 3 h for a monolayer [12]. The change in band structure is shown in Fig. 2.2 in various 2D materials upon doping as observed by angle resolved photoemission spectroscopy.

2.4 PREPARATION OF 2D MATERIALS There are various synthetic routes through which 2D materials can be prepared to make them useful for sensor applications. The most applied methods are mechanical exfoliation [14], liquid exfoliation [15], Chemical vapor deposition (CVD) [16], and intercalation [17]. Novoselov et al. successfully reported various single-layer 2D crystals like h-BN, MoS2, NbSe2, and Bi2Sr2CaCu2Ox from the bulk. The prepared single crystals were of good quality and analyzed by optical microscopy, atomic force microscopy (AFM), scanning tunneling microscopy (STM), and transmission electron microscopy (TEM) [18]. However, such methods have limitations owing to the small size of the crystals. The liquid exfoliation method involves the dispersion and ultrasonication of each starting inorganic bulk material in solvents. Using optical absorption spectroscopy it was demonstrated that the desirable solvents are those having surface tension close to 40 mJ m22. Coleman et al. proposed that successful solvents are those that minimize the energy of exfoliation like N-methyl-pyrrolidone (NMP) and isopropanol (IPA) [15]. Fig. 2.3 shows various monolayers of TMDs prepared by mechanical exfoliation and liquid exfoliation and subsequently observed by AFM and TEM. TMDs bulk materials, due to their layered structures, can be intercalated by various kinds of intercalates. The intercalates range from organic molecules, transition metal halides to lithium ions, which can be reduced to layered compounds, preferably few layers by ultrasonication in a suitable solvent. Eda et al. used the chemical exfoliation technique to obtain single-layer MoS2 nanosheets and demonstrated that the intercalation-induced phase transformation could be almost fully reversed via mild annealing using n-butyl lithium in hexane as the intercalation agent to insert lithium ions into the layered structures [20]. Zheng et al. developed a controllable electrochemical lithiation method to produce single-layer TMDs nanosheets. As shown in Fig. 2.4, the

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FIGURE 2.2 Band structure evolution of 2H-TMDs with surface doping. A doping series of ARPES spectra taken for WS2 (A 2 D), MoS2 (E 2 H), WSe2 (I 2 L), MoSe2 (M 2 P), and MoTe2 (Q 2 T) along the ΓK direction. The data were collected at 80K 2 90K with the photon energy of 45 2 64 eV. Source: Printed with permission from M. Kang, et al., Universal mechanism of band-gap engineering in transition-metal dichalcogenides. Nano. Lett. 17 (3) (2017) 1610
1615 [13].

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FIGURE 2.3 (A) Optical image of graphene, MoS2, NbSe2, and h-BN flakes prepared by mechanical exfoliation method where the red dotted squares represent the AFM scan areas. (B) AFM friction images measured from the indicated areas in the red dotted squares. S indicates SiO2 substrate. (C) Photographs of dispersions of MoS2 (in NMP), WS2 (in NMP), and BN (in IPA). (D)
(F) Low resolution TEM images of BN, MoS2, and WS2 nanosheets prepared by liquid exfoliation method. Source: Printed with permission from C. Lee, et al., Frictional characteristics of atomically thin sheets. Science 328 (5974) (2010) 76
80 [19].

bulk layered materials such as MoS2, WS2, TiS2, and ZrS2 were used as a cathode in an electrochemical cell and subsequently exfoliated [21]. The CVD method was employed to make 2D materials with largescale, uniform thickness, regular shape, and high yield as shown in Fig. 2.5. Xu et al. demonstrated a vapor
solid growth method using WSe2 powder as the evaporation source to synthesize ultrathin, even monolayer WSe2 nanosheets [24]. Choudhary et al. demonstrated a layer-controllable and wafer-scale growth method of MoS2 on Si/SiO2 substrates as shown in Fig. 2.5 by combining a magnetron sputtering followed by a CVD process [26]. For ternary semiconductor growth, Li et al. [25,27] simultaneously synthesized atomically thin uniform 2D MoS2xSe2(12x) with complete composition (0 # x # 1) tenability, by a one-step temperature gradient
assisted CVD method.

2.5 PARAMETERS OF SENSOR PERFORMANCE The performance of a sensor is rated based on certain parameters, briefly introduced here. One of the foremost parameters is the sensor response, which can be defined in a number of ways, and for physical

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FIGURE 2.4

(A) Electrochemical lithiation and exfoliation processes for the fabrication of 2D nanomaterials from layered bulk crystals. Source: Reproduced with permission from Z. Zeng, Z. Yin, X. Huang, H. Li, Q. He, G. Lu, et al., Angew. Chem. Int. Ed., 50 (2011) 11093
11097. (B) MoS2 electrochemical exfoliation experimental setup. Source: Reproduced with permission from X. You, et al., An electrochemical route to MoS2 nanosheets for device applications. Mater. Lett. 121 (2014) 31
35 [22]. (C) Optical microscopy image of an electrochemically exfoliated monolayer MoS2 nanosheet deposited on the SiO2 substrate. Source: Reproduced with permission from N. Liu, et al. Large-area atomically thin MoS2 nanosheets prepared using electrochemical exfoliation. ACS Nano 8 (7) (2014) 6902
6910 [23].

and chemical sensors the change in resistance/current is used to express the sensor response. The other most important parameter is the sensitivity that is also defined as a change of sensor responses/unit change in concentration. Selectivity refers to characteristics that determine whether a sensor can respond to certain concentrations (gas, humidity,

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2. FUNDAMENTALS AND PROPERTIES OF 2D MATERIALS IN GENERAL AND SENSING

FIGURE 2.5 CVD method to prepare 2D layered materials. (A) The scheme of WSe2 precursor is transported to the sapphire substrates. (B) The optical image of ultrathin triangular WSe2 sheets on a sapphire substrate. (C) Illustration of three-zone furnace for the growth of the MoS2(1 2 x)Se2x monolayer. (D) Photograph of bare SiO2/Si substrate and MoS2 and MoS1.60 Se0.40 monolayer films on SiO2/Si substrates. Schematic diagram of two-step (E) sputtering and (F) CVD method for the growth of MoS2 thin films. (G) Schematic for the setup used for the growth of MoS2xSe2(12x) nanosheets. (H) Photoluminescence (PL) spectra of the complete composition MoS2xSe2(12x) nanosheets and atypical PL mapping of a single ternary nanosheet (the inset, scale bar, 7 μm) excited with a 488 argon ion laser. Source: Figures reproduced with permission from (A, B) K. Xu, et al., Atomic-layer triangular WSe2 sheets: synthesis and layer-dependent photoluminescence property. Nanotechnology. 24 (46) (2013) 465705; (C, D) Q. Feng, et al., Growth of large-area 2D MoS2(1-x) Se2x semiconductor alloys. Adv. Mater. 26 (17) (2014) 2648
2653; (E, F) N. Choudhary, et al., Growth of large-scale and thickness-modulated MoS2 nanosheets. ACS Appl. Mater. Interfaces 6 (23) (2014) 21215
21222; (G, H) H. Li, et al., Growth of alloy MoS2x Se2(1
x) nanosheets with fully tunable chemical compositions and optical properties. J. Am. Chem. Soc. 136 (10) (2014) 3756
3759.

or biomolecules) or even specifically any single moiety. The response time, which is most important in gauging the performance of the sensor, is the time interval over which resistance/current attains a fixed percentage (90%) of final value when the sensor is exposed to a full scale concentration of gas. A fast response time is indicative of a good sensor. Recovery time is the time interval over which sensor resistance/current reduces to 10% of its saturation value when exposed to a full-scale concentration of gas and then placed in clean air. A good sensor should have small recovery time. Stability is the ability to produce reproducible results for a certain period of time and includes sensitivity, selectivity, and response and recovery time, ideally with an extended life of up to

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2.6 2D MATERIALS IN CHEMICAL AND PHYSICAL SENSING

13

2
3 years. The detection limit is the lowest concentration of the measuring moiety that can be detected by the sensor under given conditions. Linearity is the output signal in relation to concentration. With most sensors, the initial output of a sensor is linear or close to it, but as concentration increases the output gets gradually reduced and linearity is lost. Hysteresis is the maximum difference in output when a value is approached with an increasing concentration range and is the measure of loss of device performance.

2.6 2D MATERIALS IN CHEMICAL AND PHYSICAL SENSING One of the important applications of 2D materials is physical and chemical sensing. The physical sensors work on the principle of measuring a physical stimulus, such as temperature and pressure while a chemical sensor depends on the chemical reaction to decipher the concentrations. A biosensor involves the biological interaction between the analyte and the sensor device. Gas sensing is the physical sensing mechanism of detection of concentration of a specific gas with respect to atmosphere. Low cost and high sensitivity has made metal oxide
based detectors widely applicable as gas sensors. However at high temperatures, to obtain a high response to targeted gases the power consumption increases, which sets a limitation. So a preferred solution would be to use the materials with ultrahigh surface-to-volume ratios where the interaction of gas molecules with the surfaces is very strong, hence improving the sensitivity [28]. Thus due to their excellent semiconducting performances, unique atomic arrangement, and ultra-large surface-to-volume ratios, 2D-layered nanomaterials have emerged as potential candidates for low-power consumption sensing devices. The sensing mechanism of gases by 2D materials is based on a charge transfer process in which a sensor shows variation in the resistance due to the absorption of gases while, for exposure to inert environment, the gas molecules are desorbed and normal state is reinstated [29]. Graphene-based gas sensors have been developed by Novoselov et al. using mechanically exfoliated graphene with limit of detection (LOD) in ppb levels. The Hall geometry creates a change in the carrier density near the Dirac point which is responsible for the high response. The carrier concentration variation (Δn) was found to be linearly dependent on concentration (C) of NO2 in single-layer graphene. As shown in Fig. 2.6 the detection was based on the single-molecule-detection limit attributed to the high carrier mobility of graphene with a characteristic low noise [28].

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2. FUNDAMENTALS AND PROPERTIES OF 2D MATERIALS IN GENERAL AND SENSING

FIGURE 2.6 (A) The neutrality point during adsorption (blue curve) and desorption (red curve) of strongly diluted NO2 produced changes of Hall resistivity (ρxy) are shown in the figure. The green curve is a reference exposed to pure He. (B) All changes in ρxy larger than 0.5 Ω and quicker than 10 s were recorded as individual steps. Source: Reproduced with permission from F. Schedin, et al., Detection of individual gas molecules adsorbed on graphene. Nat. Mater. 6 (9) (2007) 652
655. (C) Noise spectral density (SI/I2) multiplied by frequency (f) versus f for the sensor upon exposure to different vapor samples and open air. Source: Printed with permission from S. Rumyantsev, et al., Selective gas sensing with a single pristine graphene transistor. Nano. Lett. 12 (5) (2012) 2294
2298 [30].

FIGURE 2.7

(A) Fabrication procedure of an all-graphene sensor. Response curves of the patterned graphene sensors to three pulses of 5 ppm NO2 (B) under the increased bias voltage; (C) under 0% and 50% relative humidity conditions at 60 V; (D) without and with the bending strain. Source: Reproduced with permission from Y.H. Kim, et al., Self-activated transparent all-graphene gas sensor with endurance to humidity and mechanical bending. ACS Nano 9 (10) (2015) 10453
10460.

Jang et al. reported fabrication of self-activated transparent gas sensor based on CVD-grown graphene. The procedure, depicted in Fig. 2.7, comprises the graphene layer which was grown and directly patterned on a Cu foil with poly (methyl methacrylate) (PMMA) coated on top of the patterned graphene to transfer the sample to a target substrate.

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2.6 2D MATERIALS IN CHEMICAL AND PHYSICAL SENSING

15

FIGURE 2.8 Optical microscopy image of (A) the as-grown MoS2 monolayers on SiO2/Si substrates and (B) the MoS2 device. Real-time conductance change of MoS2 device after exposure to NO2 (C) and NH3 (D) with various concentrations. Inset in (D) is a zoom-in plot of the sensor response to low NH3 concentrations of 1, 5, and 10 ppm [33]. Source: Reproduced with permission from B. Liu, et al., High-performance chemical sensing using Schottkycontacted chemical vapor deposition grown monolayer MoS2 transistors. ACS Nano 8 (5) (2014) 5304
5314.

After etching Cu, and transfer to a polyimide substrate the device was exposed to three consecutive pulses of 5 ppm NO2 where they exhibited improved response and recovery with increasing bias voltage [31]. In case of MoS2, vapor phase synthesis was investigated by Duesberg et al., who by sulfurization of Mo thin films developed a highperformance sensor which showed a fast response toward NH3 with an LOD down to 300 ppb, but the recovery was slow [32]. A Schottky contact device was prepared by Zhou et al. by the CVD approach and triangle-shaped structures with lateral dimensions of 5
30 μm were obtained on SiO2/Si substrates. Fig. 2.8 depicts the fabricated MoS2 field-effect transistors (FETs), demonstrating ultrasensitive detection of NO2 down to a few ppb levels, and NH3 down to 1 ppm (Fig. 2.9) [33]. Another material of interest in this family is MoSe2, which consists of a sandwiched model of two atomic layers of Se atoms and one atomic layer of Mo covalently bonded. The model has a comparatively large atomic structure due to the larger atomic radius of the Se atoms than that of S atoms. MoSe2 layers can be employed as a chemical sensor for detecting toxic gas molecules owing to its 2D geometry (Fig. 2.9). Late et al. report the MoSe2-based high-performance room temperature NH3 sensor (Fig. 2.10) fabricated by micro mechanical exfoliation [34]. The single-layer MoSe2 sensor device was fabricated on a 300 nm SiO2/Si substrate by the electron beam lithography technique and its

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2. FUNDAMENTALS AND PROPERTIES OF 2D MATERIALS IN GENERAL AND SENSING

FIGURE 2.9 (A) Raman spectra of single-layer and bulk MoSe2; (B) optical microscope image of single-layer MoSe2; (C) typical optical photograph of bulk MoSe2 crystal grown using chemical vapor transport method. Source: Reproduced from D.J. Late, T. Doneux, M. Bougouma, Single-layer MoSe2 based NH3 gas sensor. Appl. Phys. Lett. 105 (23) (2014) 233103.

detection of NH3 gas at concentrations of 50, 100, 200, 300, and 500 ppm was observed. It showed a very short response time compared to single-layer MoS2 [33,34]. Another important member of the 2D family is tungsten disulfide WS2. Huo et al. and Yang et al. [35,36] developed a transistor based on multilayer WS2 nanoflakes using a conventional mechanical exfoliation technique (Fig. 2.11). The thickness of WS2 nanoflakes was 42 nm, and the width (W) and length (L) of the sensing channel in the device was observed to be of the order of 15 and 20 μm, respectively. There was a charge transfer process between adsorbed gas molecules (O2, ethanol, and NH3) and the multilayer WS2 device. When exposed to various gas atmospheres (NH3, ethanol, air, and O2), the WS2 device exhibited distinct electrical and photosensitive responses. Ethanol and NH3 molecules acted as electron donors to increase the electron concentration in WS2, thus leading to a positive response. In particular, the device exhibited a higher sensitivity toward NH3 than that of the other gases, meaning the WS2 flakes were more sensitive to NH3 molecules. In contrast, O2 was the electron acceptor and can extract electrons from the WS2 flakes, displaying a negative response. In addition, the device exhibited higher sensitivity in O2 than in air, due to the higher concentration of O2 acting as an electron acceptor to deplete electrons. FUNDAMENTALS AND SENSING APPLICATIONS OF 2D MATERIALS

2.6 2D MATERIALS IN CHEMICAL AND PHYSICAL SENSING

FIGURE 2.10

17

Single-layer MoSe2 (A) AFM phase image of single-layer MoSe2 nanosheet-based sensor device and (B) SEM image. Sensing behavior of single-layer MoSe2. (C) Optical image of gas of sensor device fabricated using E-beam lithography. (D) NH3 sensing response as function of gas concentration. (E) Linear plot of sensitivity of MoSe2 gas sensor device as function of NH3 gas concentration (ppm). (F) Raman spectrum of single-layer MoSe2 recorded at ambient, Ar environment and after exposure with 1000 ppm of NH3. Source: Reproduced from D.J. Late, T. Doneux, M. Bougouma, Single-layer MoSe2 based NH3 gas sensor. Appl. Phys. Lett. 105 (23) (2014) 233103.

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FIGURE 2.11 AFM (A) and SEM image (B) of the actual transistor based on multilayer WS2 nanoflakes. (C) Schematic diagram of the charge transfer process between adsorbed gas molecules and the multilayer WS2 nanoflakes transistor. (D) Time-dependent photocurrent response under various gas atmospheres. (E) The gas sensitivities (A) and current changes (B) under different conditions. Source: Reproduced with permission from N. Huo, et al., Photoresponsive and gas sensing field-effect transistors based on multilayer WS2 nanoflakes. Sci. Rep. 4 (2014) 5209.

The 2D materials have also been explored for being potential materials for humidity sensing due to their high surface to volume ratios, flexibility, and transparency. Graphene has been reported to change after adsorbing water molecules due to doping. However, the resistance change is not abrupt, thereby resulting in a low sensitivity [37]. Monolayers of molybdenum disulfide (ML-MoS2) is analogous to graphene and being an n-type semiconductor with remarkable mechanical and electrical properties shows signal variations with surfaceadsorbed water molecules. The sensitivity, defined as ΔR/R0, can be modulated by VG and shows a parabolic shape reaching 104 at VG 5 30 V, when the gate voltage increased from 210 to 80 V. This .4 order of resistance magnitude change indicates the ultrahigh sensitivity compared to previous reports. As compared to graphene-based devices, the gate-modulated sensitivity in these MoS2 humidity sensors could also be beneficial. The sensing performance can be improved by increasing the gate voltage higher than 30 V. Dynamic tests of relative humidity (RH) sensing was also carried out to provide the time-response as shown in Fig. 2.11. A pulsed water vapor was introduced into the measurement chamber, in which the RH can be precisely controlled through a solenoid valve. The single cycle showed the response and recovery

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FIGURE 2.12 MoS2 humidity sensor. (A) The real-time test of the resistance change versus different RHs. (B) Dynamic stability test of the humidity sensor under the RH at 10%. (C) Response time analysis extracted from one cycle of RH pulse in (B). According to the fitting line, the response time and decay time is B10 and B60 s, respectively. (D) Long-term stability test of the humidity sensor under different RHs. The device shows ultrahigh stability for humidity sensing even after 1 month measurement. Source: Produced with permission from J. Zhao, et al., Highly sensitive MoS2 humidity sensors array for noncontact sensation. Adv. Mater. 29 (34) (2017).

time to be B10 and B60 s, faster than most reported humidity sensors based on 2D materials. The devices were capable of being maintained at a fixed RH over a month, indicating excellent stability and durability (Fig. 2.12) [38]. Similarly a few layers of black phosphorous in the form of thick film sensors were reported to have good humidity-sensing performance. The thick film sensor was subjected to I 2 V characteristic measurement as a function of varying RH. It was found that the I 2 V slope increases with increasing humidity from 11% to 97%. The water is an e2 donor molecule, the resistance of the few layer thick black phosphorus nanosheets sample was found to decrease with the increasing RH. The results (Fig. 2.13) show that the charge transfer from water molecules to the black phosphorus nanosheets plays a key role in the sensing mechanism. The sensitivity versus RH for a few layer black phosphorus nanosheets thick film sensor is calculated using the equation S 5 ðR11
RΔRH Þ=RΔRH

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FIGURE 2.13 Real-time characterization of the MoS2 humidity sensor. (A) The realtime test of the resistance change versus different RHs. (B) Dynamic stability test of the humidity sensor under the RH at 10%. (C) Response time analysis extracted from one cycle of RH pulse in (B). According to the fitting line, the response time and decay time is  10 and  60 s, respectively. (D) Long-term stability test of the humidity sensor under different RHs. The device shows ultrahigh stability for humidity sensing even after one month of measurement. Source: Reproduced with permission from M.B. Erande, S.P. Mahendra, J.L. Dattatray, Humidity sensing and photodetection behavior of electrochemically exfoliated atomically thin-layered black phosphorus nanosheets. ACS Appl. Mater. Interfaces 8 (18) (2016) 11548
11556.

where R11 and RΔRH are the resistances of the device in 11% RH and change in relative humidity, respectively. The highest sensitivity was found to be B521%. From the I 2 t curve, the response time of the thick film humidity sensor was found to be B101 s, and the recovery time B26 s, which is comparable to other 2D inorganic layered materials such as MoS2 [27] and MoSe2 [39]. To see the repeatability in response and recovery time, we carried out I 2 t measurements for hundreds of cycles; three are represented in Fig. 2.13.

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2D materials have been used as biosensors based on their large surface area, which enables them to deliver high physical adsorption of biomolecules and as well as efficient fluorescence quenching. This makes them a good choice of material for optical- and fluorescencebased biosensors. MoS2 nanosheets have been reported to have efficient quenching ability and have been used for nucleic acid detection. In the presence of target DNA, dsDNA was formed and subsequently released from the surface of the MoS2 nanosheets, resulting in fluorescence recovery. The sensor showed a detection limit of 500 pM with a linear range of 0
15 nM while WS2, TaS2, and TiS2 have also been used as sensors [40]. The fluorescence quenching method of 2D materials has also been employed for the detection of proteins. Specifically, nucleic-acid aptamers have been used mostly because of their chemical stability and in vitro synthesis. MoS2 nanosheets on which dye-labeled ssDNA aptamer probe can self-assemble were used to demonstrate fluorescence resonance energy transfer from the dye to MoS2, resulting in the quenching of the fluorescence intensity. The sensor operates when an aptamer specifically binds the target (in this case human thrombin) giving rise to a rigid conformation and partial restoration of fluorescence due to detachment of probe from the MoS2 nanosheet. This was reported to be better than a graphene-based thrombin biosensor as the detection limit achieved was 300 pM. Other protein assemblies like carcinoembryonic antigen (CEA), prostate specific antigen (PSA), and cytochrome c78 have also been sensed using 2D material sensors [41,42]. The application of 2D materials has also been extended to novel electrochemical label free sensing as well. Wang et al. reports the fabrication of sandwich-type immunosensor based on the MoS2
Au composite for the detection of CEA. The CEA primary antibody (Ab1) was immobilized using the MoS2
Au composite and Ag NPs were used to support the CEA secondary antibody (Ab2) and glucose oxidase (GOx). The glucose leads to the formation of H2O2, which is further catalyzed by the MoS2
Au composite. Measuring the reduction peak of H2O2 the immunosensor exhibited a linear range from 1 to 50 ng mL achieving a detection limit of 0.27 pg mL [43]. 2D TMD nanosheet-based FET biosensors have also been used for protein detection. In a recent study, biotin and SA were chosen as the receptor and target molecules with Biotin used to functionalize the channel of the FET biosensor. On exposure to target molecules, the current of the device decreased due to the negative charge of the SA as shown in Fig. 2.14 and the strong bonding between SA and biotin [31,44
46].

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FIGURE 2.14 (A) Schematic illustration of MoS2-based FET. Source: Reproduced with permission from D.-W. Lee, et al., Field-effect transistor with a chemically synthesized MoS2. Nano Res. 8.7 (2015) 2340
2350. (B) Schematic diagram of the MoS2-based FET biosensor. Source: Reproduced with permission from Y.H. Kim, et al., Self-activated transparent all-graphene gas sensor with endurance to humidity and mechanical bending. ACS Nano 9.10 (2015) 10453
10460. (C) Schematic illustration of a MoS2 nanopore membrane for DNA translocation. Source: Reproduced with permission from Sarkar, D., et al. MoS2 field-effect transistor for next-generation label-free biosensors. ACS Nano 8.4 (2014) 3992
4003.

2.7 CONCLUSION 2D materials have distinct chemical and physical properties including layered structure, high-surface area, layer-dependent optical bandgap, and variation of chemical compositions. This makes them potential substitutes for various conventional materials in sensing. They have improved properties and detection limits which are very important when sensitivity and measuring quanta are involved. The sensor platforms range from field-effect transistors, electrochemical sensors to physical sensors. 2D materials are compatible with any sensor assembly, and many more new members with distinctive properties are being added to this family. In the near future these materials will be found in markets such as wearable electronics, optolectronics, and semiconductor technology.

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C H A P T E R

3 Synthesis, Properties, and Applications of Graphene Shivam Trivedi, Kenneth Lobo and H.S.S. Ramakrishna Matte Centre for Nano and Soft Matter Sciences, Bengaluru, India

The element carbon has been known to humankind since antiquity: It is found in all known life forms. Its uniqueness is manifested in the many allotropes in which it occurs. The same element exhibits softness like the graphite used in pencils, to the hardest known material in the form of a diamond. Its other allotropes include amorphous carbon, glassy carbon, fullerenes, and nanotubes. The property of catenation is of vital importance as it enables carbon atoms to bond to other carbon atoms to form long chains or structures. Of its many forms, the discovery of the long-hypothesized single atomic layer form of carbon called graphene drew immediate attention from the scientific community. Its existence had been ruled out on the grounds of thermodynamic instability as divergent contributions of the thermal fluctuations are expected to destroy long-range order, resulting in melting of the lattice at finite temperatures [1]. Moreover the lack of proper characterization techniques made it difficult to elucidate its existence. It was in 2004, when Novoselov and Geim successfully isolated and studied a single layer of graphene that a deep insight into the material properties could be achieved [2]. Graphene is a two-dimensional crystal composed of carbon in a honeycomb lattice to form a single layer of atoms. It is considered the mother of many carbon allotropes such as fullerenes, nanotubes, and graphite, as shown in Fig. 3.1. As we go down from bulk graphite to

Fundamentals and Sensing Applications of 2D Materials DOI: https://doi.org/10.1016/B978-0-08-102577-2.00003-8

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FIGURE 3.1 Many forms of carbon can be traced back to graphene as its building material. Source: Adapted with permission from A.K. Geim, K.S. Novoselov, The rise of graphene, Nat. Mater. 6 (3) (2007) 183 [3].

few-layers graphene (FLG), and further down to single-layer graphene (SLG), new properties start to emerge. With the carbon atoms bonded covalently within the plane, forming σ-bonds with three neighboring carbon atoms and one out-of-plane π-bond, this network of sp2 carbon atoms gives graphene its unique properties. The strong covalent in-plane bonding of the carbon atoms results in a strength that exceeds that of diamond, whereas its high optical transparency, high carrier mobility, and room temperature ballistic conduction are attributed to its unusual electronic structure [2,4]. These properties of graphene make it suitable for a variety of applications, such as devices, energy conversion and storage, field-effect transistors (FETs), tissue engineering, sensing, and membranes [5
10]. Since its discovery it has not only become of significant academic interest, but has also become a prospective material for today’s technological needs. Thus extensive research has been carried out in synthesizing and characterizing graphene. With a deeper insight into the material’s properties, it can be incorporated into emerging technologies. In the following sections, we discuss the synthesis, properties, characterization, and some applications of graphene.

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3.1 SYNTHESIS Although the properties of graphene were predicted to some extent, the unavailability of characterization techniques made it difficult to ascertain the formation of SLG [11]. This was until 2004, when it was synthesized by simple micromechanical cleavage of highly ordered pyrolytic graphite (HOPG) using adhesive tape. This discovery has since drawn a great amount of attention from various scientific streams to understand the unusual electronic structure that graphene possesses. Following this, many techniques for obtaining mono- and few-layer graphene have emerged, using both physical and chemical approaches. Other than micromechanical cleavage, other physical approaches include liquid-phase exfoliation, ball milling, laser thinning of bulk graphitic crystals, and anodic bonding [12,13]. The chemical routes involve the conversion of graphite to graphite oxide to facilitate exfoliation, followed by annealing to obtain reduced graphene oxide (RGO). Thermal or microwave-assisted exfoliation has been deployed extensively, although it is known to have less superior properties due to the introduction of defects [14
16]. Other chemical approaches include the chemical deposition of carbonaceous precursors on transition metals and precipitation of carbon from the surface of SiC [17,18]. The various physical and chemical methods used for the synthesis of mono- and few-layer graphene are featured in Fig. 3.2. The π-orbitals normal to the surface of a graphene layer overlap with those of adjacent layers to stack and form graphite. Physical approaches include exfoliation of bulk graphite by various known methods to obtain graphene. With the interlayer bonding due to weak van der Waal forces, these approaches aim at providing the required shearing force to overcome the attractive forces between layers [20].

3.1.1 Micromechanical Cleavage Micromechanical cleavage is a rather simple method and was deployed in the groundbreaking discovery of graphene and the subsequent research that followed it. The flakes obtained by this method are of highest quality, exhibiting mobilities in excess of 200,000 cm2 V21 s21. Ebbesen and Hiura observed the accidental folding and tearing of graphite sheets when an HOPG surface was scanned using an atomic force microscopy (AFM) tip [21]. By modulating the distances between the tip and the graphite, folding and tearing at step edges were observed [22]. Although indicative of exfoliation, no monolayer graphene isolation was reported until 2004 by Novoselov et al. Square HOPG meshes were pressed against a thick layer of photoresist on a glass slide. Once the

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FIGURE 3.2 Various synthesis routes to obtain graphene. Source: Adapted with permission from A.C. Ferrari, F. Bonaccorso, V. Fal’Ko, K.S. Novoselov, S. Roche, P. Bøggild, et al., Science and technology roadmap for graphene, related two-dimensional crystals, and hybrid systems, Nanoscale 7 (11) (2015) 4598
4810 [19].

meshes were attached to the photoresist after baking, Adhesive tape was used for thinning it down to a single layer. By dissolving the photoresistant acetone, the flakes are suspended. By dipping and washing in water and propanol, these flakes were transferred onto a solid substrate of SiO2 (300 nm)/Si (n 1 doped) [2]. This technique was later simplified by exfoliation using only Scotch tape. Identification and characterization of mono-, bi-, and tri-layer graphene are often carried out by optical microscope, Raman spectroscopy, and AFM [23]. This method is the simplest of all the synthesis routes and can produce sheets of graphene of the highest quality, of sizes up to a few micrometers. However, this process remains limited to the laboratory scale, as its low yield and throughput make it impractical for large-scale production.

3.1.2 Liquid-Phase Exfoliation Mono- and few-layer graphene can be obtained by exfoliating high-quality bulk graphite through mechanical shearing in a liquid.

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Liquid-phase exfoliation offers a viable method of scaling up to an industrial level. The mono- and few-layer dispersed graphene is suitable for further chemical modification and makes processing into composites and thin films easy [12,15,24,25]. Liquid-phase exfoliation relies on energetically favorable interactions between the solvent with the graphene sheets. To overcome the van der Waals force that binds the layers in graphite, 61 meV per C atom or over 2 eV nm22 energy input on graphene surface is required for exfoliation [26]. Also, the solvent
graphene interactions must be comparable to the exfoliation energy of graphene. The energy required for the process is provided mechanically by methods such as ultrasonication, shear mixing, and ball milling [27,28]. Ultrasonication in conventional laboratory bath sonicators or more powerful probe sonicators create mechanical waves through the liquid medium. Cavitation occurs during rarefaction cycles where negative acoustic pressure is sufficiently large to disturb the liquid, creating transient microbubbles (cavities). These microbubbles grow in a short span of time and collapse violently, reaching pressure up to 20 MPa and rapid heating/cooling at a rate up to 109 Ks21 [29]. In addition, microturbulence during bubble collapse can provide mechanical energy to facilitate exfoliation [27]. With increased sonication power and time, concentrations can be increased [30]. However, sonication can impart high energy to the sheets which could result in scission. Shear mixers have been used to supply the required shearing forces to graphite powders in a solvent, using a rotor
stator arrangement. Exfoliation occurs in regions where the shear rate exceeds 104 s21 and this method possesses the capability of achieving high production rates, indicating the viability of the process for commercial production [31,32]. Once the graphene has been exfoliated, it is essential that the sheets be stabilized against restacking. This can be done using suitable solvents, polymers, surfactants, or aromatic π
π interactions as illustrated in Fig. 3.3. As graphene is hydrophobic, its dispersibility in water cannot be achieved without additives. The additives provide mediation between graphene and the solvent, thereby enabling exfoliation and, additionally, providing stability toward restacking. Most commonly used surfactants like sodium cholate and sodium dodecylbenzene sulfonate bring about stabilization by electrostatic means [24,34
37]. Another approach is the use of polymers, which wrap the sheets, thereby stabilizing the dispersions via steric means [38]. However, the presence of these groups could affect the performance of graphene due to incomplete removal of the additives when fabricated into devices.

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FIGURE 3.3 Routes of liquid-phase exfoliation of graphene. Source: Adapted with permission from K. Parvez, S. Yang, X. Feng, K. Mu¨llen, Exfoliation of graphene via wet chemical routes, Synthet. Met. 210 (2015) 123
132 [33].

It is highly desirable to obtain dispersions of graphene in solvents without the use of any additives. This can be achieved by the use of solvents which match the energetics of graphene sheets, thereby reducing the enthalpic cost of mixing. This has been shown in the case of carbon nanotubes (CNTs), where good dispersibility has been observed to be limited to solvents in a limited range of surface tension [39]. The idea was extrapolated to graphene by Coleman and coworkers to obtain dispersions in N-methyl pyrrolidone (NMP) of about 63 mg mL21 [30]. Although surface tension was the basis for predicting dispersibility, not all solvents proved effective despite satisfying this criterion. Furthermore, they showed that the list of good solvents for graphene can be further narrowed down when characterized by a Hildebrand solubility parameter of δT 5 23 MPa1/2. A similar trend was observed in this case too, with some solvents failing to disperse graphene. The Hildebrand solubility parameter considers a cumulative effect of all possible interactions between graphene and the solvent molecules. Hansen solubility parameters provide a more refined insight in the interactions between nanosheets and the solvent molecules by splitting

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them into components of dispersive (δD), polar (δP), and hydrogen (δH) interactions, and is a more effective way of ascertaining dispersibility of graphene. For solvents with δD 5 18 MPa1/2, δP 5 9.3 MPa1/2, and δH 5 7.7 MPa1/2, efficient dispersibility was observed. By optimizing parameters like initial precursor concentration, exfoliation time, and usage of cosolvents, concentrations of dispersed graphene can be increased [12,40]. The performance of a solvent can be based on its ability to exfoliate the material to single and few layers, and stabilize the dispersed sheets in high concentrations, without them restacking for a time span, which provides a processibility window while being easily available, nontoxic, and cheap. For graphene with additives, a necessary step following processing is the removal of the additives by methods like annealing. This can be circumvented by the aforementioned approach. Low boiling point solvents are desirable dispersants, as processing can be made less energy intensive, enabling the use of these dispersions as conductive inks in applications such as flexible electronics. NMP, considered the best solvent for exfoliation, poses difficulties in processing due to its high boiling point of 202 C and its high toxicity. This can cause the incomplete removal of solvent which affects device performance [41]. Low boiling point solvents like isopropanol and chloroform have been shown to disperse up to 0.5 mg mL21, although more solvents need to be investigated to achieve stable dispersions of higher concentrations [42]. Ionic liquids (ILs), considered “green solvents” are organic salts having low melting points, negligible vapor pressure, low toxicity, and high thermal stability and tenability, making them a very interesting category of polar solvents. Dai et al. first reported direct exfoliation of natural graphite flakes in 1-butyl-3-methylimidazolium bis(trifluoromethane-sulfonyl)imide, achieving concentrations of 0.95 mg mL21 [43]. Their stability was attributed to π
π interactions between the graphene layers and aromatic IL cations. Quitevis et al. obtained a high yield of 5.8 mg mL21 for graphene in IL with two phenyl groups using expanded graphite as the starting material, proposing that the adsorption of ILs due to π
π interactions may be promoting exfoliation [44]. Aida et al. demonstrated the microwave-assisted exfoliation of graphene in two oligomeric ionic liquids [45]. A yield of 93%, of which 95% were found to be monolayer was achieved in low-power microwave irradiation for a short time. The isolated graphene exhibits dispersibility in oligomeric ionic liquids, IL2PF6 and IL4PF6, up to 100 mg mL21. However, the high cost of these ILs brings limitations to their use as solvents. Other methods like the intercalation of super-acids such as chlorosulfonic acid have shown to yield highly monolayer (70%) dispersions,

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even at concentrations as high as 2 mg mL21 without sonication. Addition of H2O2 can enhance the exfoliation. However, toxicity is the main inhibiting factor in their use [46,47].

3.1.3 Chemical Vapor Deposition Chemical vapor deposition (CVD) is a widely used technique in the preparation of graphene by the decomposition of a carbon precursor at high temperatures on substrates. The hydrocarbon gas that acts as the source of carbon decomposes at the surface of the substrate as in 3.2. Transition metal substrates additionally lower the required energy by acting as a catalyst and determine the deposition mechanism [48]. By this approach, high-quality graphene can be obtained. The strategy uses the knowledge of solubility of carbon in transition metals like Ni, Cu, Ir, Pt, or Pd [49
53]. Various hydrocarbons like benzene, ethylene, acetylene, and methane have been used as the carbon source. Upon heating, the carbonaceous materials decompose and the carbon diffuses through and saturates in the metal lattice, depending on the level of solubility in the metal. Upon cooling, the carbon is precipitated out from the metal surface due to decreased solubility, resulting in the formation of graphene. This enables the synthesis of large area graphene. The chemical decomposition of precursors over the substrates can be augmented by the use of microwave or radio frequency
enhanced plasma [54,55]. Moreover, this enables the growth of graphene on substrates such as Si, SiC, SiO2, Al2O3, and stainless steel, besides the conventionally used transition metals. Based on the reaction conditions, single- to few-layer graphene can be obtained. To realize the full potential of graphene, it requires a transfer from the growth substrate onto arbitrary substrates for the fabrication of devices. The most commonly used technique for transfer is the polymethylmethacrylate(PMMA)-mediated technique. PMMA is coated on the graphene, followed by the etching of the underlying metal. The PMMA-graphene stack is then transferred onto the desired substrate and the polymer is removed by organic solvents [56,57].

3.1.4 Chemical Methods Chemical methods have been widely deployed in graphene synthesis due to their high yield and throughput. Easy exfoliation is enabled by chemically modifying graphite. A commonly used technique involves the chemical oxidation and exfoliation of graphite employing the Hummers method, where strong oxidizers such as NaNO3 and KMnO4 in sulfuric or phosphoric acid yield graphene oxide (GO) [14]. The modified Hummer’s method uses K2S2O8 in place of NaNO3.

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3.1 SYNTHESIS

33

FIGURE 3.4 Chemical methods for obtaining graphene involve the transformation of graphite to graphite oxide. This facilitates exfoliation into graphene oxide, which, when reduced, yields reduced graphene oxide. Source: Adapted with permission from B.S. Lee, Y. Lee, J.Y. Hwang, Y.C. Choi, Structural properties of reduced graphene oxides prepared using various reducing agents, Carbon Lett. 16(4) (2015) 255
259 [73].

This modification ensures complete oxidation of the graphite, and is also environmentally friendly as no harmful gases are produced [58,59]. The introduced functionalities increase the interlayer ˚ to about 9.5 A ˚ , enabling them to achieve few and spacing from 3.4 A single layers by sonication in solvents. Another role played by the hydroxyl, carboxyl, and epoxide functionalities is to make the sheets hydrophilic, thereby making it possible to obtain graphene dispersions in water [60]. Although the process of oxidation enables easy exfoliation of GO, it causes the properties intrinsic to graphene to be lost. GO films exhibit sheet resistance in the order of 1012 Ω sq21 [61]. To revert GO to graphene, a reduction step is necessary. This RGO can be obtained by numerous reduction approaches like chemical, thermal, electrochemical, photocatalyst, solvothermal, and microwave [62
72] (Fig. 3.4). Density functional calculations show that it is difficult to obtain pristine graphene by reduction of GO [74]. Although the conductivity of RGO is about 108 times better than that of graphene oxide, it is still 10 times lower than pristine graphite due to defects in the sp2 carbon lattice introduced during the aggressive oxidation step (such as irremediable holes, sp3 defects with an average distance of 1
2 nm). Another approach involves increasing the interlayer spacing with the use of intercalants, followed by their rapid evaporation. One method of doing this is to soak graphite in strong acids, which act as the intercalating species. By providing enough time, the molecules find their way between the layers, thereby increasing the interlayer spacing by forming alternating layers of graphene and intercalant. By rapidly increasing the temperature, the intercalants can be forced out, leaving behind exfoliated sheets of graphene [62]. The yield from these methods can be increased by assisted mechanical processes such as ultrasonication and ball milling [75].

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Alkali metal ions species can be introduced between layers of graphite, which when treated with ethanol, results in high yields of few-layer graphene [76].

3.2 CHARACTERIZATION Characterization of graphene is crucial to determine the number of layers and defects and to tailor its properties with respect to the intended applications. Characterization involves both microscopic as well as spectroscopic measurements. Microscopic characterizations like optical microscopy, scanning electron microscopy (SEM), TEM, and AFM shine light on the morphology, flake size, and number of layers. Raman spectroscopy on the other hand is a simple, fast, and noninvasive technique that is sensitive to minute changes in the structure. Vital information on the number of layers, defects, and functionalization can be obtained by it [77].

3.2.1 Raman Spectroscopy A graphene lattice can be considered to be composed of sublattices of two types of carbon atoms (A and B) in its unit cell. Six phonon dispersion modes are associated with these two carbon atoms. Out of which, three are optic (O) which have finite energy, while the other three are acoustic (A) phonon branches that have energy tending to zero at the center of the first Brillouin zone. These phonon modes are categorized on the basis of direction of vibration with respect to carbon
carbon directions (A and B) into transverse (perpendicular) and longitudinal (parallel). For one longitudinal phonon, the vibrations are in-plane, and for one transverse phonon, vibrations are out-of-plane. Along the highsymmetry points τ K and τ M, the six vibration modes are assigned as LO, iTO, oTO, LA, iTA, and oTA based on the abbreviations designated previously. As shown in Fig. 3.5A, the most prominent spectra of monolayer graphene appears at 1580 cm21, which is due to primary in-plane vibration mode of sp2 carbon atoms. Another mode at 2700 cm21 appears that is the second-order overtone of in-plane vibrations and is known as 2D band. At half the frequency of 2D mode a band at 1350 cm21 (D-mode) is observed for disordered samples and is attributed to defects. It is due to the breathing mode of the sp2 carbon atoms [79]. As the number of layers increases, the electronic structure of graphene changes and hence the Raman spectra. A splitting and blue shift of 2D band are observed as the number of layers increases. The G-band also experiences a red

FUNDAMENTALS AND SENSING APPLICATIONS OF 2D MATERIALS

(B)

(C) 514 nm

514 nm

(D) 2500

633 nm graphite

20000

10 layers

5 layers

Intensity (A. U.)

Intensity (a. u.)

Intensity (a. u.)

30000

15000

1500 D1

D2

Edge 1 Layer 633 nm

Graphene 500 2600 2000

2500

Raman shift (cm–1)

FIGURE 3.5

3000

2700

2800

Raman shift (cm–1)

5000

D

1 layer 0 1500

514.5 nm

10000

1000

2 layers 10000

2 Layer 2D1B 2D1A 2D2A 2D2B 20000

2000

40000 Graphite

(E) Edge Graphite

514.5 nm

Intensity (A. U.)

(A) 50000

2600 2700 2800

Raman shift (cm–1)

0 1300

1350

1400 –1

Raman shift (cm )

0 2550 2600 2650 27002750 2800

Raman shift (cm–1)

(A) Comparison of Raman spectra of graphite and graphene. (B) Evolution of the spectra at 514 nm with the number of layers. (C) Evolution of the Raman spectra at 633 nm with the number of layers. (D) Comparison of the D band at 514 nm at the edge of bulk graphite and singlelayer graphene. The fit of the D1 and D2 components of the D band of bulk graphite is shown. (E) The four components of the 2D band in a two-layer graphene at 514 and 633 nm. Source: Adapted with permission from A.C. Ferrari, J.C. Meyer, V. Scardaci, C. Casiraghi, M. Lazzeri, F. Mauri, et al., Raman spectrum of graphene and graphene layers, Phys. Rev. Lett. 97(18) (2006) 187401 [78].

36

3. SYNTHESIS, PROPERTIES, AND APPLICATIONS OF GRAPHENE

shift with an increase in the number of layers [78] as shown in Fig. 3.5. One can determine the defects by the ratio of the peak intensities, ID/IG [80].

3.2.2 Microscopy 3.2.2.1 Optical Microscopy Optical microscopy offers a simple, fast, nondestructive, and large area method to characterize graphene samples. With a suitable substrate, one can determine the thickness of the sample. By having a dielectric layer (SiO2 or Si3N4) between a graphene and silicon substrate optical characteristics like refractive index and absorption coefficient of the thin layer are changed [81]. Optical imaging of graphene and GO sheets is thus possible by interference-based techniques [82] and imaging ellipsometry [83]. Since the resolution of visible light is limited by its wavelength to approximately 200 nm, atomic resolution cannot be achieved. To go down to the order of an atom, electron beams are required which have approximately 103 times shorter wavelength than visible light. Advanced microscopes like SEM and TEM can be used to obtain images. 3.2.2.2 Atomic Force Microscopy Scanning probe microscopy (SPM) is a versatile tool for probing various aspects of materials. This microscopic technique relies on the changes in parameters based on the physical proximity of a probe rastered across the surface of a sample. The physical parameter may be current, friction, or strain in the probe. Methods in SPM like AFM are based on mapping the forces experienced by a sharp probing tip, with respect to the sample topography to generate an image [84,85]. A number of variants of SPM have developed over the years. In the study and manipulation of graphene, AFM has turned out to be an indispensable tool. Numerous parameters can be probed by deciphering the signals generated by sample-tip interactions. The most straightforward application is the determination of the number of layers by scanning the edges of graphene sheets, where the stacked sheets form steps. The measurement of the step height corresponds to the thickness of the graphene sheet. A step height of 0.68 nm is observed between the graphene sheet and the SiO2 substrate, whereas the spacing of step heights between the successive graphene sheets is 0.35 nm [80]. Based on the thickness of the flake, the number of layers

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3.2 CHARACTERIZATION

37

can be determined. It is observed that the free amplitude chosen for the tapping cantilever plays an important role in determining the exact thickness of the film. The stability of two-dimensional materials is thermodynamically forbidden [1]. The setting up of ripples in graphene results in the rise of elastic energy but stabilizes the system by decreasing the total free energy. AFM studies have shown the origin of wrinkles in CVD grown graphene to arise from the difference in thermal coefficients of expansion of the substrate and graphene [86]. AFM is often used in conjunction with Raman spectroscopy and optical microscopy to determine the number of layers of graphene on a SiO2 substrate. Optical and Raman characterization produce optical contrast or produce a characteristic Raman signal due to the sp2 electronic structure of the graphene. Processes like reactive ion etching and O2 plasma etching used during fabrication can introduce defects and cause functionalization of the graphene, thereby making it lose its signatures despite its presence. AFM is capable of reliable confirmation of these flakes as its mode of detection is by mechanical contact [87,88]. Pioneering work has been carried out by Giessibl in the atomic resolution imaging of materials [89]. Gross et al. demonstrated the atomic resolution AFM by CO functionalization of AFM tips. This has been used to explain molecular structuring, as shown in Fig. 3.6 [90
92]. Parades et al. studied the variation in the extent of reduction in RGO sample using AFM. By scanning the RGO sheets in tapping mode, the extent of oxygen functionalities after the reduction step are determined by analyzing phase images. The difference in the hydrophilicity across the sheets arises due to regions with and without these functionalities. Operating in the attractive-force regime in the tapping mode has been used to elucidate such differences [93]. Lipson et al. deployed ambient condition conducting AFM to study the extent of the growth of graphene on SiC by mapping conductivity, rather than using ultrahigh vacuum techniques like STM [94].

FIGURE 3.6 (A) Optical microscope image of micromechanically cleaved monolayer graphene [20]. (B) Atomic resolution AFM micrograph of graphene showing hexagonal arrangement of carbon atoms [90]. (C) TEM image of folded graphene sheet (scale bar:500 nm) [12].

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3. SYNTHESIS, PROPERTIES, AND APPLICATIONS OF GRAPHENE

AFM is a versatile method not only for characterization but also for the manipulation of graphene sheets. Hiura et al. first observed the folding of graphene sheets when studying the surface of HOPG using an AFM tip [95]. The micromanipulation of graphene sheets has since been attempted. Vasic et al. demonstrated how an AFM tip could be used in the manipulation of the graphene sheet, including its deformation and cutting [96]. In an attempt to show the creation of conductive pathways in graphene oxide incorporated in a device, Lorenzoni et al. used an electrochemical scanning probe method. The current-based reduction resulted in conducting pathways of lateral dimensions of 65 nanometers (nm) using a conducting AFM tip [97]. 3.2.2.3 Transmission Electron Microscopy Transmission electron microscopy (TEM) is a very sophisticated technique that is used to probe structures down to the atomic scale. Modern TEMs utilize various advanced analytical techniques such as selected area electron diffraction (SAED), energy dispersive x-ray spectroscopy (EDS), electron energy loss spectroscopy (EELS), etc. to obtain a wide range of information. Freestanding or thin graphene specimens that can transmit the electron beams are ideal for TEM analysis. To achieve a higher resolution of the atomic structuring, high-energy electron beams ( . 80 kV) are used. However, controlled exposure of electron beams can help to write patterns to a host of a new generation of electronic devices [98,99]. With TEM, one can find a number of layers in the sample. For a single layer, only one dark line is observed, whereas two dark lines for a bilayer is observed when edges are studied. Individual atoms in graphene were observed by a high-angle annular dark-field (HAADF) in a scanning transmission electron microscope (STEM) at a voltage of 100 kV [100]. The variation in the electron diffraction intensities with an incidence angle can also be used to determine the number of layers. For a monolayer graphene, the intensity of diffraction peaks does not change too much by varying the incidence angle because there is only a zero-order Laue zone in its reciprocal space [101]. TEM is a perfect tool to study the defects and engineer properties based on the applications. TEM not only allows atomic resolution but also allows in situ heating and electrical measurements.

3.3 PROPERTIES OF GRAPHENE Graphene possesses specific properties due to sp2-hybridized orbitals, very thin atomic thickness (0.345 nm), and a distinctive band structure, thus making it suitable for various applications. Single-layer,

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3.3 PROPERTIES OF GRAPHENE

39

few-layer, and functionalized graphene exhibit different properties. In the sections below, the electrical, magnetic, surface area, and mechanical properties of graphene are discussed in detail.

3.3.1 Electrical Properties The two-dimensionality of graphene can be attributed as the cause of its many exotic properties. Graphene is a zero bandgap semiconductor with a very high electrical conductivity. Each carbon atom is connected to only three adjacent carbon atoms in sp2 fashion leaving behind one electron free, which is responsible for conduction. The unhybridized p-orbitals of adjacent carbon in the same layer overlap to form π-bonds. The unusual properties like chirality, ambipolar field-effect, pseudospin, anomalous quantum Hall effect, high mobility of charge carriers, and excellent thermal conductivity exhibited by graphene can be attributed to this structure [4,20,102
105]. Several theories have been proposed to understand the band structure of graphene, beginning with “tight-binding” approximations applied to model the band structure of graphite [11,106,107]. Graphene can be thought of as an infinitely large aromatic molecule; a sheet made up of hexagonal benzene rings. The electronic structure of benzene comprises of six π-orbitals, three occupied bonding and three unoccupied antibonding orbitals, separated by a bandgap. Upon fusing such rings, as in molecules like naphthalene and anthracene, the levels grow into bands, with the gap separating the HOMO-LUMO levels decreasing with an increasing number of benzene rings. In the case of these fused rings extending infinitely in a plane to yield graphene, the bandgap closes as illustrated in Fig. 3.7.

FIGURE 3.7 Electronic band structure of single-layer graphene. Source: Adapted with permission from C.N.R. Rao, K. Biswas, K.S. Subrahmanyam, A. Govindaraj, Graphene, the new nanocarbon, J. Mater. Chem. 19(17) (2009) 2457
2469 [108].

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The hexagonal lattice, consisting of two carbon atoms can be considered as a combination of two sublattices. This results in a band structure, with the bands intersecting at a point of charge neutrality called the Dirac point. At this point, the energy spectrum becomes linear, resulting in the ballistic conduction of electrons with a velocity of the order of 106 ms21. The electrons behave as massless fermions and can be explained by relativistic equations, rather than the Schro¨dinger equation. H 5 ν F σP Here ν F and σ are the electron Fermi velocity and the Pauli matrix for the pseudospin respectively, which have contributions from the two sublattices to the electron wave function. This relativistic nature of charges brings about interesting effects like the Klein paradox, where the tunneling of a particle through a barrier occurs with absolute probability. The anomalous quantum Hall effect in graphene shows the relativistic nature of carriers. The quantization of the levels is given in the form of Gxy 5

Ve2 h

where the filling factor ν 5 4(n 1 1/2). The valley degeneracy and spin are accounted in the factor 4. The factor 1/2 is a result of the linear electronic dispersion. The quantum Hall effect in graphene can be observed at room temperature due to its high carrier mobility [109]. Suspended graphene with a clean surface was found to have a mobility of 200,000 cm2 V21 s21. Experimentally observed mobilities of graphene ranged between 2000 and 15,000 cm2 V21 s21 primarily due to structural ripples, scattering, and defects [104,110
112]. By suspending the graphene, substrate scattering effects are nullified. The high mobility paves the way for ultrafast electronics in the 100
200 GHz range [113
115]. The chirality of graphene is a result of the two inequivalent carbon atoms in the unit cell. This brings about an additional degree of freedom referred to as pseudospin. The pseudospin for the electrons will be parallel to its momentum, and vice versa in case of holes [116]. Graphene also exhibits an ambipolar field effect when made into the conducting channel of a transistor; that is, based on the gate voltage, either holes or electrons can be made majority carriers. However, the absence of a bandgap poses a problem. A device with graphene as the active material cannot be switched off, and the low on
off ratios limit the use of graphene in transistors. Thus attempts are being made to introduce a bandgap into graphene.

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3.3 PROPERTIES OF GRAPHENE

41

One such method is by narrowing the dimensions of the flake in one direction to a nanoribbon. The spatial confinement gives rise to a bandgap [117]. A bandgap can also be introduced by hydrogen patterning [118]. Bilayer graphene also exhibits bandgap tuning up to 250 meV by controlling gate voltage [119]. Wang et al. showed on
off ratios of up to 105 at room temperature with high current densities and transconductance from transistors made from large bandgap and smooth-edge nanoribbons of width below 10 nm [120
122]. The high conductivity of graphene, paired with its transparency makes it an attractive choice for transparent electrodes to replace indium tin oxide (ITO). The challenge here lies in achieving this over a large area. RGO-based transparent conductors were shown to have a transparency of about 80% and a sheet resistance of 1 kΩ. The high number of defects from incomplete removal of oxygen results in low conductivity [61,65,123]. By pairing graphene with conducting polymers like PEDOT:PSS, sheet resistances of just about 80 Ω were achieved on polyethylene terephthalate (PET) substrates. The electrode demonstrated good resistance to flexure [124]. Large area electrodes achieved by roll-to-roll transfer of chemically grown graphene showed low sheet resistance due to higher quality arising from the method of synthesis. By treatment with HNO3, nitrogen can be doped into graphene, to achieve a sheet resistance of about 20 Ω and 90% optical transmission [125]. Doping is a well-known approach to tailor the electronic properties of semiconductor materials. It has been theoretically observed that substitutional doping leads to changes in the band structure of graphene and gives rise to fascinating properties [126
128]. On covalent functionalization with oxy- groups like for the case of GO and RGO, the sp2 hybridization changes to sp3, which leads to the creation of a quantum well and the introduction of scattering sites in graphene. It also introduces energy levels in the band structure, thus making it p- or n-type extrinsic graphene [129]. RGO shows reduced mobility (0.05
200 cm2 V21 s21) than graphene and finite bandgap of 0.2
2 eV [130]. Doping is thus capable of modifying the properties of graphene, making it a potential material for various applications such as electrochemical sensing and energy storage [129]. The dopant atoms form covalent bonds with carbon atoms and suppress the density of states at the Fermi level, and thus a gap is opened between valence and conduction bands [127,131]. Wei et al. [132] synthesized nitrogen-doped graphene by CVD method and found that it exhibits n-type behavior which is similar to nitrogen-doped CNTs [133]. Doping graphene with electron donors like N leads to n-type doping whereas doping with electron acceptors like boron (B) gives rise to p-type graphene [134]. Dirac points are shifted below the Fermi level in n-type and above the Fermi level in p-type doping [135].

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3. SYNTHESIS, PROPERTIES, AND APPLICATIONS OF GRAPHENE

Several other atoms like S, P, Se, O, and Si have been doped in graphene, but B and N remain most attractive because of their comparable ionic radii with carbon atom. The room temperature thermal conductivity of graphene was found to be 5300 Wm21K21 when probed with optical noncontact methods. This outperforms materials like copper and CNTs [105].

3.3.2 Magnetic Properties Magnetic materials find diverse applications in today’s technology, for example, data storage and power production. Traditionally used magnetic materials include iron, cobalt, nickel, rare earth metal, Heussler alloys, etc. These metals are capable of exhibiting magnetism owing to their d and f electrons, which are able to align their spins to setup strong magnetic moments. The coupling of the unpaired spins results in the creation of domains in the bulk of the material, which align in the direction of the applied magnetic fields. The magnetic properties of carbon-based materials have attracted a great deal of attention. The presence of s and p electrons alone makes it counterintuitive to expect magnetism. Magnetic materials based on carbon are a region of particular interest owing to the abundance of carbon, less energy-intensive production, biocompatibility, etc. Lightweight and flexible magnets can be fabricated using carbon for storage devices. The field of spintronics is based on spin and molecular electronics, in which carbon-based magnetism could potentially be of importance [136]. Magnetic carbon also finds application in bioimaging and biosensing [137]. Although initially thought to arise solely from magnetic impurities, ferromagnetism has been observed in pristine carbon forms. The magnetic susceptibility of the various forms of carbon is dependent on their band structure which can be modified by introducing impurities, defects, and by interactions of adsorbed entities. Pristine pyrolytic graphite is known to exhibit strong diamagnetism. Its structure gives rise to anisotropy in its magnetic susceptibility as well; it is small when the field is parallel to the planes, and high, when observed perpendicular to the plane due to the presence of fast-moving electrons (of the order of 106 m21, arising from the large nearest neighbor hopping energy of 2.8 eV) [138]. In graphene, one must note that for magnetism in 2D systems, long-range ordering is very challenging due to the absent d and f electrons, as in conventional magnetic materials. The observed magnetism is explained on the grounds of localized electronic states based on spin polarization. The properties are heavily influenced by the edge states [139].

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43

FIGURE 3.8 Graphene lattice split into two sublattices. Source: Adapted from D.L. Nika, A.A. Balandin, Two-dimensional phonon transport in graphene, J. Phys. Conden. Mat. 24 (23) (2012) 233203 [140].

The understanding into the magnetic properties of graphene is based on various theories. Density functional theory gives insights into the spin-resolved density of states. The Hubbard model considers the outof-plane π orbital contributions. The cumulative spin density of the system will depend on the up- and down-spin contributions of all atoms. This can be made clear by dividing the hexagonal lattice of carbon atoms into two sublattices with A-type and B-type atoms as in Fig. 3.8. By Lieb’s theorem [141], the alignment of the spins results from the difference in the spins arising from individual A and B sublattices. If NA and NB are the numbers of atoms in the two respective lattices, the total spin is given by S 5 ½NA 2 NB =2 As can be seen from the above equation, an unequal number of electrons in the two sublattices gives rise to a spin factor. The imbalance is brought about by the presence of defects, impurities, edges, and topological features [142
145]. Lesser thickness and nanoflake area favor larger magnetization in graphene. The edge geometries of graphene can heavily influence its magnetism. For graphene nanoribbons, the type of edge present will decide its susceptibility. Two kinds of edges are observed in graphene, namely zigzag and armchair as depicted in Fig. 3.9. An unequal spin contribution from zigzag-edged graphene sublattices results in localized electronic states near the edge. These form partly flat bands near the Fermi level. Armchair edges lack this localization of states as no surface states exist [147]. The large contribution to density of states near the Fermi level makes zigzag-edged graphene nanoribbons interesting. The edge states become dispersion-less when electron-hole symmetry is broken

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3. SYNTHESIS, PROPERTIES, AND APPLICATIONS OF GRAPHENE

FIGURE 3.9 Possible orientations of edge atoms in graphene. Source: Adapted from S. Wang, L. Talirz, C.A. Pignedoli, X. Feng, K. Mu¨llen, R. Fasel, et al., Giant edge state splitting at atomically precise graphene zigzag edges, Nat. Commun. 7 (2016) 11507 [146].

and the localization takes place at the edges causing ferromagnetic coupling. This edge effect also has an impact on the electrical properties; zigzag-edge graphene is metallic while armchair edge graphene is semiconducting, with the bandgap depending on the width of the nanoribbon. As localization is along the edges, the contribution to the moment decreases as we go toward the center of the ribbon [148
150]. In graphene, the total magnetism is given by χtotal 5 χspin 1 χcore 1 χPauli 1 χorbital χspin originates from the electron
electron interactions, χcore arises from the core electrons. χPauli depends on the localized edge states. Its contribution is negligible in case of armchair nanoribbons. Its temperature dependence is Curie-like. As the peak width of the density of states is of the order of few meV, comparable to room temperature, this contribution is temperature dependent unlike in metals where it remains

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45

independent. The orbital motion of the electrons contributes to χorbital diamagnetism, and is sensitive to the size of the graphene flake. Defects play an important role in the magnetism of graphene, as they give rise to localized electronic states which setup a magnetic moment. The density of states at the Fermi level rises, establishing a magnetic order. Defect engineering in graphene can be achieved by irradiation with high-energy particles [142]. Vacancies and hydrogen chemisorption can cause the disruption in its π conjugation network. Chemically obtained graphene is known to contain defects as a result of the processes involved in the synthesis. Studies on hydrazine RGO annealed in Ar showed defect-induced ferromagnetism, existing at room temperature. For nanographite prepared by the annealing of nanodiamonds, the absence of magnetization at H 5 0 T and T 5 5.0 K show the absence of any magnetic impurities. Also, from electron spin resonance measurements, the small line widths and small deviations from free electron spin g-value further confirm the absence of ferromagnetic impurities [151]. Similar modification of the spin-resolved density arises when the edges are passivated with hydrogen or in the presence of adatoms on the nanosheet. Hydrogenated nanographite exhibits spontaneous magnetism. The adsorption of water [152] and the intercalation of acids [153] can reduce the magnetism of nanographite. HCl adsorbed on the surface of nanographite can reduce magnetic moments along the edges as a result of mechanical compression of the domains.

3.3.3 Surface Area A high surface area is an important property of graphene. The surface area of graphene is usually determined by the Brunauer
Emmett
Teller (BET) model which analyzes the nitrogen adsorption capacity of the material. The surface properties of graphene are dependent on the synthesis method and number of layers as shown in Table 3.1. Graphene-based materials have been reported to have a surface area ranging from 300 to 3000 m2 g21. Surface area of SLG is theoretically predicted to be as high as 2630 m2 g21. Here we have listed some strategies to synthesize graphene-based materials. Wang et al. [154] synthesized RGO from GO by a hydrazine-based reduction with a BET surface area of 320 m2 g21. Thermal exfoliation of graphene generally yields a highly specific surface area due to more single- and fewlayer graphene. The specific area by such a method ranges from 650 to 940 m2 g21 [155,157,159]. Zhu et al. activated GO by KOH and achieved a high surface area of 3100 m2 g21 [77].

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TABLE 3.1 Surface Area Properties of Graphene-Based Material. S. No

Graphene-based materials

Surface area (m2g21)

1

GO

2 3

4

Synthesis

References

320

Gas-based hydrazine reduction

[154]

Functionalized graphene

700
1500

Thermal exfoliation of GO

[155]

Graphene sheets

670 (EG),

Exfoliation of GO (EG)

[156]

270 (HG)

Arc deposition of graphite under H2 (HG)

670 (EG),

Exfoliation of GO (EG)

210 (HG)

Arc deposition of graphite under H2 (HG)

Graphene sheets

[156]

5

Graphene sheets

940

GO subjected to thermal shock to exfoliate and then reduced by H2

[157]

6

Porous carbon

3100

Chemical activation of exfoliated GO

[77]

7

Porous 3D graphene material

3523

Hydrothermal followed by activation by KOH

[158]

Graphene, due to its peculiar structure, is a potential material for H2 storage. Ghosh et al. [160] studied H2 and CO2 uptake by GO (EG) and nanodiamond-derived graphene (DG). The graphene obtained by GO had an average of 3
4 layers while DG had 8
10 layers. The graphene showed a linear relationship between H2 uptake and BET surface area as shown in Fig. 3.10A. Hydrogen uptake of 3 wt% is observed at 1 atm, and 77 K for GO-derived graphene. DG showed 2.5 wt% of H2 uptake at 100 atm as shown in Fig. 3.10B. The CO2 uptake by GO and DG was found to be 21%
34% and 10%
38%, respectively, as shown in Fig. 3.10C and D. Due to its high surface area and electron/hole mobility, it is widely used in supercapacitors. Surface area and surface roughness are known to impact the interaction and adhesion between polymer and graphene [161,162]. Graphene, due to its small thickness, induces wrinkles. Qin et al. [163] studied changes in surface area caused by these wrinkles using molecular dynamic-based simulations. They found that the specific surface area of graphene changes by only 2% regardless of geometry and defects. This quality of high surface area in graphene makes it suitable for multidisciplinary applications such as energy storage [164], photocatalysis [165], nanocomposites [166], hydrogen storage [167], and sensing [9].

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3.3 PROPERTIES OF GRAPHENE

(B)

2.0

% wt H2 uptake

Wt% of H2

1.6 1.2 0.8 0.4 0.0

3.0

(a) EG 4

2.5

(b) DG 2

2.0

(c) EG 3 H2

1.5 1.0 0.5

0

0.0

400 800 1200 1600 Surface area (m2/g)

(C)

Volume (cc/g)

3.5

0

200

400 600 T (min)

800

1000

(D) 180 160 140 120 100 80 60 40 20 0 0.0

60 50

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FIGURE 3.10

(A) Linear relationship between the BET surface area and the wt% of H2 uptake at 1 atm of pressure and 77 K. (B) High-pressure H2 adsorption showing the variation of the weight percentage of hydrogen uptake at 100 atm and 298 K. CO2 adsorption and desorption isotherms of (C) EG1 and (D) DG1 at 1 atm of pressure and 195 K. Source: Adapted with permission from A. Ghosh, K.S. Subrahmanyam, K.S. Krishna, S. Datta, A. Govindaraj, S.K. Pati, et al. Uptake of H2 and CO2 by graphene, J. Phys. Chem. C 112 (40) (2008) 15704
15707 [160].

3.3.4 Mechanical Properties Carbon atoms in graphene are covalently bonded with each other in a hexagonal lattice. The maximum stress that a material can tolerate prior to fracture is associated to its defect-free lattice. Defects in the lattice reduce tensile strength. The intrinsic tensile strength of graphene is 130.5 GPa, which is attributed to the C
C bond strength of sp2-hybridized carbon. It is the strongest known material with a strength approximately 200 times that of steel. Lee et al. [168] measured the intrinsic strength of monolayer graphene by nanoindentation using an AFM. They found the breaking

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FIGURE 3.11 (A) Schematic of nanoindentation of suspended graphene membrane. (B) Histogram of elastic stiffness. [168]. (C) Stress
strain plots of composites with different graphene loadings. (D) Young’s moduli of the nanocomposite (circled) and Halpin-Tsai theoretical models (simulations). Source: (A) Adapted from C. Lee, X. Wei, J.W. Kysar, J. Hone, Measurement of the elastic properties and intrinsic strength of monolayer graphene, Science 321 (5887) (2008) 385
388; (D) Adapted with permission from X. Zhao, Q. Zhang, D. Chen, P. Lu, Enhanced mechanical properties of graphene-based poly (vinyl alcohol) composites, Macromolecules 43(5) (2010) 2357
2363 [169].

strength of 130 GPa and Young’s modulus of 1 TPa for monolayers (Fig. 3.11). To perform this study, the researchers made circular wells (1.5 and 1 μm diameter) on silicon substrates with 300 nm SiO2 layer with nanoimprint lithography and reactive ion etching. After this, graphite flakes were mechanically deposited on the substrates. Monolayers were identified by optical microscope and Raman spectroscopy. Indentation was done by AFM with the diamond tip over the center of the stretched films to measure the mechanical properties. No hysteresis was observed in load reversal studies indicating the flexible behavior of graphene. Solution-based reduction of graphene offers a cheap route for scalable production and its suitability for nanocomposites. Therefore, measuring the mechanical properties of RGO is crucial. Gomez-Navarro et al. studied the mechanical properties of RGO obtained by depositing GO on Si/SiO2 substrate and then reducing it by hydrogen plasma treatment [170]. The suspended flakes were then contacted by Ti/Au using electron beam lithography. The mechanical properties were then measured by nanoindentation at the center of the

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suspended sheets using an AFM tip. The high flexibility and mean elastic modulus of 0.25 TPa was measured, close to that of pristine graphene. In another study, Suk et al. used AFM in contact mode combined with finite element method (FEM) modeling [171]. Monolayer GO was found to have Young’s modulus of 207 GPa, which is lower when compared to pristine graphene. It had a prestress of 76.8 MPa, which is one order less than that of mechanically exfoliated graphene. For two- and three-layer GO membranes, the Young’s moduli are 444.8 and 665.5 GPa, respectively. With graphene possessing such high mechanical strength and flexibility it becomes obligatory to explore its potential as a reinforcement material in nanocomposites. Zhao et al. explored the utility of exfoliated graphene sheets in polyvinyl alcohol (PVA) [169]. They found a 150% improvement of tensile strength and a nearly 10-time increase in Young’s modulus with just 1.5 vol% of graphene loading. As shown in Fig. 3.11C, the tensile strength of the composite with 1.5 vol% graphene loading is 42 MPa, while that of a PVA sample is just 17 MPa. As observed in Fig. 3.11D, the Young’s modulus with graphene loading also increases and is almost 10 times greater than pure PVA. Further graphene addition causes agglomeration due to van der Walls interactions and slippage of the nanosheets during tensile testing and thus leading to no significant improvement in the mechanical properties. The aforementioned improvements in the properties are due to the high aspect ratio of the graphene sheets and strong interaction between graphene and PVA matrix. Graphene has been exploited with different polymers to improve the mechanical properties [162,166,172,173].

3.4 APPLICATIONS OF GRAPHENE 3.4.1 2D and 3D Printing Printing of texts and graphics on flat substrates has been an essential step in the technology we see around us today. Realizing a technology via printing of functional materials opens up a plethora of potential applications. Printing offers many advantages. Firstly, the process is additive in nature. By comparison, subtractive approaches like lithography involve the removal of material, resulting in the production of waste along with the use of numerous chemicals. Printing, on the other hand, is capable of dispensing the required amount of material reducing material wastage. Secondly, the process can be carried out under ambient conditions, eliminating the need for facilities such as vacuum and heating. Thirdly, multiple materials may be printed simultaneously or

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successively to obtain the desired structure. It thus becomes essential for the development of printing techniques to exploit the capabilities of nanomaterials. Two-dimensional printing technologies on planar substrates can be broadly classified into two variants: noncontact and contact. Noncontact printing techniques involve inkjet and laser-induced forward-transfer methods, where the material is transferred onto the substrate via droplets or jets of the functional ink over a small distance. The approaches in contact techniques involve such methods as flexo, gravure, offset, screen, and microcontact printing. Contact printing uses a masked transfer of material by the use of a stencil onto the substrate, implying a contact between the two. This method offers a high throughput and is often the industry choice where the goal is the large-scale production of identical devices [174]. On the other hand, noncontact processes are deployed where a mechanical touch of the surface could damage the product. These processes are used mostly in small-scale production and research and development. Of late, the development of printing in three dimensions has enabled the realization of a number of concepts. Three-dimensional printing is, simply put, printing over and over again to achieve a layer-by-layer manufacturing technology [175]. The advantages offered by 2D printing are relevant to 3D printing as well. Additionally, this technology offers the ability to rapid prototype as it is well integrated with computeraided design software. This significantly reduces process time and cost. Various additive manufacturing techniques include fused deposition modeling (FDM), stereolithography, selective laser sintering, selective laser melting, binder jetting, direct ink writing (DIW), etc. FDM and DIW are widely used for 3D printing of nanomaterials. They depend on the fluidic nature of the printing material during the dispensing step which gains rigidity when finished, to achieve definition in the final product and its dimensional stability. FDM is achieved by melting and depositing a filament of polymer onto a base in the design pattern, which then takes form on cooling. The polymer filament can be mixed with filler materials like graphene for printing nanocomposites [176]. Direct ink write method, on the other hand, involves the dispensing of a material paste. The paste viscosity must be optimum to achieve enough fluidity for dispensing and enough rigidity to retain structure. The rigidity of the printed structures can be obtained by solvent evaporation from the paste and immersed printing in cold liquids [177]. The incorporation of nanomaterials into the printing matrix can introduce a great deal of functionality to the printed devices. Also, the intrinsic properties of the material used for printing are greatly enhanced. A small fraction of the nanomaterials when incorporated into the matrix can lead to a profound effect on properties of the formed composite.

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Graphene, with its high electrical conductivity, transparency, thermal conductivity, and mechanical strength could find its way into a number of areas of applications that can be realized via printing. The process of developing printing strategies for nanomaterials includes these steps: (1) ink formulation, (2) printing, (3) drying, and (4) postprint treatment. Aspects concerning each of these steps in 2D printing are discussed below. These can be extrapolated to 3D printing as well, with some additional considerations. 1. Ink formulation: The functionality of the material can be incorporated into printing by its addition into the ink. Printing techniques are adopted based on the patterns to be designed. Each printing technique calls for certain characteristics in the printing ink (Figs. 3.12
3.14). The viscosity of the ink is a crucial parameter in the selection of the printing technique. 3D printing methods like DIW require inks that can be shear thinned on extrusion from a nozzle, while FDM heats polymer beyond glass transition. Nozzle clogging can occur with the incorporation of nanomaterials. Ratios of filler size to nozzle diameters are maintained. Additives for stabilization of the nanomaterials and viscosifiers require removal processes postprinting. 2. Printing: Ink rheology plays a crucial role in the quality of the printer device. The resolution of the printed pattern varies between

FIGURE 3.12 Screen printing of graphene for an all printed transistor. Source: Adapted with permission from W.J. Hyun, E.B. Secor, M.C. Hersam, C.D. Frisbie, L.F. Francis, Highresolution patterning of graphene by screen printing with a silicon stencil for highly flexible printed electronics, Adv. Mater. 27(1) (2015) 109
115 [178].

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FIGURE 3.13 The versatility of 3D printing lies in its ability to print complex structures. (A) Direct ink writing of freestanding structures of silver interconnects. (B) 3D printed graphene aerogels. Sources: Adapted with permission from J.J. Adams, E.B. Duoss, T.F. Malkowski, M.J. Motala, B.Y. Ahn, R.G. Nuzzo, et al., Conformal printing of electrically small antennas on three-dimensional surfaces, Advan. Mater. 23(11) (2011) 1335
1340; C. Zhu, T.Y.-J. Han, E.B. Duoss, A.M. Golobic, J.D. Kuntz, C.M. Spadaccini, et al., Highly compressible 3d periodic graphene aerogel microlattices. Nat. Commun. 6 (2015) 6962 [179,180].

FIGURE 3.14 Tunability in graphene makes it a suitable material for versatile applications. Source: Adapted with permission from F. Perreault, A.F. De Faria, M. Elimelech, Environmental applications of graphene-based nanomaterials, Chem. Soc. Rev. 44(16) (2015) 5861
5896 [181].

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techniques. The suitability of ink for the printing technique used depends on its rheology. Noncontact methods can employ lowviscosity inks, whereas contact methods require inks of high viscosities. Binder-free inks tend to be of low viscosities, which can be made up to the required viscosities by the use of viscosifiers or by the use of techniques like solvent exchange. Solvent exchange involves the exfoliation of graphene in a suitable solvent, followed by its transfer into a high viscosity solvent. 3. Drying: Upon dispensing the inks, it is required that they lose their fluidity and become rigid. The liquid phase in the ink must evaporate. The drying process is crucial as the leftover solvent can deteriorate the performance of the device [42,182,183]. It thus becomes desirable that solvent evaporation happens without the addition of additional heat energy as it enables printing onto substrates like polymers and paper [184]. A common challenge faced during the drying of 2D printing inks is the “coffee-ring effect.” When a drop dries, the outer edge of the drop is pinned to the substrate surface [185]. The liquid at the edge evaporates faster and the liquid from the interior flows outward, causing the deposition of more material at the drop boundaries. This resembles the rings formed on drying coffee drops, and hence the name. There are three approaches to prevent this. a. De-pinning of the contact line: The pinning of the drop edge onto the substrate can be avoided by substrate modification. b. Substrate temperature: An alternative approach to resolving coffee stains is the regulation of substrate temperature. Higher temperatures at the edges are the cause of the effect, and thus lowering the drying temperature can suppress the formation [186]. c. Marangoni flow: The evaporation of the liquid along the edges sets a convective flow within the drop due to the variation in surface tensions or a concentration gradient. A liquid with a region with higher surface tension will pull more than the region with lower surface tension. This sets a flow from a region of low toward high surface tension. To overcome this in printing, a solvent with a higher boiling point and a lower surface tension is added. During drying, the added solvent evaporates slowly from the edges of the drop where the surface tension is lower. This causes a reversal in the direction of the flow, setting up an inverse Marangoni flow from the drop edge toward the interior, thus suppressing the coffee-ring effect. 4. Postprint treatment: It is highly desirable that the printed patterns are performance-ready immediately after printing as production can be faster and less energy intensive. However, unlike conventional printing, the incorporation of nanomaterials to introduce

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functionality introduces other challenges. For inks with binders, the postprinting removal of these additives is crucial as the functionality of the nanomaterials may not be completely realized and the performance hampered. Also, it is important to ensure the complete removal of the solvents for stability in functioning. Films of 2D materials are annealed to degrade the binder material. Annealing can be achieved by a few approaches: photonic, thermal, and pulsed laser annealing. Photonic routes enable the annealing of printed films on flexible substrates due to the localization of the heat. The heating step also ensures the densification of the films, ensuring higher conductivity. Additives such as ethyl cellulose and polyvinyl pyrrolidone can be made to decompose on heating to 250 C
400 C. It is clear that this step can be detrimental and impractical when using flexible polymer substrates. The idea of printing in three dimensions has been around since the 1980s; however, it has received much attention of late owing to advancements in technology. The possibilities arising from these emerging interests have opened up a vast area of research. Just as in 2D printing, there are numerous approaches to realizing 3D printed structures, coming under the umbrella of additive manufacturing. ASTM for additive manufacturing segregates 3D printing into seven categories: directed energy deposition, material extrusion, binder jetting, material jetting, powder bed fusion, sheet lamination, and vat photopolymerization. Most often, the materials used for printing are polymeric, although metals and ceramics can also be printed using suitable methods. Threedimensional printing has an edge over conventional manufacturing techniques because of the ability to rapid prototype. The technique allows testing of designs in a much shorter time span without having to commit to expensive tooling processes, while also saving on storage space of raw materials. However, this process speed is limited to prototyping and many aspects need to be resolved to achieve large-scale 3D printing. The process of printing begins with the design of the desired object, using a computer-aided modeling software. The designed object is then divided to comprise a large number of layers, by software known as slicers. The resulting file comprises the coordinates of the printing path in each layer and is known as a Standard Tessellation Language (STL) file. This interfaces the computer to the printer through the G-code. The printer now takes commands from the slicer and prints as per the coordinates in the STL file. Three-dimensional printing caters to a vast area of applications and many 0D, 1D, 2D nanomaterials have been incorporated. Most often, fused deposition modeling and DIW have been implemented owing to

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their ease in the incorporation of material. Listed below are a few reports of 3D printing using graphene, which gives a flavor of the versatility of additive manufacturing to create functional structures. Jakus et al. synthesized a composite of graphene and a biocompatible elastomer, polylactide-co-glycolide, and fabricated parts via DIW. The rheology of the printing paste was suited to printing features as small as 100 μm in structures as large as 10 cm in size. The properties of the printer device are stable against deformation and exhibited good conductivities of about 800 Sm21. Owing to the use of biocompatible materials in the printed structure, it showed adhesion, viability, and growth of human mesenchymal stem cells and in vivo studies show acceptance without accumulation in the kidney, liver, and spleen [187]. It is desirable that the printability of the materials do not affect the intrinsic properties of the material. Additives like binders in the inks could lead to degraded performance. By the functionalization of oxygen in graphene between 4 and 16 wt%, obtained by the thermal reduction of graphite oxide, To¨lle et al. showed that the dispersibility of the flakes in low boiling point nontoxic solvents could be achieved with concentrations up to 15 mg mL21. As the binders have been avoided, the conductivity of the printed films and micropatterned devices showed increased electrical conductivity with no toxicity, showing its potential in biomedical applications [188]. Fused deposition modeling relies on the fabrication of structures of polymers by extruding them above their glass transition temperature and their rapid cooling on exiting the nozzle. The structures obtained from the incorporation of RGO into the filaments of polylactic acid were shown to yield conductivity of about 600 Scm21. The process of extrusion causes an alignment of the flakes in the direction of exfoliation, which could be the cause of the increased conductivity. The mechanical properties of the printed filaments along with its conductivity show application in printing conductive pathways in flexible electronics [189]. The mechanical properties of printed patterns exhibit superior properties like increased mechanical strength and good flexure due to the ordering of the fillers that can be obtained during the printing step. A similar enhancement also occurs in the case of the electrical properties. As the market moves toward having more robustness in products, flexible electronics are being realized. In the approach to add more functionality to flexible, strong, and lightweight structures by inkjet printing of graphene-based inks, freestanding graphene paper was shown to support 3D structuring of a porous graphene hydrogel. When paired with the conducting polymer polyaniline, enhanced mechanical, electrochemical, and capacitive behavior was realized in a flexible supercapacitor with the energy density of 24.02 Wh kg21 at a power density of 400.33 W kg21 [190].

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3.4.2 Detection of Volatile Organic Compounds There has been an increase in the levels of VOCs owing to the fast pace of industrialization since the 20th century. These organic compounds are known to have a negative impact on the environment and human health. The sources of these compounds can be traced to many items we surround ourselves with, like paints, fuels, aerosols, and vehicle exhausts. These have an adverse effect on health, causing allergies, breathing disorders, and many other diseases [191,192]. VOC detection can also aid in the detection of diseases like cancer [193]. The metabolism in cancerous cells causes the emanation of various aliphatic and aromatic organic compounds. The detection of these entities is possible by analyzing exhaled breath, which could aid in early detection of the disease. Thus, it is imperative to detect VOCs in hostile environments. A VOC sensor works by producing a detectable change in a physical parameter on interaction with vapor. The parameter most often probed for detection is the change in electrical properties as it can be processed easily. Detection by the sensor should be quantitative, rather than qualitative, as in colorimetry. The sensors can then be evaluated based on parameters such as selectivity, sensitivity, stability, hysteresis, and signal to noise ratio. Gas chromatography-mass spectroscopy (GCMS) [194], ion flow tube mass spectrometry [195], infrared, and optical spectroscopy [196,197] are extremely sensitive methods to use in detecting gases down to parts per billion range. However, they are expensive to use and operate, and cannot be made portable. Many different types of materials have been deployed as a cheaper alternative in the detection of VOCs. These include metal oxide [198
200] and metal organic framework sensors [201
203], intrinsically conducting polymers [204
206], microelectromechanical microsystems (MEMS) [207
209] and surface acoustic wave devices [210
212]. These are calibrated using the aforementioned techniques which offer high sensitivity. Metal oxides have been widely exploited in gas sensing. The sensing mechanism is based on the band bending that occurs on interactions with adsorbates. However, they often lack selectivity, have low sensitivity, and require a temperature of several hundred degrees for operation due to moisture sensitivity [213]. Conducting polymer sensors feature high sensitivities and shorter response times, combined with room temperature operation. However, they suffer from lower selectivity and stability. Additionally, they are highly sensitive to humidity, irradiation, and oxide-reduction [205,214,215].

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Miniaturization has resulted from advancements in silicon fabrication techniques. MEMS-based microgas chromatography has been developed. Coated micromachined resonators fabricated from silicon show extreme sensitivity (owing to their low mass), with the coating material enabling selectivity. However, fabrication is tedious and expensive [216]. Graphene, with its large specific surface area of 2630 m2 g21, along with its versatility and chemico-physical tunability has received attention for gas sensing ability [217
219]. Graphene is functionalized by different means to make it sensitive to various entities using polymers [220,221], nanoparticles [222,223], or other carbon-based materials [224,225]. For sensors developed using graphene derivatives alone, the performance was observed to vary with the synthesis route chosen [217]. Chemiresistive devices prepared by drop casting aqueous dispersions of graphene derivatives synthesized via two methods were studied in response to ethanol and n-butanol. The chemically RGO exhibited a granular morphology and a lower change in conductance of 1%, whereas the flatter solvo-thermally RGO surfaces allow for permeation of analytes, responding with a 3% toward 10 ppm ethanol. The flatter surfaces allow for diffusion into the bulk and the response toward different analytes is dependent on steric hindrances. Graphene and its derivatives can be paired with other nanomaterials to enhance response and increase selectivity. Thangamani et al. [226] incorporated graphene nanoplatelets into a composite of PVA and CuO nanoparticles. The strong interaction between the nanoparticles and nanoplatelets results in a high response to isopropanol vapors with a sensitivity of 73 to 1800
4000 ppm. Unlike metal oxide sensors, the device is operational at room temperature. Poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate) (PEDOTPSS) is a conjugated polymer that exhibits good electrical conductivity and transparency, finding applications in photovoltaics. When paired with graphene oxide obtained by Hummer’s method concentrations as low as 0.04 wt% and sensitivity of 11% could be achieved compared to pristine PEDOT:PSS, with a response time of 3 s for 35 ppm methanol vapor. Nanocomposites of conducting polymer matrices, also called quantum resistive sensors (QRS), show exponential change in resistance with increasing conducting nanoparticle fraction. The decreased gap with loading produces tunneling pathways giving rise to a profound rise in conduction. Exposure to vapors causes localized swelling, resulting in a change in conductivity. Tung et al. incorporated RGO in a PIL-PEDOT matrix to achieve a detection range of 2.5 ppm of methanol with a response time of 2 s. The differential adsorption of analytes on

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graphene, the steric hindrances faced by the analytes, and the swelling of junctions between conductive pathways give the sensor its selectivity. Detection of VOCs by colorimetry has been demonstrated by Wang et al. [227]. Polydiacetylenes are polymers formed by self-assembly of diacetylene supramolecules and exhibit chromatic variation in response to ligand
receptor interactions. This colorimetric change can be characterized by optical absorption studies. These polymer chains were grown onto graphene. The suitability of graphene is attributed to its large surface area resulting in the growth of the polymers, added with its transparency in the visible region and good mechanical strength. Exposure to organic compounds such as THF, CHCl3, CH3OH, and DMF in the vapor phase results in a change of color, quantified based on resolving the spectrum into red, green, and blue components enabling statistical study.

3.4.3 Graphene for Gas Sensors The detection of gas molecules is necessary for fields such as environmental monitoring, industry, automotive engines, medical sector, and emergencies. Therefore, efforts to develop sensors to detect different gases are required. Current concern is to detect moderate to extremely low concentrations of gas to sense gas leakages of various gases that might be toxic, flammable, or significant in one way or the other. On adsorption of these extrinsic molecules onto graphene, its electronic properties are influenced by the mobility (200,000 cm2 V21 s21), low resistivity, and low electrical noise due to its perfect crystal lattice. A wide range of chemical species and gas molecules can be detected to atomic and molecular level by graphene and its derivatives. Adsorbates like NO2, halogens, and alkali are chemically active and contribute either electron or holes to the graphene and perturb its electronic properties [219]. On the other hand, adsorbates with H and OH radicals can form a covalent bond with graphene. On adsorption of electron donating species onto p-type graphene, its conductance reduces and vice versa in the case of electron withdrawing species [219,228]. This change in conductance can be detected by different devices like FET, quartz crystal microbalance, and MEMS sensors [229]. Schedin et al. [230] fabricated a FET using mechanically exfoliated graphene by electron beam lithography. Fig. 3.15A shows the change in resistivity caused by exposure of graphene to various gases at concentrations of 1 ppm. The positive (negative) sign of the resistivity represents electron (hole) doping. Region I is the state when the device is in vacuum before exposure. Region II represents exposure to diluted chemical, III represents evacuation of the experimental setup, and IV shows annealing at

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(A)

(B) 4

NH3 Changes in ρxy (Ω)

Adsorption

Δ ρ/ρ (%)

2 CO

0

H2O

–2 –4

I

III

II

500 t (s)

1e

20

1e Desorption

10

IV

NO2 0

30

1,000

0 0

200

400 t (s)

600

FIGURE 3.15 (A) Changes in graphene’s resistivity due to exposure to various gases diluted to concentrations of 1 ppm. (B) Changes in Hall resistivity observed during adsorption of strongly diluted NO2 (blue curve) and its desorption in vacuum at 50 C (red curve). The green curve is a reference—the same device thoroughly annealed and then exposed to pure He. Source: Adapted with permission from F. Schedin, A.K. Geim, S.V. Morozov, E.W. Hill, P. Blake, M.I. Katsnelson et al., Detection of individual gas molecules adsorbed on graphene, Nat. Mater. 6(9) (2007) 652, [230].

150 C. Further, Hall measurements revealed that NO2, H2O, and iodine acted as acceptors, whereas NH3, CO, and ethanol were donors. Under the same exposure conditions chemically induced charge carriers linearly depend on the concentration of an analyte. Fig. 3.15B shows the adsorption and desorption of individual molecules at 1 ppm NO2. Hall resistivities (ρ)xy occur in steplike manner on adsorption of the analyte molecules and also desorption in a similar fashion but in the opposite direction. Chen et al. [231] fabricated a sensor with pristine graphene with detection limits as low as 158 ppq for a range of gas molecules at room temperature. The detection limit in the case of NO was found to be 158 ppq as shown in Fig. 3.16A. The inset shows the sensor response by cycling between N2 and 10 ppt of NO; reproducible results with signals of 1.4% and 80% recovery within a few minutes was observed. Similar studies were done with NO2 using N2 as a carrier gas. The conductance increased by 1% for 40 ppt NO2 exposure in 5 minutes. The detection limit was found to be 2.06 ppt as shown in Fig. 3.16A. Fig. 3.16C and D show adsorption of NH3 with and without UV light
induced surface cleaning. By cleaning with UV light the detection limit improved from 83.7 ppb to 33.2 ppt for the same graphene device and under the same conditions. Pearce et al. [232] fabricated epitaxially grown single- and multilayer graphene on SiC surface for sensitive detection of NO2. SLG showed a higher sensitivity than multilayer graphene due to an electron donation

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FIGURE 3.16 (A) Relative change of conductance versus time recorded with NO exposures ranging from 10 to 200 ppt. The inset shows the reproducibility of sensor response at 10 ppt of NO exposure. (B) Conductance change versus time recorded with NO2 exposures ranging from 40 to 800 ppt. The inset shows the reproducibility of sensor response at 40 ppt. (C) Response to NH3 without UV light illumination. The inset shows the reproducibility of sensor response at 200 ppt of NH3 exposure. (D) Response to NH3 in situ UV light illumination. Source: Adapted with permission from G. Chen, T.M. Paronyan, A.R. Harutyunyan, Sub-ppt gas detection with pristine graphene, Appl. Phys. Lett. 101(5) (2012) 053119 [231].

from SiC in a single layer whereas in the case of multilayer graphene, the uppermost layer is screened from electron donation from SiC. Not only pristine graphene but RGO prepared by thermal reduction of GO was found to be responsive to low concentrations of NH3 and NO2 in the air at room temperature [233]. The structure of the device was back gated. On NO2 exposure the conductance increased and three cycles were repeated to check the reproducibility. The conductance was found to decrease when the concentration of NO2 was reduced from 100 to 25 ppm. The defects and doping in graphene are conducive for gas sensing. First principle calculations have also supported the same [234]. Maseland coworkers found that pristine graphene with few defects is insensitive to toluene and 1,2 dicholorobenzene, but graphene with line defects and graphene ribbons of size 2
5 μm showed much better response due to reduction in the conduction path. Suspended bilayer graphene was used to detect individual CO2 molecules using a special device architecture. This provided an enhanced electric field on the

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surface of graphene which escalated the physical adsorption of CO2 even in a low concentration environment [235]. The role of graphene in wearable and flexible gas sensing of various hazardous gases (NO2, NO, CO2, SO2, H2S, NH3, H2), heavy metals (Cd, Hg, Pb, Cr, etc.), VOCs (toluene, acetone, formaldehyde, etc.) is exceptionally large and significant [236]. Combining graphene and its derivatives with various materials such as nanoparticles, nanowires, polymers, metal oxide, transition metal dichalcogenides (TMDCs), etc., will realize more sensitive, cheap and portable sensors. The present challenge is to achieve high selectivity and quantification of chemical species.

3.4.4 Graphene in Lithium Ion Batteries Since the discovery of graphene it has been used for a multitude of applications due to its specific properties. These peculiar properties qualify it as a potential and reliable material for electrochemical energy storage. Graphene has been studied for various energy devices like solar cells [66,237], fuel cells [238,239], lithium ion batteries (LIBs), and supercapacitors. Other than this, it also has the ability to serve several purposes in other energy storage devices like transparent batteries, smaller capacitors, flexible and fast-charging devices. The market for LIB has vastly expanded in the past two decades after it was first commercialized by Sony in 1991. LIBs have been merchandised on a large scale to meet ever-growing energy demands, ranging from portable devices to large electricity storage equipment. They have high-energy density, low maintenance, a decent life cycle, and are environmentally benign. Efforts continue to propel the LIB technology to its edge and meet the global energy demands. 3.4.4.1 Working of Lithium Ion Batteries LIB comprises a positive and a negative electrode with a separator in between, which is an ionic conductor but an electronic insulator dipped in a suitable electrolyte. The energy density and performance of an LIB is hugely affected by the nature of the electrode material. With the selection and engineering of suitable electrode materials, goals like higher capacity, better cycle stability, and enhanced safety are being pursued. A typical LIB works with charge and discharge processes. During a typical discharge process, lithium ions from the anode are extracted into the electrolyte, and lithium ions in the electrolyte are intercalated into the cathode material. This movement of the ions from anode to cathode is accompanied by the release of electrons which flows in the external circuit. The reverse process occurs during the charging process where lithium ions move from the cathode and intercalate in the anode through the electrolyte. Commercial LIBs typically use transition metal

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oxides such as LiCoO2, LiMn2O4, LiFePO4 as the cathode material, which is coated over an aluminum current collector. Ten to twenty percent of conductive carbon and 5%
10% of polymeric binders like polyvinylidene difluoride (PVDF) and polytetrafluoroethylene (PTFE) are also added along with active material to enhance the electronic conductivity and achieve better adhesion of the electrode material, respectively. The anode material is coated over a copper current collector with conducting carbon and PVDF if required. The two electrodes are separated by a porous separator (polyethylene or polypropylene film of thickness 10
20 μm) soaked in an electrolyte solution (LiPF6 in an organic solvent). Both separator and electrolyte solution should have better ionic conductivity. The cell is usually fabricated in a metal casing in sandwich fashion with an electrolyte-dipped separator in between the two electrodes. A schematic of an LIB is shown in Fig. 3.17, where typical charge and discharge processes are shown. See Eqs. (3.1) and (3.2) for graphite (C6)/LiCoO2 battery. At cathode: LiCoO2 5 Li1-n CoO2 1 nLi1 1 ne2 1

2

C6 1 nLi 1 ne 5 Lin C6

(3.1) (3.2)

At anode, cell reaction: LiCoO2 1 C6 5 Lin C6 1 Li12n CoO2

FIGURE 3.17 The schematic of lithium ion battery cell. Source: Adapted with permission from K.M. Abraham, Prospects and limits of energy storage in batteries, J. Phys. Chem. Lett. 6(5) (2015) 830
844 [240].

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In the sections that follow, we summarize the status of graphene in LIB, in physiochemical mechanisms that dominate, and in future prospects. 3.4.4.2 Graphene in Lithium Ion Battery Anodes Graphite has dominated the market ever since LIBs were commercialized due to its high theoretical specific capacity (372 mAh g21) and stable structure for facile insertion and removal of lithium ions. Graphite, being a stacked structure of individual graphene sheets along the cdirection, offers limited lithium insertion of one lithium atom per six carbon atoms and hence limited specific capacity. Therefore, the overall performance of graphite is not sufficient to deliver the requirements of the next-generation LIBs needed for electric vehicles. With each day the drive to develop materials with high electrochemical performance is increasing. Graphene being a single sheet of graphite has both basal planes exposed and can accommodate double lithium ions. In addition, lithium ions have high diffusivity on the graphene plane of 1027 to 1026 cm2 s21. This indicated the use of single- and fewlayer graphene as the potential anode material in LIBs. Researchers have studied various methods to exfoliate graphite to maximize the available surface for lithium ions. Several spacers like CNTs [241] and fullerenes, have been exploited to avoid the restacking of graphene sheets. Some other approach include the incorporation of metal and metal oxide nanoparticles like Sn, Si, Co3O4, Fe2O3, Fe3O4, SnO2, TiO2, and V2O5 [242
250], which not only act as spacers but have the potential of reversible interactions with lithium ions. Graphene oxide has turned out to be better anode material than graphene as it has more interlayer spacing due to the presence of functional groups. Experiments reveal that it is difficult to use pure graphene as the anode material because small lithium clusters form on the graphene sheets to facilitate the growth of dendrites. Another issue is its low Columbic efficiency, poor cyclic stability and formation of solid electrolyte interfaces in the cavities and defects due to the reaction of lithium ions with oxygencontaining functional groups on graphene [251]. Since the lithium ions can approach from either side of the graphene sheets, there remains a strong possibility of repulsion which counters the charge process and formation of LiC6 phase. To resolve these issues several strategies like doping with boron (p-type) and application of graphene-based hybrids have been adopted [251]. Typical hybrids include graphene incorporated with metal oxides and sulfides which helps in improving Columbic efficiency compared to bare graphene, and also enhances the electrical conductivity of these materials which suffer from low electrical conductivity. Most importantly, graphene helps to counter the large volume expansions that

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occur due to repeated charge and discharge processes. Composites of these oxides with graphene not only render buffer spaces but also provide good electrical conductivity and reduce particle agglomeration. Graphene has also played a major role in advancing silicon-based anodes [252
254] where it works as an electrical conductor and elastic buffer. Various assemblies of graphene with specific morphologies can be used instead of sheets. One is achieved by stacking graphene sheets in the c-direction to achieve graphene paper. It has high flexibility and mechanical strength and can be bent and rolled without fractures which makes it a potential material for use in LIBs for flexible electronics. It can be directly used as an anode material without binder or additive, consequently giving rise to a higher energy density of the electrode material. Although graphene paper is an attractive material for LIB anodes it has certain setbacks. Its specific capacity is much lower than graphene powder. At higher current densities it shows terribly poor capacity. Even at lower current densities, the specific capacity is not comparable with that of graphene powder. This could be due to densely stacked graphene sheets which act as a barrier for the diffusion of lithium ions. Therefore densely stacked graphene paper is not a good idea. Utilizing thin paper (1
2 μm) has shown better electrochemical performances. Several other modifications would be needed to improve the diffusion of lithium ions. For example, to achieve better diffusivity of lithium ions, Alsharaeh et al. [255] created 2
5 nm pores in GO (holey graphene) using silver nanoparticles by microwave irradiation in order to use it as an anode for LIBs. It showed an improved electrochemical response in terms of charge/ discharge capacity. It led to a charge/discharge capacity of 423 mAh g21 at 100 mA g21 with a high reversible capacity of 400 mAh g21 after 100 cycles. This excellent performance was attributed to increased structural stability, and more active sites for lithium insertion. Apart from energy storage, holey graphene outperforms the intact graphene in various other applications like sensors and membranes [256,257]. 3.4.4.3 Graphene in Lithium Ion Battery Cathodes Developing cathode materials with excellent electrochemical properties is one of the most significant aspects of LIBs. Therefore efforts are continuously being made to engineer high-performing cathode material. Some 40 years back in the late 1970s, the lithium batteries used dichalcogenides like TiS2 as the cathode and Li-Al alloys as anode. For the first time in 1980, Goodenough’s group found that layered oxides like LiCoO2 have the same structure as that of dichalcogenides and lithium can be extracted electrochemically [258]. Despite several advantages associated with LiCoO2, its low thermal stability and high costs diverted research effort toward other transition metal oxides like LiFePO4,

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LiMnO2, LiMn2O4, LiNi0.5Mn0.5O2, LiMn12xFexPO4, and Li3V2(PO4)3. But these materials suffer from significantly low electrical conductivities and sluggish lithium ion transport, which leads to poor rate capability and low specific capacities. Graphene has been known as an additive that improves the electrochemical performance of the active cathode material by forming a conducting network and imparting mechanical stability to the electrode material. Though other carbon materials such as carbon black also serve the same purpose, it has low conductivity when compared to more crystalline forms of carbon. Graphene, due to its high surface/mass ratio and extraordinarily high electrical conductivity, is a highly suitable material for incorporation in an LIB cathode. The structural stability and electronic conductivity of lithium nickel manganese oxides can be improved by partial replacement of nickel and manganese with cobalt. Hence Rao et al. [259] synthesized Li1/3Ni1/3Co1/3MnO2-graphene composites by microemulsion and ball milling method. The graphene composite exhibited high discharge capacities (153 mAh g21) and less irreversible capacity losses compared to LiNi1/3Co1/3Mn1/3O2 (138 mAh g21) at a current rate of 5C. At a low current rate the composite shows a capacity retention of 99.1% in contrast to 89.7% for LiNi1/3Co1/3Mn1/3O2 as shown in Fig. 3.18. Alternating current spectra revealed that the high discharge capacity and rate capability of the composite is due to enhanced lithium transfer between active material and electrolyte interface. Other probable reasons for the improved electrochemical performance were improved structural stability, suppression of dissolution of transition metal ions, and reduced heat production during charge-discharge cycles.

FIGURE 3.18

Cycling performance at different discharge rates. Source: Adapted with permission from C.V. Rao, A.L.M. Reddy, Y. Ishikawa, P.M. Ajayan, Lini1/3co1/3mn1/ 3o2
graphene composite as a promising cathode for lithium-ion batteries, ACS Appl. Mater. Interf. 3(8) (2011) 2966
2972 [259].

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FIGURE 3.19 Comparison of rate capability of LFP and nanocomposites. Source: Adapted with permission from X. Zhou, F. Wang, Y. Zhu, Z. Liu, Graphene modified LiFePo4 cathode materials for high power lithium ion batteries, J. Mater. Chem. 21(10) (2011) 3353
3358, [260].

Zhou and coworkers [260] synthesized graphene-wrapped carbonLiFePO4(LFP/G 1 C) using spray coating and annealing techniques to study them as cathodes for LIBs. The composite material delivered a capacity of 70 mAh g21 at a high discharge rate 60 C which was much higher than carbon-LiFePO4 (LFP/C) as shown in Fig. 3.19. The improved performance was attributed to synergetic effect to nano-sized LiFePO4, crucial for the shorter diffusion path of Li1 ions and graphene network, and which provided electrical conductivity as well as free spaces and facilitated Li1 diffusion. Several works have reported [261] graphene anchored/encapsulated cathode materials to enhance the electrochemical performance of LIBs. 3.4.4.4 Graphene as Current Collector Long-term cell operation often leads to degradation and corrosion of the current collectors. Aluminum and copper have long been the appropriate choice for cathode and anode current collectors respectively since they form an oxide layer that helps to prevent corrosion. The formation of an oxide layer on the current collectors has led to the rise in internal cell resistance. Researchers have utilized materials such as carbon, graphene, graphene oxide, etc. to create a barrier for oxygen diffusion. This also helps to make a better interface between an electro active material and a current collector. These modified current collectors have been

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67

known to show an enhanced electrochemical performance which could be attributed to reduced internal resistance and improved charge transfer characteristics [262,263].

3.4.5 Graphene in Supercapacitors 3.4.5.1 Introduction and Working of Supercapacitors Supercapacitors (SC) are energy storage devices that store and deliver energy at very high charge and discharge rates. Unlike batteries they store electrical energy rather than chemical energy. Characteristics like high power density, excellent reversibility, stable cyclic performance, and the low fabrication cost of these devices have prompted researchers to pursue the development for large area and high-energy density applications. An ideal energy storage device would be one that has a high-energy density as well as fast charge and discharge rates. Though supercapacitors can charge and discharge within a fraction of seconds, they cannot store enough energy. They are currently being used where a quick burst of energy is needed, but their application is limited due to their low-energy density. Therefore, it is critical to improve the energy density of SCs to realize the potential of an ideal energy storage device that can store a large amount of energy along with fast charge and discharge rates. The SC is composed of two electrodes separated by an insulating dielectric material immersed in an electrolyte. An application of potential, opposite charges accumulate on both the electrodes, which remain separated by the dielectric, and hence give rise to an electric field. The charge storage in an SC takes place by two mechanisms: formation of an electric double layer (non-Faradaic process) and pseudo-capacitance (Faradaic process). Based on the mechanism they are categorized into three types (1) electric double-layer capacitor (EDLC); (2) pseudo-capacitor; (3) hybrid capacitor which is a combination of both double-layer and pseudo-capacitance as shown in Fig. 3.20. The EDLC devices store

FIGURE 3.20 Working mechanism of different supercapacitors. Source: Adapted with permission from F. Yi, H. Ren, J. Shan, X. Sun, D. Wei, Z. Liu, Wearable energy sources based on 2D materials, Chem. Soc. Rev. (2018) [264].

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energy by the adsorption of electrolyte ions onto an electrode material that is typically high surface area
activated carbon. It gives high power density as the ion transportation is fast, but they suffer from low-energy density when compared to pseudo-capacitors where redox reactions occur. Although the Faradaic process delivers higher capacitance, it is not very desirable as it involves a chemical reaction which than leads to slower charging and discharging rates. Improving the energy density of the SCs and making them comparable to batteries is the current challenge. Therefore there is a need to develop novel materials that can fulfill the requirements of present and future generations. The performance of an SC is determined by the properties of the electrode material and the electrolyte. Electrodes with large surface area and thin separators are utilized to attain high-energy density without compromising power density. EDLCs are the most dominant and promising types of SCs on the market. Among all the different materials, various forms of carbon (like porous/activated carbon, CNTs, and graphene), conducting polymers and metal oxides are the most investigated class. Pores of activated carbon may not be sufficiently large to allow the electrolyte ions to access all its surface area. Graphene due to its high surface area and related properties has a lot of potential as an electrode material for SC. Also, its intrinsic capacitance was found to be 21 uF cm22 which sets the upper limit of EDLC capacitance among all carbon-based materials. It can reach capacitance values as high as 550 F g21 provided the whole surface area is fully utilized. If agglomeration can be avoided, both of its surfaces will be available for the electrolyte. The device performance is dependent on the intrinsic properties of the graphene used, potential window, the electrolyte (aqueous, organic, or IL) and device fabrication. To completely exploit the adsorption potential of graphene, it has been synthesized by various routes to tailor its properties. For the sake of enhancing the electrochemical performance or to avoid agglomeration or both, graphene and its nanocomposites have been investigated with a number of materials like binders, polymers, nanoparticles, CNTs, etc. Such capacitors fall under the category of hybrid supercapacitors which incorporate the relative advantages of both EDLC and pseudo-capacitors without compromising the cyclic stability that had limited the success of pseudo-capacitors. Graphene provides a high surface area backbone and helps maintaining a good electrical contact between deposited pseudo-capacitive materials which have very low electrical conductivity. The electrons can tunnel through the ultrathin (1
5 nm) coating of pseudo-capacitive material over graphene. By using pseudo-capacitive materials, one can mitigate the shortcomings of EDLC and enhance the capacitance by faradaic reactions. Various methods like CVD, micromechanical exfoliation,

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liquid phase exfoliation (LPE), arc discharge, chemical reduction from GO, etc., have been exploited for the production of graphene. Among these, chemical exfoliation of graphite to GO and then reduction to graphene remains the most followed method for supercapacitors. This is attributed to the low cost and scalability of the process. 3.4.5.2 Graphene Nanocomposites With Various Materials Experiments reveal that pseudo-capacitive materials give 10
100 times more capacitance than EDLC due to Faradaic reactions. RuO2 was the first pseudo-capacitive material that was explored followed by materials like MnO2, IrO2, TiO2, Fe2O3, ZnO, NiO, Cu2O, etc. [265,266]. Researchers have incorporated RuO2 with carbon materials such as CNTs, graphene, etc., to further enhance the electrochemical performance. Although there are numerous works [267
269] on RuO2 and its composites, it has not been commercially accepted due to its high cost and low porosity. Therefore other metal oxides have been used that moderate costs without compromising performance [270,271]. MnO2 is a promising pseudo-capacitive material with low cost, environmentally innocuous and high theoretical specific capacitance but it suffers from low electrical conductivity. Also commercially available MnO2 shows poor specific capacitance (200 F g21) because of its limited electrochemically active area, which is an obstacle for commercial applications. Incorporating it with a large surface area conducting material like graphene would solve this problem. Graphene oxide works as a suitable support to anchor MnO2 over its surface due to the presence of functional groups. Yu et al. [272] fabricated solution exfoliated graphene nanosheets on porous textile structures followed by electro-deposition of MnO2. This achieved specific capacitance of 315 F g21. This material was used as a positive electrode with a single-walled carbon nanotube-textile as a negative electrode in aqueous Na2SO4 giving a high-energy density of 12.5 Wh kg21, power density of 110 KW21, with 95% capacitive retention at operational voltage of 1.5 V as shown in Figs. 3.21 and 3.22. Since cost plays a vital role in the selection of material for commercial applications, the choice of material is based on cost and performance. This led to a focus on conducting polymers like polyaniline, polypyrrole, PEDOT, and their composites with graphene [273]. They offer reversible redox reactions on their backbone due to the presence of π electron conjugation, which can further be tuned by chemical modifications. They also provide high capacitance, low cost, and easy processing which makes them potential materials for SC. Their composites with graphene balances the volume change of the electrode material and compensates their inherent disadvantage of swelling and degradation.

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(A)

(i)

(ii)

Microfibers in textile (B)

(C) 20

Specific capacitance (F g–1)

100 mV/s 50 mV/s 20 mV/s 10 mV/s 5 mV/s G-only 5 mV/s

10

Current (A g–1)

MnO2 deposited graphene-textile

Graphene nanosheets -coated textile fibers

0

–10

G/MnO2

300

G-only

200

100

0

–20 0.0

0.2

0.4

0.6

0.8

1.0

0

Potential (V vs. Ag/AgCl)

20

40

60

80

100

Scan rate (mV/s)

FIGURE 3.21

(A) Schematic illustration for preparing hybrid graphene (G)/MnO2 nanostructured textile. (B) Cyclic voltammograms for G/MnO2-textile electrode at different scan rates in 0.5 M aqueous Na2SO2 electrolyte. (C) Comparison of specific capacitance values between G/MnO2-textile and graphene nanosheets-only textile at different scan rates. Source: Adapted with permission from G. Yu, L. Hu, M. Vosgueritchian, H. Wang, X. Xie, J.R. McDonough, et al., Solution-processed graphene/MnO2 nanostructured textiles for highperformance electrochemical capacitors, Nano Lett. 11 (7) (2011) 2905
2911 [272].

Some polymer nanocomposites for supercapacitor applications are shown in Table 3.2. The performance of the device is dependent on the operating voltage of the electrolyte. ILs can tolerate higher voltages (up to 7 V) and hence give higher energy density. Rao et al. used graphene with an IL and achieved an energy density of 31.9 Wh kg21 at 5 mV s21 and 60 C [284]. This was further enhanced by Jang et al. who reported specific energy density of 85.6 Wh kg21 at room temperature and 136 Wh kg21 at 800 C, obtained from curved graphene sheets [285]. 3.4.5.3 Doping and Surface Modifications To engineer high-performance SCs, doping, functionalization, and surface modifications are well-known strategies. Nitrogen doping in this regard has served the purpose of augmenting the specific FUNDAMENTALS AND SENSING APPLICATIONS OF 2D MATERIALS

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71

FIGURE 3.22 Asymmetric (hybrid) electrochemical supercapacitor (EC). (A) Schematic of the assembled structure of hybrid EC cells. (B) Nyquist plot for the assembled hybrid EC over the frequency range of 100 kHz 0.1 Hz. Equivalent series resistance extracted is about 8 Ohm. (C) Galvanostatic charging/discharging curves measured with different current densities. (D) Cycling performance of hybrid ECs showing capacitance retention of approximately 95% after 5000 cycles of charging and discharging at a current density of 2.2 A g21. (E) Nyquist plots showing the corresponding impedance curves measured after each 1000 cycles during cycling test for assembled ECs. Source: Adapted with permission from G. Yu, L. Hu, M. Vosgueritchian, H. Wang, X. Xie, J.R. McDonough, et al., Solutionprocessed graphene/MnO2 nanostructured textiles for high- performance electrochemical capacitors, Nano Lett., 11(7) (2011) 2905
2911 [272].

capacitance without compromising the cyclic performance. It improves the performance of the graphene electrode as it modifies the local electronic structure and distorts the graphene lattice [286,287]. Jeong et al. achieved high capacitance of 280 F g21 with nitrogen-doped graphene produced by a plasma process, which is almost four times that of pristine graphene [288]. Wen et al. [289] later synthesized crumpled graphene sheets with high nitrogen doping and ultrahigh pore volume of 3.42 cm3 g21 showing a high capacitance of 245.9 F g21 at a current density of 1 A g21. Similar studies have been done for single or dual heteroatom-doped graphene by various routes to generate electrochemical sites and thereby increase the pseudo-capacitance [290]. Boron and nitrogen co-doped graphene has been studied for solid-state SC using PVA/H2SO4 gel as separator and electrolyte.

3.4.6 Graphene Aerogels Since graphene sheets have the propensity to stack, device performance can degrade due to narrowing of ionic and electronic channels. FUNDAMENTALS AND SENSING APPLICATIONS OF 2D MATERIALS

TABLE 3.2 Graphene-Based Supercapacitors. S. No

Electrode

Electrolyte

Specific capacitance, Energydensity 21

22

Cyclic performance

References

1

RuO2/CNT/G foam

2 M Li2SO4

502 F g @ 1 mAc m , 39.28 Wh kg21

106% retention after 8100 cycles

[274]

2

RuO2/G (Ru:40 wt%)

1 MH2So4

551 F g21 @1 A g21

94% retention after 2000 cycles at 5 A g21

[275]

3

PEDOT:PSS-G/RuO2

0.5 MH2SO4

820 F g21 @0.5 A g21, 86 Wh kg21

81% after 1000 cycles

[276]

4

G wrapped MnO2 nanospheres

1 MNa2SO4

210 F g21 at 0.5 A g21

82.4% after 1000 cycles

[277]

5

Needle-likeMnO2/GO

1 MNa2SO4

211 F g21 @0.15 A g21

84% after 1000 cycles

[278]

6

PANi nanofibers sandwiched between G sheets

1 MH2SO4

210 F g21 @ 0.3 A g21, 155 @ 3 A g21

94% after 800 cycles @3 A g21

[279]

7

PANi directly coated on RGO




361 F g21 @0.3 A g21

80% after 1000 cycles

8

PANi doped graphene nanocomposite

2 MH2SO4

21

480 F g

21

21

@0.1 A g

21

9

PANi nanorodon RGO

H3PO4PVAgel

970 F g

10

PPy foam and 3DRGO

3 MNaClO4

360 F g21 @1.5 A g21

11

Hybrid SC of G/MnO2 on textile (positive electrode) SWNTs/textile (negative electrode)

0.5 MNa2SO4

21

@2.5 A g

21

315 F g @ 2 mV s , 12.5 Wh kg21

[280] 21

70% after 400 cycles at 1.5 A g

[281]

90% retained after 1700 cycles

[282]

Stable over 1000 cycles

[283]

95% after 5000 cycles

[272]

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73

In addition, graphene produced by thermal or chemical reduction may not have sufficiently large pores for facile ionic transport. Therefore, an alternate to forming 3D graphene structures (sponges, aerogels, hydrogels, etc.) that maintain the inherent property of graphene have been extensively explored. Graphene aerogels (GAs) facilitate enhanced charge transfer and ion diffusivity due to high specific surface area, porosity, and light weight. Zhu et al. reported [291] 3D graphene composite aerogel microlattices by DIW. Graphene nanoplatelets (GNP) and SiO2 fillers were added to improve electrical conductivity and electrochemical properties of the ink. The 3D electrodes fabricated with ink of composition 12.5 wt% GNP and 4.2 wt% SiO2 showed the least resistance of 0.96 Ω sq21 and surface area of 418 m2 g21 and best rate capability and capacity retention of 90% when the current density was increased from 0.5 to 10 A g21 as shown in Fig. 3.23. These electrodes were highly stable with no loss of capacitance after 10,000 cycles. The most recent trend today is miniaturization of energy devices toward thin, lightweight, and flexible devices that can be used for wearable gadgets, wireless portable devices, etc. This has driven research efforts toward micro-supercapacitors which have interdigitated electrode geometry without any separator in between. This allows fast movement of ions in the same plane and exhibits high power densities. Yoo et al. showed that the specific capacitance of graphene dramatically increases when made into planar structure (Fig. 3.24). The open architecture in the in-plane supercapacitor allows facile transport of ions within the plane as well as complete utilization of the surface area. Therefore the thinnest devices, made out of one or two graphene layers help to deliver high specific capacitances [292]. Later, several other

FIGURE 3.23 (A) Schematic of graphene composite aerogel. (B) Specific capacitance and capacitive retention at different current density. Source: Adapted with permission from C. Zhu, T. Liu, F. Qian, T.Y.-J. Han, E.B. Duoss, J.D. Kuntz, et al., Supercapacitors based on three-dimensional hierarchical graphene aerogels with periodic macropores, Nano Lett. 16(6) (2016) 3448
3456 [291].

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FIGURE 3.24 Schematic of stacked graphene SC and in-plane SC. Source: Adapted with permission from J.J. Yoo, K. Balakrishnan, J. Huang, V. Meunier, B.G. Sumpter, A. Srivastava, et al., Ultrathin planar graphene supercapacitors, Nano Lett. 11(4) (2011) 1423
1427 [292].

groups exploited the in-plane transport of ions in graphene sheets together with other potential materials to fabricate microsupercapacitors [293,294].

3.5 CHALLENGES AND OUTLOOK Graphene, a recent wonder material holds the hope of technological revolutions in coming generations. From producing graphene in laboratories, it has progressed to large scale [18] and roll-to-roll [125] manufacture; a positive step toward a technological revolution. Due to a plethora of outstanding properties, it has already been exploited for several applications with efforts carried out to realize new applications every day. Graphene has enabled lightweight wearable devices, nanoelectromechanical systems (NEMS), data storage, LEDs, flexible devices, photovoltaic, etc. One important aspect in the utilization of graphene is the quality of the material, which is heavily dependent on the synthesis method. Graphene required for electronic applications is preferably defect free and monodispersed in regard to lateral dimensions and number of layers. Along with that, for the bulk scale production of graphene, environmentally friendly protocols need to be established for commercial applications. This aspect of research needs further attention from the scientific community. In the case of liquid-phase exfoliation the challenge is to limit or stop aggregation of graphene sheets without the use of additives to maintain its intrinsic property with the high concentrations it needs. Another important method for producing large-scale graphene sheets is CVD. It is important to isolate graphene from substrate without using harsh chemicals which contaminate the graphene films [295]. Health and safety aspects need to be considered when the commercialization of graphene is discussed. In particular, toxicity studies play a critical role. In vitro and in vivo studies on graphene report that it has no adverse effects. Moreover GO has antibacterial properties toward Gram-negative bacteria [296]. GO was found not to have any obvious

FUNDAMENTALS AND SENSING APPLICATIONS OF 2D MATERIALS

REFERENCES

75

cytotoxicity at low concentrations for human epithelial cells; however, high concentrations of GO can induce oxidative stress [297]. Pristine graphene has appeared to be more toxic than GO and RGO [298]. Therefore for the judicious and safe use of graphene, standard protocols should be practiced. With respect to environmental effects, GO can undergo degradation by the enzyme horseradish peroxidize in the presence of H2O2 [299] and also by some microorganisms [300]. This needs further attention to explore methods of degradation of graphene and its derivatives using microorganisms to provide a safe environment and better health. The future of graphene depends on product strategies which will propel the technology toward new directions. An interesting review that gives a road map of the present status and future prospects of graphene and 2D materials can be seen in reference [19].

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669. [3] A.K. Geim, K.S. Novoselov, The rise of graphene, Nat. Mater. 6 (3) (2007) 183. [4] K.S. Novoselov, A.K. Geim, S.V. Morozov, D. Jiang, M.I. Katsnelson, I.V. Grigorieva, et al., Two- dimensional gas of massless dirac fermions in graphene, Nature 438 (7065) (2005) 197. [5] J.-H. Chen, C. Jang, S. Xiao, M. Ishigami, M.S. Fuhrer, Intrinsic and extrinsic performance limits of graphene devices on SiO2, Nat. Nanotechnol. 3 (4) (2008) 206. [6] F. Bonaccorso, L. Colombo, G. Yu, M. Stoller, V. Tozzini, A.C. Ferrari, et al., Graphene, related two-dimensional crystals, and hybrid systems for energy conversion and storage, Science 347 (6217) (2015) 1246501. [7] F. Xia, D.B. Farmer, Y. Lin, P. Avouris, Graphene field-effect transistors with high on/off current ratio and large transport band gap at room temperature, Nano Lett. 10 (2) (2010) 715
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C H A P T E R

4 Synthesis, Characterization, and Properties of Graphene Analogs of 2D Material Pratik V. Shinde and Manoj Kumar Singh Centre for Nano and Material Sciences, Jain Global Campus, Jain University, Ramanagaram, Bangalore, India

4.1 INTRODUCTION 2D materials open a new frontier in material science due to their excellent electronic, mechanical, and optical properties, which originate from their ultrathin thickness and 2D morphological features. In the early part of the 20th century, based on concepts from classical physics, researchers said that 2D materials were thermodynamically unstable materials at any finite temperature due to thermal lattice fluctuations. Material science had a significant scientific breakthrough in 2004, after the isolation of monolayer graphene by mechanical exfoliation. The invention of single-layer graphene has shown not only that it is possible to create stable single atom-thin layers but also these atomic layers can have fundamentally different electronic structures and properties from the parent material. Following on from the discovery of graphene, research into 2D material was stimulated in various fields such as supercapacitors, sensors, photonics, optoelectronics, transistors, biomedical applications, and so on [1
6]. 2D materials are atomically thin crystalline solids having intralayer covalent bonding and interlayer van der Waals (vdWs) bonding. These materials are unique due to unprecedented properties that are unparalleled when compared to their bulky counterparts. Due to having a

Fundamentals and Sensing Applications of 2D Materials DOI: https://doi.org/10.1016/B978-0-08-102577-2.00004-X

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limited number of atomic layers and its high mobility, 2D crystal shows different electrical properties such as high surface area, a surface state free nature, as well as a dangling bond-free surface [7]. Properties such as strong light matter interaction, high mechanical strength, high optical nonlinearity, and a strong quantum Hall effect (QHE) make 2D material optimal for many emerging applications [8]. Graphene is an atomically thin sp2 carbon-layered material with a honeycomb lattice structure [9]. Many remarkable properties such as a large surface area, high charge-carrier mobility, high thermal conductivity, high optical activity, high mechanical strength, and low Young’s modulus make graphene the most studied material in the 2D family over the last decade [10,11]. However, the lack of bandgap and low current on/off ratio made graphene unsuitable for commercial applications, so researchers have placed more attention on other 2D materials such as hexagonal-boron nitride (h-BN), TMDCs, metal oxides, metals halides, MXenes, and Xenes [12].

4.2 GRAPHENE ANALOGS The expanding portfolio of 2D crystals currently includes the representative graphene, boron family, transition-metal oxides (TMOs), TMDCs, metal monochalcogenides (MMCs), metal halides (MHs), layered metal dichalcogenides (LMDCs), MXenes, and Xenes. Fig. 4.1 shows the 2D family and their applications in various fields.

FIGURE 4.1 2D family and its applications.

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4.2.1 Boron Family h-BN and borophene are well-studied 2D members of the boron family. Although bonding between boron atoms is more complicated than carbon, the 2D members are stable. These members have particular scientific appeal due to their potential application in sensing, photodetectors, catalysts, transistor, and energy-storage fields [13
17]. 4.2.1.1 Hexagonal-Boron Nitride h-BN , an analog of graphite (so-called white graphite) has attracted considerable attention, especially because of its exceptionally high mechanical hardness, super thermal and chemical stabilities as well as high lubricity [18
23]. Boron nitride exhibits various crystalline polymorphs that are analogous to carbon, such as diamond-like cubic BN, graphite-like h-BN, and onion-like fullerenes [24]. This isoelectronic analog of graphene have some novel physical properties due to the small dimensions and special edge structures. Morphologically h-BN is similar to the honeycomb graphene, composed of alternating boron and nitrogen atoms that are bonded with sp2 hybridization [25]. The bond ˚ [26]. length between two successive boron and nitrogen atoms is 1.44 A h-BN shows a strong covalent bonding within the plane while interplane bonding is due to weak vdWs forces. Similar to graphene (0.333 nm), in h-BN the spacing between two successive layers is 0.334 nm [9]. h-BN has a bandgap of B6 eV [21] providing clear advantages over graphene for electronic and optical applications, as these extensive applications of h-BN require high quality and uniformity. In general mechanical exfoliation is the most suitable method to obtain the uniform crystalline h-BN layers from bulk [27,28]. Chemical vapor deposition (CVD) is an attractive method for growing h-BN because of high controllability, large scalability, and low cost [29
31]. The growth of large-area monolayer h-BN films using the CVD method on metallic substrates (such as Cu [32], Ni [33], Pt [34], Fe [35], Co [36], Ir [37], Ru [38], Rh [39], and Ag [40]) has been reported. Chang et al. [41] fabricated large single crystalline monolayers of h-BN by oxide-assisted chemical vapor deposition (OCVD) on a Cu substrate. The results showed that continuous films were grown with a high degree of monolayer uniformity. Fig. 4.2A shows a schematic diagram of the growth mechanism of h-BN by a conventional CVD process and OCVD process, with a preformed oxide layer on a Cu substrate. Fig. 4.2B
D shows a scanning electron microscopy (SEM) image of hBN grown on Cu substrate for 10 min via the conventional CVD method ranging from 0.5 to 5 μm. Fig. 4.2E
G shows SEM images of h-BN grown on Cu substrate for 30 min via OCVD process, having domain size B20 μm. Thus using the OCVD method the domain size of h-BN

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FIGURE 4.2 (A) Growth mechanism of the h-BN by the conventional CVD process and OCVD process with a preformed oxide layer on Cu substrates. (B)
(D) SEM images of h-BN grown on Cu substrate for 10 min via the conventional CVD method, for example, SEM images of h-BN grown on Cu substrate for 30 min via OCVD process. Source: Reprinted (adapted) with permission from: R.J. Chang, X. Wang, S. Wang, Y. Sheng, B. Porter, H. Bhaskaran, et al., Growth of large single-crystalline monolayer hexagonal boron nitride by oxide-assisted chemical vapor deposition, Chem. Mater. 29 (15) (2017) 6252
6260. Copyright (2017) American Chemical Society.

can be enhanced from 1 to 20 μm. Atomic force microscopy (AFM) and Raman analysis showed that as-grown h-BN is a monolayer with high quality and uniformity.

4.2.2 Transition-Metal Oxides The atomically TMOs offer an unprecedented opportunity for use in optics, electronics, catalyses, sensors, and energy conversion and storage devices such as batteries, supercapacitors, and solar cells [42
47]. The family of metal oxides is known for their unique combination of redox chemistry, rapid ionic-transport channels, short-distance interactions between charge carriers and their earth abundance [48]. The larger surface area of 2D planar TMOs nanostructures provide more available FUNDAMENTALS AND SENSING APPLICATIONS OF 2D MATERIALS

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active sites for catalytic redox reactions as well as enhance the kinetics of interplane ion transport and in-plane carrier transport. MOx is the generalized formula for metal oxides, where M is a metal atom and O is an oxygen atom. MoO3, WO3, TiO2, Co3O4, V2O5, etc. are examples of the metal oxide family. 4.2.2.1 Molybdenum Trioxide Molybdenum trioxide (MoO3) is a wide-bandgap (2.8
3.6 eV) n-type semiconductor [49]. It has three fundamental crystalline structures: thermodynamically stable orthorhombic phase (α-MoO3), metastable monoclinic phase (β-MoO3), and hexagonal phase (γ-MoO3) [50]. The layered crystal structure of MoO3 is formed by stacking bilayer MoO6 octahedrons with vdWs force along the [010] direction [51]. MoO3 has wide-ranging potential applications such as photodetector, sensor, supercapacitor, energy storage, field-effect transistor, and catalyst [52
57]. MoO3 can be prepared primarily by hydrothermal, sol
gel, liquid exfoliation, and electrodeposition methods [58
61]. Liu et al. [62] synthesized α-MoO3 crystals by thermal vapor transport (TVT) in a quartz tube reactor of a tube furnace without using intentional substrates. Fig. 4.3A shows a schematic diagram of the TVT setup, which was used for synthesis of α-MoO3 crystals. Fig. 4.3B shows an SEM image of α-MoO3 belt crystals. A single-crystal X-ray diffractometer (SCXRD) and a general-area detector diffraction system (GADDS) were used to identify the crystal phases of gathered α-MoO3 belts, shown in Fig. 4.3C. The belts lie on (010) atomic planes. Also, optical microscopy, AFM, and Raman spectroscopies were used for characterization of the α-MoO3 crystals. This method is helpful for the synthesis of high-quality layered α-MoO3 single crystals, which are useful for different applications. 4.2.2.2 Vanadium Oxide The vanadium oxides (VOx) family consists of several monovalent and mixed-valent oxides (V2O5, V2O3, VO2, V6O13) that exhibit distinctive catalytic, electrical, optical, and magnetic properties that are interesting for various technological applications including thermochromic, batteries, catalytic, sensing, and electrode materials [63
67]. The vanadium element exists in several oxidation states (13, 14, 15), which allow the formation of a variety of vanadium:oxygen stoichiometries. Vanadium dioxide (VO2) is well known for its temperature-dependent first-order reversible metal 2 insulator transition (MIT) accompanied by a structural transformation between monoclinic phase VO2 (M) to rutile phase VO2 (R), which implies an abrupt change in resistivity and near-infrared transmission [68]. This change shows significant modification in electrical and optical properties. Among the VOx family, sesquioxide (V2O3) has attracted considerable attention due to a high FUNDAMENTALS AND SENSING APPLICATIONS OF 2D MATERIALS

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FIGURE 4.3 (A) Schematic diagram of the TVT setup used for α-MoO3 synthesis. (B) SEM image α-MoO3 belt crystals. (C) SCXRD and GADDS systems from a single belt and gathered belts of α-MoO3 crystals. Source: Reprinted (adapted) with permission from: H. Liu, C.J. Lee, Y. Jin, J. Yang, C. Yang, D. Chi, Huge absorption edge blueshifts of layered α-MoO3 crystals upon thickness reduction approaching 2D nanosheets, J. Phys. Chem. C 122 (22) (2018) 12122
12130. Copyright (2018) American Chemical Society.

theoretical capacity, low toxicity, and natural abundance [69]. It also possesses reversible MIT transition near 170K from antiferromagnetic insulator to paramagnetic metal [70]. Orthorhombic vanadium pentoxide (V2O5) has the highest oxidation state of vanadium with the highest stability. However, the use of V2O5 electrodes for supercapacitors is limited because of its inferior electronic conductivity and poor cycling stability [71]. Several synthetic routes such as sol 2 gel, pulsed laser deposition (PLD), CVD, atomic layer deposition (ALD), solvothermal, and hydrothermal methods have been successfully explored to fabricate different VOx phases [63,72
76]. Recently, Meng [77] synthesized porous V2O5 microcrystals with different morphologies by a hydrothermal method and thermal treatment, further used to study the specific capacitances, rate capability, and cycling stability of supercapacitors. Fig. 4.4A
C shows SEM images of electrodes of the butterfly-like, rhombohedra-like, and flower-like V2O5 microcrystals before cycles respectively. Fig. 4.4D shows the X-ray diffraction (XRD) patterns of the calcined products with different morphologies and also indicates that all the products are well crystallized. These three morphologies of V2O5 microcrystals were characterized with Raman, Fourier-transform infrared spectroscopy (FTIR), energy-dispersive X-ray spectroscopy (EDAX), and X-ray FUNDAMENTALS AND SENSING APPLICATIONS OF 2D MATERIALS

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FIGURE 4.4 (A) SEM images of electrodes of the butterfly-like V2O5. (B) SEM images of electrodes of the rhombohedra-like V2O5. (C) SEM images of electrodes of the flowerlikeV2O5. (D) XRD patterns of the V2O5 microcrystals with different morphologies. Source: Reprinted (adapted) with permission from: J. Zheng, Y. Zhang, T. Hu, T. Lv, C. Meng, New strategy for the morphology-controlled synthesis of V2O5 microcrystals with enhanced capacitance as battery-type supercapacitor electrodes. Cryst. Growth Des. 18 (9) (2018) 5365
5376. Copyright (2018) American Chemical Society.

photoelectron spectroscopy (XPS) spectra. This work provided a new strategy to design a variety of morphologies in a controlled manner. 4.2.2.3 Titanium Oxide Titanium dioxide (TiO2) is known for its outstanding optical, electrical, and catalytic properties. TiO2 is found in three crystal phases: anatase (tetragonal), rutile (tetragonal), and brookite (orthorhombic) [78]. It is an environmentally friendly, easily available, cheap, and thermodynamically stable semiconductor material with a wide bandgap (3.2 eV) [79]. It has been extensively used in various applications such as photovoltaic cells, sensors, ultraviolet detectors, energy storage, and photocatalysts [80
84]. To date, the methods used for TiO2 synthesis are hydrothermal, solvothermal, sol
gel, and CVD methods [85
88]. Alotaibi et al. [89] used a CVD method to synthesize pure brookite TiO2 thin films. The film’s crystallinity and phase purity were studied with XRD and Raman spectroscopy, as shown in Fig. 4.5A
B. SEM was used to determine FUNDAMENTALS AND SENSING APPLICATIONS OF 2D MATERIALS

FIGURE 4.5 (A) XRD of brookite TiO2 thin film. (B) Raman of brookite TiO2 thin film. (C) SEM of brookite TiO2 thin film. Source: Reprinted (adapted) with permission from: A.M. Alotaibi, S. Sathasivam, B.A. Williamson, A. Kafizas, C. Sotelo-Vazquez, A. Taylor, et al., Chemical vapor deposition of photocatalytically active pure brookite TiO2 thin films, Chem. Mater. 30 (4) (2018) 1353
1361. Copyright (2018) American Chemical Society.

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the surface morphology and film thickness, as shown in Fig. 4.5C. The surface of TiO2 brookite film is composed of narrow pyramidal features. This study opens up a better way to fabricate brookite TiO2 nanomaterial, which may lead to a broader range of applications.

4.2.3 Metal Halides MHs is another group of 2D layered materials with the general formula, MH2, where M is metal (Mg, Ca, Cd, Ge, Zn, or Pb) and H is a halogen atom (Cl, Br, or I). MHs have the stoichiometric ratio of 1:2 as do TMDCs. These materials are semiconductor materials with a bandgap in the visible to UV range which becomes wider for a monolayer. Compared to other 2D material families, research on MHs is still in progress. The MHs play an important role in photodetectors, solar cells, and the catalyst field [90
92]. 4.2.3.1 Lead Iodide Lead iodide (PbI2) is a high-anisotropic intrinsic semiconductor material with a direct bandgap of 2.28
2.5 eV [93]. The layered PbI2 material is composed of layers of hexagonally close-packed iodine and lead (I
Pb
I units) atoms stacked along to the c-axis. In between layers, covalent bonding is present, while interlayers are bonded with weak vdWs interactions [94]. PbI2 has been intensively studied owing to its tunable opto-electronic properties as well as its high atomic number, and the bandgap makes it a promising candidate for X-ray and γ-ray detectors [95,96]. As a typical layered perovskite derivative, PbI2 is used in such applications as solar cells and photodetectors [97
99]. There are some reports of PbI2 fabrication by mechanical exfoliation, solution method, and physical vapor deposition (PVD) [100
104]. Wang et al. [105] synthesized a high-quality triangular PbI2 flake on a mica substrate by PVD process. The PbI2 flakes were characterized by Raman, field emission scanning electron microscope (FESEM), XRD, AFM, transmission electron microscopy (TEM), and EDAX. Fig. 4.6A shows an optical image of synthesized PbI2 flakes. Inset is an AFM of flakes. At 15 cm distance the thickness is nearly 17.2 nm. Highresolution TEM (HRTEM) is shown in Fig. 4.6B. TEM confirms the crystal structure of PbI2 flakes. A modified PPC/PMMA-mediated technique was used for TEM sample preparation. EDAX confirms the existence of Pb and I elements with the atomic ratio of 1:2, as shown in Fig. 4.6C. The structures are interesting for high-performance photodetectors.

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FIGURE 4.6 (A) Optical image of PbI2 flakes at the distance of 15 cm. (B) TEM images of PbI2 flake. (C) EDAX spectrum of PbI2 flake.

4.2.4 Metal Monochalcogenides As with a 2D TMDCs, the MMCs have attracted particular research interest due to their astonishing properties and promising future applications. This class of 2D material has the general formula MX, where M is posttransition metal from Group IIIA (Ga or In) and X is a chalcogenide from Group VIA (S, Se, or Te) of the periodic table. The structure of these materials form sheets in which atoms are packed in hexagonal lattices similar to the carbon atoms in graphene. The two layers of M atoms are sandwiched in between two layers of X atoms (X
M
M
X pattern). This layered structure exhibiting in-plane covalent bonding and weak vdWs interlayer interaction. GaS, GaSe, and InSe are the significant members of this family. 4.2.4.1 Gallium Sulfide Gallium sulfide (GaS) has a hexagonal crystal structure with space group P63/mmc [106]. It is composed of sandwich-like covalently ˚ and D3h symbonded S-Ga-Ga-S atoms, with a lattice constants 3.59 A metry [106]. GaS is the semiconductor and has an indirect gap of 2.59 eV and a direct gap of 3.05 eV [107]. As an experiment, large single-layer or few-layers of GaS were synthesized using a micromechanical cleavage technique on an SiO2/Si substrate [108,109]. Also, 1D GaS nanowires and nanobelts have been synthesized using a vapor
solid method [110]. CVD, however, has been one of the most promising approaches for the synthesis of largearea and high-quality 2D GaS materials [111
113]. Wang et al. [114] synthesized 2D GaS crystal by a simple and efficient ambient pressure CVD method using a single-source precursor of Ga2S3. GaS crystals were characterized with optical microscopy, SEM, AFM, photoluminescence (PL), Raman spectroscopy, and TEM. From SEM images, the triangular GaS domains were observed, while from low-magnification

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TEM images of GaS flake, layered structures at a large scale were observed. This method is useful for scale-up and the controlled synthesis of 2D GaS. 4.2.4.2 Gallium Selenide Gallium selenide (GaSe) is a p-type semiconducting material with layered structure, exhibiting strong in-plane covalent bonding, and weak vdWs interlayer interaction [115]. The unit cell of GaSe crystal is composed of vertically stacked Se-Ga-Ga-Se sheets. Bulk GaSe has an indirect bandgap of B2.11 eV, possessing properties like high photoresponsivity (2.8 A W21), high quantum efficiency (1,367%), and large nonlinear optical coefficients (54 pm V21) [116]. GaSe can therefore serve as a promising material for optoelectronics, photodetectors, and field-effect transistors. Hu et al. [115] prepared ultrathin 2D nanosheets of GaSe using mechanical cleavage and solvent exfoliation. While the Ajayan group used vapor phase mass transport methods to synthesize the atomically thin GaSe single crystal [117]. Recently Chen et al. [118] reported the growth of atomically thin GaSe on a GaAs (111) substrate by molecular beam epitaxy (MBE) with a two-step growth approach. Fig. 4.7A shows an AFM image of sub-μm-scale triangular high-quality grains of GaSe with well-aligned edges. A scanning transmission electron microscopy (STEM) technique is used to determine the morphology of as-grown GaSe films. The STEM images are shown in Fig. 4.7B. This method is helpful for synthesizing high-quality GaSe films.

FIGURE 4.7 (A) AFM image of ultrathin GaSe grown on the surface of GaAs using MBE. (B) STEM image with the schematic of GaSe lattices (Scale bar: 0.5 nm).

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4.2.4.3 Indium Selenide Indium selenide (InSe) is a black crystalline n-type layered semiconductor [119]. Its structure is composed of stacked layers of SeIn-In-Se atoms with vdW force bonding [120]. The bulk InSe has a narrower direct bandgap nearly B1.26 eV, which can be increased up to 2.11 eV for the monolayer due to quantum confinement [121,122]. InSe displays higher electron mobility (B103 cm2 V21 s21), high on/off ratio (B108), and excellent flexibility and ambient stability [123
126]. Recently, the QHE was also observed in few-layer InSe in argon [127]. These properties make InSe a most suitable material for optoelectronics, transistors, photovoltaic, and strain engineering applications [124,125,128,129]. Several reports have been made on few-layer InSe films made by mechanical exfoliation and liquid exfoliation, which all produce nanosheets with small and uncontrollable size [124,130]. Yang et al. [131] used a PLD technique for direct growth of wafer-scale layered InSe nanosheets. XPS was used to confirm the chemical composition of pure InSe. Characterization techniques such as Raman, TEM, and AFM were also used to study the growth of InSe. XRD clarifies the crystallographic phase of the synthesized InSe films, as shown in Fig. 4.8A. The peaks correspond to the hexagonal crystal structure of β or ε phase. The welldefined hexagonal sheet and layered stacking morphology are observed in low-resolution TEM images, shown in Fig. 4.8B. AFM characterization was used to measure the thickness of monolayer InSe films, shown in Fig. 4.8C. This method provides a better way of synthesizing InSe layers, which exhibit good uniformity and high crystallinity with precise controllability.

4.2.5 Layered Metal Dichalcogenides Group IV metal dichalcogenides attracted extensive attention due to their potential applications in next-generation electronics and optoelectronics. These low-cost compounds are earth abundant and environmentally friendly. Like TMDCs their general formula is MX2, where M is a metal atom from Group IVA (Sn) and X is chalcogenide atom of Group VIA (S or Se). The metal atom is sandwiched in between two chalcogenide layers. In contrast with graphene and black phosphorous, these materials exhibit a suitable bandgap making them suitable candidates in different fields such as field-effect transistors, photodetectors, and photocatalysts [132
135]. SnS2 and SnSe2 are the members of this 2D material family.

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FIGURE 4.8 (A) XRD results of a PLD grown InSe film. (B) Low resolution TEM image of bilayer stacking InSe nanosheets. (C) AFM image of monolayer InSe nanosheets. Source: Reprinted (adapted) with permission from: Z. Yang, W. Jie, C.H. Mak, S. Lin, H. Lin, X. Yang, et al., Wafer-scale synthesis of high-quality semiconducting two-dimensional layered InSe with broadband photoresponse, ACS Nano 11 (4) (2017) 4225
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4.2.5.1 Tin Disulfide Tin disulfide (SnS2) belongs to an extended family of LMDCs and crystallizes in the hexagonal CdI2-type lattice structure [136]. In the crystal structure, S-Sn-S trilayers are internally bonded with covalent bonding and held together by vdW forces. In recent years, SnS2 has attracted much interest due to its earth abundance, low cost, and high chemical stability [137]. SnS2 exhibits n-type semiconductor characteristics and has a large bandgap in the range B2.033 to B2.4 eV, which is wider than most LMDCs [138]. Its high sensitivity, high current on/off ratio, high carrier mobility, and strong anisotropy of optical properties make it a promising candidate for field-effect transistors, gas-sensing materials, opto-electronic devices, and holographic recording systems [132,139
142]. A scalable, high-quality, and high-efficiency production of mono- or few-layer SnS2 materials has attracted much attention. Like other 2D materials, SnS2 can be obtained by mechanical exfoliation, solvothermal, ALD, and CVD methods [143
146]. But there are some issues with these methods, such as less control of the size, thickness, yield, and uniformity of products; it is also time consuming. Jia et al. [147] used a simple and high-efficiency modified CVD to prepare a vertically aligned 2D SnS2 crystal nanoflake with high production and quality. Fig. 4.9A shows a schematic diagram of a CVD system with four temperature zones for the growth of 2D SnS2 crystal samples. Fig. 4.9B
C shows an SEM image and an enlarged SEM image of SnS2 nanoflakes grown on an Si substrate. The numerous vertical SnS2 nanoflakes have an average size of up to 30 μm. The step-shaped terrace-like morphology of SnS2 nanoflakes, further verified with AFM, is shown in Fig. 4.9D. Fig. 4.9E shows an HRTEM image of the asgrown SnS2 nanoflake, indicating a high-single crystalline structure. The selected area electron diffraction (SAED) pattern of SnS2 crystal, shows single-crystal quality with the expected hexagonal crystal structure, shown in Fig. 4.9F. The Raman spectrum of an SnS2 crystal will have a strong Raman peak at 313.3 cm21 (A1g phonon mode), as shown in Fig. 4.9G. XRD spectra of the phase structure of the as-prepared SnS2 crystal are shown in Fig. 4.9H. This method gives high-quality crystalline synthesis of 2D SnS2, which can be extended to other potential applications. 4.2.5.2 Tin Diselenide Tin diselenide (SnSe2) is a naturally abundant emerging member of LMDCs. Bulk SnSe2 crystal has a hexagonal CdI2-type structure with vdW gap separating the (001) planes, in which the Sn planes are sandwiched between two layers of the Se plane [148,149]. In monolayer

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FIGURE 4.9 (A) Schematic diagram of a modified CVD system with four temperature zones for the growth of 2D SnS2 crystal samples. (B) SEM image of large-area as-grown SnS2 nanoflakes. Inset: Enlarged SEM image of large-scale SnS2 nanoflakes. (C) High-resolution SEM image of a single flake. (D) Tested AFM image. (E) HRTEM image of a single SnS2 nanoflake. (F) SAED pattern of the SnS2 crystal. (G) Raman spectrum of SnS2 crystals. (H) XRD of as-grown SnS2 nanoflakes. Source: Reprinted (adapted) with permission from: X. Jia, C. Tang, R. Pan, Y.Z. Long, C. Gu, J. Li, Thickness-dependently enhanced photodetection performance of vertically grown SnS2 nanoflakes with large size and high production, ACS Appl. Mater. Interf. 10 (21) (2018) 18073
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FIGURE 4.10 (A) Raman spectrum of annealed SnSe2. (B) STM topography image of single-crystalline SnSe2. (C) FFT of single-crystalline SnSe2. Source: Reprinted (adapted) with permission from: Z. Tian, M. Zhao, X. Xue, W. Xia, C. Guo, Y. Guo, et al., Lateral heterostructures formed by thermally converting n-type SnSe2 to p-type SnSe, ACS Appl. Mater. Interface. 10 (15) (2018) 12831
12838. Copyright (2017) American Chemical Society.

˚ and the Se-Se distance is 3.293 A ˚ SnSe2, the Sn-Se distance is 2.793 A ˚ and has a lattice constant of 3.321 A [150]. It is an n-type semiconductor with a direct bandgap B1 eV for bulk crystal, but B1.4 eV for monolayer, which facilitates the fabrication of switchable transistors [134,151,152]. It shows higher carrier mobility and superior sensitivity for photodetectors [133,151]. Tian et al. [153] successfully synthesized SnSe2 crystals using a CVD method. In Fig. 4.10A, Raman spectra of both SnSe2 and SnSe are shown. The peak corresponding to SnSe2 is A1g mode at 182 cm21, while the peaks Ag mode at 65, 125, 148 cm21; B3g mode at 105 cm21 are corresponding to SnSe. The triangular lattice of Se atoms is clearly visible in a scanning tunneling microscope (STM) image, as shown in Fig. 4.10B. Fast Fourier transform (FFT) of the topography image is shown in Fig. 4.10C. This method provides a better way to synthesize SnSe2 crystals with controllability.

4.2.6 Transition-Metal Dichalcogenides The general formula of TMDCs is MX2, where M is a transition-metal atom (such as Mo, W, Ti, or Ta) and X is a chalcogen atom (such as S, Se, or Te). In TMDCs, the transition-metal (M) layer is sandwiched in between two chalcogens (X) atomic layers and forms a layered structure X-M-X, for example, MoS2, WS2, TaS2, TiS2, etc. TMDCs have out-ofplane mirror symmetry and broken in-plane inversion symmetry. These 2D materials show complementary characteristics to graphene and in some cases surpass graphene. The factor that makes TMDCs unique is

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their strong spin-orbit interaction due to the d orbitals of the heavy metals, which leads to valley polarization. 4.2.6.1 Molybdenum Disulfide Molybdenum disulfide (MoS2) is one of the most studied analogs of graphene. The unit cell of bulk-phase 2H-MoS2 is composed of a bilayer with space group P63/mmc [154]. It is a promising semiconducting TMDC material with tunable bandgap. It can be tuned from indirect bandgap (1.2 eV) to direct bandgap (1.9 eV) [155]. Monolayer MoS2 has an in-plane Young’s modulus of about 200
300 GPa [156,157]. In addition, it has high mobility ( . 190 cm2 V21 s21), high on/off current ratio (108), current density (20 μA μm21), and valley polarization [158]. As such, MoS2 has a wide range of potential applications in various fields such as electronics, optoelectronics, sensing, energy storage, and catalysis [159
164]. Several processes such as mechanical exfoliation, hydrothermal synthesis, liquid exfoliation, and CVD have been applied to derived different morphologies of MoS2 [165
168]. Recently, Vashishta and colleagues synthesized MoS2 layers from the direct sulfidation of MoO3 surfaces using S2 gas precursors by CVD synthesis [169]. Huang et al. [170] successfully synthesized MoS2 nanosheets using liquid exfoliation assisted by formamide solvothermal treatment. AFM was used to examine the morphology of the MoS2. The size of exfoliated of MoS2 was 40 nm laterally and 0.9 nm thick. Fig. 4.11A depicts an AFM image. TEM detected the lath-like image of exfoliated MoS2, as shown in Fig. 4.11B, while Fig. 4.11C shows the SAED pattern of exfoliated MoS2. This

FIGURE 4.11 (A) AFM image of the exfoliated MoS2 nanosheets with solvothermal temperature 130 C. (B) TEM image of the exfoliated MoS2 nanosheets with solvothermal temperature 130 C. (C) SAED pattern of the exfoliated MoS2 nanosheets. Source: Reprinted (adapted) with permission from: J. Huang, X. Deng, H. Wan, F. Chen, Y. Lin, X. Xu, et al., Liquid phase exfoliation of MoS2 assisted by formamide solvothermal treatment and enhanced electrocatalytic activity based on (H3Mo12O40P/MoS2)n multilayer structure, ACS Sust. Chem. Eng. 6 (4) (2018) 5227
5237. Copyright (2018) American Chemical Society.

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characterization confirms the MoS2 nanosheets were single crystal in 2H-MoS2 structure. 4.2.6.2 Tungsten Disulfide Tungsten disulfide (WS2) is a promising layered semiconductor material of the TMDC family. The 2H-WS2 has the hexagonal space group ˚ and c 5 12.323 A ˚ [171]. P63/mmc with lattice parameters of a 5 3.1532 A In monolayer form, WS2 has a direct bandgap of 2.1 eV, while bulk WS2 has an indirect bandgap of 1.4 eV [172]. According to theoretical prediction, WS2 has the highest carrier mobility among the TMDC family [173]. Also, it has broadband light absorption, high carrier dynamics (B140 cm2 21 s21), high third-order nonlinear susceptibility (B1028), high photoconductivity, and strong PL [174,175]. Due to these attractive properties, WS2 is one of the attractive candidates in electronic and optoelectronic applications [176
178]. WS2 can be prepared by various synthesis approaches such as liquid exfoliation, hydrothermal route, solvothermal route, and CVD method [179
182]. Currently, McDonnell et al. [183] has grown WS2 flakes using CVD onto a thick oxide-coated silicon substrate. The monolayer nature of WS2 flakes was confirmed by AFM and Raman spectroscopy. The Raman spectra, AFM, and optical image are shown in Fig. 4.12A
B respectively.

FIGURE 4.12 (A) Raman spectra for WS2 monolayer flake. (B) Optical microscope image of WS2 flake. Inset: AFM topography profile of WS2 flake. Source: Reprinted (adapted) with permission from: L.P. McDonnell, C.C. Huang, Q. Cui, D.W. Hewak, D.C. Smith, Probing excitons, trions, and dark excitons in monolayer WS2 using resonance Raman spectroscopy, Nano Lett. 18 (2) (2018) 1428
1434. Copyright (2018) American Chemical Society.

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4.2.7 MXenes The family of 2D transition-metal carbides, carbonitrides, and nitrides collectively referred to as MXenes. MXenes are created by selective etching of the A element from the MAX phases, which are layered solids connected by strong metallic, ionic, and covalent bonds. It is a large family of hexagonal layered materials with P63/mmc symmetry, where M layers are nearly close-packed and the X atoms fill the octahedral sites [184]. The M
A bonds are weaker than the M
X bonds, however, the M
A bonds are more chemically active than the M
X bonds, which lead to highly selective etching of A-element layers. MXenes have a general formula Mn11AXn, where M is an early transition metal (Sc, Ti, V, Cr, Mo, etc.), A is mainly a Group IIIA or IVA (i.e., groups 13 or 14) element (Al, Si, Sn, In, etc.), X is C and/or N, and n 5 1, 2, or 3. Because the n values vary from 1 to 3, the corresponding single MXene sheets consist of 3, 5, or 7 atomic layers for M2X, M3X2, and M4X3 respectively. There are three ways to tune the properties of MXenes: altering the composition, surface termination, and structure/morphology. MXenes offers good metallic conductivity as well as hydrophilicity, a rare combination of 2D materials [185]. Ti3C2 was the first MXene reported in 2011, synthesized by the selective etching of Al from Ti3AlC2 using aqueous hydrofluoric acid at room temperature (RT) [186]. The MXenes family includes Ti2C, Nb2C, V2C, (Ti0.5, Nb0.5)2C, (V0.5, Cr0.5)3C2, Mo2C, Ti3CN, Nb4C3, etc. [187
193]. Also, Mo2CTx was the first MXene synthesized from non-MAX-phase precursors by etching Ga layers from Mo2Ga2C [194]. Another example is Zr3C2Tx, which was synthesized by selectively etching aluminum carbide (Al3C3) layers from non-MAXphase precursor Zr3Al3C5 [195]. The tuning of inherent physio-chemical properties of MXenes allows the properties for applications including electrode materials, energy storage, supercapacitors, hybrid devices, water purification, gas and biosensors, lubrication, optical devices, thermoelectric materials, and even topological insulators [196
208]. 4.2.7.1 Ti3C2Tx Ti3C2Tx is a widely studied MXene and has a high electrical conductivity and good environmental stability. Kim et al. [209] synthesized Ti3C2Tx by etching Al from Ti3AlC2 powders using the LiF/HCl route and used SEM, AFM, and TEM to study the morphology of individual flakes. XPS and Raman spectroscopy were used to study the chemical composition. 4.2.7.2 V4C3Tx Recently, Tran et al. [210] successfully introduced a new member of the MXene family: V4C3Tx (here, T represents surface groups) by

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FIGURE 4.13 (A) SEM and EDAX (inset) of MAX phase V4AlC3. (B) SEM and EDAX (inset) of MXene V4C3Tx. (C) STEM image of V4C3Tx. (D) HAADF image of V4C3Tx. Source: Reprinted (adapted) with permission from: M.H. Tran, T. Scha¨fer, A. Shahraei, M. Du¨rrschnabel, L. Molina-Luna, U.I. Kramm, et al., Adding a new member to the MXene family: synthesis, structure, and electrocatalytic activity for the hydrogen evolution reaction of V4C3Tx, ACS Appl. Energy Mater. 1 (8) (2018) 3908
3914. Copyright (2018) American Chemical Society.

chemical exfoliation of the 413 MAX phase V4AlC3 by treatment with aqueous hydrofluoric acid. The successful removal of aluminum from the MAX phase structure was confirmed by using X-ray powder diffraction and scale-bridging electron microscopy. Fig. 4.13A shows the layered morphology of V4AlC3; inset depicts EDAX measurements showing the presence of V, Al, and C alongside Si. An exfoliated MXene V4C3Tx SEM image is shown in Fig. 4.13B, with EDAX inset. A very minor amount of Al remains in the structure. Also shown is a STEM image (Fig. 4.13C) and a high-angle annular dark field image (HAADF) (Fig. 4.13D), which shows the exfoliated 2D V4C3Tx. 4.2.7.3 Nb1.33C The Rosen group [211] first synthesized the quaternary MAX solid solution, (Nb2/3Sc1/3)2AlC, where the M elements Nb and Sc are not ordered. They then selectively etched both Al and Sc atoms and

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produced Nb1.33CTx MXene, further characterized with XRD, XPS, and STEM.

4.2.8 Xenes A monoelemental class of 2D crystals termed 2D-Xenes comprise Group IVA, Group VA, and Group VIA atoms. Group IVA includes silicene, germanene, stanene; Group VA includes phosphorene, arsenene, antimonene, bismuthene; while Group VIA includes tellurene and silicene. Their electronic structure ranges from insulators to semiconductors with tunable gaps, to semimetallic, depending on the substrate, strain, and chemical functionalization. Like graphene, these materials are arranged in a honeycomb-buckled lattice due to the interplay of sp2 and sp3 hybrid bonds. However, compared to graphene their interatomic distance is larger, resulting in up-and-down atomic buckling about a honeycomb lattice, which provides a new way toward covalent functionalization. This fact may offer a potential to access to the quantum spin Hall state in Xenes, enabling new classes of nanoelectronic and spintronic devices. 4.2.8.1 Group IV Elemental Analogs Germanene

Germanene is another appealing germanium-based 2D cousin of graphene, which is predicted to be stable as a freestanding novel germanium 2D allotrope in a low-buckled honeycomb geometry [212,213]. This geometry is composed of two vertically displaced sublattices. Germanene is one of the youngest members of the 2D family and has not yet been studied extensively. The spin-orbit gap in germanene (B24 meV) is much broader than in graphene (,0.05 meV), which makes germanene the ultimate candidate to exhibit the quantum spin Hall effect at experimentally accessible temperatures [214]. Researchers ˚ and predict that the germanene structure with a small buckling of 0.7 A ˚ bond lengths of 2.44 A is energetically the most favorable, and it does not exhibit imaginary phonon mode [215]. Despite this buckled structure, the 2D Dirac nature of electrons is preserved in germanene [216,217]. The electronic configuration of carbon, silicene, and germanium are similar since the outermost orbital s and p have four electrons. Germanium is stable due to an energetically most favorable diamond structure [218]. Germanene deposited onto a substrate can exhibit a mini bandgap due to symmetry breaking between up and down out-ofplane atomic displacements. This tunable bandgap value can be modified by a transverse electrical field. This “magical” phenomenon of

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FIGURE 4.14 (A) LEED patterns of a clean Al (111) surface. (B) LEED patterns of germanene grown on Al (111) substrate. (C) STM image of germanene taken with a sample positive bias V 5 1.3 V and a current I 5 0.3 nA. The line showing the Al [ 2 1 2 2 1] crystal direction. (D) FFT image shows a single hexagonal lattice periodicity. Source: Reprinted (adapted) with permission from: M. Derivaz, D. Dentel, R. Stephan, M.C. Hanf, A. Mehdaoui, P. Sonnet, et al., Continuous germanene layer on Al (111), Nano Lett. 15 (4) (2015) 2510
2516. Copyright (2015) American Chemical Society.

tuning the bandgap with a choice of substrate plays a fundamental role in semiconductors [219]. Unfortunately, these graphene analogs of germanium (germanene) do not occur in nature and therefore have to be synthesized artificially. Until now, germanene has been successfully synthesized on substrates like Pt, Au, Al, and Ge2Pt [220
223]. Pirri and his group [224] successfully synthesized germanene on Al (111) substrate in an ultrahigh vacuum (UHV) system with a base pressure of about 3 3 10211 mbar. Fig. 4.14A
B shows the low-energy electron diffraction (LEED) pattern for clean Al (111) and germanene-covered Al (111) surface. In Fig. 4.14B the circles show the integer spots of the substrate. After germanene deposition on a substrate, the LEED pattern shows the LEED superstructure superimposed to the Al (111) integer spots. Fig. 4.14C shows an STM image of the long-range structure of germanene on a substrate. The substrate is completely covered with a flat 2D germanene layer. A single hexagonal lattice periodicity studied by FFT is shown in Fig. 4.14D. This work indicates that Al (111) is also one of the best substrates to grow 2D germanene. The physical and chemical properties of germanene grown on Al (111) still need to be explored before this material is used in fundamental applications. Silicene

Silicene, a 2D monolayer of silicon, consists of a honeycomb lattice of atoms with a buckled configuration [217,225,226]. Sometimes 2D silicene is referred to as the alter ego of graphene. However, silicene is different

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from graphene in terms of stability, atomic structure, and electronic properties. Silicene is probably the first artificial honeycomb-like lattice structure made of Si atoms. The buckling of Si atoms brings them closer together to facilitate a stronger overlap of their π-bonding pz orbitals, resulting in a mixed sp2
sp3 hybridization, which gives stability to the hexagonal arrangement. Silicene endorses sp3 hybridization due to its ˚) larger ionic radius [227]. Silicene has a larger bond length (B2.28 A ˚ compared to the graphene bond length (B1.42 A), which prevents the Si atoms from forming strong π bonds [228]. This quality leads silicene to deviate away from an sp2 hybridization. Although silicene has a lowbuckled geometry, the π and π* bands cross linearly at the Fermi level and form the famous “Dirac cone” at the symmetric point K in the reciprocal space with an estimated Fermi velocity of 106 m s21 [229]. Silicene shows novel quantum phenomenon such as the quantum spin Hall effect and the quantum anomalous Hall effect, due to interplay bandgap engineering in between the nonnegligible spin-orbit coupling (SOC) and electromagnetic field [230]. In 2007 Guzma´n
Verri and Voon showed that silicene is a zero-gap semiconductor [216]. The silicene bandgap can be opened by strengthening the properties such as the SOC or its intrinsic buckled structure by applying a gate voltage in the out-of-plane direction. Furthermore, as compared to graphene, silicene has an easier valley polarization [227]. These silicene properties hold great promise for a variety of novel applications, such as energy devices, topological bits, and quantum sensing. Due to the absence of a graphite-like form of silicon in nature, silicene is synthesized by a bottom-up approach, namely epitaxial growth on a substrate. A few reports have shown that monolayer silicene sheets were successfully synthesized on various substrates, including Ag, Ir, ZrB2, ZrC, and MoS2 surfaces [231
235]. Crescenzi et al. [236] used a highly oriented pyrolytic graphite (HOPG) substrate that, due to its sp2 configuration, provides chemical inertness. At RT, high-purity silicon deposition occurs in UHV conditions (base pressure of 10210 Torr) at a constant rate of 0.01 nm min
1. XPS was used to investigate the composition of the grown silicon layer. From XPS, both Si 2p and Si 2s peaks are highly symmetric, and the data did not show any additional peaks. An XPS spectrum is shown in Fig. 4.15A. AFM provides evidence for the formation of large nanosheets and small Si 3D clusters on the substrate, shown in Fig. 4.15B. An AFM image show a flat and clean bare surface with well-resolved monatomic steps. Fig. 4.15C
D shows AFM images of the same substrate after a single monolayer deposition of Si by maintaining the substrate at RT. Scanning tunneling spectroscopy (STS) shows a straight line around the Fermi level located at 0.0 eV which shows the metallic nature of the silicene areas; see Fig. 4.16A
B. STM

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FIGURE 4.15 (A) XPS of Si 2p and Si 2s core levels obtained after the deposition of one monolayer of silicon on HOPG at room temperature. (B) AFM image of HOPG showing a flat and clean surface with several monatomic steps. (C) Silicon monolayer deposited at RT on HOPG. (D) AFM image of silicene patchworks (light blue arrows) and Si clusters (white spots). Source: Reprinted (adapted) with permission from: M. De Crescenzi, I. Berbezier, M. Scarselli, P. Castrucci, M. Abbarchi, A. Ronda, et al., Formation of silicene nanosheets on graphite, ACS Nano 10 (12) (2016) 11163
11171. Copyright (2016) American Chemical Society.

measurements show a silicene unit cell and reveal small buckling as shown in Fig. 4.16C. The images show the formation of Si islands (very bright regions) rising above flatter zones. Density functional theory (DFT) calculations show the geometry and structural stability of a silicene monolayer on a HOPG surface. Ab initio molecular dynamics (AIMD) simulations explored the thermal stability of the bidimensional structure at two different temperatures (RT and 350 C). AIMD was also helpful to investigate the growth mechanism of silicene on the graphite substrate. Stanene

Stanene is a graphene-like 2D crystal, formed from a tin (Sn) hexagonal lattice in buckled arrangement [237,238]. In Group IV, tin is one of the heaviest elements, consisting of strong SOC. The strong SOC in

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FIGURE 4.16 (A) I
V curve registered on silicene on HOPG. (B) Normalized conductance and red dotted line is the theoretical density of states. (C) STM image after one silicon monolayer deposited on a HOPG substrate at RT. Source: Reprinted (adapted) with permission from: M. De Crescenzi, I. Berbezier, M. Scarselli, P. Castrucci, M. Abbarchi, A. Ronda, et al., Formation of silicene nanosheets on graphite, ACS Nano 10 (12) (2016) 11163
11171. Copyright (2016) American Chemical Society.

stanene gives a sizable bandgap up to 73.5 meV [239]. Stanene has much weaker π
π bonding, but its 2D structure is mainly stabilized due to low-buckling arising from σ
π bonding [240]. Stanene exhibits sp2
sp3 hybridization due to the preferential state of sp3 bonding. Due to its unique Dirac cone electronic structure, it shows enhancement in thermoelectric performance and topological superconductivity, as well as large-gap quantum spin Hall effect near RT [241
244]. Currently, monolayers and few-layer stanene were successfully grown on Bi2Te3 (111), InSb (001), Au (111), and Sb (111) substrates [245
248]. Le Lay and coworkers [249] successfully grew in situ epitaxial 2D stanene on an Ag (111) single-crystal template under an ultrahigh vacuum system and their crystalline structures were studied using LEED, STM, synchrotron radiation core-level spectroscopy (CLS), and angle-resolved photoemission spectroscopy (ARPES). Fig. 4.17A shows a LEED pattern of Ag2Sn surface alloy prepared on Ag (111) single crystal with an Sn deposition of 0.33 monolayer at 200 C. Fig. 4.17B shows an HRSTM image of stanene as well as a striking honeycomb structure. Fig. 4.17C shows an experimental STM image of an Sn film deposited straight onto a clean bare Ag (111) for 0.9 monolayer at 150 C. Fig. 4.17D shows HRSTM image of the Ag2Sn surface alloy. From the

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FIGURE 4.17 (A) LEED pattern of Ag2Sn surface alloy prepared on Ag (1 1 1) single crystal with a Sn deposition. (B) High-resolution STM image of stanine. (C) STM images of a Sn film deposited straight onto a clean bare Ag (1 1 1). (D) High-resolution STM image of the Ag2Sn surface alloy. (E) ARPES intensity plot of stanene prepared on the Ag2Sn surface alloy after Sn deposition.

STM images, we can clearly see that perfect planar stanene is formed on the Ag2Sn surface alloy. Fig. 4.17E shows electronic band structure by an ARPES intensity plot of stanene prepared on the Ag2Sn surface alloy after Sn deposition of 0.5 monolayer at 150 C. This fabrication method showed that Ag (111) substrate is an ideal candidate for growing largearea, high-quality monolayer stanene. 4.2.8.2 Group V Elemental Analogs Phosphorene

Phosphorene, a 2D monolayer of black phosphorus, has attracted intense attention recently due to its intriguing structures and fascinating electronic properties. Phosphorene has phenomenal properties such as direct bandgap (B2 eV), high charge-carrier mobility (B1000 cm2 V21 s21), moderate on/off ratio (104
105), as well as strong anisotropic nature of both electro-optical and thermo-mechanical properties [250
253]. The structure of phosphorene is orthorhombic, which makes it unusual compared with other 2D materials [254,255]. According to theoretical predictions, monolayer phosphorene can sustain tensile strains up to 27% and 30% in the zigzag and armchair directions respectively [256]. Phosphorene has a direction-dependent Young’s modulus; in the zigzag direction it is 166 GPa while in the armchair direction it is 44 GPa [257]. However, the Young’s modulus of phosphorene is smaller than graphene (1 TPa) and MoS2 (270 Gpa) [258,259], yet phosphorene shows excellent flexibility. Along the zigzag

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direction phosphorene exhibits an anisotropic Poisson’s ratio is 0.93, while in the armchair direction it is 0.17 or 0.4 [260]. These properties make phosphorene a most suitable contemporary 2D semiconductor material that combines the eminence of graphene with transitional metal dichalcogenides. Its lack of stability under ambient conditions and high reactivity toward oxygen, light, and water, mean that more effort is required to synthesize phosphorene for modern applications [261]. Currently, phosphorene can only be fabricated by a top-down approach whereas bottom-up approaches like CVD or hydrothermal methods are still to be developed. The failure of a bottom-up approach may be due to the high reactivity of phosphorene [262,263]. Among the top-down approach, an important method for high-quality fabrication of phosphorene is mechanical exfoliation [264,265]. But this is only for laboratory purpose, and is unsuitable for scale-up. Lu et al. [266] successfully fabricated phosphorene by a combination of mechanical exfoliation and plasma thinning. An alternative route is liquid exfoliation [267], which is able to produce high-quality and large quantities of monolayer phosphorene with controllable size [252,268]. Kaur et al. [269] exfoliated phosphorene using a mixture of Nmethyl-2-pyrrolidone and deoxygenated water as a subphase medium. This exfoliated phosphorene was assembled on a substrate by the Langmuir
Blodgett (LB) method. This method offers an inexpensive way for the production of large-area thin films of layer materials for mass production. The material was characterized by XRD, Raman spectroscopy, FESEM, EDAX, and HRTEM. Fig. 4.18A shows the FESEM a low-magnification (0.8 3 0.8 mm2) image of exfoliated large nanosheets of phosphorene assembled on SiO2/Si substrates. The small dark contrast shapes are formed from the curling or rolling of large nanosheets

(A) FESEM low magnified (0.8 mm 3 0.8 mm) image of exfoliated large nanosheets assembled on SiO2/Si substrates. (B) Magnified FESEM image of large nanosheets. (C) AFM of large nanosheets of phosphorene. (D) Honeycomb microstructure of phosphorene.

FIGURE 4.18

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during LB assembly under high surface pressure. The continuity in the nanosheets clearly appears in the magnified image shown in Fig. 4.18B. The AFM image in Fig. 4.18C clearly shows that the large nanosheets have a thickness of B4 nm. Fig. 4.18D shows an HRTEM image of the honeycomb microstructure of phosphorene. This method provides great promise for the fabrication of large-area semiconducting phosphorene thin films as well as the possibility of depositing other 2D materials that are sensitive to oxygen. Phosphorene presents many challenges and opportunities for researchers to explore the fundamentals and to discover novel applications for this new emerging material. Antimonene

This 2D material of Group V has attracted attention due to its unique properties like tunable bandgap, high carrier mobility, and topological nontrivial states. Antimonene monolayer sheets of sp3-hybridized antimony atoms possess a buckled honeycomb-like structure, in contrast with the flat graphene monolayer. The puckered atoms held together by weak vdWs forces were predicted to be stable. This thermodynamically stable material has significant fundamental properties such as good electrical conductivity (1.6 3 104 S m21), tunable bandgap (2.28 eV), large ˚ ), as well as fast ion diffusion properties interlayer channel size (3.73 A [270
273]. Moreover antimonene possesses strong SOC. Fortin-Descheˆnes et al. [274] synthesized an antimonene layer on a Ge (111) substrate using solid-source MBE. Antimonene was grown by low-energy electron microscope (LEEM) under UHV with LEED used for the identification of the optimal parameters for the 2D growth. Fig. 4.19A
B shows STM measurements that provide a deeper insight into the nature of antimonene. The buckled honeycomb structure of a monolayer antimonene is shown in Fig. 4.19B. Fig. 4.19C shows LEED (30 eV) with the main orientations circled (green and red) of antimonene. The STEM diffraction pattern shown in Fig. 4.19D confirms the antimonene lattice parameter remains constant across the layers thickness. Fig. 4.19E shows local XPS spectra (at photon energy 200 eV) on islands and the surface region. The Raman spectra shown in Fig. 4.19F shows a small decrease in Raman intensity for layers of thicknesses down to 4 nm. The stability of antimonene and electronic properties was studied with ab initio calculations. The growth antimonene on Ge (111) substrate is a very promising approach for the development of scalable devices. Arsenene

Arsenene, a monolayer of gray arsenic, has emerged as a novel 2D semiconducting material with buckled layer structure [275]. The properties of stanene include a sizable bandgap (2.49 eV), ultrahigh mobility, and high on/off ratio [276,277]. The tunable bandgap of arsenene can be

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FIGURE 4.19 (A) STM image of single-layer antimonene (Vt 5 2V, It 5 0.79 nA). Insets: Showing the structure of single-layer Antimonene Island (bottom left) and Ge
Sb chains (top right). (B) STM image of buckled honeycomb structure of monolayer antimonene (Vt 5 2V, It 5 0.14 nA). (C) LEED (30 eV) with the main orientations circled (green and red). (D) STEM antimonene diffraction pattern. (E) Local XPS spectra (at photon energy 200 eV) on island shown by (A). Inset: XPS spectrum of the surface region indicated by (B). Red curve showing raw data while brown curve showing fit curve. (F) Raman spectra (at 633 nm excitation wavelength) for samples of average antimonene layer height of 4 nm, 8 nm, and 30 nm. Source: Reprinted (adapted) with permission from: M. Fortin-Descheˆnes, O. Waller, T.O. Mentes, A. Locatelli, S. Mukherjee, F. Genuzio, et al., Synthesis of antimonene on germanium, Nano Lett. 17 (8) (2017) 4970
4975. Copyright (2017) American Chemical Society.

changed from indirect bandgap to direct bandgap after applying a small tensile strain. Arsenene is one of the 2D materials, and has the lowest anisotropic thermal conductivity [278]. To date, fabrication and studies of arsenene remain a challenge because the substance undergoes fast degradation due to the formation of arsenic oxides [279,280]. Still there is no evidence of a practically successful exfoliation of monolayer arsenene by mechanical exfoliation method. Tsai et al. [277] synthesized multilayer arsenene on InAs using a plasma-assisted process. First, nitrogen ions introduced into InAs form InN and simultaneously force arsenic atoms out of the surface to form arsenene layers during the thermal treatment. Fig. 4.20A and B shows XPS spectra of multilayer arsenene/InN/InAs and As 3d spectrum of the multilayer arsenene/InN/InAs. In the first 3d spectrum the In 2 N peak splits into 3d5/2 and 3d3/2 bands, located at B443.1 and B450.6 eV, respectively. While in the second spectrum, the As 2 As peak located at B42.4 eV

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FIGURE 4.20 (A) 3D spectrum of the multilayer arsenene/InN/InAs. (B) As 3d spectrum of the multilayer arsenene/InN/InAs. (C) TEM image of the multilayer arsenene/ InN/InAs. Source: Reprinted (adapted) with permission from: H.S. Tsai, S.W. Wang, C.H. Hsiao, C.W. Chen, H. Ouyang, Y.L. Chueh, et al., Direct synthesis and practical bandgap estimation of multilayer arsenene nanoribbons, Chem. Mater. 28 (2) (2016) 425
429. Copyright (2016) American Chemical Society.

infers formation of arsenic of the elemental structure. To protect the thin film from focused ion beam during TEM, the film was first coated with SiO2. The TEM is shown in Fig. 4.20C. From the TEM it is clearly seen that the heterogeneous structure consists of three parts including multilayer arsenene, InN, and InAs substrate. The thickness of arsenene can be controlled from the plasma exposure time. This cost-effective synthesis process for arsenene may be helpful for different applications. 4.2.8.3 Group VI Elemental Analogs Tellurene

Tellurene is a highly anisotropic 2D layered structure of the Group VI element tellurium (Te). At equilibrium, the α-phase is the most stable phase for few-layer tellurene and the tetragonal β-phase is more stable for monolayer tellurene [281]. This semiconductor material has a direct bandgap of nearly 0.33 eV in bulk, with an indirect bandgap of 0.92 eV for the monolayer form, grown epitaxially on graphene [282,283]. Zhu et al. predicted an indirect bandgap for α- and β-tellurene of 1.15 and 1.79 eV, respectively [284]. For bilayer tellurene, Ji et al. reported an indirect bandgap of 1.17 eV [281]. Tellurene exhibits significant air stability as well as high on/off ratio (B106) and high mobility (B700 cm2 V21 s
1) for field-effect transistors [285].

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Wang et al. synthesized 2D hexagonal Te nanoplates on flexible and transparent fluorophlogopite mica sheets by vdWs epitaxy, and the material had large lateral dimensions (6
10 μm) and thickness (30
80 nm) [286]. Recently Wu et al. [287] grew 2D tellurene by the reduction of sodium tellurite. They reduced it with hydrazine hydrate in an alkaline solution at temperatures from 160 C to 200 C in the presence of crystal-face-blocking ligand polyvinylpyrrolidone. The 2D Te flakes can be transferred onto various substrates by an LB processor or ink-jet printing for characterization. The structure, composition, and quality of 2D tellurene crystals were analyzed by XRD, STEM, HRTEM, and EDAX. This method provided a promising approach to synthesize tellurene crystals in a simple and cost-effective manner.

4.3 APPLICATIONS OF GRAPHENE ANALOGS 4.3.1 Optoelectronics Optoelectronic devices are light-emitting or light-detecting devices, either producing light or using light in their operation. Photodetectors, light-emitting diodes (LEDs) and laser diodes, optical amplifiers and optical modulators are some examples of optoelectronic devices. The tunable bandgap ranges from insulator to metal depending on layer thickness, making 2D materials suitable candidates for optoelectronic devices. The optical modulator is a device that modulates the optical signal of light in a well-controlled manner. Li et al. [288] used CVD-grown monolayer MoS2 exfoliated on a SiO2/Si substrate to fabricate the electro-optic modulator shown in Fig. 4.21A. An LED is a semiconducting device that emits narrow bandwidth light when activated by passing an electric current through it. Yang et al. [182] studied valley LEDs made by a CVD-grown monolayer of WS2. Fig. 4.21B shows a schematic diagram of the p1-Si/i-WS2/n-ITO heterojunction LED device. To simplify, the photodetector is a device that converts light into electrical signals. Tanigaki and colleagues fabricated photodetectors based on multilayer GaTe flakes [289]. Fig. 4.21C is the 3D view and cross-section schematic of the multilayer GaTe flakes
based photodetectors.

4.3.2 Energy Storage The demand for efficient energy storage and conversion technologies is increasing every day. Electrochemical capacitors and rechargeable batteries are the two most useful devices for large-scale production and energy storage. Batteries are essential components in portable electronic devices, vehicles, and textiles, which transform our daily lifestyle. Lithium-ion, sodium-

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FIGURE 4.21

(A) Schematic diagram of the electro-optic modulator based on MoS2 monolayer. (B) Schematic diagram LED based on monolayer WS2. (C) Three-dimensional schematic view and the cross-section view of the photodetector based on multilayer GaTe flakes. Source: (A) Reprinted (adapted) with permission from: B. Li, S. Zu, J. Zhou, Q. Jiang, B. Du, H. Shan, et al., Single-nanoparticle plasmonic electro-optic modulator based on MoS2 monolayers, ACS Nano 11 (10) (2017) 9720
9727. Copyright (2017) American Chemical Society. (B) Reprinted (adapted) with permission from: W. Yang, J. Shang, J. Wang, X. Shen, B. Cao, N. Peimyoo, et al., Electrically tunable valley-light emitting diode (vLED) based on CVD-grown monolayer WS2, Nano Lett. 16 (3) (2016) 1560
1567. Copyright (2016) American Chemical Society. (C) Reprinted (adapted) with permission from: F. Liu, H. Shimotani, H. Shang, T. Kanagasekaran, V. Zolyomi, N. Drummond, et al., High-sensitivity photodetectors based on multilayer GaTe flakes, ACS Nano 8 (1) (2014) 752
760. Copyright (2014) American Chemical Society.

ion, magnesium-ion, and potassium-ion batteries are available based on the 2D material [290
293]. 2D materials offer a high surface area and open ion diffusion channels for energy storage. Among these, lithium-ion batteries are used the most because of their excellent energy density and power density, good safety, and long lifespan. Fig. 4.22A shows a schematic diagram of V2O3/C hybrid-based Li-ion batteries [294]. A V2O3/C hybrid acts as an anode while the commercial LiMn1/3Co1/3Ni1/3O2 acts as a cathode. Fig. 4.22B shows a schematic diagram of Na-ion batteries based on amorphous TiO2 material as an anode [295]. Na-ion batteries also show nearly the same potential as a Li-ion battery.

4.3.3 Transistor A transistor is an electronic component that regulates current or voltage flow and acts as a switch and an amplifier for electronic signals.

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FIGURE 4.22 (A) Schematic diagram of V2O3/C anode based Li-ion batteries. (B) Schematic diagram of TiO2 anode based Na-ion batteries. Source: (A) Reprinted (adapted) with permission from: P. Yu, X. Liu, L. Wang, C. Tian, H. Yu, H. Fu, Urchin-like V2O3/C hollow nanosphere hybrid for high-capacity and long-cycle-life lithium storage, ACS Sust. Chem. Eng. 5 (12) (2017) 11238
11245. Copyright (2017) American Chemical Society.

There are mainly two types of transistors: field-effect transistor (FET) and bipolar junction transistor (BJT). In an FET, a voltage at the gate controls a current between the source and drain while in BJT, the base terminal controls a larger current flow between the collector and the emitter terminals. Transistors are important components in analog, digital, and sensor applications. Recently, Stampfer et al. fabricated the ultra-scaled Schottky barrier MoS2 FETs on a 20 nm thick SiO2 gate dielectric, as shown in Fig. 4.23A [296]. The device is a three-terminal structure containing source, drain, and highly doped Si back gate. Agnihotri et al. fabricated BJTs of 2D WSe2, as shown in Fig. 4.23B [297].

4.3.4 Supercapacitor Supercapacitors are a passive, static energy-storage system, and can store and deliver energy at relatively high rates. The energy-storage mechanism is due to charge-separation at the electrochemical interface between the electrode and the electrolyte. When a voltage is applied, the electrons gather at one electrode and the electrical charge is stored. Supercapacitors have several orders of magnitude higher energy density than dielectric capacitors, so they can store more energy than conventional capacitors. Supercapacitors offer high-power density, good operational safety, and long cycling life, so they have been considered to be an excellent energy-storage platform with significant potential. Supercapacitors play an important role in video recorders, TV satellite receivers, alarm clocks, radios, process controllers, home appliances, and solar panels [298].

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On the basis of their energy-storage mechanism, supercapacitors can be classified as electrochemical double-layer capacitors (EDLC) and pseudocapacitors [298]. In EDLC, a “double layer” of ions is created at the interface of the electrode surface and the electrolyte, while in a pseudocapacitor, energy is stored through fast surface redox reactions which take place at the electrode-electrolyte interface. Fig. 4.24A shows a schematic diagram of the charging and discharging mechanism of EDLC supercapacitor and Fig. 4.24B shows the mechanism of a basic pseudocapacitor [299]. Banks and colleagues reported antimonene material
based supercapacitors. This supercapacitor shows excellent performance, has good

FIGURE 4.23

(A) Schematic diagram of MoS2 FET. (B) Schematic diagram of WSe2 BJT. Source: (A) Reprinted (adapted) with permission from: B. Stampfer, F. Zhang, Y.Y. Illarionov, T. Knobloch, P. Wu, M. Waltl, et al., Characterization of single defects in ultra-scaled MoS2 field-effect transistors, ACS Nano 12 (6) (2018) 5368
5375. Copyright (2018) American Chemical Society. (B) Reprinted (adapted) with permission from: P. Agnihotri, P. Dhakras, J.U. Lee, Bipolar junction transistors in two-dimensional WSe2 with large current and photocurrent gains, Nano Lett. 16 (7) (2016) 4355
4360. Copyright (2016) American Chemical Society.

FIGURE 4.24 (A) Schematic diagram of the charging and discharging process in EDLC supercapacitor. (B) Schematic diagram of the mechanism of pseudocapacitor.

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FIGURE 4.25 (A) Schematic diagram of the Pt/ZnO single nanowire H2-sensing mechanism. (B) The testing principle of the gas sensor of CuO sheets. (C) Schematics diagram of the ethanol gas sensor based on TiO2 nanobelts. (D) The schematic diagram of the humidity sensors of the titania nanotubes. (E) Kretschmann configuration of SPR biosensor based on MoS2 nanosheets and silicon. Source: (A) Reprinted (adapted) with permission from: S.N. Das, J.P. Kar, J.H. Choi, T.I. Lee, K.J. Moon, J.M. Myoung, Fabrication and characterization of ZnO single nanowire-based hydrogen sensor, J. Phys. Chem. C 114 (3) (2010) 1689
1693. Copyright (2010) American Chemical Society. (B) Reprinted (adapted) with permission from: P. Hu, G. Du, W. Zhou, J. Cui, J. Lin, H. Liu, et al., Enhancement of ethanol vapor sensing of TiO2 nanobelts by surface engineering, ACS Appl. Mater. Interf. 2 (11) (2010) 3263
3269. Copyright (2010) American Chemical Society.

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capacitance (1578 F g21), high charging current density (14 A g21), highly competitive energy (20 mW h kg21), power densities (4.8 kW kg21), and good cycling capabilities [300]. TMDCs materials are also a promising material for a supercapacitor. Geng et al. reported a multilayer pure metallic M-MoS2 2 H2O based ultrafast rate supercapacitor with extraordinary capacitance [301]. To date, a number of 2D materials
based supercapacitors were reported [54,302
304].

4.3.5 Sensor A sensor is a device that is frequently used to detect and respond to electrical or optical signals from the physical environment. The physical parameters could be light, temperature, gas, humidity, speed, heat, motion, pressure, or any one of a great number of other environmental phenomena. The sensor converts these physical parameters into an electrical signal. Sensors are important indicators for quality of life, providing comfortable living environments by measuring and controlling physical parameters. The ideal sensor is the one that has high sensitivity, high stability, and fast response-recovery time. Many 2D materials fulfill these characteristics, so they have attracted much attention. Fig. 4.25A
E shows a schematic diagram of a hydrogen sensor, gas sensor, ethanol sensor, humidity sensor, and surface plasmon resonance (SPR) biosensor based on 2D materials [305
309].

4.4 SUMMARY AND PERSPECTIVE The rediscovery of graphene has started a new era of materials science, and researchers have devoted intense efforts to enlarge the field of 2D materials. Beyond graphene, other subfamilies of 2D materials have also shown great potential in such fields as electronics, optoelectronics, spintronics, mechanical devices, electrochemistry, and biological applications. The development and maturity of 2D materials will inspire new insight into the modern industry and find ways to wide-ranging applications. In this chapter we briefly discussed the state-of-the-art of graphene and, beyond graphene, 2D materials. We started with the current progress of 2D materials and graphene in different fields, and summarized their properties. Then we presented the growth of 2D materials beyond graphene by discussing different graphene-like materials. 2D analogs of graphene such as h-BN, TMDCs, metal oxides, MHs, MXenes, and Xenes have 2D structures similar to graphene but very distinct electronic and optical properties. These graphene analogs can be insulator,

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semiconductor, conductor, or superconductor depending on the composition and phases. This chapter summarizes 2D materials of each type of family. Many of the structural characteristics and unique electronic and optical properties of 2D materials were also discussed briefly. Synthesis in a well-controlled manner is always a first and critical step for any new material. This chapter provides information about different synthesis methods for synthesizing 2D materials. Mechanical or chemical exfoliation, hydrothermal or solvothermal methods, and CVD methods are the widely used synthesis methods for 2D materials. This chapter also offers a comprehensive overview of characterization techniques which will be helpful for the study of the materials. XRD, XPS, EDAX, or Raman spectroscopies are mainly used to find out the chemical composition or crystallinity and to identify the molecule, while optical microscopy, FESEM, STEM, or AFM are commonly used for imaging and morphological studies. This chapter also provides information regarding material applications in different fields. These new graphene analog materials offer unprecedented opportunities for new advanced applications. Despite considerable progress in the synthesis and characterization techniques, manufacturing techniques for large-scale practical applications have a long way to go.

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C H A P T E R

5 Electronic Structure and Theoretical Aspects on Sensing Application of 2D Materials Brahmananda Chakraborty High Pressure and Synchrotron Radiation Physics Division, Bhabha Atomic Research Centre, Trombay, Mumbai, India

5.1 IMPORTANCE OF THEORETICAL MODELING AND SIMULATIONS FOR SENSING APPLICATIONS Various two-dimensional (2D) materials, theoretically predicted and experimentally synthesized are potential candidates for sensing gases and biomolecules. The gas-sensing process involves very weak interactions that are difficult to detect in experiments. Simulations can provide important insight on the sensing process, such as possible adsorption configurations, preferred adsorption sites, adsorption energy, electron transfer, changes in electronic and optical properties, and different approaches to enhance adsorption which can play a vital role in the fabrication and development of efficient gas and biomolecule sensors. The results predicted by theoretical simulations can support the experimental measurements and provide understanding for the mechanism of sensing. With the advent of high-speed, large-memory supercomputers extensive electronic structure simulations are now quite feasible, and the simulations methods and procedures are becoming well established. Many well-established methods for quantum simulations are supported by many versatile and popular codes, both commercial and open access.

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But there are challenges for theoretical simulations: Sensing parameters are very sensitive to the choice of the simulations parameters, and there are merits and demerits for the simulations methods. So one needs to be very careful regarding the simulation procedures and interpreting the results.

5.2 INTRODUCTION TO QUANTUM SIMULATIONS The origin of electronic structure calculations dates back to the development and formulation of quantum theory in the first half of the 20th century. Quantum theory not only gave a new insight in our understanding of physics but also brought with it precision and predicting power. The applications of quantum mechanics are limitless, whether it is in applied or pure branches of science. Most importantly a real-world process or system can be modeled using the equations of quantum mechanics. However, equations such as the Schrodinger equation cannot be solved analytically except for a few simple systems that are for the most part irrelevant because of their small scales. Solving such an equation requires sophisticated numerical techniques for it to be applicable. These quantum mechanical methods are unique in the sense that modeling of a system does not require any empirical model or fitting parameters as they are first principle or ab initio in nature. This means such methods do not require any prior data from experiments and can be used to study any given system. Most importantly electronic structure calculations enable researchers to calculate physical properties that cannot be measured directly or are inaccessible for the experimentalists such as the binding energy of the atom or a molecule in a system. Such methods can be employed in finding new compounds which can have a particular property and can be compared to present ones saving both experimental resources and time. Various properties of such hypothetical compounds can be predicted beforehand making it easier for the researcher when searching for a particular property. Overall simulations of materials provides another level of understanding which may not be available from the experiment. These methods can be implemented in various ways in terms of different simulations packages that are tailored to deal with particular problems. For example, some packages like Gamess [1] or Gaussian [2] are used to solve chemistry problems of molecular structures; for these a localized basis set is more suitable like the Gaussian-centered orbitals or atomic orbitals. On the other hand, codes that deal with metals and other solids where the band structure is more important use a plane wave basis set like VASP [3] or Quantum Espresso [4]; others use

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hybrid methods like CP2K [5]. Some codes such as WIEN2K [6] or ELK [7] are designed primarily to treat heavy elements where the pseudopotential methods fail and the all-electron potential must be considered. In the following sections we discuss more about the plane wave pseudopotential density functional theory (DFT) and how it can be used to model 2D materials and calculate sensing properties.

5.3 OVERVIEW OF QUANTUM SIMULATION METHODS Solving the Schrodinger equation for many body systems is challenging and involves different methods with various approximations. In this section we will outline different methods and approximations employed to develop some scheme for solving many body quantum system.

5.3.1 The Many-Body Hamiltonian Electronic structure calculations aim to numerically solve the Schrodinger equation for a system with only the basic description provided. Some popular electronic structure methods include DFT [8
10], Hartree
Fock (HF) method [11,12], Quantum Monte Carlo [13
15], coupled cluster [16,17], multireference configuration interaction [18], etc. These methods have an inherent advantage since the convergence to the exact solution is systematic; however, one method can be better than the other depending on the property of concern. In this chapter we apply more emphasis on understanding and applying DFT. So what is DFT? DFT is the most used and successful quantum mechanical method to treat real-world materials. It is fast and accurate when compared to other methods and used routinely to solve problems in chemistry and physics like the molecular binding and band structure of solids. It has also seen some application in biology, geoscience, mineralogy etc. With that said it can also be used to capture more exotic effects such as noncollinearity, magnetism, superconductivity, lattice dynamics, electron-phonon interactions, and many more. Although DFT is based on a conceptually rigid framework it is still versatile when it comes to implementing it as there are many ways in which DFT is applied through the means of various simulation packages available. The main focus of the quantum mechanical simulations is to calculate the electronic structure of a system by solving the many-body Schro¨dinger equation. For a solid that contains positively charged nuclei

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and negatively charged electrons one can write the exact nonrelativistic many-particle Hamiltonian of such electromagnetically interacting particles as: b 5 Tbnucl 1 Tbelec 1 V b n2e 1 V b e2e 1 V b n2n H

(5.1)

where ħ2 X rR2 i b ħ2 X rr2i b ; T elec 5 2 ; V n2e Tbnucl 5 2 2 i Mi 2 i mi 52 5

X e2 1 X Zi e2 b e2e 5 1 b n2n  ;V  ;V 4πє0 i;j Ri 2 rj  8πє0 i6¼j ri 2 rj 

1 X e2 Zi Zj 8πє0 i6¼j jRi 2 Rj j

(5.2)

Here the first two terms are the kinetic energy operators for the nuclei and the electrons while the last two terms describe the Coulomb interactions between nuclei
electron, electron
electron, and nuclei
nuclei, respectively. Mi and mi are the masses of the nuclei and electrons at the positions Ri and ri, respectively. Using this Hamiltonian one can obtain the eigenstates, i.e., the wave functions ðѱRn ; re ) of the many-particle system by solving the corresponding Schro¨dinger equation: ˆ fRn ; re g 5 EѱfRn ; re g Hѱ

(5.3)

However, it is well known that because of its nature it is impossible to solve such an equation unless one makes few approximations. To obtain the approximate eigenstates, approximations are made on various levels.

5.3.2 The Born
Oppenheimer Approximation The mass of electrons is much smaller when compared to that of the nuclei, but the electromagnetic forces acting on the electrons are very similar. This results in the electronic motion being significantly faster than the nuclear motion. Because the velocities of the nuclei are much less we can assume them to be frozen at a fixed position and the electrons to be in instantaneous equilibrium with them [19]. Hence the nuclei becomes an external particle with a potential in which the electrons move. This approximation results in neglecting the kinetic energy operator from the Hamiltonian; also the last term becomes a constant. Hence the Hamiltonian of the many-electron system (note the problem is reduced to solving for the electronic wave functions; however, the

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ionic part can also be taken into consideration in the similar way) takes the form: b 5 Tbelec 1 V b ext 1 V b e2e H

(5.4)

This leaves us with a total of three terms which are the kinetic energy of the electron gas, the potential due to electrons interacting with each other, and the potential of the electrons interacting with the external nuclei, i.e., the external potential. The nuclear repulsion term in the Hamiltonian is just a constant term.

5.3.3 Hartree
Fock Method The HF method [20] is a wave function
based method which is meant to be a correction to the Hartree method. In the Hartree method the many-electron wave function is represented by a simple product of singleelectron orbitals. This, however, is against the Pauli exclusion principle as the wave function has to be antisymmetric under particle exchange—that is to say that the wave function must change sign if two electrons are exchanged. The HF method tackles this issue by representing the antisymmetric many-electron wave function using the Slater determinant. 2 3 ϕ ðx Þ ? ϕ1 ðxN Þ   1 4 1 1 ^ & ^ 5 ѱHF x1; x2; . . . :; xN 5 pffiffiffiffiffiffi (5.5) N! ϕ ðx Þ ? ϕ ðx Þ N

1

N

N

Here we can easily verify that the properties of determinant satisfy the condition of the wave function being antisymmetric. One can get the exchange term from the aforementioned Slater determinant which leads to the Hartree
Fock equation. ð 1X 1 b b b  ϕi ð~ ϕj ð~ εi ϕi ð~ r 0 Þ  r Þd3~ r Þ 5 ½T elec 1 V ext 1 V e2e ϕi ð~ rÞ 2 r 0 Þϕi ð~ r0 0 2 j r 2~ r ~ (5.6) The major drawback, however, is the form of the exchange term being nonlocal in nature, making the equation difficult to solve. Also this method completely neglects the electron correlations as the instantaneous repulsion electron
electron is replaced with the electron interacting with average electron gas. It particularly fails when dealing with metals. That being said the HF method is successful in quantum chemistry calculations where it gives reasonably good results for molecules. This formulism forms a basis for the post HF methods including coupled cluster, multireference configuration interaction, Møller
Plesset perturbation theory, etc., which include some of the correlation effects.

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5.3.4 Density Functional Theory DFT was established in 1964 with the two theorems proposed by Hohenberg and Kohn [21], which were followed by a set of equations given by Kohn and Sham [22]. The reason DFT is considered a major breakthrough in computational physics is because of its accuracy and feasibility. This makes DFT superior to quantum chemistry methods such as the HF method mentioned in the previous section, which is based on many-body wave function as the fundamental variable of the system, unlike DFT, which considers the electron density. 5.3.4.1 The Hohenberg and Kohn Theorems Hohenberg and Kohn formulated their two theorems as follows: First theorem: There is a one-to-one correspondence between the ground-state density ρð~ rÞ of a many-electron system (atom, molecule, solid) and the external b ext . An immediate consequence is that the ground-state expectation potential V b is a unique functional of the exact ground-state value of any observable O electron density. D   E  b ѱ O ѱ 5 O½ρ

(5.7)

b being the Hamiltonian H, b the ground-state total Second theorem: For O energy functional: H½ρ  EVext ½ρ is of the form:  E D   E D   b ѱ 1 ѱV b ext ѱ (5.8) EVext ½ρ 5 ѱTb 1 V |fflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflffl} FHK½ρ

ð

5 FHK½ρ 1

ρð~ r ÞVext ð~ r Þd~ r

(5.9)

where the Hohenberg
Kohn density functional FHK½ρ is universal for any many-electron system. EVext ½ρ reaches its minimal value (equal to the groundb ext . state total energy) for the ground-state density corresponding to V Proving these theorems is beyond the scope of this chapter, as these proofs are easily available in the literature [22], but we will briefly discuss the consequences. The theorem states that there is a one-to-one correspondence between the external potential and the electron density. Hence, if the functional FHK is known, one knows everything about the ground state of the system as minimizing FHK would find the exact

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energy and density of the many-electron system. Even though the theory remains exact, the form of FHK is not known, making it impossible for the theory to be applied in practice. 5.3.4.2 The Kohn
Sham Equations A year later after the two theorems were published, Kohn and Sham introduced equations that could be used to construct suitable energy functional making DFT applicable. The fundamental idea was to solve for a fictitious system of noninteracting particles which would result in the same ground-state properties as that of the real system of interacting electrons. This was done by defining the Hamiltonian of the system as: b KS 5 Tb0 1 V bH 1 V b xc 1 V b ext ; H

(5.10)

bH; V b xc , and V b ext are the where Tb0 is the kinetic energy operator and V Hartree, exchange-correlation, and external potentials respectively. The electron density of the noninteracting system of N electrons is defined as: ρð~ rÞ 5

N X

r ÞΨi ð~ r Þ; Ψi ð~

(5.11)

i51

with the kinetic energy functional as: T0 ½ρ 5 2

N     ħ2 X Ψi  r 2  Ψi ; 2me i51

(5.12)

where jΨi i represent one-electron orbitals and the electron density is obtained by summing all the occupied orbitals. The occupation of these orbitals follows the Pauli exclusion principle. Furthermore, the energy functional is written as: ð EVext ½ρ 5 T0 ½ρ 1 EH ½ρ 1 Exc ½ρ 1 ρð~ r ÞVext ð~ r Þd~ r; (5.13) |fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl} FHK½ρ

where FHK½ρ is defined as the sum of the first three terms and T0 ½ρ is the kinetic energy functional defined in Eq. (5.10) with EH ½ρ and Exc ½ρ representing the Hartree and exchange correlation functional. With this is in mind the ground-state density can be calculated by solving by evaluating Eq. (5.8) as:

ħ2 b r Þ Ψi ð~ r 1 V eff ð~ r Þ 5 εi Ψi ð~ r Þ; (5.14) 2 2me

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where b eff ð~ b xc 1 V b ext bH 1 V V rÞ 5 V

(5.15)

b xc and V b H are defined as and V 0 2 ð 0 ρð~ rÞ bH 5 e b xc 5 δExc ½ρ ; V r: V 0 d~ δn 4πє0 j~ r 2~ rj

Considering the above the only unknown is the exchange-correlation functional Exc ½ρ, which can be approximated. Further details are provided in next section. Note that the Ψi and εi , i.e., the single particle wave function and the single particle energy, are only mathematical objects, hence they are physically meaningless. The solution of the above equation can be found using a self-consistent procedure as shown in Fig. 5.1 as both the Hartree potential and exchangecorrelation potential depend on the electron density from which the ground-state energy can be calculated iteratively. Here the starting b KS density ρ0 ð~ r Þ is a guess from which the Kohn
Sham Hamiltonian H b H and the exchangeis constructed by calculating the Hartree V b xc . This is followed by an eigenvalue problem correlation potential V from which the Ψi and εi are obtained. Using Ψi new density ρk ð~ r Þ is constructed, and if it differs from the original density ρk21 ð~ r Þ then the procedure is started over again by constructing a new Hamiltonian and finding the next value for density. This continues until the convergence is achieved upon which self-consistent solution for the Kohn
Sham equations is obtained. 5.3.4.3 Approximations to the Exchange-Correlation Functional From the previous section we know that the only unknown term remaining to solve the Kohn
Sham equations is the exchangecorrelation functional Exc ½ρ, which has to be approximated. This is the only approximation used apart from the Born
Oppenheimer approximation since the exact solution is impossible to obtain. The two most commonly used approximations for the exchange-correlation functional are the local density approximation (LDA) [21
25] and the generalized gradient approximation (GGA), the former being the oldest and the simplest. LDA was proposed by Kohn
Sham in their groundbreaking work which proved to be a reasonably good approximation and where the exchange-correlation functional is defined as: ð LDA Exc ½ρ 5 ρð~ r Þєxc ðρð~ r ÞÞd~ r: (5.16)

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153

Guess density: ρ0(r )

Initial density: ρk–1(r )

Calculate: Veff (r ) = VH + Vxc + Vext

Set up: HKS

Solve: HKSΨi (r ) = εi Ψi (r )

Obtain: εi,Ψi (r )

Construct: ρk (r ) = Σ iN= 1Ψ*i (r )Ψi (r )

Convergence: is ρk (r ) = ρk–1(r )? No Yes ρk (r ) is self-consistent

FIGURE 5.1 The flowchart representing the self-consistent cycle used to solve the Kohn
Sham equations iteratively.

Here the exchange part of the єxc can be found analytically while the correlation is numerically known from the quantum Monte Carlo simulations [23]. Based on the homogenous electron gas model this exchange-correlation energy is approximated to be the same as that of the homogenous electron gas with the same density at the given point. An even better approximation to the exchange-correlation functional is the GGA, where even the gradient of the density is considered. GGA exists in various forms and can be switched depending on the problem; some may perform better than others for chemical problems. The most popular GGA forms are the Perdew
Burke
Ernzerhof (PBE) [26] and the Becke
Lee
Yang
Parr (BLYP) [27,28].

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5.4 CORRECTIONS TO DENSITY FUNCTIONAL THEORY Even with the above description, K-S DFT within LDA (and even GGA) is not successful for all the system properties, the most common example being the bandgap of the system where it is underestimated because of lack of any self-interaction correction. In such situations methods like the Hubbard correction, hybrid functionals and GW are considered to describe the system properly. These methods will be discussed in the following section.

5.4.1 Local Density Approximation 1 U It is observed that when dealing with partially filled d(f) shells of strongly correlated materials which either have a transition metal or rare earth element the LDA, and often even GGA fails in describing the ground-state electronic structure. Transition metal oxides like NiO, FeO, and even rare earth compounds have a large bandgap, and the nature of d(f) electrons is localized. When systems like these are treated with LDA the ground-state electronic structure is found to be metallic with the d(f) electrons being partially filled in as the case of FeO [29]. The main reason behind this failure is that LDA is not an orbital-dependent potential: It considers the exchange interaction of the homogenous electron gas rather than the screened on-site Coulomb interaction which results in the orbital polarization not being described correctly. Fig. 5.2 describes a possible scenario in which the Hubbard correction is important for determining the ground-state electronic structure. Here the ground state of FeO is found to be metallic with the standard GGA while adding a Hubbard correction results in an insulating nature that is found experimentally. Overall LDA 1 U is a computationally cheap method to obtain the correct ground of such strongly correlated materials but suffers from transferability as the U value is system dependent and has to be calculated for each system. Even though there are methods to obtain U value just from first principles [29
32], there are scenarios in which multiple elements require the U correction leading to multiple U values. In this type of situation, performing a calculation with fewer parameters is more desired.

5.4.2 Hybrid Functionals The results obtained from (semi-)local functionals like L(S)DA, PBE can be further improved by adding nonlocal terms to the exchangecorrelation energy in an attempt to reduce the self-interaction errors. In

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FIGURE 5.2

The projected density of states of FeO in the cubic phase with antiferromagnetic configuration at the experimental lattice constant obtained with GGA (top panel) and LDA 1 U (bottom panel) with the Hubbard U value of 4.3 eV. The transition from a metallic to insulating nature is seen when the Hubbard correction is incorporated. Source: Reproduced with permission from M. Cococcioni et al., Linear response approach to the calculation of the effective interaction parameters in the LDA 1 U method. Phys. Rev.B 71 (2005) 035105. Copyright (2005) American Physical Society.

this method a fraction of exact exchange is added to standard GGAs as there is no limit in choosing any particular combination of exchange and correlation functionals. Such functionals are known as hybrid functionals, which are generally defined as: EHybrid 5 αEEXX 1 ð1 2 αÞEGGA 1 EGGA : xc x x c

(5.17)

Here EEXX is the exact exchange and α is the mixing parameter for x the exact exchange which can be defined semiempirically. Such a

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method was initially suggested by Becke, which later led to the development of some popular hybrid functionals like the B3LYP [33]. The B3LYP is widely used in quantum chemistry problems because of its improvement to calculating molecular properties over the standard (semi-)local functionals. Another popular hybrid functional frequently used in solid-state calculations is the HSE06 [34] functional, which separates the exchange interaction into two parts: short range and long range. Such a hybrid functional has the form: EXX;SR EHSE ðωÞ 1 ð1 2 αÞExωPBE;SR ðωÞ 1 ExωPBE;LR ðωÞ 1 EPBE xc 5 αEx c ;

(5.18)

where ExEXX, SR is the short-range exact exchange, ExωPBE, SR and ExωPBE, LR are the short-range and long-range components of the PBE exchange, and EcPBE is the full PBE correlation. The α value is set to a constant of 0.25, which is derived from perturbation theory. The method of hybrid functionals uses only a single parameter α which is preferred to the LDA 1 U approach. However this comes at the price of a much higher computational cost and also the fact that they perform far worse when calculating cohesive energies when compared to PBE.

5.4.3 Corrections for Dispersion Forces Last but not least, standard DFT does not incorporate the corrections for dispersion forces; however, over the course of time sophisticated techniques to include this correction have been developed. The inclusion of these forces is particularly important when calculating binding energies of molecules. Some semiclassical treatments of the dispersion forces include the DFT-D2 [35], DFT-D3 [36], Tkatchenko 2 Scheffler model [37], XDM [38], etc. Other methods such as the van der Waals density functionals, which are nonlocal densitybased dispersion corrections, also attempt to include the dispersion forces.

5.5 SENSITIVITY OF THE SIMULATION RESULTS Some parameters are very important for obtaining reasonable accurate results and those parameters must be taken into consideration for any DFT calculation. These parameters can also be property dependent as the convergence of the property may rely on some totally different parameter values; for example, one might need a denser k-point grid to treat metals. In this section we discuss a few of the important parameters.

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5.5.1 Energy Cutoff The energy cutoff is an important parameter used to obtain precise values for the ground-state properties. One must always check the convergence of total energy with respect to the energy cutoff (Ecut) before carrying out any serious calculation. Solutions of the Schro¨dinger equation for a periodic system follow Bloch’s theorem, which in turn leads to the solution being written as the product of a cell periodic part and a wavelike part: Φn ðrÞ 5 expðik 3 rÞfn ðrÞ;

(5.19)

where fi(r) is the cell periodic part of the wave function. With the periodicity for fi(r) in mind one can expand it in terms of a finite number of plane waves with the reciprocal lattice vectors of the crystal being their wave vectors: X fn ðrÞ 5 cn;G exp½iG 3 r: (5.20) G

According to the equation the summation is carried over the reciprocal lattice vectors defined by G 5 m1b1 1 m2b2 1 m3b3 where the mi takes integer values. This set of lattice vectors defined by G in reciprocal space is defined such that G 3 a 5 2πm, where a is any real space lattice vector and m takes integer values. Combining the above two equations results in the solutions being written as a sum of plane waves: X Φn ðrÞ 5 cn;k1G exp½iðk 1 GÞ 3 r; (5.21) G

where cn;k1G are the coefficients of the plane waves describing the electronic wave functions. Using Bloch’s theorem, the solutions for an infinite system with periodicity require only the solution of a finite number of electronic wave functions. However the solutions are in terms of an infinite number of reciprocal space vectors within the first Brillouin zone (BZ) of the periodic system. This problem can be tackled by using a special set of k-points as the solution varies slowly over small regions of reciprocal space. The k-point sampling is discussed briefly in the next section. We are still left with the problem of an infinite number of possible values for G as they are required for evaluating the wave functions even for a single k-point in the reciprocal space. Fortunately cn;k1G , which are the coefficients for the plane waves, have kinetic energy: E5

h2 jk1Gj2 : 2m

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(5.22)

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This means the solutions obtained for lower values of kinetic energy are much more physically important than those obtained at higher values of kinetic energy. This lets us introduce a kinetic energy cutoff for the plane waves to reduce the basis set to a finite size making it computationally possible, yet retaining accuracy when calculating the solutions as the parameter can be easily tuned. This results in solutions having kinetic energy less than: Ecut 5 reducing the infinite sum to X Φi ðrÞ 5

h2 2 G ; 2m cut

(5.23)

ci;k1G exp½iðk 1 GÞ 3 r:

jG 1 kj , Gcut

With this we have introduced Ecut, which is an important parameter in any DFT calculation as the convergence to the ground-state energy is dependent on it. Moreover this parameter is much simpler to define when compared to defining k-points as in most cases the pseudopotentials are provided with the default value of Ecut. The rule of thumb when dealing with multiple elements is that the cutoff that is the highest for an element is taken as the overall cutoff of the supercell calculation. It must be noted that when performing a DFT calculation to obtain energy differences for multiple systems this parameter must have the same values for all the calculations. Fig. 5.3 displays the convergence of total energy with respect to Ecut.

FIGURE 5.3 Total energy convergence with respect to energy cutoff for fcc aluminum. Source: Reproduced from https://wiki.fysik.dtu.dk/dacapo/Solution_1#convergence-in-k-points-andplanewave-energy-cutoff.

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5.5.2 Convergence With Respect to k-Point Grid The other important parameter for an accurate DFT calculation is k-point sampling. Unlike the Ecut, choosing a proper grid to sample the BZ isn’t a simple task, and sometimes lower values for the sampling can be better than those at higher value. Below is a brief discussion on the choice of the k-point grid. To obtain various important properties like the density of states (DOS), charge density, etc. in a DFT calculation we have to calculate the integrals over the BZ which have the form: ð ð Ωcell 1 gi 5 g ð k Þdk 5 gi ðkÞdk; (5.24) i ΩBZ ð2πÞd BZ

BZ

where evaluating g i gives the average value over the unit cell. The index i corresponds to the number of discrete states at each point and Ω corresponds to the volume of the primitive or the BZ. This particular integral is defined in the k space and the integration requires all the possible values of k in the BZ. For practical applications this integral has to be discretized and summed over the values of the function and their appropriate weights. Hence the integral becomes: X gi 5 gi ðkÞwðkÞ: (5.25) kAIBZ

For computational reasons we want a minimum number of k-points; so instead of integrating over the whole BZ only the irreducible part of the BZ is taken when evaluating. This is done by assigning proper weights to the k-points. Over many decades people have studied various efficient methods for solving the integral and reducing the computational time. One of the most important contributions to evaluating this integral was provided by Monkhrost and Pack in 1976 [39]. This method for sampling the BZ is used widely and is available in most of the DFT simulation packages. In order to specify such a grid we need to specify the number of k-points in each direction of the BZ, for example, in a cubic structure with equal lattice vectors. In each direction we can specify 10 3 10 3 10 k-points as the reciprocal lattice vectors, which are the same in all directions, and we want the k-point density to be the same in all directions. Note that not all of the k-points are used in the calculation, i.e., only the k-points in the irreducible Brillouin zone (IBZ) are used which greatly reduces the computational effort by considering various symmetries of the crystal. The higher the symmetry the lower the number of k-points required for the calculation. For example, in a face-centered cubic (fcc) crystal the aforementioned grid consists only of 35 k-points instead of

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FIGURE 5.4 Total energy convergence with respect to k-point sampling for fcc aluminum. The x-axis represents the number of k-points in each direction. Source: Reproduced from https:// wiki.fysik.dtu.dk/dacapo/Solution_1#convergence-in-k-points-and-planewave-energy-cutoff.

1000. Another key point is that when increasing the volume or multiplying the cell the number of k-points required to achieve convergence are reduced since increasing volume in real space reduces the volume of the BZ. For systems with periodicity in only two dimensions k-points need to be specified only in the two dimensions, else they should be kept to 1, that is, N 3 N 3 1 for a square lattice. Fig. 5.4 displays the convergence of total energy with respect to the number of k-points.

5.6 MODELING OF GRAPHENE-BASED 2D MATERIALS FOR GAS SENSING Conventionally, metal oxides, e.g., NiO, Fe2O3, ZnO, Cr2O3, SnO2, and so on [40], were used for gas sensing in portable gas detection systems because of their advantages, such as compact size, easy synthesis, and low cost. However, the performance of such sensors is greatly dependent on several parameters such as surface reactions, morphology, operation temperature, etc. [41], which creates obstacles for gas sensors based on bulk materials to achieve high performance. An efficient gas sensor should possess (1) high sensitivity and selectivity; (2) fast response; (3) low analyst consumption; (4) low operating temperature and temperature independence; and (5) stability in performances. Because gas-sensing performance depends on the adsorption and desorption of gas molecules on sensing materials, the sensing

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performance can be significantly enhanced by increasing the surface interaction between the analyte and the sensing material. So nanomaterials were considered to be promising candidates for gas sensing with improved sensitivity, selectivity, and response speed due to their large surface
volume ratio and massive reactive sites [42]. This has generated a thrust to synthesize various nanomaterials with different shapes and sizes, for example, porous nanotubes [43] or one-dimensional nanowires [44]. A huge number of 2D layered materials have been synthesized and theoretically predicted [45,46] in recent years. They possess a chemically active or inert surface along with semiconducting or metallic electronic properties depending on the thickness, stacking pattern, and composed elements. They are excellent candidates for efficient gas sensing due to their atomic-thin large surface-to-volume ratio, layered structure, strong surface activities, and large adsorbing capacity of gas molecules. Graphene is the first fabricated 2D material with atomic thickness and large surface
volume ratio [47]. Graphene sensors can detect individual events when a gas molecule attaches to or detaches from the surface due to the electrical conductivity changes induced by adsorbed molecules [48]. Plenty of graphene derivatives, e.g., graphene oxides (GOs), reduced graphene oxides (RGOs) [49], and hydrophilic and hydrophobic graphene [50] can also be used as gas sensors.

5.6.1 Gas-Sensing Mechanism In general, there are two gas sensing mechanism: the surface adsorbed oxygen ions mechanism and the charge transfer mechanism. The surface-adsorbed oxygen ions mechanism can be described as follows. In metal oxide
based gas sensors, e.g., ZnO, SnO2, etc. the sensing mechanism is related to the surface-adsorbed oxygen ions. The gases adsorbed on the metal oxide interact with the oxygen negative ions and change the conductivity of the metal oxides. For a reducing gas, it releases a negative charge due to the oxidation, which increases the conductivity of metal oxides. For the reducing-gas CO, the process is as follows [51,52]: 2 COðgÞ 1 O2 ðadÞ -CO2ðgÞ 1 e :

(5.26)

For an electron acceptor gas, it will accept the charge and decrease the conductivity of the metal oxide. For a gas such as NO2 the process will be: NO2 1 e-NO22 :

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(5.27)

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5.6.2 Charge Transfer Mechanism The gas-sensing mechanism in graphene and graphene-related layered inorganic analogs are different from conventional metal oxides. It is based on the charge transfer process where the sensing material acts as charge acceptor or donor. This charge transfer process changes the resistance of the sensing material which helps to sense the gases. During the desorption process the resistance of the sensing material returns to its initial state. By computing the optical absorption of the sensing materials before and after adsorption of gases the adsorbed gases can be detected. Theoretical modeling of 2D sensing materials is very useful and can give detailed insight regarding charge transfer, bonding, and orbital interactions which are difficult to get through experiments. It can also study the sensing mechanism and sensing performance of various materials to design suitable material for the experimentalist to tailor it. Although there are many electronic structure methods, e.g., DFT [8
10], Hartree
Fock [11,12], quantum Monte Carlo [13
15], coupled cluster [16,17], multireference configuration interaction [18], etc., the most widely used approach is the first-principles calculation based on DFT simulations as it reasonably and efficiently describes the weak interaction between adsorbates and sensing materials. The results predicted by DFT calculations can support experimental measurements and provide theoretical understanding of the mechanism of gas sensing. Various well-developed commercial or open-access quantum software products perform electronic structure simulations. The most popular and widely used electronic structure simulation packages are VASP [53,54], SIESTA [55], Quantum Espresso [56], Abinit [57], among others.

5.6.3 Modeling of Graphene Graphene is the first theoretically predicted and experimentally confirmed 2D gas-sensing material with high sensitivity and selectivity. As it possesses high electron mobility with atomic thickness [58], it is considered to be one of the most valuable materials for post silicon electronics. The structure of a 4 3 4 graphene layer is shown in Fig. 5.5. To simulate a graphene layer we need to consider a periodic boundary condition in the graphene plane (e.g., X and Y direction) with no periodicity in the perpendicular to the plane (e.g., Z direction). The DOS for single-layer graphene is displayed in Fig. 5.6, which exhibits a characteristic V-shaped DOS with zero bandgap. To obtain V-shaped DOS we need to consider higher k-points, e.g., 20 3 20 3 1 in a gamma-centered K grid. One of the promising applications of graphene is gas sensing. Using DFT simulations, Wehling et al. [59]. studied NO2 and N2O4

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FIGURE 5.5 Structure of 4 3 4 graphene. 5 Graphene

Density of states/eV

4 Fermi level

3

2

1

0 –2

–1

0 E–EF (eV)

1

2

FIGURE 5.6 Density of states for single-layer graphene; dotted magenta line indicates the Fermi level showing zero bandgap.

adsorption on graphene. It was observed that although an open shell NO2 molecule is strong, its closed shell dimmer N2O4 adsorbed only with weak van der Waals (vdW) interactions. Leenaerts et al. [60] carried a comprehensive study for the adsorption of H2O, NH3, CO, NO2, and NO on pristine graphene using first-principle calculations. It was observed that H2O and NO2 are electron acceptors, whereas NH3, CO, and NO are electron donors, which support experimental findings by Schedin et al. [48]. Due to a small charge transfer the adsorption was weak, typically 10s of meV. A theoretical study determined that gas adsorption on pristine graphene is too weak in comparison with the requirement of gas sensing. So for pure graphene it is difficult to have ultrahigh sensitivity and selectivity.

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5.6.4 Modeling of Graphene Oxides and Reduced Graphene Oxides Graphene structure with low O content (7%
8%) is known as RGO [61] whereas those with higher oxygen content are known as partially oxidized graphene oxides or GOs. Wang and Hu synthesized GOs structures with varying O content [62]. In the simulations we need to replace some C atoms with the O atoms depending on O content and relax the structure. When O is introduced, O adatoms prefer the bridge positions between two C atoms to form epoxy groups. In the bonding, 2p orbitals of O are bonded with 2pz states of two C atoms to form more stable sp3 hybridized orbitals. DFT-optimized structures of graphene oxides with various O content are displayed in Fig. 5.7 [63]. Table 5.1

FIGURE 5.7 DFT-optimized structures of graphene oxides with various O content. Source: Reproduced with permission from F. Nasehnia et al., Optical conductivity of partially oxidized graphene from first principles. J. Appl. Phys. 118 (2015) 014304. Copyright (2015) AIP Publishing.

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TABLE 5.1 Bond Length, Elevation of the Oxidized Carbon Atom from the Graphene Plane, Adsorption Energy, and the Bandgap for Graphene Oxide with Different O at % [63] O/C ratio (%)

0

2

4

6

8

12

25

50

˚) dC
C (A

1.42

1.53

1.50

1.51

1.50

1.53

1.48

1.49

˚) dC
O (A

...

1.52

1.53

1.53

1.54

1.53

1.51

1.51

˚) h (A

...

0.41

0.32

0.51

0.44

0.29

0.32

0.17

Eads (eV)

...

1.82

2.14

2.32

2.42

2.60

2.86

3.05

Eg (eV)

0

0.02

0.04

0.05

0.06

0.25

1.25

3.93

From F. Nasehnia, and M. Seifi. J. Appl. Phys.118 (2015) 014304

tabulates the structural parameters, for example, adsorption energy, CaO and CaC bond length, elevation of the oxidized carbon atom from the graphene plane and the bandgap [63]. We notice from Fig. 5.7 that O atoms are chemisorbed on graphene plane and distort carbon atoms vertically, with almost no change in the in-plane structure. The adsorption energy per O atom increases with increase in O content. The elevation of the oxidized carbon atom from the graphene plane reduces with O concentration. The opening of bandgap due to the functionalization of O depends on O concentration and adsorption configuration. For O concentration lower than 8%, the gap is not noticeable and the electronic properties of RGO is almost like that of pure graphene. The bandgap and change in electronic properties are significant beyond 12% O concentration. Fig. 5.8 [63] displays the total DOS of the most stable configurations with O coverage of 12%, 25%, and 50%. Attachment of oxygen on graphene cannot only open up a bandgap but also can modify electronic, optical, and other properties with controlled oxidation. Due to the presence of massive reactive sites on the surface, such as oxygen functional groups or defects, GOs or RGOs are more promising for gas sensing than pristine graphene. Despite extensive experimental research on gas-sensing performance of GO and RGO, the theoretical investigations of gas-sensing properties of GO or RGO are not explored that much, probably due to the versatile structures and difficulties in simulating the real experiments of the oxides. Using first-principle calculations, Peng and Li [64] investigated NH3 adsorption on GO and predicted that the adsorption can be promoted by surface epoxy or hydroxyl groups. In hydroxyl functionalized graphene, the charge transfer is higher than that of epoxy groups. Hydroxyl functionalized graphene exhibits better gas adsorption performance compared to epoxy group attached graphene. Tang and Cao [65] FUNDAMENTALS AND SENSING APPLICATIONS OF 2D MATERIALS

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(A) 60 50

12% O

40 30 20 10 0 –10 –9 –8 –7 –6 –5 –4 –3 –2 –1 0

1

2

3

4

5

6

7

1

2

3

4

5

6

7

(B) 60 50 DOS (1/eV)

25% O 40 30 20 10 0 –10 –9 –8 –7 –6 –5 –4 –3 –2 –1 0 (C) 15

10

50% O

5

0 –10–9 –8 –7 –6 –5 –4 –3 –2 –1 0 1 2 3 4 5 6 7 8 9 10 E–EF (eV)

FIGURE 5.8 Total DOS of the most stable configurations of graphene oxide with O coverage of 12% (A), 25% (B), and 50% (C). Source: Reproduced with permission from F. Nasehnia et al., Optical conductivity of partially oxidized graphene from first principles. J. Appl. Phys. 118 (2015) 014304. Copyright (2015) AIP Publishing.

reported that GO cannot only adsorb NH3 for gas-sensing purpose, but it can also dissociate the molecule into smaller species. The surface oxygen sites in GO can make OH?N and O?HN hydrogen bonds with NH3 which increases the charge transfers from NH3 to the GO. The attached NH3 would be dissociated into chemisorbed NH2 or NH species through the H atom, which results in hydroxyl group hydrogenation and ring-opening of epoxy group.

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5.6.5 Modeling of Graphyne and Graphdiyne A frequently investigated material is graphyne [66], an allotrope of graphene, which can be formed from graphene via the simple replacement of CaC bonds by acetylene linkages CQC. Graphyne was theoretically predicted in 1987 and its molecular variant has been synthesized successfully. γ-graphyne, the most stable structure in the graphyne family, possesses an intrinsic bandgap due to Kekule distortion [67
69], compared to zero bandgap in graphene, and has a porous structure and large surface area that opens the door to a variety of potential applications including gas sensing. Graphdiyne is formed when two acetylene linkages are connected between carbon hexagons. Graphdiyne belongs to the same family as graphyne and is expected to have similar characteristics to that of graphyne. Graphdiyne has recently been synthesized and it exhibits semiconducting behavior [70]. A DFT-optimized structure of 3 3 3 3 1 supercell of graphyne and graphdiyne and their band structures are depicted in Fig. 5.9 [71]. The band structure plot computed through PBE functionals as displayed in Fig. 5.9 reveals that both species are associated with direct bandgaps. Unlike graphene, graphyne and graphdiyne possess bandgap and the DFT computed bandgap employing the PBE functional are 0.47 and 0.52 eV, respectively [71]. A charge density plot using Bader charge density analysis is displayed in Fig. 5.10. There is charge transfer from the carbon ring to the nearby sp carbon for both graphyne and graphdiyne resulting in electrons deficient in C6 rings as shown in Fig. 5.10. As graphyne and graphdiyne contain both sp2 and sp carbon atoms, their binding energy is expected to be much less than that of graphene graphyne and graphyne that contains well-established structures with only sp2 carbon atoms. The formation energy of graphyne and graphdiyne are 12.4 kcal mol21 and graphyne 18.3 kcal mol21, respectively, which is lower than the formation energy of any carbon allotrope containing acetylene linkages [72]. 5.6.5.1 Influence of Acetylene Linkage Unlike graphene, which has only sp2 hybridized C atoms, graphyne has both sp2 and sp hybridized C atoms, allowing more p orbitals to interact with the adsorbed elements [73], and different bonding and charge transfer mechanisms than in the family of sp2 hybridized carbon nanostructure such as graphene, carbon nanotubes, etc. Consideration of the graphyne structure reveals that it has two sites for adsorption, known as the triangle hollow and the hexagonal hollow. The hexagonal hollow site is surrounded by the sp2 hybridized C atoms with an out-ofthe-plane pz (π/π*) orbital, while at the triangle hollow site there are both sp2 and sp hybridized C atoms, the latter being the C atoms on the acetylene linkage(
CC
). Due to the presence of sp hybridized C

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(A)

(C)

(B)

Energy (eV)

(D)

4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 –0.5 –1.0 –1.5 –2.0 –2.5 –3.0 –3.5 –4.0 –4.5 –5.0

Γ

Energy (eV) 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 –0.5 –1.0 –1.5 –2.0 –2.5 –3.0 –3.5 –4.0 –4.5 –5.0

K

M

Γ

Γ

K

M

Γ

FIGURE 5.9 3 3 3 3 1 supercell of the optimized geometries of (A) graphyne and (B) graphdiyne, and the calculated band structure of (C) graphyne, and (D) graphdiyne. Source: Reproduced with permission from K. Srinivasu et al., Graphyne and graphdiyne: promising materials for nanoelectronics and energy storage applications. J. Phys. Chem. C 116 (2012) 5951
5956. Copyright (2012) American Chemical Society.

FIGURE 5.10 Charge density plots of (A) graphyne and (B) graphdiyne. Source: Reproduced with permission from K. Srinivasu et al., Graphyne and graphdiyne: promising materials for nanoelectronics and energy storage applications. J. Phys. Chem. C 116 (2012) 5951
5956. Copyright (2012) American Chemical Society.

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atoms, there are in-plane px
py orbitals which also contribute to the π (bonding) and π* (antibonding) states in graphyne. Theoretically, one needs to investigate the bonding and charge transfer mechanism at both of the graphyne sites. While analyzing DOS, it is better to plot the three suborbital px, py, and pz for C p orbital.

5.7 MODELING OF 2D MATERIALS BEYOND GRAPHENE FOR GAS SENSING Although graphene is the most well-known 2D crystal with a plethora of unique properties, it also has disadvantages which limit its applications. The lack of an intrinsic bandgap is one of the largest obstacles for graphene to be fully utilized. Fortunately, graphene’s discovery has generated enormous interest in other 2D materials and 2D nanostructures with possible superior characteristics. It has generated a new area of material science called “2D material beyond graphene,” which is growing extremely rapidly at present. This includes isolated monolayers and few-layer crystals of hexagonal boron nitride (hBN), transition metal dichalcogenides (TMDCs: MoS2, MoSe2, WS2, WSe2, etc.), transition metal oxides (TMOs: LaVO3, LaMnO3), transition metal chalcogenides (NbSe3, TaSe3) [74], 2D analogs of the classical semiconductors, silicene and germanene [45], phosphorene [75], and others (Li7MnP4, MnP4) [74]. So the resulting pool of 2D crystals is huge and exhibit a range of properties—from the most insulating to the best conductors, from the strongest to the softest. Fig. 5.11 depicts a schematic diagram of various 2D materials [76]. In this section we will mention the gassensing capability of some of the “2D material beyond graphene” from theoretical perspectives.

5.7.1 Silicene, Germanene, and Stanene The group IV heavy elements of Si, Ge, and Sn can also form graphene-like monolayer structures with hexagonal lattices, generally called silicene, germanene, and stanene and have been successfully synthesized using epitaxial growth mechanism on silver or gold substrate. They are chemically more active and more suitable for gas sensing when compared to graphene as they have low buckling of sp3-like hybrid orbitals due to the weakened π
π overlaps. Prasongkit et al. [77] theoretically studied the gas-sensing performance of silicene nanosensors for four different gases: NO, NO2, NH3, CO. It was observed that the pristine silicene can adsorb NO and NO2 strongly with adsorption energies of 0.73 and 1.3 eV, respectively. But it has very weak adsorption

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Layered materials

Graphene family

2D chalcogenides

2D oxides

Graphene

Fluorographene

Graphene oxide

hBN “white graphene”

BCN

MoS2, WS2, MoSe2, WSe2

Semiconducting dichalcogenides: MoTe2, WTe2, ZrS2, and so on

Layered semiconductors: GaSe, GaTe, InSe, Bi2Se3, and so on

Metalic dichalcogenides: NbSe2, NbS2, Tas2, TiS2, NiSe2

Micas BSCCO

Layered Cu oxides

MoO3, WO3

TiO2, MnO2, V2O5, TaO3, RuO2

Perovskite-type: LaNb2O7, Ca2Nb3O10

Hydroxides: Ni(Oh)2, Eu(OH)2

Materials are stable in ambient room temperatures

Potentially stable materials in ambient conditions

Materials are stable only an inert atmosphere

3D compounds that have been exfoliated into momolayers

FIGURE 5.11 Chart illustrating the categorized library of 2D materials. Source: Reproduced with permission from A.K. Geim et al., Van der Waals heterostructures. Nature 499 (2013) 419
425. Copyright (2013) Nature Publishing Group.

for CO and NH3 due to weak vdW interaction. When pristine silicene is doped with either B or N atoms, the binding energy and charge transfer for CO and NH3 improves, and doped silicone is also capable of sensing CO and NH3. Fig. 5.12 presents the most stable configurations for the four gas species, NO, NH3, NO2, and CO, adsorbed on pristine, B-doped, and N-doped silicene. The variation of transmittance after gas adsorption is displayed in Fig. 5.12C where red, green, orange, and blue lines indicate the transmittance for CO, NO, NH3, and NO2 adsorbed on the devices, respectively. From Fig. 5.12C, we see that a distinction between the four gases is feasible on a silicone-based sensor. Germanene can also form chemical bonding with some small gas molecules due to its high active surface. First-principle DFT simulations [78] predicted that NH3, NO, NO2, and O2 would be chemisorbed on germanene with strong covalent bonds which open significant bandgaps near the Dirac cone of germanene. It was found that NO2 is chemisorbed with dissociation of O2 at room temperature, whereas the inert gases such as N2, CO, CO2, and H2O are physisorbed via weak vdW interactions. Different adsorption behaviors of common gas molecules on germanene signify germanene can be employed as a selective and sensitive gas-sensing 2D material. Stanene also has the properties to sense gases due to its strong chemical reactive surface. Through DFT simulations it was observed that [79] H2O, NH3, and CO molecules are physisorbed whereas NO and NO2 molecules are strongly chemisorbed on stanene with large charge transfer, sizable adsorption

FUNDAMENTALS AND SENSING APPLICATIONS OF 2D MATERIALS

FIGURE 5.12 (A) Pristine (upper) and doped silicone (lower) where X marks the site for B and N doping. (B) The most stable configurations for the four gas species NO, NH3, NO2, and CO when adsorbed on either pristine, B-, or N-doped silicone. (C) The transmittance variation after gas adsorption for pristine, B- and N-doped silicones. Red, green, orange, and blue lines represent the transmittance for CO, NO, NH3, and NO2 adsorbed on the devices, respectively, while black dashed lines represent that of the reference system (P-, B-, and N-silicene devices without gas). Source: Reproduced with permission from Prasongkit et al., Highly sensitive and selective gas detection based on silicone. J. Phys. Chem. C 119 (2015) 16934
16940. Copyright (2015) American Chemical Society.

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energy, and strong covalent (SnaO) bonds. The adsorption energies and charge transfer between gas molecules and stanene monolayers can also be improved by employing biaxial strains and an electric field.

5.7.2 Phosphorene, Arsenene, and Antimonene Two-dimensional layer materials corresponding to group V elements of P, As, Sb, and Bi are called phosphorene, arsenene, antimonene, and bismuthene. For phosphorene, there are two different allotropes in 2D form: black and blue phosphorene. Both allotropes have been synthesized experimentally [80] and are predicted to have potential in gassensing application [81] from both theoretical and experimental studies. Kou et al. [82], using both DFT and nonequilibrium Green’s function (NEGF) calculations, studied the adsorption of CO, CO2, NH3, NO, and NO2 on monolayers of black phosphorene. They predicted that the charge transfer between the molecules and phosphorene is the driving mechanism for the high adsorption strength. Fig. 5.13 [82] presents top views (A
E) and side views (F
J) of CO, CO2, NH3, NO, and NO2 adsorbed on phosphorene monolayer. The I
V curves along (K) armchair and (I) zigzag directions of pure phosphorene and phosphorene with the NH3 adsorption are also shown. The transmission spectra under zero bias are shown in (M); the transmission spectra along the armchair and zigzag directions are presented in (N). It is clear from Fig. 5.13 that the transport features exhibit anisotropy in different (armchair or zigzag) directions. There is also noticeable difference in the I
V curve for pure phosphorene and NH3 adsorbed phosphorene. The above theoretical predictions were experimentally validated by Abbas et al. [81] and Cui et al. [83]. Cai, Ke, Zhang, and Zhang [84] also studied the adsorption of CO, H2, H2O, NH3, O2, and NO2 and found that CO, H2, H2O, and NH3 molecules are weak donors, whereas O2 and NO2 are strong acceptors. From this study we can conclude that phosphorene is a superior gas sensor that promises wide-ranging applications. 2D blue phosphorus monolayer has buckled honeycomb hexagonal lattice. Using DFT simulations, Liu and Zhou [85] investigated the adsorption of O2, NO, SO2, NH3, H2O, NO2, CO2, H2S, CO, and N2 on a blue phosphorus monolayer. All the studied gas molecules, except O2, stably physisorb on monolayers of blue phosphorus with different interaction strengths, whereas O2 gets dissociated and chemisorbs on the blue phosphorus sheet which implies that having monolayers of blue phosphorene is a promising candidate for novel gas sensors. Although arsenene and antimonene were theoretically predicted in 2015 [86
89], antimonene was synthesized experimentally in 2016 [90
92], and arsenene is yet to be synthesized. But their gas-sensing

FUNDAMENTALS AND SENSING APPLICATIONS OF 2D MATERIALS

FIGURE 5.13 (A
E) Top views and (F
J) side views of CO, CO2, NH3, NO, and NO2 adsorbed on phosphorene monolayer. The I
V curves along (K) armchair and (K) zigzag directions of pure phosphorene and phosphorene with the NH3 adsorption. The transmission spectra under zero bias are shown in (M), the transmission spectra along the armchair and zigzag directions are presented in (N). Source: Reproduced with permission from Kou et al., Phosphorene as a superior gas sensor: Selective adsorption and distinct I
V response. J. Phys. Chem. Lett. 5 (2014) 2675
2681. Copyright (2014) American Chemical Society.

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performance was theoretically investigated by Chen et al. [79]. Using first-principles calculations, they studied the adsorption of SO2 and NO2 on pristine, B-doped and N-doped arsenene and predicted that both molecules chemisorbed on B-doped arsenene, while weakly physisorbed on N-doped and pristine arsenene. The conductivity of the N-doped arsenene (pristine arsenene) is considerably enhanced after SO2 (NO2) adsorption, signifying that N-doped arsenene can be treated as an excellent sensing material for an SO2 gas sensor, whereas pristine arsenene can act as a potential application for NO2 gas sensor. Gas-sensing properties of pristine antimonene were investigated by Meng et al. [93]. Interestingly, atmospheric gas molecules, e.g., N2, CO2, O2, and H2O bind weakly to antimonene, whereas the polluted gas adsorbents, e.g., NH3, SO2, NO, NO2 bond strongly due to considerable adsorption energies and higher charge transfers. This signifies that single-layered antimonene is a potential candidate for detecting polluting gases.

5.7.3 Transition Metal Dichalcogenides TMDCs are a class of 2D materials with the formula MX2, where M denotes a transition metal (including Ti, Zr, Hf, V, Nb, Ta, Mo, W, Re, and so on), and X indicates a chalcogen (Se, S, or Te). Each TMDC sheet is trilayered having an M atom in the middle which is covalently bonded to six X atoms located in the top and bottom layers. The interlayer force between the adjacent X
M
X layers is very weak and dominated by vdW interaction, therefore facilitating mechanical exfoliation [94]. A variety of 2D TMDCs exhibit excellent molecular sensing capability due to their high surface
volume ratio, sizable bandgaps, and availability of reactive sites. 5.7.3.1 MoS2 Among the 2D TMDCs, MoS2 is the most promising due to its chemical stability and high carrier mobility and has been widely explored and used in electronics and optoelectronics. It consists of a metal Mo layer sandwiched between two S layers and exhibits semiconducting property with a direct bandgap of around 1.2 eV. Its gas-sensing capabilities for NO and NH3 have been experimentally studied by He et al., [95] and Li et al. [96] in MoS2-based field-effect transistors and sensing films with an ultrahigh sensitivity. Theoretically, using first-principles calculations, Yue et al. [97] reported the adsorption of H2, O2, H2O, NH3, NO, NO2, and CO gases on pristine monolayer MoS2. Fig. 5.14A displays the most stable adsorption configuration of the gas molecules on pristine MoS2 monolayer. All the molecules are adsorbed weakly and act as charge acceptors for the monolayer, except NH3. From Fig. 5.14B

FUNDAMENTALS AND SENSING APPLICATIONS OF 2D MATERIALS

FIGURE 5.14 (A) Top views and side views of different gases (H2, O2, H2O, NH3, NO, NO2, and CO) adsorbed on monolayer MoS2. (B) Electronic band structure changes after NO and NO2 adsorption. (C) Electric field effect on electron transfer. (Left) Representation of the applied perpendicular electric field, where the arrows denote its positive direction. (Right) Variation of charge transfer as a function of electric field strength for NO, and NO2, adsorbed on monolayer MoS2. Source: Reproduced with permission from Yue et al., Adsorption of gas molecules on monolayer MoS2 and effect of applied electric field. Nanoscale Res. Lett. 8 (2013) 425. Copyright (2013) Springer.

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we notice that there is no significant change in the band structure of monolayer MoS2 upon molecular adsorption, although certain molecules such as O2, NO, and NO2 introduce adsorbate states in the bandgap region. The charge transfer between the adsorbed molecule and MoS2 can be significantly improved by applying an electric field as shown in Fig. 5.14C. Like an electric field, strain also acts as an alternative way to modulate the gas adsorption on MoS2 [98]. It was observed that the adsorption of NO and NH3 can be substantially improved on strained monolayer MoS2 whereas adsorption of NO2, CO, and CO2 does not change to the applied strain. This signifies that sensitive strain engineering can be applied for selective chemical adsorption on MoS2 and opens new design strategies for tailoring MoS2-based ultrahigh sensitivity nanoscale sensors and electromechanical devices. Like pristine graphene, the gas adsorption on pristine MoS2 is very weak as it is due to the physisorption through vdW interaction. To increase the sensitivity and selectivity of MoS2 Yu, Wang, et al. [99] studied the adsorption of nonpolar gases CO2 and CH4 on pristine MoS2 and MoS2 with single S vacancy and double S vacancies using first-principle calculations and grand canonical Monte Carlo simulations. It was observed that although pristine MoS2 shows little or no adsorption for nonpolar gas CO2 and CH4, the MoS2 with a single S vacancy and double S vacancies possess excellent adsorption ability for CO2 and CH4. The orbital coupling between d orbital of Mo and p orbital of CO2 (or CH4) molecule makes stronger adsorption with higher adsorption energy.

5.8 INTERACTION MECHANISM OF GASES ON 2D MATERIALS The gas-sensing process involves complicated and precise adsorption mechanisms as the interactions are in general not strong. The gases can be chemisorbed or physisorbed on 2D materials. If it is chemisorbed there will be charge transfer either from gas to the sensing materials or in the reverse direction and the adsorption energy is considerably higher. For physisorption, the interaction is weak and difficult to detect. Simulations can provide important information on sensing processes such as possible adsorption configurations, preferred adsorption sites, adsorption energy, electron transfer, change in electronic properties and different approach to enhance adsorption, which can play a vital role for the fabrication and development of efficient gas sensors. But there are challenges for theoretical simulations. Gas-sensing parameters are very sensitive to the choice of exchange functional, energy cutoff, and k-point grid used and also on the dispersion correction schemes. In this section we discuss the details of various adsorption parameters from a simulation point of view.

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177

5.8.1 Adsorption Site Finding stable adsorption configurations is a crucial step in the simulation of gas sensing. As weak interactions are involved it takes longer for force convergence. Sometimes the gas molecules may get stuck around local minima (energy) and actual stable configurations cannot be reached. In that case the user may restart the simulation with a new initial configuration. If the 2D material containing transition metals have magnetic moment, the structural relaxation as well electronic energy calculations should be performed considering spin polarization along with proper magnetic configuration. Choice of improper magnetic configuration can lead to error in adsorption energy as well as to wrong electronic properties. It is advisable to test the energy convergence with respect to cutoff energy as well as number of K-points. The bond length between the gas molecules and the nearest element of the 2D material is an important parameter from a theoretical point of view. The lower the bond length, the stronger the adsorption. Fig. 5.15 displays the absorption configurations of CO, H2O, NO, and O2 on a WS2 monolayer obtained from DFT simulations [100]. Here two different configurations for H2O and three different configurations for O2 were considered. Both the configuration of H2O has similar adsorption energy, but for O2 the third configuration is not stable whereas the other two are stable with the same adsorption energy. In the case of CO absorption, it was found that in the stable configuration the C atom tends to move toward W, while the O atom points away.

5.8.2 Adsorption Energy The adsorption energy of the gas molecules on sensing material is the key parameter for the detection of the gases. The adsorption energy is defined as: Eads 5 E2D1gas
E2D
Egas ;

(5.28)

where E2D1gas is the energy of the 2D system with gas adsorbed, E2D is the energy of the 2D system, and Egas is the energy of isolated gas molecules. According to this definition, a negative value of adsorption energy indicates that the adsorption of gas molecules on the surface is energetically favorable. As the energy is very sensitive to the choice of the exchange functional and other simulation parameters, we need to take the same setting for computing E2D1gas and E2D . The adsorption energy may be different for different adsorption sites. For example, in graphyne there are two adsorption sites, namely hexagonal and triangular, which give different adsorption energy. In many cases the adsorption energy is very small, so the simulations should have higher precision to

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5. ELECTRONIC STRUCTURE AND THEORETICAL ASPECTS

FIGURE 5.15 Absorption configurations (including top and side views) of (A) CO, (B) H2O(1), (C) H2O(2), (D) NO, (E) O21, (F) O22, and (G) O23. Source: Reproduced with permission from Viet Q Bui et al., A first-principles investigation of various gas (CO, H2O, NO, and O2) absorptions on a WS2 monolayer: stability and electronic properties. Phys. Condens. Matter 27 (2015) 305005. Copyright (2015) IOP Publishing.

account for small changes in energy. Optimization needs to be performed with care so that there are negligible forces on the atoms. There are certain codes which automatically take periodic boundary conditions, in that case the simulation cell should have sufficient vacuum to avoid the interaction between periodic images. It is advisable to compare the adsorption energy obtained from the different exchange functional.

5.8.3 Charge Transfer and Orbital Interactions Charge transfer between the sensing element (gas) and the sensing material is a very important parameter for the design of the sensor. The sensing material can act as charge acceptors or donors and when FUNDAMENTALS AND SENSING APPLICATIONS OF 2D MATERIALS

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179

exposed to different gases charge transfer takes place which changes the resistance of the sensing material. So the amount of charge transfer decides the sensitivity of the device. The higher the charge transfer the better the sensitivity. In DFT simulations, one needs to take care of the number of valence electrons considered for each element to correlate the charge transfer. Mulliken’s charge analysis [101] or Bader charge analysis [102] are extensively used to find the amount of charge transfer during the interaction between various elements. The charge transfer can be pictorially visualized by plotting the spatial variation of charge density. There exists various sophisticated software to visualize charge density distribution, e.g., XCRYSDEN [103], VESTA [104]. Yue et al. [97] studied the charge transfer between n-type MoS2 and different adsorbed gas molecules, which include O2, H2O, NH3, NO, NO2, and CO. Fig. 5.16 displays the charge density difference plots for the above gases interacting with monolayer MoS2, where the red region indicates the

FIGURE 5.16 Charge transfer process and density difference plots for (A) O2, (B) H2O, (C) NH3, (D) NO, (E) NO2, and (F) CO interacting with monolayer MoS2. Source: Reproduced with permission from Yue et al., Adsorption of gas molecules on monolayer MoS2 and effect of applied electric field. Nanoscale Res. Lett. 8 (2013) 425. Copyright (2013) Springer.

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charge accumulation and the green region is the charge depletion [97]. For n-type MoS2 monolayer, there are some electrons in the conduction band. When MoS2 is exposed to electron acceptor gases such as O2, H2O, NO, NO2, and CO, there is charge transfer from MoS2 to the sensitive gases, resulting in a decreased carrier density in MoS2. As a result, the resistance of MoS2 increases. On the other hand, when MoS2 is exposed to electron donor gases like NH3, there is charge transfer from NH3 to the MoS2 monolayer, increasing the electron carrier density of the n-type MoS2 monolayer and reducing its resistance. Charge transfer occurs due to interaction between the orbital of gases and that of the sensing material. In the case of transition metal dichalcogenide-based sensors, the d orbital of the transition metal interacts with p orbital of O (for O2), N (for NH3, NO, NO2), C (for CO). By plotting the partial density of states (PDOS) of the interacting elements one can qualitatively explain the charge transfer between different orbitals of the interacting elements. To get a clear picture, one can plot five suborbitals of d orbitals, namely, dxy, dxz, dzx, dz2, dx2
y2 and three suborbitals of p orbitals namely, px, py, and pz. Theoretically one can correlate the charge transfer process through Bader charge analysis, PDOS analysis, and charge density plot.

5.8.4 Importance of Dispersion Corrections As the interaction of gases on 2D material is in general very weak, it is difficult to model such weak interactions by standard GGA or LDA functionals as they cannot account for long-range electron correlation effects. Simulations using GGA functional without long-range electron correlation tend to underestimate effects whereas those with LDA functionals tend to overestimate effects. To account for the weak interaction more accurately vdW corrections have been included in the standard DFT simulation. With the introduction of vdW interaction the adsorption energy increases slightly. Here we mention that there are various schemes for describing vdW interactions and the adsorption energy varies a little bit for different exchange functionals (GGA, LDA) as well as for different schemes of vdW interactions. Here we outline Grimme’s DFT-D2 and DFT-D3, the optPBE
vdW and vdW
DF method for describing dispersion corrections in DFT [35,36,105,106]. 5.8.4.1 Grimme’s Density Functional Theory-D2 In the Grimme’s DFT-D2 approach [35], the total energy is described as a sum of the Kohn 2 Sham energy, EDFT and vdW semiempirical pair (2) correction, Edisp : EDFT
D 5 EKS
DFT 1 Eð2Þ disp

FUNDAMENTALS AND SENSING APPLICATIONS OF 2D MATERIALS

(5.29)

5.8 INTERACTION MECHANISM OF GASES ON 2D MATERIALS

Eð2Þ disp 5
s6

NX Nat at
1 X i51 j5i11

C6ij R6ij

fdmp ðRijÞ ;

181

(5.30)

where Nat is the total number of atoms, Cij6 is the dispersion coefficient, Rij is the bond distance for atom pair ij, and s6 is a scaling factor. A damping factor, fdmp is included to prevent singularities at small distances. 5.8.4.2 Grimme’s Density Functional Theory-D3 Grimme’s DFT-D3 method improves the accuracy of the DFT-D2 approach by incorporating the environment-dependent dispersion coefficients during the DFT simulations and including a three-body component to the Edisp term. Now the Edisp term is expressed as: ð3Þ Edisp 5 Eð2Þ disp 1 Edisp ;

where Eð3Þ disp is given by Eð3Þ disp 5

X

ffð3Þ ðrABC ÞEABC :

(5.31)

(5.32)

ABC

Here the sum is overall triplet ABC and geometrically averaged radii rABC are used in the damping function, ff(3). 5.8.4.3 van der Waals
DF In the vdW
DF approach, no external input is required and dispersion interactions are treated directly using the electron density. The exchange-correlation energy is given by [105
108]: Exc 5 EGGA 1 ELDA 1 Enl x c c

(5.33)

Here ExGGA denotes exchange energy for a given GGA functional, Ec is the LDA correlation energy, and dispersion is included directly using a nonlocal correlation, Ecnl, which is expressed as a double space integral of the form: ðð nl Ec 5 dr1 dr2 nðr1 Þϕðr1 ; r2 Þnðr2 Þ; (5.34) LDA

where n(r) is the electron density and ϕ is an integration kernel. 5.8.4.4 optPBE
van der Waals optPBE
vdW is a variant of vdW
DF. Table 5.2 displays the adsorption energy of CO on graphene and defected graphene using six different approaches of vdW corrections [109]. We notice that different vdW-inclusive methods give a relatively wide range in adsorption FUNDAMENTALS AND SENSING APPLICATIONS OF 2D MATERIALS

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TABLE 5.2 Comparison of Adsorption Energy and Equilibrium Molecule-Substrate distance of CO on Graphene and Defected Graphene Using Six Different Approaches of vdW Corrections [109] Method

Graphene

Defected graphene

Ead (eV)

˚) d (A

Ead (eV)

˚) D (A

PBE 1 vdW

2 0.11

2.88

2 2.15

1.33

DFT-D2

2 0.09

2.87

2 2.25

1.33

DFT-D3

2 0.10

2.92

2 2.17

1.33

vdW
DF

2 0.14

3.02

2 1.80

1.33

optB88
vdW

2 0.11

2.77

2 2.04

1.33

optPBE
vdW

2 0.16

2.93

2 2.01

1.33

From Y. Jiang, S. Yang, S. Li, W. Liu, Y. Zhao, J. Nanomater. (2015) 504103. http://dx.doi.org/10.1155/2015/ 504103.

energies. Again, the PBE-vdW and DFT-D3 give almost identical binding energies, which are about 100 meV higher than those from the DFT-D2 method. This is because the former methods use the environmental-dependent vdW parameters in DFT calculations. The vdW
DF functional gives in general higher binding energy due to the well-known overly repulsive exchange part used in this approach. So we advise computational researchers to be cautious in choosing the exchange functional and long-range corrections. It is also extremely important to verify the theoretical results with experimental measurements.

5.9 CHANGE IN PROPERTIES DUE TO ADSORPTION OF SENSING ELEMENTS Due to the adsorption of sensing elements, there are some changes in the properties of the sensing material which helps to detect the sensing element. The major modifications are in electronic, electrical, and optical properties. In some cases there may be changes in magnetic properties.

5.9.1 Electronic Properties Most of the sensors are based on charge transfer due to adsorption of gas molecules or biological molecules. This charge transfer modifies the electrical conductivity of the sensing material. Due to charge transfer there is a change in band structure of the material as well as in density of states. For semiconducting material, there is a change in bandgap

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183

due to adsorption of gas molecules or biological samples. The bandgap may induce color change in the sample which is employed to detect the sensing elements in optical sensors. In this section we will focus how to accurately compute the bandgap of the material before and after adsorption of the sensing element. 5.9.1.1 Bandgap Modifications In earlier sections we emphasized the importance of charge transfer and adsorption energy for detecting the sensed element. Here we focus on the importance of changes in bandgap of the material and how to account for the small change in bandgap. The bandgap (Eg) is a major factor in determining the electrical conductivity (σ) of a material; σ is related to the bandgap Eg through the expression [110,111]: 
Eg σ ~ exp ; (5.35) 2kT where k and T are the Boltzmann constant and the temperature, respectively. From the above expression, the smaller the bandgap, the higher the electrical conductivity at any given temperature. As the change in bandgap is in general very small, one needs to compute it very accurately. If the error in computing the bandgap is of the order of the change in bandgap then the sensor can give the wrong results. The accuracy of the DFT result depends on how precisely we can describe the unknown exact exchange correlation interactions. The most commonly used LDA and GGA are inadequate to describe localized electronic states such as localized d or f states in transition metals and other high Z elements. The shortcomings of DFT due to the difficulty in describing the exchange and correlation interactions become apparent for materials with strongly correlated electrons where LDA and GGA suffer from errors in describing their physical properties. This inadequacy of LDA and GGA approaches is associated to the self-interaction error and the absence of discontinuity in the exchange-correlation functional derivative with respect to electron occupation number [112
115]. In order to overcome the limitations of standard GGA and LDA approaches we need to incorporate some advanced technique in DFT. Here we outline some corrections to DFT to get accurate bandgap in systems involving localized d or f electrons. GGA 1 U Method

The GGA 1 U approach aims to correct standard LDA or GGA exchange correlation for self-interaction error by incorporating the on-site Coulomb interaction in narrow, spatially localized bands based on the Hubbard model approach for treating strongly correlated

FUNDAMENTALS AND SENSING APPLICATIONS OF 2D MATERIALS

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5. ELECTRONIC STRUCTURE AND THEORETICAL ASPECTS

TABLE 5.3 Bandgaps of WSe2 Absorbed With Various Gas Molecules Predicted by PBE and PBE 1 U Calculations (With D2 Empirical Corrections) [100] PBE

PBE 1 U (U 5 2.87 eV for 5d of W)

Absorbed gas on WSe2

Bandgap (eV)

Bandgap (eV)

CO

1.82

1.72

H2O

1.82

1.72

H2O

1.82

1.72

NO

0.95

0.85

(2)

0.81

0.75

(2)

0.81

0.81

(2)

1.36

1.32

O O O

From V.Q. Bui, T.-T. Pham, D.A. Le, C.M. Thi, and H.M. Le, J. Phys. Condens. Matter 27 (2015) 305005.

electrons [116
118]. This approach is simpler and computationally less expensive compared to hybrid functional approaches or GW approach. An important issue of the GGA 1 U method is the choice of U parameter. In practice, U parameter can be empirically determined by relying on experimental results, i.e., the value of U can be varied until the experimental value of the bandgap is reached. The Hubbard U term can also be determined using a method proposed by Cococcioni and de Gironcoli [29], the so-called “linear response” approach, where experimental data is not required. Viet Q Bui et al. [100] computed the bandgap of WSe2 and WSe2 adsorbed with several gas molecules (CO, H2O, NO, and O2) using PBE and PBE 1 U (U 5 2.87 eV for 5d of W) and the value of bandgap for different configuration are presented in Table 5.3. We can see that the incorporation of Hubbard U decreases the bandgap compared to pure PBE calculations. Hybrid Functional

Hybrid functionals based on a screened Coulomb potential for HF exchange can provide better accuracy and overcome some of the shortcomings of standard DFT [34,119,120]. In this method, a fraction of nonlocal HF exchange is included in the GGA exchange potential within a fixed radius, called the screening length. There are two adjustable parameters: (1) screening radius length and (2) the fraction of added exchange. In practice, fraction of nonlocal exchange is varied to reproduce experimental bandgaps whereas the screening length is kept fixed. The method is computationally very expensive and sometimes

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185

FIGURE 5.17 The spin-resolved total DOS for perfect Y2O3 with (A) GGA exchange functional and (B) hybrid functional HSE06. Source: Reproduced with permission from Brahmananda Chakraborty et al., Room temperature d(0) Ferromagnetism in hole Doped Y2O3: widening the choice of host to tailor DMS. J. Phys. Condens. Matter 28 (2016) 336001. Copyright (2016) IOP Publishing.

convergence is an issue. It is advisable to first perform a normal GGA calculation and store the wave functions and then start calculations with hybrid functionals using saved wave functions. Fig. 5.17 displays [121] the DOS for Y2O3 using GGA and using hybrid functionals based on Heyd
Scuseria
Ernzerhof (HSE06). We see that the GGA calculated bandgap is 4.224 eV, much lower than the experimental value of 5.8
6 eV [122,123], whereas hybrid functionals yield a bandgap of 5.6 eV, closer to the experimental bandgap. GW Corrections

The GW approximations [124] may be considered as a generalization of the Hartree
Fock approximation (HFA) with dynamically screened Coulomb interactions. Here an approximation is made to compute the self-energy of a many-body system of electrons. The approximation is that the expansion of the self-energy Σ in terms of the single particle Green’s function G and the screened Coulomb interaction W (in units of ¯h 5 1). As the method demands expensive computational effort GW calculations are restricted to rather small systems, and various approximations have been made to make GW calculations feasible. Fig. 5.18 [125] shows the band structure of 2 H hexagonal phase WSe2 using LDA (black) and G0W0 (red) and LDA. Here we mention that the spin-orbit coupling gives rise to a splitting of the bands at various

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FIGURE 5.18 Band structure of 2H-WSe2 using LDA (black) and G0W0 (red) and LDA projected density of states. Note the spin-orbit coupling gives rise to a splitting of the bands at various regions of the Brillouin zone. The red line connecting the G0W0 points is obtained from a cubic spline interpolation. Source: Reproduced with permission from Filip A. Rasmussen et al., Computational 2D materials database: electronic structure of transition-metal dichalcogenides and oxides. J. Phys. Chem. C 119 (2015) 13169
13183. Copyright (2015) IOP Publishing.

regions of the Brillouin zone. The red line connecting the G0W0 points is through a cubic spline interpolation. LDA predicted direct bandgap is 1.22 eV whereas G0W0 predicted direct bandgap is 2.08 eV.

5.9.2 Optical Properties Like resistivity-based gas sensors, optical gas sensors are also of interest. In optical sensors, the change in optical properties is used to detect gas molecules. Due to adsorption of gas molecules, the absorption spectrum of the sensing material gets modified. The gas molecule can act as an acceptor or a donor. There are various methods to compute the optical absorption spectra and each has its own advantages and disadvantages. Post HF-based methods such MRSDCI [126] give greater numerical accuracy. Timedependent density functional theory (TDDFT) [127,128] includes the exchange correlation functional within an adiabatic approximation, which leads to an improved electronic correlation effect for both ground states and excited states. TDDFT improves transition energy due to

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187

improved electronic correlations. VASP [129] works on the independent particle model and uses non-normconserving ultrasoft pseudopotential. In HF-based methods, when the size of a Boron cluster increases the excitation into very high-energy, unoccupied orbitals need to be neglected in order to save extensive computational cost. This will limit the calculation of the linear optical absorption spectrum up to the visible region only. There is no restriction on the size of the system used to estimate the linear optical absorption spectrum using TDDFT, and a spectrum can be obtained in both visible and ultraviolet region. Rajendra K. Shivade et al. [130] computed the optical absorption spectra of various boron clusters using the TDDFT method
based OCTOPUIS code and DFT-based VASP code. A comparison of optical absorption spectra of B5 triangular bipyramid clusters obtained using TDDFT-based OCTOPUS code (LDA) and DFT-based VASP code (GGA and LDA) is shown in Fig. 5.19. We notice that although the number of peaks are almost the same, there is a slight shift in peak position in VASP-computed spectra compared to OCTOPUS-computed spectra.

FIGURE 5.19 Linear optical absorption spectrum of B5 distorted bipyramid isomer computed using OCTOPUS with LDA exchange correlation functions (top panel), VASP with GGA exchange correlation functions (middle panel), VASP with LDA exchange correlation functions (bottom panel). Source: Reproduced with permission from R.K. Shivade et al., Optical absorption spectra of boron clusters Bn (n 5 2
5) for application in nano scintillator
a time dependent density functional theory study. Eur. Phys. J. B 89 (2016) 198. Copyright (2016) EDP Sciences, Springer Science 1 Business Media, Societa` Italiana di Fisica.

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Using DFT simulations, Nayeri et al. [131] investigated the optical adsorption of monolayer semiconducting transition metal dichalcogenides (STMD), MoS2, MoSe2, WS2, and WSe2 in the absence and presence of the NH3, NO, NO2, and O2 gas molecules. Due to the adsorption of gas molecules there are noticeable changes in the optical absorption spectrum, which helps in detecting those gases. They computed the imaginary part of the dielectric function which is directly related to the absorption spectrum. Fig. 5.20 [131] shows the imaginary part of the dielectric function in the range of 0
3 eV for MoS2, MoSe2, WS2, and WSe2 before and after gas adsorption. We see from Fig. 5.20 that the dielectric function for pristine STMDs contains several peaks (black solid lines) which correspond to direct bandgaps at k-points. Due to the adsorption of O2 and NO2 molecules, an extra peak appears at N energy lower than the first peak in the pristine STMD. The position of the extra peak depends on the type of adsorbed gas. Table 5.4 [131] presents the position of the extra peaks for various gases on the monolayer STMD. The extra peak for NO2 appears at higher energies with larger intensities as compared to O2 molecule for all four STMDs. The adsorption of NO molecule on the MoSe2 and WSe2 surface results in an increase in the intensity of the third peak. For NO molecules on MoS2 and WS2, an

FIGURE 5.20 The imaginary part of the dielectric function versus the photon energy for (A) MoS2, (B) MoSe2, (C) WS2, and (D) WSe2 without and with the gas molecules. Source: Reproduced with permission from Nayeri et al., The transport and optical sensing properties of MoS2, MoSe2, WS2 and WSe2 semiconducting transition metal dichalcogenides. Semicond. Sci. Technol. 33 (2018) 025002. Copyright (2018) IOP Publishing.

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TABLE 5.4

189

Position of the Extra Peaks for Various Gases on the Monolayer STMD [131]

Extra peak (eV) for STMD

O2

NO2

NO

MoS2

0.514

0.63

0.08

MoSe2

0.06

0.47

WS2

0.38

0.58

WSe2

0.26

0.46

0.06

From M. Nayeri, M. Moradinasab, M. Fathipour, Semicond. Sci. Technol. 33 (2018) 025002.

extra peak appears at lower energies compared to O2 and NO2 molecules. The change in absorption spectrum signifies that the STMDs meet the criteria needed for sensors based on optical absorption monitoring and are suitable for detecting NO, O2, and NO2.

5.10 MODELING OF GLUCOSE SENSING IN 2D MATERIALS Various transition metal
based 2D oxides, e.g., MoO3, WO3, MmO2, NiCo2O4 are potential candidates for glucose sensing [132]. Transition metals can have different oxidation states with a very low energy gap between the oxidation states [133]. They show high catalytic activities that favor the oxidation of glucose molecules. In some cases the presence of RGO can improve the glucose sensitivity of 2D materials [134]. To explore the theoretical insight on the interaction of glucose molecules on 2D materials it is essential to simulate the isolated glucose molecule, the 2D materials, and glucose molecules attached on 2D materials.

5.10.1 Simulations of Glucose Molecule Glucose, C6H12O6, [135] is a simple sugar (monosaccharide) which circulates in the blood of animals as blood sugar and is used as an energy source in most organisms, from bacteria to humans. Two isomers of the aldohexose sugars are found but only one is biologically active, known as D-glucose. The D-glucose has two different structures: alpha-D-glucose and beta-D-glucose. Although alpha glucose and beta glucose have the same number of carbon atoms, hydrogen atoms, and oxygen atoms they form two different structures when these atoms are formed into molecules. The structures of alpha glucose is shown in

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5. ELECTRONIC STRUCTURE AND THEORETICAL ASPECTS

FIGURE 5.21 DFT relaxed structure of alpha glucose; blue, pink and orange sphere represents C, O and H atoms respectively.

Fig. 5.21 [135]. Chains of alpha glucose form starch whereas cellulose, or fiber, is made up of chains of beta glucose. To simulate glucose molecules we need to consider a large vacuum to avoid the interaction between periodic images of glucose molecule, especially in DFT codes where a periodic boundary condition is automatically taken. It is very important to consider van der Waals interactions separately as DFT does not incorporate the weak dispersion forces. Grimme’s DFT-D2 [35] is one of the schemes to incorporate weak dispersion forces, which uses a pairwise force field for describing vdW interactions.

5.10.2 Simulations of 2D Materials Here we mention theoretical aspects for simulating 2D TMOs as having been successfully used for glucose sensing. It is important to choose the most stable and reactive surface for simulating the interaction of glucose molecule on 2D transition metal oxide surfaces. If the surface is not stable there will be a convergence problem during relaxation. The interaction will be stronger for the most reactive surface. Rajeswari et al. [136] considered an O-terminated (0 0 1) surface of WO3 for simulating glucose sensing as it has better sensing properties as reported by Xiao Han and Xiaohong Yin [137] in the context of NO2 sensing on WO3. Before constructing the surface the researcher should relax the bulk structure and compare the lattice parameters with the experimental value. Computing and comparing DOS of the bulk structure can also FUNDAMENTALS AND SENSING APPLICATIONS OF 2D MATERIALS

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FIGURE 5.22 Total density of states for bulk MnWO4 with GGA and GGA 1 U correction; Fermi level is shown in dotted green line. Source: Reproduced with permission from K.K. Naik et al., Facile hydrothermal synthesis of MnWO4 nanorods for non-enzymatic glucose sensing, supercapacitor properties and density functional theory investigations. Chem. Select, 2 (2017), 5707
5715. Copyright (2017) Wiley-VCH.

provide the researcher with confidence regarding the accuracy of the simulation procedures and structural details. If the system is a semiconductor it is required to match the computed bandgap with the available experimental data. As the normal DFT generally underestimates the bandgap one should use GGA 1 U [29] or hybrid functional [33,34] or the GW [124] method. Naik et al. [138] computed the bandgap of bulk MnWO4 using GGA and GGA 1 U methods as shown in Fig. 5.22. While the GGA 1 U method underestimates the bandgap (0.88 eV), the GGA 1 U method produced a bandgap (2.4 eV) close to the experimental value of 2.50 eV [139]. The orientation of glucose molecules on a 2D transition metal oxide surface is also an important aspect from the simulation point of view. As the oxidation of glucose molecules is the main concern here it is advisable to place the O molecule of glucose close to the transition metal of the transition metal oxide.

5.10.3 Interaction of Glucose Molecule on 2D Materials For transition metal
based 2D oxides, glucose molecules generally interact with the transition metal. There is a charge transfer from the p orbital of O of glucose to the d orbital of transition metal of 2D oxides and the O of glucose makes a bond with the transition metal. The bonding may be ionic in nature as there is charge transfer. There may be hybridization between p orbital of glucose O atom and d orbital of FUNDAMENTALS AND SENSING APPLICATIONS OF 2D MATERIALS

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transition metal. Getting the binding energy of glucose on a 2D surface is one of the important features of the DFT simulation. The binding energy of glucose is computed using the expression:     (5.36) Eb glu 5 E ðTMO 1 GluÞ 2 EðTMOÞ 2 E glu ; where E(TMO) is the energy of the transition metals
based 2D oxide, E (Glu) is the energy of the single glucose molecule, and E(TMO 1 Glu) is the energy of transition metal
based 2D oxide 1 glucose system. If the binding energy is negative, it indicates that the binding of glucose on transition metals
based 2D surface is energetically favorable. Naik et al. [138] studied the interaction of glucose on the surface of MnWO4 and computed binding energy as 20.45 eV, the negative binding energy infers that the interaction is exothermic. Charge transfer is an important aspect for studying the glucosesensing mechanism. Glucose gets oxidized due to charge transfer between glucose and transition metal
based 2D oxides. Charge transfer generally takes place between 2p orbital of O and d orbital of transition metal. The transition metal in TMO generally acts as a communicating medium to transfer charge from glucose by participating in the redox reaction. Redox metal oxides like NiO, Co3O4, CuO, etc. possess multiple oxidation states having small energy gaps [133] which are more efficient for sensing glucose. In DFT simulation, charge transfer can be analyzed quantitatively through Bader charge or Mullikan charge analysis or qualitatively through partial density of states analysis. Fig. 5.23 displays the [140] PDOS of the p orbital of an O atom when in isolated glucose molecule (upper panel) and when the O atom is bonded with Mo of MoO3 (1 0 0) surface (lower panel). We notice that the intensity of PDOS for the p orbital of the O atom near the Fermi level reduces when it is bonded with Mo of MoO3 (1 0 0) surface. The reduction of the intensity of PDOS close to the Fermi level for occupied p orbitals of the O atom in a glucose molecule signifies charge transfers from the glucose molecule. The PDOS of Mo and O of MoO3 surface and the PDOS of Mo bonded to glucose and O of glucose bonded to Mo are shown in Fig. 5.24 [140]. It is clear from this figure that for MoO3, the conduction band is dominated by the Mo d orbital whereas the O p orbital has a larger contribution in the valence band. Interestingly, there is strong hybridization between the Mo d orbital and p orbital of the bonded O atom around 27 eV. When glucose is bonded to the MoO3 surface, the intensity of PDOS for the Mo d orbital near the Fermi level increases, which may occur because of charge transfer from the p orbital of the bonded O atom of the glucose molecule to the d orbital of the bonded Mo atom. The bonding of glucose on MoO3 and the charge transfer may indicate that the glucose is being oxidized.

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FIGURE 5.23 Total density of states for MoO3 (1 0 0) surface of monoclinic structure (upper panel) and glucose attached on MoO3 (1 0 0) surface (lower panel). Source: Reproduced with permission from M. Sharma et al., Non-enzymatic glucose sensing properties of MoO3 nanorods with insight from density functional theory investigations. J. Phys. D: Appl. Phys. 50 (2017) 475401. Copyright (2017) IOP Publishing.

FIGURE 5.24 Partial density of states of (A) O and Mo atom of (1 0 0) surface of monoclinic MoO3, (B) O atom of glucose bonded with Mo of MoO3 (1 0 0) surface and Mo bonded with glucose. Source: Reproduced with permission from M. Sharma et al., Nonenzymatic glucose sensing properties of MoO3 nanorods with insight from density functional theory investigations. J. Phys. D: Appl. Phys. 50 (2017) 475401. Copyright (2017) IOP Publishing.

5.10.4 Metal-Doped Transition Metals Oxides When TMO are doped with metals, the conductivity and reaction rate increases which leads to enhancement of electrochemical activity and results in higher sensitivity. Metals generally used are P, Pt, Au, Ag. Metal should be bonded strongly on the TMO surface and should

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be stable without clustering. From the simulation point of view, the binding energy of the metal on a TMO surface is an important parameter and we need to compute it by taking the same set of parameters for both TMO and metal plus TMO. It is advisable to perform ab initio MD simulations to check the stability of the system at the highest operating temperature. The wt% of the metal should be low enough to avoid clustering between metals. Metal
metal clustering may affect the interaction of glucose and result in reduction in sensitivity. The metal on TMO generally interacts with the O molecule of glucose and enhances the oxidation of glucose. There is charge transfer between the metal d orbital and p orbital of O of glucose. First, it is important to investigate the difference in DOS for TMO and metal-doped TMO. Rajeswari Ponnusamy et al. studied the glucose sensitivity of Pd-doped WO3 nanostructures [141]. Pd is bonded on a WO3(0 0 1) surface with a binding energy of 21.82 eV; negative binding energy means that the binding is energetically favorable. Fig. 5.25 [141] displays the total DOS of (0 0 1) surface of WO3 (lower panel) and Pd doped on (0 0 1) surface of WO3 (upper panel). It can be seen that the DOS near the Fermi level is enhanced when Pd is doped on the WO3 (0 0 1) surface. There may be charge transfer from d and s orbitals of Pd to WO3. The enhancement in the DOS near the Fermi level may mean that in the presence of Pd,

FIGURE 5.25 Total density of states of WO3 (0 0 1) surface and Pd doped on WO3 (0 0 1) surface. Fermi level is shown with a pink line. Source: Reproduced with permission from Rajeswari Ponnusamy et al., Pd doped WO3 nanostructures as potential glucose sensor with insight from electronic structure simulations. J. Phys. Chem. B 122 (2018) 7636
7646. Copyright (2018) American Chemical Society.

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TABLE 5.5 Binding Energy of Pd and Au on MnO2 (1 0 0) Surface and Binding Energy of Glucose MnO2 (1 0 0) Surface, Pd 1 MnO2 (1 0 0) Surface and Au 1 MnO2 (1 0 0) Surface [142] System

Binding energy of metal (eV)

Binding energy of glucose (eV) 2 1.88

MnO2 (1 0 0) Pd-doped MnO2 (1 0 0)

2 3.27

2 2.16

Au-dopted MnO2 (1 0 0)

2 1.83

2 2.02

From R. Ponnusamy, A.S. Gangan, B. Chakraborty, D. Late, C.S. Rout, J. Phys. Chem. B 122 (2018) 7636
7646.

FIGURE 5.26 (A) Contour plot of charge density of glucose and (B) charge density difference between NiCo2O4 1 Pd 1 α-glucose and α-glucose. The blue region is the charge-depleted region, and the red region is the charge-rich region. Source: Reproduced with permission from Naik et al., Enhanced non-enzymatic glucose sensing properties of electrodeposited NiCo2O4-Pd nanosheets: experimental and DFT investigations. ACS Appl. Mater. Interf. 9 (2017) 23894
23903. Copyright (2017) American Chemical Society.

the conductivity and charge transfer capability of WO3 increases. The binding energy of glucose is more on the Pd-doped (0 0 1) surface of WO3 compared to a bare WO3 surface. The nonenzymatic glucose-sensing properties of MnO2 nanosheets and Pd- and Au-doped MnO2 were studied by Ponnusamy et al. [142] both experimentally and theoretically. It was shown from bonding and charge transfer analysis that Pd is bonded more strongly on MnO2 compared to Au and the binding energy of Pd-doped MnO2 is higher than Au-doped MnO2 and bare MnO2. Table 5.5 [142] displays the binding energy of Pd and Au on MnO2 and the binding energy of glucose MnO2, Pd 1 MnO2, and Au 1 MnO2. The higher binding energy of glucose and enhanced charge transfer from glucose to Pd-doped MnO2 compared to bare MnO2 imply that Pd-doped MnO2 possesses superior glucose-sensing performance.

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Naik et al. [143]. reported the nonenzymatic glucose-sensing performance of NiCo2O4 and Pd-doped NiCo2O4. From a total density of states plot it was shown that the appearance of occupied and unoccupied DOS near the Fermi level signifies that both Ni and Co ions in NiCo2O4 can act as communicating media to transfer the charge from glucose by participating in the redox reactions. Fig. 5.26 displays the contour plot of charge density of glucose and charge density difference between NiCo2O4 1 Pd 1 α-glucose and α-glucose. The blue region refers to a charge-depleted region whereas the red region corresponds to a charge-rich region. Note from the plot that there is charge transfer from glucose to the Pd as the charge-loss (blue) region corresponds to the bonded O atom of glucose, whereas the charge-gain (red) region corresponds to the bonded Pd atom.

5.11 CONCLUSIONS AND FUTURE DIRECTIONS In summary, this chapter has presented an overview of quantum simulations methods, simulation procedure, and analysis techniques relevant to sensing gas and biomolecules on various 2D materials. In the first sections, we described the many body Hamiltonian and various methods for solving Schro¨dinger equations, e.g., HF, DFT, etc. The sensitivity of results on various parameters such as cutoff energy, k-point grid, and exchange correlation functional was highlighted. The structures of various 2D materials, modeling procedures, and their properties were discussed from a sensing aspect. We have also highlighted the change in properties due to gas sensing and how to quantify this through DFT using adsorption energy, charge transfer, modification in geometry, modification in band structures, and change in resistivity. We emphasized the importance of dispersion corrections in gas sensing and different schemes for implementing vdW corrections in standard DFT simulation. The shortcomings of DFT to use in predicting accurate bandgap has been mentioned and various correction methods such as GGA 1 U, GW, hybrid functional, etc. have been outlined with their merit and demerit. The change in optical properties due to gas sensing and how to analyze the modification through theoretical simulations have been mentioned in the aspect of optical sensors. Simulation procedures for glucose molecules and their interaction with 2D TMOs have been described in terms of charge transfer, binding energy, and geometry for glucose sensing. Overall, this chapter provides the basics of quantum simulations, procedures to model 2D materials and their interactions on gas and biomolecules; changes in electronic, optical, and structural properties and how to analyze those from a theoretical aspect; the sensitivity of the results on various simulations procedure and

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parameters; and finally how to provide theoretical insight for sensing study on 2D materials. Although there are plenty of reports on the sensing of gases and biomolecules using change in resistivity, the change in optical properties have not yet seen much focus. An optical sensor exhibits a change in color of the sensing material due to adsorption of gases or other sensed material. Future sensing mechanisms may work with changes in color of the material on adsorption of the sensed material. In last few years, new members of the graphene family, e.g., graphyne, graphdiyne, etc. have been theoretically predicted and synthesized in molecular form. In the future we may see implementation of new 2D materials such as graphyne and graphdiyne for sensing. From the simulation point of view, accurate and sophisticated methods such as the dynamic mean field theory, GW approach, etc. are still not widely used as they are computationally very expensive. Due to the advent of high-speed, large-memory supercomputers, future simulation procedures may involve the use of more sophisticated methods resulting in improved sensitivity.

Acknowledgments The author would like to thank Dr. C.S. Rout for useful discussion regarding formulating the theme of this chapter. BC would like to thank Mr. Abhijeet Gangan for numerous help in preparing this book chapter. Thanks to Dr. A.K. Mohanty for support and encouragement. BC would also like to thank Dr. S. Banerjee for inspiration and scientific discussions. Thanks to Mr. Manikandan for helping in referencing & formatting.

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C H A P T E R

6 Gas Sensors Based on Two-Dimensional Materials and Its Mechanisms K. Rajkumar1,2 and R.T. Rajendra Kumar3 1

2

Department of Physics, Bharathiar University, Coimbatore, India Department of Physics, Indian Institute of Technology, Chennai, India 3 Department of Nanoscience and Technology, Bharathiar University, Coimbatore, India

6.1 INTRODUCTION Gas sensors [1,2] which can detect the presence and various concentrations of a specific gas in ambient atmosphere are highly useful for environmental monitoring, industries, agricultural production, military, public safety, and in medical diagnostics [3
8]. The advent of nanomaterials has led to an unimaginable revolution in sensing. The nanomaterials’ high surface-to-volume ratio is ideal for gas adsorption, as the surface is highly reactive to the gases [7]. Over the past three decades, many investigations into using nanomaterials for sensors have been carried out. Materials such as metal oxides [9
12], semiconductors [13,14], carbon-based materials [15
17], and conducting polymers [18
20] have been widely investigated. Using metal oxide nanostructures as gas sensors has been investigated and current gas sensors are based on them. Metal oxide nanostructures have a high surface area; therefore they have high sensitivity, fast response time, and low cost; however, they also have high operating temperatures and low selectivity [21
23]. Polymers that have room temperature (RT) sensing capability are sensitive to humidity and therefore degrade quickly [24
26].

Fundamentals and Sensing Applications of 2D Materials DOI: https://doi.org/10.1016/B978-0-08-102577-2.00006-3

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Carbon-based nanostructures are interesting candidates for gas sensor. Carbon nanotubes have high sensitivity, RT operation, but poor recovery time, which makes them unsuitable for being used as gas sensors [2,27,28]. High sensitivity materials with good selectivity and response/recovery time, which work at RT, are essential to be used as a gas sensor. Two-dimensional (2D) nanostructures opens a way to meet these requirements. 2D nanostructures have many advantages including large surface area, many active sites, facile surface functionalization, good compatibility with device integration, and the possibility of assembly into three-dimensional (3D) architectures [29,30]. 2D materials are highly advantageous because atomically thin 2D sheets have high surface-to-volume ratios, and most of the atoms are exposed to the atmosphere [31,32]. For example, graphene, which is an atomically thin 2D allotrope of carbon, has an area per unit mass of 2600 m2 g
1, in which the whole volume is exposed to atmosphere [31,32]. Moreover graphene has high carrier density (B1012 cm22), high carrier mobility (200,000 cm2 V21 s21), and very low resistivity and noise at RT [33,34]. The properties of 2D nanostructures can be changed by varying the thickness and lateral size. Carbon nanostructures based on graphene, graphene oxide (GO), and reduced graphene oxide (rGO) are widely investigated for gas sensing. Metal oxides and metal hydroxides are another class of 2D materials. Because of their semiconducting and thickness-dependent physical and chemical property, layered inorganic transition metal dichalcogenides (MoS2, WS2, ReS2, MoSe2, WSe2, and ReSe2), layered group III
VI semiconductors (GaS, GaSe, and SnS2), layered metal oxides (MoO3 and SnO2), phosphorene and hexagonal boron nitride (h-BN) have recently attracted enormous attention, offering wide opportunities to tune the transport properties. In this chapter we will discuss various configurations used for fabricating a gas sensor and fundamentals of gas-sensing mechanisms. A detailed focus on various 2D materials used for gas sensors, the state-ofthe-art device fabrication, and sensing performance from the material point of view will be highlighted. We focus on 2D nanostructures of graphene, metal dichalcogenides, metal oxides, phosphorene, BN, and MXene. Finally, we will present the challenges and future prospective of 2D materials for gas-sensing applications.

6.2 GAS SENSING: FUNDAMENTALS 6.2.1 Important Parameters of a Gas Sensor Usually gas sensors are characterized by the following parameters: sensor response, sensitivity, selectivity, stability, detection limit, resolution, response and recovery time, and cycle life. FUNDAMENTALS AND SENSING APPLICATIONS OF 2D MATERIALS

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Sensor response: The definition for sensor response S (%) varies based on the measured change of the sensor. In the case of a chemiresistortype sensor, a change in resistance is observed; in the case of a conductometric sensor, a change in conductance is observed. For chemiresistor sensing the sensor response is defined as the ratio of the change in the resistance of the sensor in the presence and absence of analyte gas molecules with respect to the initial resistance of the sensor. The sensor response is given as Sð%Þ 5

Rg 2 Rair 3 100 Rair

where Rair is the resistance of the sensor in air and Rg is the resistance of the sensor in the presence of target gas [35,36]. Sensitivity (S): Also defined as the change of measured signals per unit analyte concentration, this value can be extracted from the slope of the calibration graph from concentration versus sensor response plot [36]. Selectivity: This is a measure of the response to detect or sense specifically a particular analyte of our interest among various other analytes [36]. Response and recovery time: The time taken to reach 90% of the full response after a stimulus is the response time. The recovery time is the time taken to return to 90% after the stimulus is removed [36]. Stability: An important measure that characterizes the quality of a signal overtime. It is the ability of the sensor to produce reproducible results for the sensor parameters over a certain period of time [36]. Detection limit: The maximum and minimum limit of detection (LOD) of the analytes that a sensor can detect [36]. Resolution: The lowest concentration difference that a sensor can distinguish [36]. Cycle life: The period of time over which a sensor can operate continuously [36]. Ideally a chemical sensor should possess high sensitivity, selectivity, dynamic range and stability; low detection limit; good linearity; small response and recovery time; and long life cycle.

6.2.2 Various Types of Device Configurations The sensing element used for making devices can be fabricated into different configurations including chemiresistive sensors, field-effect transistor (FET) sensors, impedance sensors, surface acoustic wave (SAW) sensors, surface work function (SWF) change transistors, quartz crystal microbalance (QCM) sensors and one using Schottky diodes and heterojunctions. FUNDAMENTALS AND SENSING APPLICATIONS OF 2D MATERIALS

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6.2.2.1 Chemiresistive Sensors A chemiresistor is the simplest and easiest configuration to fabricate and it is a widely studied sensor type. Chemiresistors consume low power, can be miniaturized, are reusable, and are low cost [37]. Generally a chemiresistive sensor consists of two inert electrodes between which the sensing element is fabricated. The resistance change between the two electrodes is measured. When the gas is fed over the sensor, gas molecules interact with the sensing material surface and are adsorbed, which induces changes in carrier concentration; thereby a change in electrical resistance from which a sensor’s gas response can be deduced. A schematic representation of a chemiresistive sensor with two electrodes is shown in Fig. 6.1. Interdigitated electrodes are most commonly used as electrodes for chemiresistive sensors. Commonly, drop casting or spin coating of the 2D material dispersed in some solvent is done to transfer the sensing material over the electrodes. Another method that can be used is chemical vapor deposition (CVD) over the electrodes, which deposits electrodes over the 2D material on the substrate using microfabrication to use to fabricate chemiresistive-based sensors. Fowler et al. fabricated single-layer chemiresistive sensors by spin coating chemically

FIGURE 6.1 Schematic representation of a 2D material: chemiresistive gas sensor.

FIGURE 6.2 Photograph of graphene-based (A) interdigitated electrode sensor with microplate and (B) four-point interdigitated electrode sensor. Source: Reproduced with permission from J.D. Fowler, M.J. Allen, V.C. Tung, Y. Yang, R.B. Kaner, B.H. Weiller, Practical chemical sensors from chemically derived graphene, ACS Nano 3 (2009) 301
306 [38].

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FIGURE 6.3 Schematic diagram of the 2D material
based field-effect transistor (FET) sensor. Source: Reproduced with permission from G. Lu, L.E. Ocola, J. Chen, Reduced graphene oxide for room-temperature gas sensors, Nanotechnology 20 (2009) 445502 [32].

exfoliated graphene dispersions on interdigitated electrode arrays. Fig. 6.2 shows the image of the microhotplates and four-point electrodes chemiresistors. The fabricated sensor was used to sense NO2, NH3, and 2,4-dinitrotoluene [38]. 6.2.2.2 Field-Effect Transistor Sensors In a FET sensor the sensing material acts as a channel, with metal electrodes as source and drain. Usually the gate electrode lies at the back, with the gate and channel sensor material separated by a thin dielectric insulator. A schematic representation of a FET sensor is shown in Fig. 6.3. The conductance is measured across source and drain, while the gate controls the charges in the channel. Drain current was measured before and after it is exposed to the analyte gas. Lu et al. fabricated a gas sensor using partially rGO sheets. Here, rGO serves as the channel and Au fingers as source and drain. The low concentrations of NO2 were sensed [32]. 6.2.2.3 Conductometric Sensors The electrical conductivity of the sensor material changes upon interaction with the gas molecules [22,39]. The surface reactions play a vital role in conductometric gas sensors. Various other factors such as surface areas, surface additives, temperature, and humidity plays a major role in the performance of the sensor. Conductometric gas sensors are advantageous because they have been used in the detection of gases especially under atmospheric conditions. They are simple to use and diverse gases are detectable. Perkins et al. used MoS2 monolayers as channels to fabricate a conductometric gas sensor on SiO2/Si wafer. The channel conductance alters when gas is purged over the sensor. Fig. 6.4 depicts a schematic and optical image of the fabricated sensor. The gases interacting with the MoS2 monolayer get physiosorbed and act as either an electron donor or acceptor [40]. When triethylamine (TEA) and acetone, which

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FIGURE 6.4 (A) Schematic of monolayer MoS2 conductometric gas sensor and (B) the optical image of the fabricated sensor device. Source: Reproduced with permission from F.K. Perkins, A.L. Friedman, E. Cobas, P. Campbell, G. Jernigan, B.T. Jonker, Chemical vapor sensing with monolayer MoS2, Nano Lett. 13 (2013) 668
673 [40].

are electron donors, were exposed to the sensor, a rapid increase in conductance was measured. The conductance decreased for electron acceptors, such as nitromethane, nitrotoluene, dichloropentane, dichlorobenzene (DCB), and water vapor. 6.2.2.4 Impedance Sensor An impedance sensor works by measuring the change in impedance of a sensor while applying a sinusoidal voltage [41]; the change in impedance is measured by measuring the current. The sensing response is evaluated from sub-hertz to mega-hertz. An impedance sensor consists of a sensing material fabricated on two metal electrodes. Bi et al. used GO as a sensing material deposited over two sets of interdigitated electrodes as humidity sensor. Fig. 6.5A shows photographs of the fabricated humidity sensor. Fig. 6.5C and D shows an SEM image of the interdigitated electrodes without and with GO. The huge sensitivity of up to 37,800% was obtained for 15%
95% relative humidity [42]. This sensor also exhibited very fast response time and recovery time.

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FIGURE 6.5 (A) Photographs of sensor, (B) its scanning electron microscopy (SEM) image of the red square, and (C and D) SEM image of interdigitated electrodes without and with graphene oxide (GO). Source: Reproduced with permission from H. Bi, K. Yin, X. Xie, J. Ji, S. Wan, L. Sun, et al., Ultrahigh humidity sensitivity of graphene oxide, Sci. Rep. 3 (2013) 2714 [42].

6.2.2.5 Surface Acoustic Wave Sensors In a surface acoustic wave(SAW) sensor, SAWs are used to sense a physical change, and the variation in the SAWs is measured. The electrical signal that is given as input is transduced into a mechanical wave by the sensor [43]. Any physical change in the active material influences the mechanical wave. This change in mechanical wave is transduced into an electrical signal. Changes in frequency, phase, amplitude, or time delay between the input and output electrical signals can be used to measure the presence of the desired gas molecules. The gas molecules adsorbed on the surface of the sensor affects the mass loading of the surface wave, thereby resulting in a change of conductance of the sensing materials [44,45]. Using SAW for sensor devices is advantageous due to low cost, good sensitivity, small size, and the ability to detect a wide variety of gases. Le et al. fabricated a SAWbased humidity sensor and a corresponding schematic, and SEM images are shown in Figs. 6.6 and 6.7, respectively. A thin GO layer is used as sensing layer. The sensor is highly sensitive with a wide detection range at low and high humidity levels. The sensitivity of SAW sensors depends on the thickness of the coating, resonant frequency, and surface coverage of the GO sensitive layer. The shift in frequency takes place when there is change in humidity, as a result of change in mass loading [46].

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FIGURE 6.6 Schematic diagram of a surface acoustic wave sensor without graphene oxide and the device surface coated with a graphene oxide (GO) film. Source: Reproduced with permission from X. Le, X. Wang, J. Pang, Y. Liu, B. Fang, Z. Xu, et al., A high performance humidity sensor based on surface acoustic wave and graphene oxide on AlN/Si layered structure, Sens. Actuat. B Chem. 255 (2018) 2454
2461 [46].

FIGURE 6.7 Scanning electron microscopy (SEM) images of (A) surface acoustic wave sensor device, (B) before graphene oxide (GO) coating, and (C) after GO. Source: Reproduced with permission from X. Le, X. Wang, J. Pang, Y. Liu, B. Fang, Z. Xu, et al., A high performance humidity sensor based on surface acoustic wave and graphene oxide on AlN/Si layered structure, Sens. Actuat. B Chem. 255 (2018) 2454
2461 [46].

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6.2.2.6 Schottky Diodes Sensors Schottky diodes are generally metal semiconductor heterojunction device [47]. 2D Schottky diodes as gas sensors are made by fabricating metal/graphene or its analogs as junctions. Modulation of the Fermi level in semiconductor heterojunctions or the interface Schottky barrier height (SBH) when analyte gas molecules are adsorbed is the mechanism of operation of Schottky diode sensors [48,49]. When gas is adsorbed the SBH is varied and as a result the current across the device varies. Singh et al. fabricated a reverse-biased atomically thin graphene/Si heterojunction diode. The device displayed bias-dependent molecular sensitivity. Any interaction between graphene and the gas molecules has a direct effect on the interface barrier height, which affects the reverse current under the applied reverse bias [49]. The device was operated in reverse bias, so that the bias can control the barrier height at the graphene/Si heterointerface. Thus the device has wide tunability with good molecular detection sensitivity. The graphene/Si Schottky diode showed 13 times improved sensitivity and consumed very low power for detecting NH3 and NO2 compared to graphene-based chemiresistor under normal atmospheric conditions. 6.2.2.7 Other Sensor Configurations A heterojunction semiconductor gas sensor is formed of two different (p- and n-type) semiconductors. When gases adsorb on the surface of the heterojunction on both sides, the interfacial barrier is modulated which has a direct effect on the charge transfer characteristics of the heterojunction [50,51]. SWF change transistor is another type of gas sensor that depends on the modulation of the SWF of the sensing materials when gas is adsorbed [52]. A Si microcantilever is used make contact with the sensing element to make amperometric and potentiometric measurements. Highly sensitive graphene is used as a low-cost sensing material. When the sensor is exposed to analyte gas, adsorption of the gas molecules takes place, which results in a change in the surface dipole moment and electron affinity [17,53].

6.2.3 Influential Parameters of a Gas Sensor Apart from the inherent material property of the sensing element, there are external parameters that greatly influence the sensing property of a sensor, including humidity, temperature, gas flow rate, pressure, and the dimensions of the sensing element.

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Temperature greatly influences the performance of a gas sensor. Sensors that operate at RT will be ideally advantageous. For explosive sensing, sensors without heaters are required because use of high temperatures may trigger explosion [54]. In this sense, a 2D material would be highly advantageous because of its atomically thin layer; the atoms in this thin layer would be sensitive to the environment [31,32]. However, to improve the selectivity and other sensing parameters, the effect of temperature has to be studied. Though investigation on the effect of temperature on the sensing parameters of 2D materials is still lacking. Most metal oxide
based sensors are sensitive at temperatures higher than 150 C [55]. Higher temperature accelerates gas adsorption, which increases the reactivity of the gas with the sensor [22]. The performance of a metal oxide
based sensor with respect to operating temperature has been studied: Korotcenkov et al. investigated the reducing gas [R] adsorption/desorption behavior of SnO2 at different temperatures. The sensor response was found to vary as a function of operating temperature, and the sensor response depends on (1) adsorption of dissociated oxygen at temperature between 170 C and 200 C, (2) adsorption of [R] molecules converted into RO, (3) desorption of [R], and (4) chemisorbed oxygen desorption [56]. According to Korotcenkov et al., at low operating temperatures the sensitivity decreases because of the first two processes; the last two factors decide the decrease of sensitivity at high temperatures. Different gases have different adsorption/desorption reaction rates with respect to different temperature profiles, which can be useful in the selective recognition of particular gases. For example, the oxidation of H2S and C2H5OH on the metal oxide takes place at relatively low temperatures. In the case of alcohols and ketones the temperatures is above 200 C and alkanes oxidize at high temperatures above 400 C [57]. Thus by controlling the operating temperature it is possible to create conditions in which one particular gas has higher sensitivity, while other gases are less sensitive. However, these approaches alone cannot mitigate the issues of low selectivity, where other parameters have to be taken care of. Humidity is an important element that affects the sensitivity of a gas sensor. The interference of humidity can be reduced by proper design with materials that are insensitive to water vapors. The other way in which humidity can be reduced is by using filters. There are different types of filters (powders, porous layers, and membranes) which physically absorb gas molecules. Materials such as zeolites, SiO2, Al2O3, and Teflon are used as filters. Use of catalytic films decomposes the gases and noble metals (Pt, Pd), while metal oxides (Fe2O3, CuO) are being used as catalytic filters [56]. The gas flow rate into the sensing chamber is another important parameter because it has direct influence on the initial base resistance of

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the sensing material. Zhou et al. explored the influence of gas flow rates on the gas-sensing mechanism of rGO; the resistance of rGO increases at higher flow rates and decreases for lower flow rates [58]. Therefore the flow rate has to be optimized.

6.3 GAS-SENSING MECHANISMS 6.3.1 Gas-Sensing Mechanism in Graphene The mechanism of sensing in 2D materials is still uncertain and more investigation is required. The mechanism of sensing in 2D materials cannot be generalized as it differs from material to material. The most commonly predicted sensing mechanism in graphene is by adsorption of gas, either physisorption or chemisorption. Adsorption of gas molecules on graphene results in the changes in carrier concentration and resistivity [59]. Physisorption occurs because of van der Waals interaction or through hydrogen bonding whose binding energy varies less but is highly suitable for recovery of the sensor after sensing. The binding energies of a gas molecule by chemisorption are higher because it involves charge transfer; however, the recovery of the graphene sheet with chemisorbed molecules is more difficult than for physiosorbed gas molecules [60]. During chemisorption, transfer of charges between the gas molecule and the sensing material takes place. The sensing material can act either as acceptor or donor. When the highest occupied molecular orbital state of the adsorbent gas molecule is above the graphene’s Fermi level, then Fermi level upshift and charge transfer from the adsorbate to graphene. On the other hand, if the lowest unoccupied molecular orbital state of adsorbent is below the Dirac point, the graphene Fermi level downshifts and the charges transfer from graphene to the adsorbent molecule [60]. The number of charges involved in transfer and the binding energy between the adsorbate and graphene can be calculated using density functional theory (DFT). DFT calculations help to predict the sensitivity by estimating the binding energy and the amount of charge transferred. Low binding energy suggests physisorption and high binding energy suggests chemisorption; these studies will therefore help engineer the graphene surface to yield a sensitive, selective detection of gas molecules. You et al. investigated the charge transfer properties of graphene [pristine(0vG), defective graphene (monovacant(1vG), divacant(2vG), trivacant(3vG), and tetravacant(4vG) graphene)] adsorbed with NO2 [61]. The adsorption energy was calculated for pristine and defective graphene; the results are presented in Fig. 6.8A. The 0vG has less

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FIGURE 6.8 (A) The graph between adsorption energy and the number of carbon vacancies to explain the effect of carbon vacancies on the adsorption energy of graphene. (B) Depiction of charge transfer between adsorbed NO2 adsorbed and graphene both pristine and defective graphene (brown represents carbon, green represents iron, purple represents nitrogen, and red represents oxygen). Source: Reproduced with permission from Y. You, J. Deng, X. Tan, N. Gorjizadeh, M. Yoshimura, S. Smith, et al., On the mechanism of gas adsorption for pristine, defective and functionalized graphene, Phys. Chem. Chem. Phys. 19 (2017) 6051
6056 [61].

adsorption energy while 3vG has highest adsorption energy. It was revealed that in pristine graphene(0vG), because of its iso-charged surface and lack of active sites and dangling bonds, only physisorption of NO2 molecules takes place with very little charge transfer of 0.003e2 from graphene to NO2 (Fig. 6.8B). In the cases of defective graphene,

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the nitrogen atom of the NO2 molecule prefers to interact with the graphene, that is, the unsaturated carbon attracts the unpaired electrons in the nitrogen head of NO2. The vacancies created in odd numbers of carbon (1vG, 3vG) are more favorable for NO2 graphene interactions than even numbers of carbon vacancies (2vG, 4vG). In the case of 1vG and 2vG the charge transfer is favorable from graphene to NO2, whereas in 3vG and 4vG the charge transfer is from NO2 to graphene. It has been suggested that the reason for the strongest charge transfer from a 1vG to NO2 molecule with a charge value of 0.029e2 is due to the dangling bonds derived from sp2 carbon of graphene at the defective site [61]. In another study, Zhang et al. investigated the interactions between various graphenes (pristine, defective, and B- and N-doped) and gas molecules (CO, NO, NO2, and NH3) using DFT calculations to explore their potential applications as gas sensors [62]. All four investigated molecules show physical adsorption (physisorption) on pristine graphene (PG) with low adsorption energies and with very little charge transfer. The N-graphene weakly interacted with CO, NO, and NH3, but has strong binding with NO2 with an adsorption energy of 20.98 eV. The B-doping enhanced the graphene’s interactions with NO, NO2, or NH3. Strong interaction resulted in case of defective graphene with CO, NO, and NO2 but weakly with NH3 [62]. Thus the study suggested that graphene without any modification will not be an ideal material for gas sensing: Modifying graphene either by doping or by creating defects would greatly improve the sensitivity and selectivity of the sensors. Dan et al. also suggested that the nonexistence of dangling bonds in PG results in weak physisorption of gas molecules; consequently the sensitivity of the sensors is low. Thus from these studies it is inferred that gas sensing in graphene is mainly due to adsorption-induced charge transfer mechanism.

6.3.2 Gas-Sensing Mechanism in Two-Dimensional Transition Metal Dichalcogenides The gas-sensing mechanism of 2D transition metal dichalcogenides is based on transfer of charges. Yue et al. used first principle calculation to study the adsorption of different gas molecules (O2, H2O, NH3, NO, NO2, and CO) on MoS2 monolayer. They found that, primarily the molecules were physiosorbed on a monolayer MoS2 accompanied with a small charge transfer; MoS2 acts either as charge acceptors or donors [63]. Charge transfer from MoS2 to the gas molecules such as O2, CO, H2O, NO2, and NO takes place when adsorbed, whereas for NH3 adsorption charge transfers from NH3 to MoS2.

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Cho et al. proved that charge transfer takes place between gas (NH3, NO2) molecules and MoS2 sensing layers by in situ photoluminescence (PL) measurement. PL measurements were carried out before gas exposure and after gas exposure. The resistance of the MoS2 layer was found to increase and decrease on exposure to NO2 and NH3 gas respectively [64]. The charge transfer in MoS2 with NO2 and NH3 is represented in Fig. 6.9E and F. The PL of MoS2 has two emission peaks, A1 and A0, which correspond to 1.8413 and 1.8424 eV and to trions and neutral excitons, respectively (Fig. 6.9C and D). When NO2 gas molecules are adsorbed, excess holes are generated as a result of extraction of the electrons which converts a neutral exciton (A0) to a quasiparticle (A1) from MoS2. As a result the PL intensity of A1 peak increases and the intensity of A0 peak is suppressed (Fig. 6.9C). In the case of NH3 exposure, the PL intensity of A1 peak is suppressed due to the dissociation of

FIGURE 6.9 Schematic image of the most favorable configurations of MoS2 with (A) NO2 and (B) NH3. Photoluminescence (PL) spectra (in situ) of MoS2 with (C) NO2 and (D) NH3. Schematics describing the charge transfer between MoS2 and (E) NO2 and (F) NH3. Source: Reproduced with permission from B. Cho, M.G. Hahm, M. Choi, J. Yoon, A.R. Kim, Y.-J. Lee, et al., Charge-transfer-based gas sensing using atomic-layer MoS2, Sci. Rep. 5 (2015) 8052 [64].

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FIGURE 6.10 IDS versus VBG of MoS2 FETs with (A) NO2 and (B) NH3 exposure with various concentrations. Vth variation with concentrations for (C) NO2 and (D) NH3. Source: Reproduced with permission from B. Liu, L. Chen, G. Liu, A.N. Abbas, M. Fathi, C. Zhou, Highperformance chemical sensing using Schottky-contacted chemical vapor deposition grown monolayer MoS2 transistors, ACS Nano 8 (2014) 5304
5314 [65].

the positive trions from the neutral excitons as a result of the addition of electrons, resulting in enhanced intensity of neutral excitons (A0), as shown in Fig. 6.9D. Liu et al. explored NO2 and NH3 gas-sensing performance of MoS2 grown by CVD, fabricated into a FET device [65]. When MoS2 is exposed on NO2, being a strong oxidizer it withdraws electrons from MoS2, decreasing the electron concentration in the conduction band. As a result a more positive gate voltage is necessary to switch on the transistor; therefore the threshold voltage (Vth) takes a positive shift (Fig. 6.10C). NH3 possesses a lone pair of electrons which transfers it to the conduction band of MoS2, resulting in an increased electron concentration in MoS2. Therefore, the threshold voltage (Vth) takes a negative shift (Fig. 6.10D).

6.3.3 Gas-Sensing Mechanism in Metal Oxide
Based TwoDimensional Material The basic gas-sensing mechanism in metal oxides is associated with surface-adsorbed oxygen ions. Essentially, when a metal oxide is exposed to atmospheric oxygen, oxygen species (O22, O2, O22) are ionosorbed onto the metal oxide surface [66]. These adsorbed species

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act as electron acceptors, and these charges are trapped to form an electron depletion layer or space charge region. The presence of these negatively charged oxygen species leads to band bending. When an n-type metal oxides is exposed to an analyte gas, the gas molecules interact with the negatively charged preadsorbed oxygen species 2 22 (O2 2 , O , O ) resulting in a change in the carrier concentration and the conductivity of the sensor. When exposed to a reducing gas (CO), the negative charges are released due to oxidation, consequently the barrier height is reduced and the conductivity increases [67]. The opposite process takes place when an oxidizing gas is exposed. This mechanism depends also on the type of semiconducting metal oxide (p- or n-type).

6.4 GRAPHENE-BASED MATERIALS Honeycomb carbon lattice arranged in a single atomic layer is called graphene. Monolayer graphite sheet consists of carbon atoms sp2 hybridized and covalently bonded to its neighboring three other carbon atoms [68]. Since its discovery by Geim and Novoselov it has become one of the most intensively studied materials because of its fascinating structural, electrical, and mechanical properties. It possesses excellent mechanical strength with a Young modulus of B0.05 TPa, and also ultrafast electron transport with a highest mobility of B200,000 cm2 V
1 s
1. Its high surface-to-volume ratio, high conductivity (high electron transport along the graphene base plane), and low Johnson noise makes it a favorable RT chemical sensors [60]. Graphene monolayer exhibits ballistic charge transport to over a 300 nm length and has zero bandgap [69]. Because of these unique properties, graphene is inherently sensitive even to a single gas molecule. The smallest quantum change in conduction due to the interaction between a single molecule and graphene can be measured. Therefore, graphene is very sensitive to single molecules of a wide range of gases, highly selective gas sensors using graphene need to be developed. Intrinsic graphene has no dangling bonds on its surface and therefore has weak interaction with the gas. To enhance the sensitivity and selectivity further, graphene has to be modified. Generally graphene is functionalized with organic molecules, polymers, or metals which act as a trapping center for target analyte.

6.4.1 Pristine Graphene-Based Gas Sensors In 2007 Novoselov et al. investigated the sensing behavior of the mechanically exfoliated grapheme [59]. The graphene gas sensor has a LOD as low as parts per billion (ppb).

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Hall measurement studies provided evidence for single molecule detection. A steplike behavior (blue line) in the resistivity was obtained as a result of NO2 adsorption. NO2 adsorption modifies the carrier concentration of graphene as a result of transfer of electrons one by one. The statistical analysis shown in Fig. 6.11B also supports single molecule detection (Fig. 6.11A). This pioneering work inspired both experimentalists and theoreticians to study the sensing performances of graphene. Rumyantsev et al. first reported the selective detection of chemical vapors using a transistor with pristine single-layer graphene (SLG) [70]. They claim that the noise obtained at low frequencies can be used as a (B) 600

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FIGURE 6.11 (A) Hall resistivity changes observed during dilute NO2 adsorption (blue line) and desorption (red line). The green line represents adsorption of He gas as a reference. (B) Representation adsorption/desorption of NO2 step heights by statistical distribution. (C) Noise spectral density (SI/I2) multiplied by frequency (f) versus frequency for various gas vapor exposure. Source: (A and B) Reproduced with permission from F. Schedin, A. Geim, S. Morozov, E. Hill, P. Blake, M. Katsnelson, et al., Detection of individual gas molecules adsorbed on graphene, Nat. Mater. 6 (2007) 652 [59]. (C) Reproduced with permission from S. Rumyantsev, G. Liu, M.S. Shur, R.A. Potyrailo, A.A. Balandin, Selective gas sensing with a single pristine graphene transistor, Nano Lett. 12 (2012) 2294
2298 [70].

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parameter to improve the selectivity of the graphene sensor. Fig. 6.11C shows different frequency characteristic noise induced by different vapors. The noise frequency range and the relative resistance change show distinct signatures for different gases. The origin of the Lorentzian noise in graphene when exposed to different gases is explained as follows: first, adsorbed gas molecules generate specific trap states and scattering centers locally in graphene, which leads to a variation in the number of carrier and their mobility. Second, the gas adsorption/ desorption kinetics also contribute to the noise [70]. Nemade et al. fabricated chemiresistive gas sensors by electrochemical exfoliation of few-layer graphene and evaluated the sensing performance toward CO2 and LPG [71]. The sensor’s sensitivity was 3.83 for CO2 and 0.92 for LPG. The response time for CO2 was 11 and 5 s for LPG. The detection limit was 3 ppm for CO2 and 4 ppm for LPG. Reina et al. and many others have fabricated large area graphene by CVD on Cu, Co, and Ni substrates [72]. This graphene can be transferred to flexible substrates and can be fabricated into a gas sensor. Choi et al. used a flexible substrate to coat graphene as channels and also as a heater [73]. The authors used SLG as the channel (sensing element) and bilayer graphene as the heater with Cr/Au as the electrodes. The sensor sensitivity was 10% for 0.5 ppm of NO2. This sensor also had a fast response and recovery time of less than 20 s. Rigoni et al. transferred graphene onto E-beam lithographically patterned electrodes on SiO2/Si [74]. Briefly, polymethyl methacrylate (PMMA) was deposited on a CVD-grown copper substrate and Cu was dissolved in iron nitrate solution, after which the PMMA thin film with graphene was transferred onto SiO2/Si prepatterned substrate. The response to ammonia and nitrogen dioxide at different relative humidity, in the dark and under 254 nm ultraviolet (UV) light, was studied. To measure the sensor selectivity, gas response was measured for CO2, CO, acetone, ethanol, and hydrogen. Kumar et al. fabricated a resistive gas sensor using CVD-grown graphene that was transferred onto a smooth paper [75]. The sensor had an LOD of 300 parts per trillion (ppt) for NO2. The sensitivity of the sensor was B118% per ppm of NO2. The sensitivity was further increased by a factor of 2.5 by exposing the sensor to UV light for 10 min. The UV exposure ensured fast recovery, typically in tens of seconds. Kulkarni et al. investigated the physisorption of chloroform, dimethyl methylphosphonate (DMMP), dichloromethane, 1,2-dichlorobenzene (DCB), chlorobenzene, and N,N-dimethylformamide on graphene using a graphene nanoelectronic heterodyne sensor [76]. They studied the temperature-dependent molecular desorptions and their kinetics. Electrostatic gating of graphene electrically tunes the gas

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molecule
graphene binding kinetics. They demonstrated that electrical tuning of molecular physisorption can be made possible by electrostatic gating of graphene. Kim et al. developed a patterned, transparent, self-activated graphene with NO2 sensors which is flexible and consumes less power. Without external heating, when increasing the bias voltage, the micropatterned channel is heated and the graphene sensor is self-activated, which results in fast response and also reversible sensing behavior [77]. Thus all graphene sensors exhibited high sensing performances to NO2 with high humidity at RT. Another method of fabricating large area graphene is by an epitaxial method using a SiC substrate. When heated in an ultrahigh vacuum, sublimation of silicon atoms from the substrate takes place, and the carbon atoms are rearranged to form graphene layers. Pearce et al. fabricated a SLG device by epitaxial method which was sensitive to very low concentrations of NO2 gas [78]. Fig. 6.12A displays an SEM image of the multilayer graphene below the interdigitated finger electrode with bond pads. Fig. 6.12C and D

FIGURE 6.12

SEM images of (A) device without graphene. (B) Multilayer graphene. Optical microscopic image of (C) wire-bonded multilayer device. (D) Wire-bonded singlelayer device. Source: Reproduced with permission from R. Pearce, T. Iakimov, M. Andersson, L. Hultman, A.L. Spetz, R. Yakimova, Epitaxially grown graphene based gas sensors for ultra sensitive NO2 detection, Sens. Actuat. B Chem. 155 (2011) 451
455 [78].

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shows a microscopic image and photographic image of the fabricated device. When the sensor is exposed to low concentration NO2, a switch in response direction occurs due to the conversion between n- and p-type. Graphene exhibits a switch from n- to p-type behavior due to the hole doping from NO2 molecules when exposed to higher concentrations. It was demonstrated that by tuning graphene’s electronic properties it is possible to attain extremely high sensitivity.

6.4.2 Defective and Functionalized Graphene-Based Gas Sensors A number of theoretical and experimental reports have shown that structure defects, oxygen-containing groups, heteroatom dopants, and surface functional groups have a substantial effect on the gas-sensing performances [61,79]. First principle calculations implied that the defective graphene interacts strongly with NO, NO2, and CO, but weakly with NH3 [62]. N-doped graphene strongly binds with NO2, B-doped graphene interacts strongly with NO, NO2, and NH3. Singh et al. calculated the adsorption energy of PG and Al-doped graphene to different gas molecules such as CO2, CO, CCl4, CH4, NH3, NO2, CCl2, F2, SO2, CF4, and N2O [80]. PG displayed weak sensitivity to all the above gases, while Al-doped graphene displayed higher reactivity toward all the gas molecules especially with NO2, NH3, and CO2. Recently You et al. studied the gas adsorption mechanism in pristine, defective, and functionalized graphene sheets [61]. In vacancy-induced defected graphene structures, the vacancy offers higher sensitivity toward NO2 gas adsorption. The vacancies that were generated by the removal of oddnumbered carbon have higher NO2 graphene surface interactions than the vacancies generated by removing the even-numbered carbons. Adsorption energies of Fe-doped mono- and tetravacant graphene is higher than Fe-doped bi- and trivacant graphene. In the case of functionalized graphene with carbonyl and ether groups, carbonyl group facilitates higher adsorption. Compared to other Fe-doped vacant graphene, the oxygenated functional groups have smaller adsorption energies. Dai et al. studied the adsorption of numerous gas molecules such as NO2, NO, O2, CO, CO2, SO2, H2, and H2O for nitrogen-, sulfur-, boron-, and aluminum-doped graphene using DFT [81]. They found that NO and NO2 alone binds to B-doped graphene and only NO2 binds to S-doped graphene. Al-doped graphene has enhanced reactivity with many gases. These studies suggest that graphene with defects, selective dopants, and functionalization is highly suitable for specific gas sensing. Khojin et al. prepared monocrystalline graphene by mechanical exfoliation and defective polycrystalline graphene (PCG) by CVD and

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evaluated its sensing performance [82]. Trace amount of toluene (T) and DCB were tested and the change in the conductance recorded. PG is insensitive to DCB and T and the gas response of PCG was higher than PG. The graphene sheet was cut into ribbons 2
5 mm wide and gas sensing was examined. The response to DCB increased two times (Fig. 6.13B). This study reveals that by engineering the geometry of graphene defects the sensitivity can be enhanced. Other treatments such as ozone and various plasma treatments induce functional groups, and doping of hetero-atoms result in variation in the Fermi energy. Chung et al. introduced oxygen functional groups on a graphene surface by treating them with ozone [83]. NO2 sensing performance: response, response time, and detection limit was found to be better suited to ozone-treated graphene sensors than intrinsic graphene. The response percentage of ozone-treated graphene for 200 ppm NO2 was two times higher and exhibited eight times faster response time than that of a PG sensor. This superior sensing response was ascribed to high gas binding energy induced by the oxygen functional groups. GO and rGO has been extensively studied for gas sensing. rGO is prepared from GO which is synthesized from graphite by oxidation in acidic KMnO4 solution. GO synthesis is relatively lower cost than other methods and can be prepared on a large scale. GO contains hydroxyl and epoxy groups on sp3 hybridized carbon on the basal plane, and carbonyl and carboxyl groups on sp2 hybridized carbon at the edges [84]. GO offers wide opportunities for tuning, since it contains defects, reactive sites, and functional groups. Because of the presence of oxygencontaining groups, the conductance of GO is low, which is not suitable for electronic applications [17]. Reducing GO either chemically or thermally partially restores its conductivity. Apart from increasing

FIGURE 6.13 (A) Atomic force microscopy (AFM) images of polycrystalline chemical vapor deposition (CVD) graphene. (B) Response of the pristine graphene (PG), polycrystalline graphene (PCG), and microribbon graphene to dichlorobenzene (DCB). Source: Reproduced with permission from A. Salehi-Khojin, D. Estrada, K.Y. Lin, M.H. Bae, F. Xiong, E. Pop, et al., Polycrystalline graphene ribbons as chemiresistors, Adv. Mater. 24 (2012) 53
57 [82].

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the conductivity, the reduction process introduces defects such as vacancies and structural defects, which can act as an active adsorption site [17,85]. Defects play an important role in the adsorption and desorption of gases during sensing. The defects formed during reduction have higher adsorption energy than the oxygenated GO; therefore it is difficult to desorb the gas, and the defect density has to be tuned and optimized to obtain high sensing performances. Shen et al. demonstrated selective detection of SO2 utilizing nanosheets of GO which was chemically exfoliated from graphite and treated with acids periodically to obtain edge-tailored GO sheets [86]. The GO FET was fabricated by simply drop-casting GO sheets onto Au electrodes on an insulating SiO2 layer of highly doped Si. This GO-based sensor displayed enhanced sensing performance with fast response and recovery time upon SO2 exposure. The GO sensor displayed no response to any other organic vapors. Robinson et al. demonstrated that chemically rGO sheets using hydrazine hydrate (HH) showed high sensitivity to chemical-warfare agents and explosives at ppb level [87]. The extent of the reduction of GO is tunable by changing the exposure time of the HH vapor. The conductance changes as a function of chemical reduction and the extent of the chemical reduction has a large influence on the sensor sensitivity. Hu et al. used p-phenylenediamine (PPD) to reduce GO. This PPDRGO had excellent sensing performance; better than hydrazine-reduced GO. PPD-RGO sensing devices displayed good response to DMMP [88]. Because PPD is a weaker reducing agent than hydrazine, more oxygenated groups will be retained resulting in higher sensing performance. Dua et al. used ascorbic acid (a weak reducing reagent) and inkjet printing on flexible substrate (polyethylene terephthalate) to fabricate a sensor device [89]. These devices were able to detect chemically aggressive vapors such as NO2 and Cl2 with limits of detection in the range of 100 ppm to 500 ppb. Strong interactions of target molecules with graphene were observed which made them too long for recovery. UV light illumination was performed to obtain full signal recovery. Gosh et al. used NaBH4 as a reducing agent to prepare rGO. The rGO gas sensors were fabricated on a ceramic substrate and NH3 detection was carried out at RT. The reduction time of GO was optimized to obtain good sensing performances for NH3 gas. The response of the rGO sensors was 5.5% for 200 ppm and 23% for 2800 ppm of NH3, and the recovery was also fast. This rGO sensor had high selectivity toward ammonia detection [90]. Lu et al. demonstrated sensitive NO2 detection utilizing partially rGO. The GO was reduced thermally by step annealing at a maximum of 300 C under Ar flow [32]. This rGO exhibited p-type behavior in ambient conditions and was sensitive to low concentrations of NO2 at

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RT. The enhanced sensitivity results from e2 transfer from rGO to NO2, resulting in higher hole concentration. Guo et al. fabricated humidity sensors by using lasers as an effective method to reduce GO to rGO [91]. They used a two-beam-laser interference (TBLI) method to reduce, pattern, and nanostructure GO on flexible substrates. Fig. 6.14A and B shows a schematic of GO reduction by the TBLI method. When two similar lasers are irradiated onto the GO film at one position, interference takes place as shown in Fig. 6.14B. The amount of oxygen-containing groups was tuned by tuning the laser power. Response and recovery of rGO sensors reduced with a laser power of 0.15 and 0.2 W are shown in Fig. 6.14C and D. The device

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FIGURE 6.14 (A and B) Schematic illustration of fabrication of rGO by TBLI reduction of GO film. Response and recovery curves of the sensor fabricated using the laser power of (C) 0.15 W and (D) 0.2 W. Source: Reproduced with permission from L. Guo, H.-B. Jiang, R.-Q. Shao, Y.-L. Zhang, S.-Y. Xie, J.-N. Wang, et al., Two-beam-laser interference mediated reduction, patterning and nanostructuring of graphene oxide for the production of a flexible humidity sensing device, Carbon 50 (2012) 1667
1673 [91].

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fabricated using a 0.2 W laser displayed a fast response and recovery time because of the moderate amount of oxygen functional groups. The response of the device fabricated using 0.15 W was higher; however, its recovery was poor compared to the other device fabricated with a 0.2 W laser. Apart from these methods, gas sensors based on 3D porous structures of rGO has been reported by Huang [92], Koratkar [93], and others. Large porous structures enable gas molecules to infiltrate into the 3D structures to obtain high sensitivity. However reversibility is poor. In order to achieve reversibility, adsorbed gas molecules were removed by heat treatment. Yuan et al. prepared sulfonated rGO (S-rGO) and ethylenediaminemodified rGO (EDA-rGO) and fabricated a simple chemoresistor-type NO2 sensors [94]. Fig. 6.15A shows the sensing response of rGO, S-rGO, and EDA-rGO to 50 ppm NO2. Fig. 6.15B shows the response of S-rGO sensors with different sensing layer thicknesses [50 nm (1), 10 nm (2), and a few layers (1
6 nm) (3)] after exposure to 50 ppm NO2. Compared to rGO, S-rGO and EDA-rGO sensors exhibited 16 and 4 times higher responses, respectively. Fig. 6.15C shows the response of S-rGO sensor to (A) 30

(B) 30 50 ppm

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FIGURE 6.15 (A) Sensing response comparison of rGO-, S-rGO-, and EDA-rGO-based sensors to 50 ppm NO2. (B) Response of the S-rGO with different thicknesses [50 nm (1), 10 nm (2), and a few layers (1
6 nm) of S-G sheets] for 50 ppm NO2 exposure. (C) Sensor response of S-rGO to 50 ppm NO2, NH3, H2O, or toluene. Source: Reproduced with permission from W. Yuan, A. Liu, L. Huang, C. Li, G. Shi, High-performance NO2 sensors based on chemically modified graphene, Adv. Mater. 25 (2013) 766
771 [94].

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50 ppm NO2, NH3, H2O, or toluene. S-rGO sensor is highly selective to NO2. The functional groups play a significant role in improving the selectivity of the sensor. The electron-deficient NO2 molecules are more susceptible to the lone-pair electrons in the functional groups.

6.4.3 Graphene/Polymer-Based Gas Sensors Dan et al. fabricated a graphene sensor by using mechanically exfoliated grapheme [95]. Conventional nanolithography was used to pattern metal electrodes for contacts. Polymer photoresist PMMA was used as an electron beam resist, which left residues on the graphene surface. This resist residue chemically dopes with graphene. The residue site in the graphene acts as an adsorbent layer for gas molecules and also increases the carrier scattering. Fig. 6.16A shows an Atomic force microscopy (AFM) image of the fabricated graphene sensor. The AFM thickness profile of graphene

FIGURE 6.16

(A) Atomic force microscopy (AFM) image of a section of graphene sensor. (B) AFM line scans of the sensor. (C) Sensor responses to nonanal vapors, before and after Ar/H2 cleaning. Source: Reproduced with permission from Y. Dan, Y. Lu, N.J. Kybert, Z. Luo, A. C. Johnson, Intrinsic response of graphene vapor sensors, Nano Lett. 9 (2009) 1472
1475 [95].

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measured before and after Ar/H2 cleaning is shown in Fig. 6.16B. The sensing response to nonanal vapors of graphene before and after Ar/H2 cleaning is shown in Fig. 6.16C. It is evident that PMMA residue enhances the electrical response to nonanal vapor up to the ppm level. After removing the PMMA residues, the response declines. After this report several others started working on graphene/polymer composites to improve the gas-sensing performance. Polymers such as poly(3,4-ethylenedioxythiophene) (PEDOT), poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS), mixed polymer poly(vinyl alcohol); polypyrrole (PPy), poly(etherimide), polypyrene (PPr), poly(diallylimethyammonium chloride), and polyaniline (PANI) were used to make composites with graphene to enhance the gas-sensing performances. Mashat et al. prepared PANI-decorated graphene by ultrasonicating aniline and ammonium persulfate along with graphene dispersion [96]. The H2 gas-sensing performance was compared with PANI nanofibers and individual graphene sensors. The sensing performance was higher for PANI-decorated graphene. The sensitivity was 16.57% for 1% H2. Bai et al. used an in situ polymerization method using their respective monomers to synthesis GO/PANI, GO/PEDOT, and GO/PPy composites [97]. GO/PPy composite displayed high sensitivity to NH3. Zhang et al. used codeposition of GO and PPr by electrochemical methods to prepare GO/PPr composite [98]. The fabricated sensor has high sensitivity of 9.87 3 1024 ppm21. Yuan et al. prepared self-assembled rGO sheets on electrospun polymer fibers [99]. This composite of nanofibers was used to sense NO2. The sensor displayed a sensitivity as high as 1.03 ppm21 at RT also with good reversibility and selectivity. The LOD was as low as 150 ppb. Sun et al. prepared PPy-rGO hybrid by in situ polymerization [100]; 5 wt.% rGO-PPy possessed the highest response at 10 ppm NH3 (2.5 times higher than pure PPy). The sensor exhibited selectivity toward few volatile organic compounds. The high response enhancement is attributed to the stacking of PPy on rGO and H-bond formation between PPy and rGO, offers large surface area and fast carrier transport. Recently Zhang et al. used the QCM method to demonstrate humidity sensing with a layerby-layer self-assembled PANI/GO film [101]. The fabricated nanocomposite sensor displayed high sensitivity and repeatability with a fast response/recovery time (13 s). The sensing mechanism was explained based on the Langmuir adsorption model.

6.4.4 Graphene/Metal Nanocomposites
Based Gas Sensors Various graphene
nanoparticle (NP) nanocomposites, especially with noble-metal NPs (Pt, Pd, Ag, and Au) have been widely investigated for gas-sensing applications. Metal-decorated graphene/rGO have been

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proven to have good sensitivity and selectivity, due to the large changes in the electronic properties of graphene/rGO [102,103]. The noble-metal nanostructure-decorated graphene also exhibits high catalytic activity. Graphene can easily transfer the e2 from the noble metal to the electrodes [104]. The mechanism of sensing in graphene
NP nanocomposites is different from graphene. The sensing mechanism in metal nanostructuredecorated graphene is mainly due to their catalytic action in dissociating the gas molecules and charge transfer from or to graphene from the gas molecules. Moreover the size and morphology critically influences the catalytic activity [105,106]. The mechanism of sensing is explained schematically in Fig. 6.17. The interaction forces for noble metals with graphene is in the order of Ag . Pd . Pt . Au [108]. Chemically exfoliated graphene exhibits p-type semiconducting behavior, as it contains high defect states [109]. Metals having lower work function (Ag) compared to graphene tend to form a Schottky barrier at the metal
graphene interface [110]. When the metal with lower work function is decorated on graphene, transfer of electrons from the metal to the graphene takes place leading to annihilation of holes in the metal, which is depicted in Fig. 6.17. Fermi level aligning takes place and a Schottky barrier forms restricting the transfer of electrons back from graphene to the metal [107]. Thus the hole density in graphene is reduced and the device resistance increases. When an electron donating gas such as NH3, H2S, or H2 interacts, the electrons donated by the gas to the metal further shift the metal Fermi level upward further increasing the SBH, which leads to further promotion of electrons from the metal to graphene [107]. When exposed to electron donor gas, the conductance of the device decreases. Upon exposure to electron withdrawing gas such as NO2 and NO, the SBH decreases; however, reverse-biased transfer of electrons from graphene to the electron withdrawing gas via the metal nanoparticle is not facilitated. Therefore these show poor response for NO2 and NO gas [107]. For higher work function metals (such as Au, Pd, Pt) decorated graphene, electrons from the graphene is withdrawn to the metal leading to an increase in the Fermi level of the metal to align with the bulk graphene Fermi level. Therefore the Fermi levels at equilibrium lead to accumulation of holes (majority charge carriers) in graphene. Upon exposure to electron withdrawing or donating gases, charge transfer can take place from graphene to NP or NP to graphene, respectively. The mechanism is depicted in Fig. 6.17. Noble metals such as Pt and Pd possess high catalytic and sensing efficiency toward H2. However since their cost is higher, ways to use them in minute quantity without compromising their sensing ability has to be found. This can be achieved by using graphene/rGO to make nanocomposites. Pak et al. decorated Pd on a graphene nanoribbon

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Higher work function NP (Au, Pd, Pt)

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FIGURE 6.17 Schematic explaining the effect of NP decoration and gas exposure on charge carrier density of graphene sheets. Source: Reproduced with permission from A.V. Singhal, H. Charaya, I. Lahiri, Noble metal decorated graphene-based gas sensors and their fabrication: a review, Crit. Rev. Solid State Mater. Sci. 42 (2017) 499
526 [107].

(GNR) array for hydrogen sensing [111]. The Pd-decorated GNR sensors had excellent response (90% response within 60 s at 1000 ppm) and recovery (80% recovery within 90 s). Hong et al. used a Pd NP-coated polymer membrane decorated on an SLG hybrid, to sense H2 gas. Here SLG was grown by the CVD method on Cu foil [112]. SLG/Cu foil was deposited with Pd NPs by galvanic displacement reaction. PMMA was then coated with Pd NPs/SLG to selectively filtrate H2. The response to 2% H2 gas is shown in Fig. 6.18A, the response to increasing concentrations of H2 ranging from 0.025% to 2% is shown in Fig. 6.18B. The sensor did not respond to CH4, CO, or NO2, thus it is selective to H2 (Fig. 6.18C). PMMA/Pd NPs/SLG hybrid sensor

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FIGURE 6.18 Sensing response of hybrid PMMA/Pd NP/SLG sensor. (A) Resistance changes to 2% H2 exposure. (B) Response to increasing concentrations of H2 ranging from 0.025% to 2%. The inset is the sensitivity versus H2 concentration in log scale. (C) Response of PMMA/Pd NPs/SLG hybrid sensor to various gases to check selectivity. Source: Reproduced with permission from J. Hong, S. Lee, J. Seo, S. Pyo, J. Kim, T. Lee, A highly sensitive hydrogen sensor with gas selectivity using a PMMA membrane-coated Pd nanoparticle/ single-layer graphene hybrid, ACS Appl. Mater. Interf. 7 (2015) 3554
3561 [112].

displayed a 66.37% response to 2% H2. The response and recovery time was 1.81 and 5.52 min, respectively. Liu et al. prepared Pd-rGO nanocomposites by alternating current dielectrophoresis [113]. The sensors displayed good sensitivity and showed good recovery for NO gas in the range from 2 to 420 ppb at RT. Tran et al. developed NH3 gas sensor using graphene
silver nanowires composite [114]. They prepared GO by chemical exfoliation method adopting modified Hummer’s method and Ag NWs by polyol method. Graphene was spin-coated on the electrodes and silver nanowires were spray-coated to form a sensor device. NH3 sensing response yielded a sensitivity of B28% which is eight times higher than “intrinsic” graphene sensor. The response and recovery times were B200 and B60 s, respectively. The silver nanowires act as a bridge to connect graphene islands thereby improving the electrical transport. Recently Ovsianytskyi et al. reported an H2S gas sensor based on AgNPs decorated grapheme [115]. The resistance change of doped

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graphene was 37%, for 500 ppb H2S. The H2S molecules are dissociated by the AgNPs; as a result the concentration of the charge carriers is affected. Ag decorated graphene exhibits p-type behavior; therefore the number of holes is reduced because of the release of electrons and as a result of charge recombination the resistivity increases. The sensor has negligible response to gases such as CH4, CO2, O2, and N2, which shows that the presence of other gases in high concentrations would not interfere with H2S detection. Therefore the sensor is highly sensitive to H2S gas. Apart from individual metal NPs, bimetallic NPs were also employed to make composites with graphene for sensing applications. Phan et al. synthesized graphene-supported Pt/Pd core
shells hybrid structures by a simple chemical method for hydrogen detection [116]. 30 nm Pd with a thin overlayer of Pt was coated onto the graphene. H2 sensing showed a good response in the range of 1
40,000 ppm, and fast response or recovery time. The maximum response was 36%. The response and recovery times were 3/1.2 min for 10,000 ppm of H2 concentration at RT.

6.4.5 Graphene/Metal Oxide Nanocomposite
Based Gas Sensors Generally, metal oxide semiconductors possess good gas-sensing performance but require high operating temperatures (150 C
600 C) and low selectivity which limit their applications [117]. Graphene/ rGO-decorated/functionalized metal oxide NPs are promising for sensitive and selective gas detection. There are a large number of reports of graphene/rGO-decorated or functionalized metal oxide gas sensors. The advantages of a combination of graphene and metal oxides are: (1) graphene increases the conductivity of metal oxides that is decorated on the surface. The metal oxide readily transfers the charge carriers acquired from the surface reaction of gas molecules to the graphene which in turn transfers to the electrodes [3]. (2) Graphene may control the size and the morphology of metal oxides during synthesis. (3) Metal oxide nanostructures may prevent the aggregation of graphene sheets [3]. Metal oxide nanostructures such as SnO2, ZnO, Cu2O, WO3, In2O3, Fe2O3, and NiO along with graphene have been widely explored for sensing applications. Among these metal oxides there are a variety of morphologies that have been employed for gas sensing. Gases such as H2, liquefied petroleum gas (LPG), H2S, CO2, CO, NH3, NO2, ethanol, and acetone gases have been sensed. Although large quantities of work based on metal oxide/graphene have been done, there remain challenges to prepare graphene-based metal oxide sensor with good sensitivity, fast response, and recovery times operating at

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RT. Here are some of the methods that have been reported to improve the gas-sensing properties of metal oxides. Zhang et al. fabricated transistor gas sensors based on graphene and metal oxide NPs [118]. Fig.6.19D shows a top view optical image of the CVD-grown graphene-based transistor gas sensor arrays. They tested sensor performance with three kinds of NPs: ZnO, SnO2, and CuO. Out of the fabricated sensors, SnO2 NP graphene transistors exhibited fast response and short recovery times (B1 s) at 50 C for hydrogen (100 ppm) sensing (Fig. 6.19C). Fig. 6.19A and B shows the dynamic gas-sensing response of a graphene-decorated sensor exposed to different H2 concentrations. The mechanism was elucidated based on charge transfer and band alignment between graphene and SnO2 NPs. Bo et al. recently fabricated a RT formaldehyde sensor using vertical graphene (VG) decorated with SnO2 NPs [119]. VG was grown on the sensor electrodes using a microwave plasma-enhanced CVD (MW-PECVD) method. SnO2 NPs were decorated on the VG by electrochemical deposition. The formaldehyde response of a VG/SnO2 sensor

FIGURE 6.19 (A) The sensitivity versus temperature plot of graphene gas sensor decorated with SnO2 NPs. (B) Dynamic gas-sensing response graphene decorated with SnO2 NPs sensor exposed to different H2 concentrations at RT and at 50 C. (C) The sensor response to 100 ppm H2 concentration. (D) Optical image of CVD-grown graphene-based transistor gas sensor arrays. (E
F) Ids
Vg and Ids
Vds curves of the gas sensor arrays at RT, respectively. Source: Reproduced with permission from Z. Zhang, X. Zou, L. Xu, L. Liao, W. Liu, J. Ho, et al., Hydrogen gas sensor based on metal oxide nanoparticles decorated graphene transistor, Nanoscale 7 (2015) 10078
10084 [118].

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is shown in Fig. 6.20B. The LOD was 0.02 ppm. The response time and recovery time were 46 and 95 s for 5 ppm. The sensor possesses excellent selectivity toward formaldehyde (Fig. 6.20D) with good stability. Liu et al. synthesized hierarchical flower-like rGO-In2O3 composite by hydrothermal method [120]. The rGO-In2O3 composites possess better sensing performance to NO2 than the In2O3 sample. The operating temperature was tuned by changing the percentage of rGO in the composites. The 5 wt.% rGO-In2O3 sensor worked at RT. This sensor responded to 1 ppm NO2 at RT; 3 wt.% rGO-In2O3 composite responded to 10 ppb to NO2. The hierarchical structure of In2O3 and the presence of rGO in the composites provided an enhanced surface area and local p
n heterojunctions in rGO/In2O3 composites which was responsible for the higher sensing performance (Fig. 6.21). Wang et al. prepared WO3 nanorods decorated on sulfonated rGO (S-rGO) to sense NO2 [121]. The WO3/S-rGO sensor response to (B)

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FIGURE 6.20 (A) Sensing response to 5 ppm formaldehyde at RT of various SnO2/VG mass ratio. (B) Dynamic gas response curves for different concentrations of formaldehyde of SnO2/VG(1:4) and (C) calibration curve obtained for VG/SnO2 sensor. (D) Response of VG/SnO2 sensors to various gases to show the selectivity. Source: Reproduced with permission from Z. Bo, M. Yuan, S. Mao, X. Chen, J. Yan, K. Cen, Decoration of vertical graphene with tin dioxide nanoparticles for highly sensitive room temperature formaldehyde sensing, Sens. Actuat. B Chem. 256 (2018) 1011
1020 [119].

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FIGURE 6.21 (A) The response curves of 3.0 wt.% rGO-In2O3 and pristine In2O3. (B) Response comparison of the two sensors toward NO2 in the range of 10
1000 ppb. (C) Response of In2O3 and rGO-In2O3 composites to various tested gas at 74 C. Source: Reproduced with permission from J. Liu, S. Li, B. Zhang, Y. Wang, Y. Gao, X. Liang, et al., Flower-like In2O3 modified by reduced graphene oxide sheets serving as a highly sensitive gas sensor for trace NO2 detection, J. Coll. Interf. Sci. 504 (2017) 206
213 [120].

20 ppmNO2 was 149% in 6 s, which is 4.7 times higher sensitivity and 100 times faster than WO3/rGO sensors. The sensor displayed good reproducibility, selectivity, and fast recovery times. The superior sensing performance of the S-rGO/WO3 sensor was attributed to the synergic high transport capability of S-rGO as well as its excellent NO2 adsorption ability, and also due to charge transfer occurring at the S-rGO/WO3 interfaces. Although there is much literature on metal oxide/rGO or graphene, good gas sensors which have high sensitivity, high selectivity with less response and recovery times fabricated by a simple cost-effective method that work at RT are yet to be developed. Metal oxides/graphene and rGO provide many possibilities to improve gas-sensing performance.

6.5 GRAPHENE ANALOGOUS TWO-DIMENSIONAL MATERIALS Graphene has fascinating properties, but it lacks an electronic bandgap which has stimulated researchers to prepare 2D materials with semiconducting characteristics. Researchers started exploring layer

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structured materials that are the nearest possibility to obtain 2D nanostructured materials. The materials that have layer structures are transition metal dichalcogenides, layered III
VI semiconductor, phosphorene, layered metal oxides, and other materials like silicene, and BN sheets.

6.5.1 Transition Metal Di-chalcogenide
Based Gas Sensors Transition metal dichalcogenides are a class of semiconducting materials with the formula of MX2, where M indicates a transition metal element (including Ti, Zr, Hf, V, Nb, Ta, Mo, W, Re, etc.), and X represents a chalcogen (Se, S, or Te) [122]. These materials have attracted enormous interest because of their unique physical and chemical properties, including semiconducting property, high surface-to-volume ratio, sizable bandgaps, high absorption coefficient, and availability of reactive sites for redox reactions [123]. MoS2 is a widely explored layered 2D semiconductor and has various applications. An MoS2 monolayer consists of one layer of Mo atoms sandwiched between two layers of S atoms, and the adjacent SMoS sheets are held together by van der Waals interactions. The weak interaction between the interlayer interactions allows single- or few-layer MoS2 nanosheets to be created through exfoliation or grown by CVD [124]. Bulk MoS2 possesses an indirect bandgap (1.2 eV). The bandgap of monolayer MoS2 becomes direct and wider (1.8 eV), confirmed by strong PL of monolayer MoS2 [124]. Li et al. first demonstrated excellent sensing characteristics of micromechanically exfoliated MoS2 flakes [125]. They fabricated an MoS2 FET (Fig. 6.22B) by micromechanical exfoliation. They were able to obtain single- and multilayer MoS2 films and use them as a sensing channel in FETs for detection of NO. The MoS2 FET device showed n-type doping behavior. The FET sensors based on bilayer (2 L) (optical image shown in Fig. 6.22A), trilayer, and quadrilayer MoS2 films displayed good sensitivity to NO. A detection limit of 0.8 ppm (Fig. 6.22C) was obtained. The single-layer MoS2 device showed a fast but unstable response. Late et al. fabricated transistor-based gas sensors using single- and multilayer MoS2 sheets that were exfoliated micromechanically to detect NO2, NH3, and H2O vapor at RT and in ambient condition [126]. Fig. 6.23A and B shows SEM images of a two-layer MoS2 transistor and an optical image of the MoS2 gas sensor device mounted on the chip, respectively. The single-layer MoS2 sensor was unstable. The sensing performance was improved either by applying gate bias or by light exposure. Fig. 6.23C and D shows the sensing behavior of two FETs upon exposure to NH3 and NO2. The resistance of two-layer and

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FIGURE 6.22 (A) Optical microscope image of a 2 L MoS2 film on Si/SiO2. (B) Optical microscope image of fabricated FET using 2 L MoS2 film. (C) Dynamic response during the exposure of the 2 L MoS2 FET to NO with increasing gas concentration. Source: Reproduced with permission from H. Li, Z. Yin, Q. He, H. Li, X. Huang, G. Lu, et al., Fabrication of single- and multilayer MoS2 film-based field-effect transistors for sensing NO at room temperature, Small 8 (2012) 63
67 [125].

five-layer FETs decreases when they are exposed to NH3 but increases upon exposure to NO2. Given that MoS2 is an n-type, NH3 acts as an electron donor while NO2 acts as an electron acceptor. The sensor fabricated with five-layer MoS2 displayed better sensitivity than that of the two-layer device. Perkins et al. fabricated a FET sensor (Fig. 6.24A) by mechanical exfoliation using Scotch tape from bulk samples. They exfoliated monolayers of MoS2 deposited on a 270 nm SiO2 on Si substrate [40]. E-beam lithography was used to make electrical contacts. The sensor exhibited good response to TEA with concentrations ranging from 1 to 100 ppm (Fig. 6.24B) at RT. Due to good response and low signal-to-noise ratio, the detection limits of TEA was claimed to be 10 ppb. Liu et al. made Schottky contacts with monolayer MoS2 sensors grown by CVD method [65]. The sensor was ultrasensitive toward NH3 and NO2 at RT. The LOD was 20 ppb and 1 ppm for NO2 and NH3, respectively. The significant improvement in the sensitivity is attributed to the modulation of Schottky barrier at the MoS2
metal junctions. Like graphene, MoS2 was also modified with functional molecules, metal, and metal oxide nanostructures to improve the sensitivity and

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FIGURE 6.23 (A) SEM image of a 2LMoS2 transistor. (B) Optical image of MoS2-based transistor sensor device. Dynamic sensing responses of two-layer (2-layer) and five-layer (5-layer) MoS2 devices to (C) NH3 and (D) NO2. Source: Reproduced with permission from D.J. Late, Y.-K. Huang, B. Liu, J. Acharya, S.N. Shirodkar, J. Luo, et al., Sensing behavior of atomically thin-layered MoS2 transistors, ACS Nano 7 (2013) 4879
4891 [126].

FIGURE 6.24 (A) An optical image of the devices with monolayer MoS2 with Au fingers for electrically contact (B) sensor responses to TEA with concentrations in the range from 1 to 100 ppm. Source: Reproduced with permission from F.K. Perkins, A.L. Friedman, E. Cobas, P. Campbell, G. Jernigan, B.T. Jonker, Chemical vapor sensing with monolayer MoS2, Nano Lett. 13 (2013) 668
673 [40].

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selectivity. Apart from these, MoS2 was also used to make heterojunctions with other 2D materials for gas sensors. Other metal dichalcogenides such as WS2, MoSe2 and WSe2, ReS2, ReSe2, and SnS2 have also been shown to possess good gas-sensing properties. Monolayer WS2 forms trigonal prismatic arrangement of the form S
W
S. A tungsten layer is sandwiched between two sulfur layers to form a layered structure [127]. Huo et al. fabricated an FET sensor with exfoliated WS2 and studied its photoelectrical gas-sensing properties [128]. Fig. 6.25A shows the AFM image of the exfoliated WS2 flake. The sensor response to various gases (ethanol, NH3, O2, and air) was studied. Photocurrent response of WS2 to different gases is shown in Fig. 6.25B. The current decreases in O2 and air, but increases in ethanol and NH3, compared to vacuum. This increase and decrease in current is ascribed to the charge transfer between WS2 and gas species (Fig. 6.25C). O2 molecules act as electron acceptors while ethanol and NH3 acts as electron donors to WS2, resulting in a decrease or increase in current respectively. Recently Xu

FIGURE 6.25 (A) An atomic force microscopy (AFM) image of the exfoliated WS2 flake. (B) Photocurrent response of WS2 to various gases. (C) Schematic to explain charge transfer process between adsorbed gas molecules and the multilayer WS2 nanoflakes transistor. Source: Reproduced with permission from N. Huo, S. Yang, Z. Wei, S.-S. Li, J.-B. Xia, J. Li, Photoresponsive and gas sensing field-effect transistors based on multilayer WS2 nanoflakes, Sci. Rep. 4 (2014) srep05209 [128].

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et al. adopted a hydrothermal method to prepare ultrathin WS2 nanosheets. The thickness was measured to be about 5 nm. The WS2 nanosheets interconnected to form a 3D wall-like structure. The resistive gas sensors based on as-prepared WS2 nanosheets showed p-type behavior, and had a response of 9.3%
0.1 ppm NO2 gas at RT. Ou et al. prepared SnS2 flakes by a wet chemical method and performed NO2 sensing [129]. At 120 C the SnS2 sensor displayed a good sensitivity of 10 ppm NO2. The detection limits were ,30 ppb. The response factor was high (B36). This selectivity toward NO2 is attributed to the strong physical affinity of SnS2 to NO2 gas molecules. Like metal di sulfides, layered metal di-selenides such as MoSe2, WSe2, and ReSe2 are gaining enormous interest for gas sensors. Late et al. prepared single-layer MoSe2 by micromechanical exfoliation and used them as RT NH3 gas sensors [130]. The sensor exhibited a detection limit of 50 ppm. Charge transfer takes place between NH3 molecule and single-layer MoSe2 which led to the change in carrier concentration. Their results indicate that single- and few-layer MoSe2 nanosheets can be used as gas sensors. Fang et al. fabricated a p-type FET with mechanical exfoliated singlelayer WSe2 whose source/drain (S/D) contacts were chemically doped; a high-k gate dielectric was used [131]. The schematic of the sensor is shown in Fig. 6.26A. The Pd contacts were used to reduce the contact resistance for hole injection during chemisorption of NO2 on WSe2. Chemisorbed NO2 molecules induce p-type doping of the S/D regions of the WSe2 FET to reduce the metal contact resistance. After S/D doping by NO2, the ON current (ION) was enhanced 1000 times without any change in the OFF current (IOFF). A positive shift in threshold

FIGURE 6.26 (A) Schematic of FET using monolayer WSe2. (B) Transfer characteristics of the device before and after NO2 p-doping of the S/D contacts. Source: Reproduced with permission from H. Fang, S. Chuang, T.C. Chang, K. Takei, T. Takahashi, A. Javey, Highperformance single layered WSe2 p-FETs with chemically doped contacts, Nano Lett. 12 (2012) 3788
3792 [131].

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voltage after NO2 doping took place because of the increase in the Pd metal gate work function (Fig. 6.26B). Yang et al. fabricated FETs using single- and few-layer ReSe2 as a conductive channel [132]. The single-layer ReSe2 FET exhibited p-type conductivity at RT with a mobility higher than that of few-layer FET. The number of layers has direct influence on the bandgap and electrical properties. The photoresponsivity of a single-layer device is superior to fewlayer devices. The adsorbed O2 molecules acts as electron acceptors increasing the hole carrier density of the p-type ReSe2 monolayer, thus improving its photoresponsivity. The photoresponsivity of single-layer ReSe2 under different gas environment showed different responses.

6.5.2 Layered III
VI Group Materials
Based Sensors Layered group III
VI semiconductor-based sensor materials include GaS, GaSe, GaTe, and InSe. These materials have unique 2D characteristics induced by anisotropic crystallography; they also possess excellent electrical, optical, and mechanical properties [133]. Among these materials, GaS and GaSe have attracted attention owing to its crystal structure. It crystallizes into a hexagonal structure with each layer consisting of S
Ga
Ga
S and Se
Ga
Ga
Se repeating units held together by weak van der Waals forces [134]. Layered GaS is an n-type semiconductor with an indirect bandgap of 2.5 eV. GaSe exhibits p-type electrical characteristics with indirect bandgap behavior. Recently, these semiconductors were synthesized by vapor-phase deposition, liquid exfoliation, micromechanical exfoliation, and van der Waals epitaxial growth. Yang et al. synthesized few-layer GaS and GaSe and studied its photoresponsivity in various gases [135]. They fabricated ultrathin GaS nanosheets by mechanical exfoliation and a two-terminal photodetectors was fabricated on a SiO2 substrate (Fig. 6.27A and B). The GaS photodetector exhibited different photo-responses in different gas environments (Fig. 6.27C). It had higher on
off current ratio, photo-response, and external quantum efficiency in an NH3 environment than in air or in O2. The photoresponse time was 10 ms and the device showed good stability. Yang et al. studied the photoelectric response of few-layer GaSe phototransistors in different gas atmosphere [136]. The fabricated few-layer GaSe phototransistor showed p-type conductivity at RT and exhibited better performance in ambient O2 than in air. The annealed device showed better photo response than the exfoliated version. The photo-response was B0.21 s, with good stability after many on
off cycles. The unique characteristics of few-layer GaSe may find interesting applications in multifunctional optoelectronic devices and sensitive sensors.

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FIGURE 6.27 (A) Optical image of GaS nanosheet device. (B) Schematic of the device operation. (C) Photocurrent response to different gas environments. Source: Reproduced with permission from S. Yang, Y. Li, X. Wang, N. Huo, J.-B. Xia, S.-S. Li, et al., High performance few-layer GaS photodetector and its unique photo-response in different gas environments, Nanoscale 6 (2014) 2582
2587 [135].

6.5.3 Layered Metal Oxide
Based Sensors Layered MoO3, WO3, and SnO2 are the only 2D semiconducting metal oxide analogs of graphene. They are particularly interesting because of their stability at elevated temperature in ambient atmosphere. Either physisorption or chemisorption takes place on the surface of 2D metal oxides and as a result a change in electronic and optical properties will take place in 2D metal oxides [137]. Apart from the 2D layered semiconducting metal oxides analogs of graphene, there are metal oxides which form 2D morphology such as ZnO, WO3, SnO2, CuO, NiO, and Co3O4. These metal oxides form nanosheet and nanowall structures. Ji et al. combined grinding and sonication to exfoliate bulk α-MoO3 crystals into single- and few-layer nanosheets (Fig. 6.28A) [138]. A surfactant was used to stabilize them. The senor response of 2D-MoO3 nanosheets was far more sensitive than bulk MoO3 powder. The ethanol-sensing response of 2D-MoO3 nanosheets increases from 7 to 33 FUNDAMENTALS AND SENSING APPLICATIONS OF 2D MATERIALS

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FIGURE 6.28 (A) TEM image of MoO3 nanosheets. Sensor response to100 ppm alcohol vapor at different temperatures. The inset in the figure displays the response and recovery curves of the senor at its optimum working temperature. (B) Bulk MoO3. (C) MoO3 nanosheets. Source: Reproduced with permission from F. Ji, X. Ren, X. Zheng, Y. Liu, L. Pang, J. Jiang, et al., 2D-MoO3 nanosheets for superior gas sensors, Nanoscale 8 (2016) 8696
8703 [138].

compared with the sensor using bulk MoO3 (Fig. 6.28B and C). The 2D-MoO3 nanosheets have significantly shorter response and recovery times. Among the tested volatile organic compounds, 2D-MoO3 nanosheets were selective to C2H5OH. Wang et al. synthesized ultrathin 2D WO3 nanosheets (Fig. 6.29A) of thickness B10 nm by the hydrothermal method using surfactant Pluronic P123 (polyethylene oxide
polypropylene oxide polyethylene oxide) [139]. By varying surfactant concentration the morphology was tuned to produce nanosheets. This gave ultrathin 2D WO3 nanosheets with controlled oxygen vacancies. The ultrathin structure with defectrich WO3 exhibited a high response (B6) for 50 ppb nitrogen dioxide at 140 C (Fig. 6.29B). Also it possessed good selectivity toward NO2 against other examined gases (ethanol, CO, H2, and NH3) and environmental factors (CO2 and humidity) (Fig. 6.29C). FUNDAMENTALS AND SENSING APPLICATIONS OF 2D MATERIALS

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FIGURE 6.29 (A) TEM image WO3 nanosheet. (B) Response and recovery curves of WO3 nanosheet to 50 ppb NO2 for three cycles at 140 C. (C) Responses of M1 (nanosheet) and M2 (nanoparticle) to various gases at 140 C. Source: Reproduced with permission from Z. Wang, D. Wang, J. Sun, Controlled synthesis of defect-rich ultrathin two-dimensional WO3 nanosheets for NO2 gas detection, Sens. Actuat. B Chem. 245 (2017) 828
834 [139].

6.5.4 Black Phosphorous
Based Two-Dimensional Material for Sensors Phosphorene is a thin monolayer of single- or few-layer black phosphorus (BP) [140]. A weak van der Waals interaction exists between the layers that can be broken during exfoliation. Phosphorene has p-type conductivity and has received increasing interest for application in electronic devices due to its high-charge carrier mobility and tunable bandgap. Both theoretical and experimental studies have been performed on the sensing behavior of phosphorene. Kou et al. examined the adsorption property of phosphorene to different gas molecules using first principle calculations and predicted that phosphorene should have superior sensing performance to graphene and MoS2 [141]. The sensing mechanism is similar to that of graphene and is based on charge transfer on adsorption of gas molecules. Among the studied molecules, binding of nitrogen-based gas molecules, such as NO and NO2, is the strongest. Moreover the

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adsorption of gases on phosphorene is much stronger than on graphene and MoS2. Thus phosphorene or few layers of BP is highly promising as a gas-sensing material. Abbas et al. prepared and studied a mechanically exfoliated, multilayer phosphorene-based FET sensor [142]. The schematic and the optical image of the BP FET is shown in Fig. 6.30A and B. They performed measurements with varying NO2 exposure in the range 5
40 ppb and detected as 5 ppb (Fig. 6.30C). The sensor had good stability and better recovery after flushing with argon. The sensitivity obtained by this sensor is surprising compared to other multilayer 2D materials. The high sensitivity was credited to the high adsorption energies of NO2 on BP and the less out-of-plane conductance of BP compared to graphene and MoS2. Cho et al. performed a comparative study of the gas-sensing properties of BP, MoS2, and graphene [143]. The three identical-sized sensing films (BP, MoS2, and graphene nanosheets) were obtained from a

FIGURE 6.30

(A) Scheme of a multilayer BP FET. (B) Optical image of the multilayer BP flake sensor fabricated between two Ti/Au electrodes (black line is to guide the eye). (C) Its sensing performance with varying NO2 concentrations (5
40 ppb). Inset shows a zoomed-in image of a 5 ppb NO2 exposure response. Source: Reproduced with permission from A.N. Abbas, B. Liu, L. Chen, Y. Ma, S. Cong, N. Aroonyadet, et al., Black phosphorus gas sensors, ACS Nano 9 (2015) 5618
5624 [142].

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sonication exfoliation. When these films were subjected to various chemicals, BP exhibited significantly higher sensitivity compared to graphene and MoS2. Fig. 6.31A shows the response of BP, MoS2, and graphene to 10 ppm NO2. The response time of BP was 5 s, whereas the response times of MoS2 and graphene were 200 s. The response time of BP is 40 times faster than that of MoS2 and graphene nanosheets. Also, BP showed a highly selective response to NO2 (Fig. 6.31B), while being insensitive to oxygen functionalized molecules, whereas MoS2 and graphene had similar response to all the chemical compounds (Fig. 6.31C). The superior selectivity of BP to NO2 molecules might be because the adsorption of paramagnetic molecules on BP can induce spin polarization, which leads to spin-polarized current on sensing film. Their DFT study also suggested that for NO2, BPhad an adsorption energy of 20.273 eV, much higher than MoS2 (20.157 eV) and slightly higher than graphene (20.257 eV).

FIGURE 6.31 (A) Dynamic NO2 gas response of BP, MoS2, and in the range of 0.1
100 ppm. (B) The response time τ (90%) of each sensor to dilute NO2 (0.1
100 ppm) gas molecules. (C) The maximal resistance change (ΔR/Rb) max (%) onto various analytes. (D) Molar response factor of BP, MoS2, and graphene. Source: Reproduced with permission from S.Y. Cho, Y. Lee, H.J. Koh, H. Jung, J.S. Kim, H.W. Yoo, et al., Superior chemical sensing performance of black phosphorus: comparison with MoS2 and graphene, Adv. Mater., 28 (2016) 7020
7028 [143].

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6.5.5 Other Two-Dimensional Materials Other interesting 2D material includes BN, Silicene, and MXenes. BN has a layered structure and is highly insulating with a wide bandgap of 5
6 eV [144]. BN are thermally and chemically more stable and therefore have high resistance to oxidation. It has potential application in electromagnetic devices, field emitters, semiconductor diodes, etc. [145]. The wide bandgap of BN nanosheets limits the application for sensors, but theoretical studied based on DFT calculations have predicted that BN nanostructures have good adsorption capacity for NH3, NO2, PH3, CH4, O2, etc. [146
148]. Ma et al. studied the interaction of Cr-doped BN sheet with gas molecule using DFT [149]. The adsorption energy of Cr-doped BN sheet had strong sensitivity to gas molecules such N2, NO, NO2, O2, H2S, CO, CO2, CH2O. Therefore much effort has been devoted to manipulating BN by surface functionalization and doping. Feng et al. fabricated Schottky diode sensors using C-doped BN nanosheets. BN was prepared by using CO2-pulsed laser deposition [150]. The sensing behavior of BN nanosheets-based gas sensor was tested with CH4 and O2 (100 ppm). The resistance change for CH4 was higher than that of O2, which indicates that BN has high sensitivity and selectivity toward CH4. The silicon atom in a 2D buckled hexagonal honeycomb structure is silicone [151]. Yang et al. examined the adsorption behaviors of NO2, NO, and NH3 on silicene using DFT [152]. They found that NH3, NO, and NO2 strongly adsorb on silicene, and the charge transfer that takes place from silicene to the gas molecules resulted in p-type doping. The adsorption energy for NO2 was higher than that of NH3 and NO. These results indicate that silicene can be a potential candidate for sensing. MXenes are two-dimensional inorganic compounds such as transition metal carbides, nitrides, or carbonitrides. MXenes have gained much interest because these materials have found success in energy storage materials, optoelectronic devices, and electrocatalysts [153]. Xiao et al. performed DFT calculation and found the Ti2CO2 monolayer to be very selective to NH3 compared to other gas molecules such as H2, CH4, CO, CO2, N2, NO2, and O2 [154]. Thus Ti2CO2 could be used as a NH3 sensor with high sensitivity and selectivity.

6.6 CONCLUSION AND FUTURE PERSPECTIVES In this chapter, we presented various 2D materials for gas sensors, described different sensors that can be fabricated and discussed possible mechanisms of gas sensing. 2D nanostructured materials, including

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graphene-based material, metal dichalcogenides, metal oxides, phosphorene, and BN show promising gas-sensing performances. The sensing mechanism of graphene, MoS2, BN, MXenes, and phosphorene is based on a charge transfer process. These 2D nanostructures have RT sensing capability. Factors influencing the gas-sensing property of 2D materials, such as the number of atomic layers, presence or absence of defects/dopants, are discussed. Graphene, rGO, and 2D metal dichalcogenides nanosheets offer opportunities to engineer them through surface functionalization, metal/metal oxide, polymer nanostructures decoration for sensitive and selective detection at RT. These 2D materials offer opportunities for good sensing efficiency, but at a huge cost. A cost-effective method to prepare the required 2D material is still required. Recently, experimental and theoretical studies have shown that few-layer BP has strong adsorption of gas molecules, better than graphene and MoS2, and also possess excellent selectivity and sensitivity to NO2. BP, BN, MXene, silicene, and germanene are 2D materials that are still in the early stage of research. Much effort is required to overcome their drawbacks like wide bandgap and stability. In spite of significant progress in gas sensors, many unresolved challenges are yet to be overcome. Lowering the detection limit, selective detection, and reducing the recovery time are still challenging. Many methods are prescribed to overcome these challenges such as engineering 2D materials with defects or dopants, modifying 2D materials with metal, metal oxides, and polymers, controlling the operating temperature, using filters, shining UV light into gas sensors for in situ cleaning the sensing material, and preconcentrating the analyte gas. However one has to keep in mind the cost of the sensor while mitigating these challenges. Great effort is required to explore and engineer 2D material properties and understand the sensing mechanism to achieve highly efficient sensors at less cost. Efforts are needed not only in engineering the material property but also in fabricating a cost-effective sensor.

Acknowledgment Author K.R thanks Science and Education Research Board (SERB), Govt. of India for providing National Postdoctoral Fellowship (NPDF) (PDF/2017/003000).

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C H A P T E R

7 Enzymatic and Nonenzymatic Electrochemical Biosensors C. Revathi1,2 and R.T. Rajendra kumar3 1

Department of Physics, Bharathiar University, Coimbatore, India Nanobiotechnology Laboratory, PSG Institute of Advanced Studies, Coimbatore, India 3Department of Nanoscience and Technology, Bharathiar University, Coimbatore, India 2

7.1 SENSORS A sensor is a device that detects and responds to some type of input (light, heat, motion, moisture, pressure, etc.) from the physical environment. The output is then converted into human-readable display. According to the measurement of input, the sensors can be classified into two different groups: physical and chemical sensors. Physical sensors measure physical quantities like distance, pressure, temperature, and mass. Chemical sensors measure the chemical substances by change in chemical parameters.

7.1.1 Biosensors The development of biosensors started with the detection of glucose for managing diabetes mellitus. To date, utilization of biosensors has expanded considerably in many different areas including pharmaceutical, environmental, food and agriculture, and industrial treatment. Eggins defined the biosensor as “A device incorporating a biological sensing element connected to a transducer to convert an observed response into a measurable signal, whose magnitude is proportional to the concentration of a specific chemical or set of chemicals” [1]. Biosensors can be classified into

Fundamentals and Sensing Applications of 2D Materials DOI: https://doi.org/10.1016/B978-0-08-102577-2.00007-5

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various categories based on the type of receptor used and transduction mechanism. Based on the receptor type, they are (1) enzymatic sensors, (2) immune, (3) aptamer, (4) DNA, and (5) genomic sensors. Based on the transduction mechanism, they are differentiated as electrochemical (EC), optical, piezoelectric, and thermal/calorimetric biosensors. In this chapter we focus on EC biosensors utilizing different receptor types mentioned above. Among the various types of biosensors, enzymatic biosensors are mostly in commercial use due to their high performance, cost, compactness, and simple operation; they do not require trained personal or lab facilities. The desired characteristics of biosensors are given below: • High specificity/selectivity—one of the most important characteristic so that sensors have the ability to identify different substances. • Fast response time—generally expected no more than 30 s. • Easy preparation/operation—the working area of the sensor should be designed with minimum volume and manipulation reduced to a minimum. • Requirement of small volume of analyte—to recognize even micro molar level of analyte. Usually mM range but expected to go down to the range of fM (10215 M) level. • High sensitivity and repeatability—one of the vital characteristic sensors should possess with reproducibility/repeatability over many days/cycles.

7.1.2 History of Biosensors The development of biosensors started with the immobilization of proteins on activated charcoal (1916) and then was further developed by Clark, for amperometric detection of glucose [2]. In 1969 Guilbault and Montalvo tried to immobilize urease on an ammonia electrode to detect urea [3]. Ion-selective field-effect transistor (ISFET) sensors were developed in the 1970s. The first enzymatic sensors were successfully marketed by Yellow Spring Instruments in 1956 based on Clark’s invention of the oxygen electrode. Clark is known as the “father of biosensors,” and his invention of the oxygen electrode bears his name—Clark electrode—and comes under first generation [4]. After that, pH, optical, and immunosensors were developed in the years 1976
1983 [5,6]. Enzyme-based blood glucose sensors were successfully launched by Medi Sense in 1987, overcoming the problems of oxygen electrodes [7]. The development of biosensors is tabulated in Table 7.1. Currently, biosensor applications are mainly focused on medical diagnostics and food analysis.

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7.1 SENSORS

TABLE 7.1

261

Development of Biosensors

Year

Development of the biosensors

1916

First report on the immobilization of proteins: adsorption of invertase on activated charcoal

1956

Invention of the first oxygen electrode [Leland Clark]

1962

First description of a biosensor: an amperometric enzyme electrode for glucose [Leland Clark]

1969

First potentiometric biosensor: urease immobilized on an ammonia electrode to detect urea [Guilbault and Montalvo]

1970

Invention of the ion-selective field-effect transistor (ISFET)

1972

First commercial biosensor: Yellow Springs Instruments glucose biosensor

1976

First bedside artificial pancreas [Clemens et al.]

1980

First fiber-optic pH sensor for in vivo blood gas

1982

First fiber-optic-based biosensor for glucose

1983

First surface plasmon resonance (SPR) immunosensor

1987

Launch of the blood glucose biosensor [MediSense]

1990

Launch of the Pharmacia BIA core SPR-based sensor

1992

Launch of the i-STAT handheld blood analyzer

1996

Launch of glucocard; Abbott acquires MediSense for $867 million

1998

Launch of LifeScan fast take blood glucose biosensor

2001

LifeScan acquires Inverness Medical’s glucose testing machines

2006

Dexcom receives approval for its short-term sensor (STS) continuous monitoring of glucose

2007

Launch of GlucoTel Bluetooth-enabled glucose meter

1999
Current

BioNMES, Quantum dots, Nanoparticles, Nanocantilever, Nanowire, and Nanotube The biosensor development and demand market is expected to reach approximately USD 360 million by the end of 2023 [https://www. marketresearchfuture.com/press-release/biosensors-developmentdemand-market]

The market for global biosensor development and demand has prominent key players, including Siemens Healthcare (Germany), LifeSensors (United States), Abbott Laboratories (United States), Johnson & Johnson (United States), Medtronic plc (United States), Nova Biomedical Corporation (United States), Koninklijke Philips N.V. (Netherlands),

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Nokia Corporation (Finland), F. Hoffmann-La Roche AG (Switzerland), TiaDoc Technology Corporation (Taiwan), Bayer AG (Germany). From Table 7.1 and recent publications we can see that the market is based primarily on electrochemical biosensors. This chapter will mainly focus on 2D nanomaterial-based biosensors.

7.2 ELECTROCHEMICAL BIOSENSOR Electrochemistry is a good field for the analysis of the contents of a biological sample due to the direct conversion of a biological event to an electrical signal. The electrical signal, which is obtained from the interaction between the target molecule and receptor, results in an output or analytical information. According to the type of input electrical signal (electrical current, or voltage), electrochemical biosensors are classified as potentiometric or voltammetry, amperometry, and conductometric biosensors. These biosensors possess advantages with respect to other types of biosensors in terms of portability, rapid measurement, repeatability, robustness, selectivity, sensitivity, and reduction of the volume of sample to realize the recognition.

7.2.1 Enzymatic Biosensors Enzymatic biosensors use enzymes and require an appropriate environment to maintain activity. The local chemical and thermal environment can have an unknown effect on the enzyme stability. The maximum activity of the enzyme typically depends on the nature of the biological element, type of transducer, physicochemical properties of the analyte, and operating conditions [9]. Enzymes are immobilized through approaches such as (1) adsorption. Physical adsorption is weak and occurs mainly via van der Waals interaction; chemical adsorption is stronger and involves the formation of covalent bonds. (2) Covalent bonding occurs between a functional group and transducer and substance. It requires mild conditions such as low temperature, low ionic strength and pH in the physiological range. (3) Entrapment refers to a mixture of the enzyme with a monomer solution which is polymerized to a gel, trapping the enzyme. (4) Cross linking is where a biomaterial is chemically linked to another supporting material to stabilize an adsorbed biomaterials. The advantages and disadvantages associated with the methods of enzyme immobilization is found in research papers [10,11]. The first enzymatic glucose sensor developed by Clark uses glucose oxidase enzyme (GOx) immobilized on glucose permeable

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membrane. The sensor produces gluconolactone, H2O2, and electrons in the presence of glucose and oxygen as shown by Eq. (7.1): Glucose 1 O2



Glucose oxidaseðGOxÞ-Gluconolactone 1 H2 O2 1 2e2 (7.1)

The next-generation biosensors were developed by coupling enzymes with redox mediators such as dye molecules, biomolecules, quantum dots, micro/nanomaterials, and their composites to enhance the electron transfer characteristics associated with the nanomaterials [12]. Fig. 7.1 shows a schematic of different types of sensors classified into different generations on the basis of the sensing mechanism [13]. Though enzymatic sensors are highly selective for the target analyte, enzyme immobilization is highly challenging. An enzymatic sensor has poor enzyme immobilization and shelf life, and thus has stringent storage requirements.

7.2.2 Nonenzymatic Electrochemical Sensors In addition to the difficulties in storage, handling, and stability of enzyme-based sensors, there are problems with interference by compounds. If the concentration of the interfering compound is high compared with the analyte of interest, then a selectivity problem will arise. Sometimes these types of sensors suffer from simultaneous determination of different analyte molecules which is more important in pharmaceutical applications [14,15]. Also there is a change in the background current due to electrode surface fouling. To replace the enzymes, nonenzymatic sensors have been developed. These types of sensors do not involve enzymatic bioactivity and are very stable. Nonenzymatic biosensors use nanomaterials to obtain stability, reproducibility, and simplicity. Nanomaterial plays a vital role in improving electrocatalytic behavior and selectivity due to their high surface area, morphology, and electron transport. However, the use of bare electrodes leads to slow kinetics, low sensitivity, high over potentials, and therefore is not suitable for analytical applications [16]. To overcome these problems different materials are used to modify the working electrode. The different types of nanomaterials used include noble metals (Au, Pt) and carbon-based materials (CNTs, graphene, quantum dots), which are highly useful as active conductive supports for high-performance nonenzymatic electrochemical sensors (nEECSs) [17,18]. Metal oxide (ex-ZnO, SnO2, NiO, SiO2, Fe2O3, MnO2, etc.) nanomaterials are used because of their high catalytic activity and cost effectiveness. Composites materials [19
25] are widely used due to enhancement in physical and chemical characteristics.

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(A) First-generation glucose sensors O2

Glucose

2e– Gluconic acid

Electrode

H2O2 GOx (B) Second-generation sensors

Glucose

Mediator (oxidized) 2e–

Gluconic acid

Electrode

Mediator (reduced) (C) Single nanomaterial sensors

Glucose

O2 2e–

Gluconic acid

Electrode

H2O2

(D) Nanocomposite sensors Glucose

O2 2e–

Gluconic acid

Electrode

H2O2 TRENDS in Molecular Medicine

FIGURE 7.1 The classification of biosensors according to the materials used [13].

7.3 CURRENT STATUS To improve the performance (selectivity, sensitivity) of sensors, chemically modified electrodes (CME) are widely used to replace bare carbon electrodes. The CME provides different redox properties for different nanomaterials. Bare electrodes such as platinum (Pt), gold (Au),

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carbon paste electrodes (CPEs), and glassy carbon electrodes (GCEs) can be chemically modified in different ways such as adsorption, covalent bonding, and deposition of modifiers on the bare electrode surface [26,27]. In recent years nanomaterials, enzymes, biomolecules, carbonbased materials, polymers, hybrid materials, etc. have been used to enhance the sensitivity, selectivity, speed, and stability—referred as the 4s—of biosensors [28].

7.4 PARAMETERS INVOLVED IN ELECTROCHEMICAL SENSING AND THEIR SENSING MECHANISM In a nEECS, the electrocatalytic behavior of the CME depends on parameters such as electrolyte, applied potential (V), accumulation time (s), amount of dispersing material (working material), scan rate (ʋ), and pH. The electrical current passes through an electrolyte and different electrolytes exhibit different electrochemical behaviors. Optimization of the electrolyte may help improve the chemical stability of the CMEs and improve the sensitivity and efficiency of the biosensors. Accumulation time is one of the effective parameters in electrochemical sensing and is the time frame for adsorption of analyte molecules on the surface of CME. A redox reaction will occur after applying the potential difference between a CME and reference electrode (RE) when adsorption of analyte molecules on the surface of CME. The amount of material on the electrode working area is important because excess conducting/electrocatalytic material could lower peak current values due to the changes in conducting behavior and sometimes act as a barrier by forming an insulating layer. According to the Butler
Volmer equation, the applied potential controls the rate of electrode reaction [29,30].

7.5 NANOMATERIALS FOR NONENZYMATIC SENSING Nanostructured biosensors can (1) act as good electrocatalyst/reactant and speeding up the electrochemical reaction; (2) enhance electron transfer and reduce the interfacial resistance; and (3) provide active sites to immobilize biomolecules [31]. Metal nanoparticles (MNPs) (Au, Ag, Pd, Pt) and semiconductors (PbS, CdS) combined with metal nanoparticles have been used for labeling or immobilizing molecules leading to improvement in SSS performance and the LOD of sensor. Due to their high surface area, conductivity, low dimensionality, and chemical activity, metal oxides (ZnO, MnO2, CuO, NiO, MgO, IrO2, WO3, PbO2, RhO2,

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V2O5, Fe3O4, etc.) combined with metal nanoparticles and polymers (PANI, ILs, PMMA, chitosan, etc.) have been used [32
40]. From the mid-1970s, conducting polymers have been used in EC biosensors due to their high electron mobility, large surface area, scalability, anticorrosion, and ease of use [38]. They possess intrinsic conductivities ranging from 10210 to 1025 S cm21. To improve sensing performance, the conductivity of conjugated polymers is tuned by modifying their backbones through doping. Polymer-based composites are materials that consist of conductive polymers with other nanomaterials (metals, metal oxides, carbon based); they are also used for biosensors. Polymer nanocomposites show remarkable improvement in sensitivity, selectivity, stability (SSS), and detection limit. In enzymatic biosensors, polymer-based materials are widely used as substrates to immobilize specific biorecognition elements (oligonucleotides, enzymes, antibody/ antigen, aptamers) through physio- or chemisorption. Jia et al. [41] used Au nanoparticles (NPs) for immobilizing horseradish peroxidase (HRP) and reported the effect of nanoparticle size on immobilization capability and sensing performance. Cai and coauthors [42] developed a sensor by tapping oligonucleotide with a mercaptohexyl group onto the surface of cystaminemodified gold nanoparticles as shown in Fig. 7.2. Their results show that with trapped biomolecules the sensor performance is 10 times higher than that of the bare electrode. Mercury- (Hg), bismuth- (Bi), and antimony- (Sb) modified electrodes were used as macro or commercial electrodes for metal detection. Al-Furjan et al. [43] improved electron transfer between the enzyme and electrode by adding dopants. To improve sensor performance Mg ions were doped into TiO2 nanodots with different compositions. Fig. 7.3 shows a schematic of Nafion/HRP/Mg-TND/Ti-modified electrode. The authors show that 2% doped Mg
TiO2 had high-sensing

FIGURE 7.2 Trapping of oligonucleotide on the surface of cystamine-modified Au nanoparticles [42].

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FIGURE 7.3 Schematic of Nafion/HRP/Mg-TND/Ti-based H2O2 sensor [43].

performance with a sensitivity of 1377.64 μAm M21. In perovskite materials, A sites and B sites are responsible for thermal resistance and catalytic behavior respectively. Perovskite materials can be considered for nonenzymatic biosensor applications. The electrocatalytic behavior of strontium palladium perovskites (Sr2PdO3) were improved by doping with Ca ions at different ratios (Sr2-xCaxPdO3 with x 5 0
0.7); 0.3 mole Ca21 ions give better sensing of glucose [44]. This work has excellent antiinterference and sensor performance and does not suffer from the presence of Cl2 ions. Naik et al. [45] used a spinel type of metal oxide of NiCo2O4 nanostructures for enzyme-less glucose sensing. To attain a high surface to volume ratio and fast mass transport behavior the researchers prepared hybrid nanostructure by adding Au and Ag to make Au-NiCo2O4 and Ag-NiCo2O4 successively on Ni foam by electrodeposition. The modified NiCo2O4, Au-NiCo2O4, Ag-NiCo2O4 electrode had sensitivity values of 20.8, 44.86 and 29.86 μA μM21 cm22 for glucose detection respectively [46]. Zhou et al. reported on the effects of Ag doping of ZnO rods/ITO and the catalytic activity and electron transfer characteristics for glucose. Contact angle analysis showed that doping with Ag minimizes the contact area of the solid
liquid interface between the analyte/electrode (Fig. 7.4) and does not favor high-sensing performance. The sensor showed enhanced electron transfer and catalytic properties by addition of Ag. Sharma et al. [48] researched the fabrication of a monoclinic MoO3-based nonenzymatic glucose sensor. MoO3 structures are made by MoO6 octahedrons and possess layered-type structure, which con˚ distance. MoO3nect through weak van der Walls forces with a few A based sensors show high sensitivity for glucose sensing owing to the cagelike crystal structure that facilitates intercalation and deintercalation of ions and glucose molecules. In a similar way to doping,

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FIGURE 7.4 (A) Effect of Ag doping on contact angle. (B) Pictorial representation of glucose-sensing mechanism using Ag-doped ZnO on ITO [47].

functionalization of nanomaterials with conducting or biopolymers could help improve the SSS performance of enzyme-less sensors. Ethylene di amine (EDA) modified multiwalled carbon nanotubes (MWCNTs) show fast electron transfer compared to dimethylamine and triethyl amine groups for detection of H2O2 [49]. Carbon-based materials (single-walled carbon nanotubes [SWNTs], MWCNTs, graphene, graphene-based quantum dots) modified electrodes were used to detect analytes such as heavy metals, drug molecules, biomolecules, pathogens, DNA, and chemical compounds [50].

7.6 TWO-DIMENSIONAL MATERIALS-BASED ELECTROCHEMICAL BIOSENSORS Generally 2D materials can be defined as materials that have a thickness of less than a few nanometers including nanosheets, nanoplates, nanoflakes, nanowalls, etc. Electrons in these materials are restricted in one direction and are free to move in the other two dimensions. The exploitation of 2D materials encompasses techniques based on approaches such as exfoliation, extraction, wet chemical and vaporbased deposition [51]. Sometimes a combination of these different techniques is used to make hybrid 2D materials. 2D materials include metal oxides, polymers, graphene and nongraphene materials of the metal dichalcogenides, phosphorene, metal hydroxides, etc. [52,53]. This section briefly discusses the role of 2D materials in electrochemical sensors using low-cost and simple fabrication procedures. Graphene was the first 2D material discovered in 2004. Graphene is a one-atom-thick carbon nanosheet separated from its parent graphite

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that exhibits several unique characteristics compared with that of zero-dimensional (0D) nanoparticles, one-dimensional (1D) and threedimensional (3D) materials. Graphene has fascinated researchers in the field of electrochemistry and is used in electronics, photonics, and sensors. The research in graphene electrochemistry/electrochemical sensors and storage devices is because of its layer-dependent physio-chemical properties, electron confinement, large specific surface area (2630 m2 g
1), conductivity, good flexibility, and mainly wide electrochemical potential window, low charge transfer resistance, high thermal conductivity, high oxidation resistance up to 600 C, and good mechanical properties with Young’s modulus of 1 TPa [54
58]. It provides more active sites and facilitates the surface functionalization, thereby improving sensing performance.

7.6.1 Synthesis of Graphene Sheets 2D-based graphene materials have been synthesized using mechanical exfoliation, chemical vapor deposition (CVD), and chemical reduction of exfoliated graphene oxides (CFG) [59,60]. A single-layer graphene (SLG) sheet was synthesized by mechanical exfoliation. SLGs exhibit semimetallic properties with zero energy bandgap. SLGs show high mechanical strength and a high carrier mobility that plays a versatile role in field-effect transistor (FET) based sensor devices. But the SLG suffers in practical application because of difficulties in large-scale industrial production. CVD methods can produce large sheets that are used in device applications and have the possibility of doping with atoms such as boron or nitrite. It is essential to coat the film of insulating substrates to avoid impurities that can affect the sensing device performance. A single graphene film was coated on silicon carbide to produce hundreds of integrated circuits on one sheet of epitaxial grown graphene [61,62]. The term “open energy bandgap” was used because the interaction between graphene and SiC creates bandgap value 0.26 V. Generally, the EC properties depend on the electron transfer rate between electrode surface and electroactive species [63]. In graphene, the electron transfer rate depends on the density of the edge/basal plane defects and sites in graphene structure. Carbon-based bioelectrodes are functionalized or decorated with metal NPs, polymers, and biomolecules through physical and chemical approaches to improve efficiency [12]. Its well known that there are various steps are involved during the preparation of graphene sheets and also different methods were followed to prepare 2D-based graphene and functionalized graphene materials for biosensing [56]. Er et al. reported Pt NPs decorated GO for detecting alpha 1-adrenoceptor (α1-AR) antagonists [64].

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Wang et al. investigated graphene and graphene-related nanomaterials and their role in biomolecule detection for cancer diagnosis [65]. The detection of nucleic acids is important for the early detection of cancer. de Avila et al. researched rGO and CMC (O-carboxymethylcellulose) modified disposable screen printing electrode (SPE) for breast cancer detection [66]. A disposable graphite pencil electrode (GPE) was fabricated by Kilic et al. for miR-21 cell detection using differential pulsed voltammetry (DPV) and electrochemical impedance spectroscopy (EIS) measurement [67]. This graphene-modified GPE could detect miR-21 in cell lysates of miR-21 positive breast cancer cell line (MCF-7) with LOD of 2.09 mg mL
1. Graphene-based electrodes have also been used to detect VEGFR2, cyclin A2 proteins [68]. Fig. 7.5A and B shows the steps involved in functionalization of immunosensors on graphenebased materials for protein detection. In Fig. 7.5A, thionine was coated on a GCE to act as mediator to improve the signals from chitosan (CS) functionalized rGO sheets. Covalent binding of biotinylated Ab2 (antibody of VEGFR2) were accomplished via glutaraldehyde (GTA) as a cross linker. Modified GCEs were then incubated with bovine serum albumin (BSA), Ag, and Ab1 (VEGFR2). From EIS analysis it could be noted that thionine/CS-rGO/GCE has low electron transfer resistance [69]. For the BSA, Ag, Ab1, Ab2 modified thionine/CS-rGO/GCE reveals high electron transfer resistance. The change in resistance value confirms successful preparation of modified electrode for detection of Ab1. Fig. 7.5B shows a schematic diagram of an immunosensor preparation using graphene-based composites [65]. For simultaneous detection of multiple analytes (tumor biomarkers of carcinoembryonic antigen [CEA], squamous cell carcinoma antigen [SCCA]) rGO-tetra ethylene pentamine (TEPA) was used as a substrate material and Au mesoporous carbon-CMK-3 (replicating mesoporous silica SBA-15) was used for immobilizing secondary antibodies (Ab2) as well as to maintain the activity of biomolecules. After immobilization, SCCA detection is shown in Fig. 7.6. DPV analysis showed that both CEA and SCCA oxidized simultaneously at 0.62 and 0.17 V respectively, using a rGO-TEPA modified immunosensor [70]. This immunoassay method confirms there is no formation of crossreaction between the analyte molecules. It could be used in clinical diagnostics due to selectivity and reproducibility. For graphene, the carboxyl, hydroxyl, ketone groups present in the basal and edge planes are active sites for the immobilization of biomolecules. Li et al. prepared a 3D paper-based device using CuS decorated GO for α-fetoprotein (AFP) detection in human cells with an LOD of 0.5 pg mL
1 [68]. Kumar et al. made a Whatman paper-based biosensor for CEA detection using poly (3,4-ethylenedioxythiophene): poly(styrene sulfonate) (PEDOT:PSS)

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FIGURE 7.5 (A) Biosensing model for trapping the VEGFR2. (B) Assembly of graphene-based immunosensors for protein molecules [65,69].

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FIGURE 7.6 (A) Schematic illustration of paper-based PEDOT:PSS/rGO electrode fabrication. (B) CA response of carcinoembryonic antigen. (C) Concentration of CEA vs. peak current values. (D, E) Foldable sensor and demonstration of LED emission when current flows through electroactive paper [70].

and rGO composites. PEDOT:PSS/rGO acts as an electroactive paper and shows a sensitivity of 25.8 mA ng cm
2 with a linear range detection of 2
8 ng mL
1 in human serum collected from cancer patients [70]. Fig. 7.6 shows a schematic illustration of electrode fabrication (Fig. 7.6A), chronoamperometric (CA) response of CEA (Fig. 7.6B), change in current against CEA concentration (Fig. 7.6C), and demonstration of LED emission when current flows through the electroactive paper (Fig. 7.6D and E). Simultaneous determination of six biomarkers (AFP, AFP-L3, APT, AFU, DCP, and c-GT) was achieved using a graphene nanosheets/PdPt bimetallic nanocomposites
based immunosensor [71]. Efforts have been made to adjust the physical and chemical properties of graphene [71], including chemical doping with nitrogen (N), boron (B), and sulfur (S) to impact biosensing performance [72]. N-doped graphene oxide was used to measure H2O2 release from living cells at an applied potential value of 20.4 V with an LOD of 0.05 mM [73]. Inorganic nanomaterials such as CuS were decorated with rGO to detect H2O2. Pt NPs, Fe3O4, CoO4, and MnO2 decorated rGO also used to real-time monitoring of H2O2. These types of approaches promote electron transfer reactions at a low applied potential. Zerodimensional graphene quantum dots also give better performance for

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FIGURE 7.7 (A) Steps involved in preparation of the rGO-Qd/ZnO modified electrode. (B) Preparation of cell-based H2O2 sensor [75].

H2O2 [74]. The modified electrode was used to detect H2O2 from MCF-7 cells. rGO-QD was hybridized with ZnO to enhance the electrocatalytic activity [75]. Fig. 7.7A and B shows the preparation of an rGO-Qd/ZnO electrode and the preparation of a cell-based H2O2 sensor. The sensing mechanism is explained below using Eqs. (7.2) and (7.3). Briefly, ZnO was responsible for the reduction of H2O2 and edge plane defect sites in GO provide more active sites for fast electron transfer. Oi2 =Oi22 1 H2 O2 -O2ðgÞ 1 H2 O 1 e2 =2e2

(7.2)

rGO 2 ðC2OHÞn 1 H2 O2 -O2ðgÞ 1 H2 O 1 rGO

(7.3)

We can expect that these kinds of modifications on graphene-based materials extend the detection limit for analyte molecules as compared with metal, metal oxide nanoparticles, and polymers.

7.6.2 Nongraphene 2D Materials for Biosensors One of the difficulties with using graphene sheets is tuning the bandgap and electrical properties. Recent research focused on overcoming this in graphene using 2D nongraphene materials which are capable of improving sensing performance by solving the bandgap issue. Fig. 7.8 shows a flowchart of different 2D nongraphene materials based on their functions in biosensing. Dichalcogenides consist of MX2 where M is the transition metal atom (Cr, Mo, W, Tc, Re) and X is chalcogen (S, Se, Te) atoms [57]. A hexagonally packed single layer of M atoms is sandwiched between the two layers of X atoms. Generally M-X and sandwich layers are bonded by covalent and van der Waals forces respectively, and this

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FIGURE 7.8 Flowchart of different 2D-based nongraphene materials based on their functions in biosensing [57].

arrangement allows the crystal to be cleaved into a layered structure. Among the various transition metal chalcogenides MoS2, SnS2, SnSe2, WSe2, and WS2 are interesting for biosensing. In this section, we focus on the development of MoS2, SnS2, and WS2 biosensors.

7.7 MOS2-BASED ELECTROCHEMICAL SENSORS Molybdenum disulfide (MoS2) is a 2D material that has attracted attention due to its graphene-like structure and is widely used in nanoelectronics. MoS2 is a layered structure consisting of Mo atoms sandwiched between two sheets of S atoms. MoS2 is interesting for gas sensors and electrochemical biosensors because of its catalytic property and ease of surface functionalization that can lead to more adsorption sites for biomolecules. Various MoS2 structures such as spheres, flowers, wires, plates, rods, and tubes have been fabricated using different synthesis methods [76]. Micromechanical cleavage, lithium intercalation, liquid exfoliation, and hydrothermal methods were used for synthesis of MoS2 nanostructures [77]. Lithium intercalation is a most effective technique for the mass production of fully exfoliated MoS2 nanosheets [78]. Fan et al. reported on the preparation of MoS2 nanosheets with nbutyl lithium in hexane through sonication assisted lithium intercalation [79]. Generally, the conducting state of 1T phase of MoS2 is interesting for biosensing applications due to different electronic and chemical properties [80]. Fig. 7.9 shows different structure/phase of MoS2. Normally the electronic structure of MoS2 is controlled by “d” orbital of molybdenum and the layers end with a lone pair of electrons because four electrons from “Mo” fill the bonding states around them. Therefore

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FIGURE 7.9 Different structure/phase of MoS2 with different atomic layer thickness. (A) 2H (hexagonal), (B) 3R (rhombohedral), (C) T (tetragonal symmetry) [80].

it is expected that tuning the band structures helps raise the excitation and fluorescent properties for optical biosensing applications [80]. Many materials used for biosensors are toxic and not biologically compatible. Reports show that MoS2 has less toxicity compared with graphene oxides and could be suitable for in vivo biosensing and bioimaging applications. Due to the strong vibrational sensitive characteristic of MoS2, it is possible to conduct biosensing via Raman and FTIR spectroscopy that will be more sensitive to adsorbates on the surface of MoS2. Generally thiol/silane groups are used to modify the surface of chalcogenides. In the case of MoS2, functionalization can be done by creating defects during synthesis. In situ reduction of metal ions, esterification, and creation of free radicals on the surface are used for functionalization of MoS2. It is interesting to note that functionalization helps change the state of MoS2 from metallic 1T to 2H semiconducting. 2D-based MoS2 materials are widely used in different biosensors such as electrode and optical sensors [81]. Zhai et al. reported the synthesis of MoS2 nanoflowers for detecting glucose. MoS2 nanoflowers were synthesized by a hydrothermal method using CTAB as a surfactant by tuning the pH of the growth solution [82]. Post-synthesis annealing of MoS2 gives the flowerlike morphology. TEM studies revealed that after annealing petal-shaped MoS2 was obtained. The results indicate that morphology has an impact on sensing performance with high-sensitivity value of 570.71 μA mM21 for glucose detection. Fig. 7.10 shows XRD (Fig. 7.10A), SEM (B
D), and TEM images (Fig. 7.10E and F)

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FIGURE 7.10 (A) XRD and SEM images of MoS2 obtained with a various concentration of CTAB. (B) 0 mM, (C) 8.2 mM, (D) 16 mM. TEM images of (E) annealed and (F) not annealed MoS2 [82].

FIGURE 7.11

Schematic of GO@MoS2 preparation and immobilization of GO@MoS2 on Au electrode via electrostatic bond [83].

of MoS2 nanoflowers obtained by varying the CTAB concentration from 0 to 16 mM. Yoon et al. reported a hydrogen peroxide sensor using myoglobin (Mb), which is a metalloprotein. Mb (as biomolecular probe) immobilized on MoS2 encapsulated GO (GO@MoS2) via electrostatic bond on an Au electrode is shown in Fig. 7.11 [83]. Kim et al. measured parathyroid hormone concentration (PTH) in serum samples using immunoassay biosensors [84]. Cysteine was functionalized on graphene
MoS2 (MG) composites, which provide
SH,
NH, COO
groups. Fig. 7.12A and B shows SEM and HRTEM images of MG composites. Phosphate (ALP)/HRP)-IgG were immobilized on MG modified Au electrodes. PTH antibodies were cross-linked on enzyme-modified Au electrode to detect antigen of PTH in serum. HRTEM shows that few layers of

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FIGURE 7.12

277

(A) SEM and (B) HRTEM images of MG composite [84].

FIGURE 7.13 (A) MoS2-modified screen printing electrode. (B) Cyclic voltammetry (CV) and amperometry analysis in the presence of BSA in serum samples (calibration plot) [86].

MoS2 were linked with GO and their presence confirmed by electron energy loss spectroscopy (EELS) measurement. MoS2 utilization eliminates the pretreatment step of the sensor surface with harsh chemicals. BSA [85] was detected using few layer of MoS2 nanoflakes modified Au SPE. Kukkar et al. observed that MoS2 is in detecting BSA with a detection limit of 0.006 mg mL
1 [86]. Fig. 7.13A and B shows an MoS2-modified screen-printed electrode and the measured change in current from CV and amperometry in the presence of BSA.

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FIGURE 7.14 (A) Schematic of Au-Pd nanoparticles decoration on MoS2 nanosheets. (B) DPV sensing. (C, D) CV sensing the presence of H2O2 using Au- and Pd-modified MoS2-based electrodes [87].

Li et al. believed that bimetallic nanoparticles, which possess high electrocatalytic behavior, can be used to modify the surface of MoS2 [87]. In their work, they decorated Au-Pd nanoparticles on MoS2 nanosheets (Fig. 7.14A) through facile thermal co-reduction based on the synthesis of Au-Pd/GO for H2O2 detection. Fig. 7.14B shows DPV peak current values after successive addition of H2O2 in the range 0.8
10 mM. The sensitivity value was 184.9 μA mM21 cm22 at a reduction potential B 20.1 V. However, MoS2-modified GCE does not show catalytic behavior; bimetallic decorated MoS2 enhances the electrocatalytic ability toward glucose and H2O2 and results revealed that Pd shows a synergetic electrocatalytic effect compared with Au nanoparticles as shown in Fig. 7.14C and D. The mechanism of glucose sensing was explained as dehydrogenation of the anomeric C1 atom of glucose adsorbed on the surface of the modified electrode in the positive potential range that may hinder the electro oxidation of glucose. From cyclic voltammetry (CV) analysis (Fig. 7.14C), upon increasing the positive potential value, hydroxyl groups were formed on the surface of Pd due to the NaOH electrolyte. Pd-OH groups facilitate the oxidation of adsorbed intermediates and results peak at 20.3 V (Fig. 7.14D). Remaining uncovered (free from OH, intermediates) active sites on the Pd surface were responsible for direct oxidation of glucose at a potential

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FIGURE 7.15

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Preparation of ZnO nanosheet on 2D thin MoS2 for DNA sensing [88].

of 20.1 V (Peak 2). This work proves that bimetallic nanoparticles enhance the electrochemical properties of MoS2 and motivates the development of nonenzymatic-based sensors. Yang et al. demonstrated the detection of acute promyelocytic leukemia (APL) using a ZnO/MoS2-based DNA sensor [88]. ZnO nanosheets were electrodeposited on MoS2 scaffold. Retinoic acid receptor alpha (RARA) was used to detect APL. The scheme of the GCE-modified ZnO/MoS2 is shown in Fig. 7.15. Their results reveal that freestanding ZnO sheets were formed from the electrostatic interaction between positively charged ZnO and negatively charged MoS2. Positively charged ZnO gives more adsorption sites for DNA immobilization and improves the catalytic activity and oxidize at an applied potential value of 20.3 V. This high-sensitivity DNA sensor has a detection limit up to 6.6 3 10216 M. Yang et al. detected guanine and adenine using electro-polymerized MoS2. Poly (xanthurenic acid), a low-toxicity and stable polymer were polymerized on a CPE to get novel composites [89]. Fig. 7.16A
E shows the preparation method, CV analysis, and DPV sensing of polymermodified MoS2 showing better electrocatalytic behavior in the absence and presence of adenine and guanine. It is noted that composites show better performance than MoS2. Negatively charged surface of composites enhance the sensing ability of positively charged analyte molecules with a detection limit of 2.7
3.8 3 1028 Lin et al. demonstrated accurate determination of H2O2 using CNTs
MoS2-based composite-modified GCE [90]. CNT
MoS2 composites were prepared through a one-step

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FIGURE 7.16 (A) Preparation of PXa
MoS2. (B, C) CV analysis. (D, E) DPV sensing of adenine and guanine using poly(xanthurenic acid) polymer-modified MoS2 [89].

solvothermal approach and coated on GCE and utilized for H2O2 detection. MoS2 on CNTs increases the surface area and number of adsorption sites. H2O2 was found to reduce at a reduction potential of 20.6 V with a detection limit of 5 nM. It shows better selectivity against ascorbic acid, glucose, uric acid (UA), glutathione, L-cysteine, and sucrose and shows best reproducibility in milk samples. Lin et al. reported H2O2 sensors using MoS2 nanoflowers decorated with platinum (Pt) nanoparticles. CV analysis reveals that Pt-decorated MoS2 nanoflowers shows a twofold increase in the electrochemical behavior compared with MoS2 and bare GCE (Fig. 7.17A). The formation of nanohybrids was confirmed by HRTEM analysis (Fig. 7.17C) showing uniform decoration of Pt nanoparticles with a size in the range of 3
5 nm. The amperometric measurements (Fig. 7.17B) show linear response on the addition of H2O2 at a potential of 20.35 V [91]. The selectivity, reusability, and stability (Fig. 7.17D
F) of the nano-hybrid-modified electrode were studied using DPV measurements. Zang and Wang groups studied different metal nanoparticles (Au, Ag, Pd, and Pt) decorated MoS2 through different preparation methods and results were compared with commercially Pt catalyst [92]. MoS2/ noble metal nanohybrid structures show enhanced catalytic ability. Similar research has been done using metal-decorated MoS2 for the detection of H2O2, dye molecules, neurotransmitters, protein, and biomolecules. Catechol (CC) is a toxic environmental pollutant that leads to the depression of the central nervous system. Utilizing different pairs of metal-decorated MoS2 sheets gives different sensing ability and Au/Pt-MoS2 nanosheets have a high detection limit of 0.4 mM in the linear range of 2
100 mM, as shown in Fig. 7.18.

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FIGURE 7.17 (A, B) CV and amperometry sensing of H2O2. (C) HRTEM image. (D) Antiinterference study. (E) Reusability. (F) Stability of the Pt-decorated MoS2/GC [91].

This modified electrode was used to detect CC in real water with acceptable recovery values. Fig. 7.18A shows the preparation of Au-, Pt-, and Au/Pt-decorated MoS2 nanoparticles and DPV detection of CC (Fig. 7.18B
E). Chao et al. reported Pt-decorated MoS2 for simultaneous determination of UA and dopamine (DA) at a reduction potential of 0.3 and 0.15 V, respectively, and showed considerable recovery values in serum (real sample analysis) [93]. Yang et al. synthesized PANI-MoS2 (PMo) composites by sonication followed by drop casting on a CPE [94]. The 18-base synthetic oligonucleotides pDNA (50 -TCT TTG GGA CCA CTG TCG) were immobilized on PMo/CPE to get ss DNA on the surface to detect cauliflower mosaic virus 35S (CaMV35S) gene. It should be noted that from Fig. 7.19C, different concentration of MoS2 gives different sensing performance against the CaMV35S virus. The optimized concentration of MoS2 is 0.054 g and which gives more adsorption sites for CaMV35S with a detection limit of 2.0 3 1026 M. Huang et al. synthesized 2D-based MoS2 nanostructures by a hydrothermal method and PANI/ MoS2 composites were prepared through in situ polymerization [95]. MOS2-PANI composites were drop cast on GCE and incubated in Au solution to give Au/MoS2-PANI/GCE electrode for DA detection. After modification with Au NPs, the charge transfer resistance of the electrodes was found to decrease and facilitate simultaneous detection of DA and UA. The device is promising for real applications compared with graphene oxide, multiwalled carbon-based modified electrodes. The same group developed MoS2-CS-based composites with Au NPs to detect bisphenol A (BPA) [96]. The effective

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FIGURE 7.18 (A) Schematic of the preparation of Au-, Pt-, and Au@Pt-decorated MoS2 nanoparticles. (B
E) DPV detection of CC using Au-MoS2, Au-MoS2, Pt-MoS2, Au@PtMoS2-modified GCE [92].

FIGURE 7.19

(A) MoS2-PANI preparation. (B) EIS spectra of PANI, MoS2, PANI-MoS2 and (C) Effect of MoS2 concentration on sensing performance [94].

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surface area of GCE increased from 0.02 to 0.08 cm2 after modifying with Au-CS/MoS2/GCE. This increases the availability of adsorption sites for BPA attachment and gives high sensitivity with a detection limit of 5 nM. The electrochemical sensor was fabricated by dispersing MoS2 on GCE, and Au NPs in CS were placed on the MoS2 surface and used as an electrode. The results also showed that the modified electrode shows better interference against 200-fold concentrations of inorganic ions such as Al31, Fe31, Ca21, Mg21, Cu21, Zn21, SO422, Br2, Cl2, F2, and NO32. Also these interfering compounds do not affect the signals from BPA. This result provides a good platform for novel electrochemical sensor development. Zhang et al. found that reduced MoS2 (rMoS2) nanosheets possess high conductivity, and fast electron transfer [97]. Their invention inhibits the semiconducting nature of the MoS2 and has potential use in the field of electroanalytical chemistry. KimuraSuda et al., Opdahl et al., and Liu et al. observed that DNA, particularly single standard DNA (ssDNA), can adsorb easily on Au surface with high affinity and act as an efficient binding agent [98,99]. A previous report implies that ssDNA can be bound on graphene through π
π stacking, hydrophobic interaction, and van der Walls forces. Following these observations, Wang et al. prepared an electrochemically rMoS2
graphene/A32/Au electrode using 32-mer homoadenine ssDNA oligonucleotides (A32) on graphene, which act as a binding block for MoS2/Au. Riboflavin (vitamin B2) was detected using an ssDNA/ GO-rMoS2/Au-modified electrode [100]. Fig. 7.20 shows the synthesis process and fabrication of the rMoS2
graphene/A32/Au-modified electrode. From CV, note that after A32 immobilization on Au, there is a tremendous change in anodic and cathodic peak separation potential. This may be due to electrostatic repulsion between the negatively charged backbones of ssDNA immobilized on the Au electrode. Further, the peak current values increase while attaching the rMoS2-GO. The authors point out that GO-MoS2 improves the conductivity through fast electron transfer. In the presence of riboflavin, the ssDNA/GO-rMoS2/ Au electrode shows a shift in the CV curve with high peak current values due to the strong interaction between modified electrode and functional group of riboflavin.

7.7.1 Tungsten Disulfide- (WS2-) Based Materials WS2 possesses similar structure to MoS2. WS2 consists of W metal atoms sandwiched between two layers of sulfur chalcogen atoms and can be prepared easily at a large-scale level. Compared with MoS2, WS2 provides high intrinsic electronic conductivity, as well as thermal and oxidative stability. Fang et al. prepared graphene-like WS2 and

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FIGURE 7.20 Fabrication of rMoS2
graphene/A32/Au for riboflavin detection by DPV [100].

FIGURE 7.21 (A) TEM analysis of WS2. (B) WS2
GO [102].

plate-like WS2 structures through facile solid-state reaction and revealed that graphene-like WS2 has a better electrochemical performance [101]. Huang et al. used layered WS2
GO for the detection of benzene derivatives (dihydroxybenzene isomers) of catechol (CT), resorcinol (RS), and hydroquinone (HQ) [102]. In Fig. 7.21A TEM images show WS2 and WS2
GO composites. WS2
GO
modified GCE acts as a transducer for the determination of benzene derivatives and shows better sensitivity with an LOD of 1 3 1027 M. In a sample analysis, CT, RS, HQ in water from river, sewage, and pond were analyzed. Hybridization chain reaction (HCR) is a “chain reaction of recognition and hybridization events between two sets of DNA hairpin molecules” and has been used for enzyme-based sensors [103]. Liu et al.

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fabricated hybrid MWCNTs
WS2 structures through a hydrothermal method [104]. The authors demonstrate sensitive detection of hepatitis B virus genomic DNA with HCR amplification. The sensing platform includes capture DNA probe (cDNA), auxiliary DNA, and two DNA hairpins; the detection was via HRP on the DNA duplex. Biotin H1 and 2 was labeled at the open end of two hairpin DNAs, and HRP was immobilized via biotin H. The target DNA was detected by measuring the change in current from DPV signals. In the absence of target DNA, biotin does not participate in the catalytic reaction. Nucleic acids are often used in biosensing [105]. Among the different oligonucleotides, aptamers have molecular recognition abilities through receptor-ligand interactions and aptamers have been used for the detection of metal ions, proteins, and cells. Aptamer-based sensors have high specificity, biocompatibility, and simple preparation [106]. Aptamers have been used to detect immunoglobin G with WS2 combined with GO and Au. A schematic of GO-Au modified aptamer-based sensor is shown in Fig. 7.22B. The influence of reaction time and temperature of aptamer binding was studied from DPV peak current values. This aptamer sensor has detection limit 1.2 3 10213 M. Recently Huanag et al. developed a WS2-Au-based label-free aptamer-based biosensor for 17βestradiol detection [107]. The authors used a thiol-tailored aptamer as a capture molecule on WS2-Au/GCE. From the CV it was noted that an addition of aptamers to Au/Ws2/GCE, the peak current values were decreased. This is because of electrostatic repulsion between the negatively charged phosphate backbones of oligonucleotides. The WS2-AuDNA modified electrode accelerates charge transfer and shows good selectivity with an LOD of 2 3 10212 M. Shuai et al. synthesized 2D-based layered tungsten disulfide/acetylene black composites for DNA sensors [108] as shown in Fig. 7.23. As prepared WS2
AB composites were placed on GCE and Au NPs were deposited over the WS2
AB/GCE. The thiolated DNA sequence was immobilized on Au NPs/WS2
AB/GCE through Au-S bond. Target DNA was hybridized with probe DNA on the modified electrode surface and auxiliary DNA hybridizes with target DNA. DNA hairpins of bio-H1 and bio-H2 are opened with the help of bio recognized probe (H1 and H2 are stable and do not open at room temperature). The nicked double helices used to immobilize HRP through biotin—avidin reaction leads to a fast current response. This HCR amplification is used to detect target DNA via the catalytic reduction of H2O2 and HQ system. This HCR-based WS2
AB modified electrode has a detection limit of 0.12 fM with a linear range of 0.001 pM to 100 pM. It possesses good selectivity and differentiates a single-based mismatched DNA sequence. To verify the general use of this sensor, the presence of target DNA was analyzed in serum samples with good results for clinical applications.

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FIGURE 7.22

Schematic illustration of the sensor construction and for (A) DNA detection with HCR amplification [104]. (B) IgE aptamer sensor based on WS2-graphene nanosheets and AuNPs [106].

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FIGURE 7.23 Pictorial representation of working principle of DNA detection based on HCR [108].

Theophylline (1, 3-dimethyl-7H-purine-2, 6-dione, TP) is an amethyl xanthine derivative and one of the natural alkaloids found in tea, coffee, chocolate, etc. The accepted concentration for daily life is about 55
110 μM or 5
20 μg mL
1 [109]. Excess theophylline leads to respiratory and cardiac disease. Wang et al. prepared WS2 nanoflower/Ag NPs composites for theophylline sensing [110]. WS2 act as a substrate for Ag NPs growth and well dispersed Ag NPs would give high electrocatalytic ability. Fig. 7.24A
D shows SEM and TEM images of as prepared WS2-Ag NPs composites. The oxidation peak current value of Ag-Ws2/GCE is five times higher (13.75 μA) compared with bare GCE (2.65 μA). TP were oxidized at 1.38 V with a linear range of 2
50 μM. The sensor was used to detect TP present in commercial products such as tea and drugs with recovery values of B98%. Toh et al. [111] fabricated nonenzymatic sensors (microfluidic device) for H2O2 detection and their amperometric response against H2O2 and shown in Fig. 7.25A
D. They reported an immune-based H2O2 sensors modified with 1T phase WS2 sheets and compared with 1T-phase MoS2, MoSe2, and WSe2 nanoparticles [112]. GTA was used to immobilize hemoglobin (Hb) to maintain structural stability and entrap or frame the 1T-WS2/Hb structure on the surface of GCE [112]. Fig. 7.26A and B shows the preparation of GTA/Hb/1T-WS2/GCE and the

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FIGURE 7.24

(A, B) SEM images. (C, D) TEM images of WS2 nanoflowers/Ag NPs

composites [110].

FIGURE 7.25 Schematic of lab on chip and its amperometric response for H2O2 detection. (A, B) Schematic representation and photo image of lab on chip set up, (C) lab on chip contains coin S$1 for H2O2 detection, (D) amperometric response of H2O2 detection using 1T phase WS2 at applied potential value of 2 0.5 V. [111].

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FIGURE 7.26 (A) Preparations of GTA/Hb/1T-WS2/GCE. (B) TEM image of GTA/ Hb/1T-WS2 [112].

corresponding TEM. 1T-WS2 provides more catalytic active edge sites and uniform immobilization of Hb results in a high S/V ratio and lowers the electron transfer resistance between GCE surface and Hb. They also prepared modified electrodes with different types of 1T-based dichalcogenides GTA/ Hb/1T-MoS2/GCE, GTA/Hb/1T-MoSe2/GCE, and GTA/Hb/1T-WSe2/GCE, respectively. An experiment confirms that GTA/Hb/1T-WS2/GCE serves as a biosensor compared with others. Loading of 5 mg mL
1 of 1T-WS2 on GCE at an applied potential value of 20.4 V results in the detection of H2O2 in the linear range of 2 3 1026 to 38 3 1026 M (R2 5 0.98) and from 48 3 1026 M to 1728 3 1026 M (R2 5 0.99) with LOD of 36 3 1029 M. This modified electrode has antiinterference ability against ascorbic acid, DA, and UA.

7.7.2 Tin (IV) Sulfide- (SnS2-) Based Materials 2D-based tin disulfide (SnS2) is an intrinsic n-type semiconductor (IV
VI) in applications such as gas sensing, batteries, solar cell [53,113,114]. The crystal structure of SnS2 consists of S-Sn-S (Sn atoms are sandwiched between the layer of S) triple layer and linked together through weak force interactions and therefore it can easily be split into thin layers with different structures (sphere, flower, tube, plates, etc.). It is a CdI2-type crystal structure and possesses orthorhombic tin monosulfide (SnS) and hexagonal-based SnS2. Depending on the staking layer style it exhibits 1T, 2H, and 4H polymorphs and gives different physical and electron transport characteristics. To prepare different types of SnS2

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nanostructures, researchers have used chemical methods (precipitation, hydrothermal), vapor phase, or template-based techniques. At present, semiconducting-based metal sulfides are suitable and interesting in the field of biosensors due to their highly conducting nature. Gan et al. described molecularly imprinted- (MIP-) based sensor for 6-benzylaminopurine (BAP) determination. BAP is a pesticide used in agriculture [115]. Excess exposure to BAP leads to severe vomiting and nausea and can damage the upper respiratory tract, eyes, and skin. Gan et al. developed a sensor by modifying GCE with core shell type MWCNTs/SnS2 composites with molecularly imprinted CS to detect BAP [116]. A substantial improvement in electron transport and surface area were observed due to the core shell architecture while using this modified electrode, confirmed by electrochemical analysis (CV, DPV). Fig. 7.27 shows SEM and TEM images of the core
shell type structure of MWCNTs@SnS2. In this experiment, CHIT/MWCNTs@SnS2/GCE was immersed in BAP to give BAP-CHIT/MWCNTs@SnS2/GCE. The authors show after imprinting and removing BAP, the specific recognition cavities enhance the electrocatalytic ability and selectivity toward BAP with an LOD of 50 pM. This MIP-based electrode was used for sample analysis in soya bean sprout, potato, tomato, pear, apple, and mung bean sprout with a recovery of 93%
102%. The structure [117] (Fig. 7.28A and C) and SEM images (Fig. 7.28B and D) of SnS and SnS2 respectively were shown in Fig. 7.30. Chia et al. and Li et al. used SnS2 flakes to enhance direct electron transfer (DET) between protein for glucose biosensing [117,118]. Li et al. fabricated reagent-less biosensors from nanomaterials for glucose sensing. The researchers prepared a substrate for glucose oxidase immobilization using MWCNTs with SnS2 flake composites for enzymatic

FIGURE 7.27 (A) SEM and (B) TEM images of the core
shell MWCNTs@SnS2 [116].

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FIGURE 7.28

291

(A, C) Structure and SEM images of (B) SnS layer, (D) SnS2 [117].

glucose biosensing. The GOx/MWCNTs
SnS2/Nafion/GCE shows well-defined redox peaks at 20.43 and 20.47 V and the peak potential separation is 36 mV, lower than graphene-modified electrodes (80 mV). Fig. 7.29D shows amperometric sensing of glucose with an LOD of 0.004 nM at an applied potential of 20.43 V; also shown are SEM images (Fig. 7.29A
C) of SnS2 flakes, MWCNTs
SnS2, and GOx/ MWCNTs
SnS2. Li et al. used Au NPs due to their catalytic efficiency as a base for protein immobilization with enhanced conductivity [119]. They prepared metal-based composites of SnS2-Au NPs to accelerate the DET characteristics for biomolecular-based devices/sensors. Fig. 7.30B and C shows TEM images of SnS2 and Au-SnS2. The GCE electrode was modified with SnS2-Au NPs and GOx for glucose sensing with CS as a binder (GOx/SnS2-Au NPs-CS/GCE). This modified electrode shows well-defined redox peaks at
0.40 and
0.44 V with peak potential separation of 39 mV, similar to SnS2-MWCNTs composites. Fig. 7.30A shows amperometric sensing of glucose at an applied potential value of
0.43 V with LOD of 0.1 μM. Wang et al. reported detecting glucose in human serum samples using an SnS2-Pt NPs composite-modified electrode [120]. After immobilizing GOx on SnS2-Pt NPs/GCE the charge transfer resistance increased from 665 to 4127 Ω, confirming GOx molecules are effectively immobilized on SnS2-Pt NPs/GCE. In the presence of anlayte (glucose) redox peak obtained at 20.351 and 20.378 V with peak potential

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FIGURE 7.29 FESEM images of (A) SnS2, (B) MWCNT-SnS2, (C) GOx/MWCNT-SnS2. (D) Amperometry sensing of glucose using GOx/MWCNTs
SnS2/Nafion/GCE [118].

FIGURE 7.30 (A) Amperometry sensing of glucose using GOx/SnS2-Au NPs-chitosan/GCE and TEM images of (B) SnS2 and (C) Au-SnS2 [119]. FUNDAMENTALS AND SENSING APPLICATIONS OF 2D MATERIALS

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FIGURE 7.31 TEM images of (A) SnS2, (B) SnS2-Pt NPs, (C) amperometry sensing of H2O2 using SnS2-Pt NPs [120].

separation of 27 mV, lower than SnS2 modified with Au NPS and MWCNTs. The GOx/PtNP@SnS2/Nafion/GCE shows a glucose detection limit of 0.0025 mM. The amperometric sensing (Fig. 7.31C) and TEM images of SnS2 and SnS2-Pt NPs (Fig. 7.31A, B) are shown in Fig. 7.31. The sensing can be written as follows: GOX ðFADÞ 1 2e2 1 2H1 -GOX ðFADH2 Þ

(7.4)

GOX ðFADH2 Þ 1 O2 -GOX ðFADÞ 1 H2 O2

(7.5)

Glucose 1 GOX ðFADÞ-Gluconolactone 1 GOX ðFADH2 Þ

(7.6)

Li et al. prepared a SnS2-Pt NPs hybrid through a facile solvothermal approach for H2O2 detection [121]. In this system Pt NPs possess fast electrocatalytic behavior and SnS2 hold high surface area. These characteristics influences the effective sensing performance,which attained a detection limit value of 0.33 μM at an applied potential value of 0 V. The electrode has a reproducibility rate of 92% relative to its initial performance.

7.8 SUMMARY AND FUTURE PERSPECTIVES The interlinking of 2D and hybrid 2D nanomaterials with electroanalytical methods has led to advancement in biosensing applications, and has demonstrated promising capabilities of these nanomaterials in

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enzymatic and nonenzymatic sensors. It is expected that 2D-based nanohybrid structures with multifunctionalities will continue to grow rapidly in the field of biosensors. 2D-based biosensors improve the sensitivity, selectivity, and detection limit, although the use of these materials in biosensing is still a new field, and there are many ways to develop them further. Most 2D materials are not uniform, and there are requirements to develop new techniques to synthesize 2D-based composites or decoration using metal particles with large scale and reproducibility. Moreover while using 2D materials in sensing, further research is needed on the electrode/electrolyte interface, charge transfer, and sensing mechanisms. In the case of 2D-based materials few devices are available for real applications other than those reported in publications, and these types of materials are limited in lab scale only. Therefore, there is a need for sensing devices such as SPEs, paper strip based, and microfluidic devices for practical applications with good sensitivity and detection limits. Improvement is still needed in selectivity for simultaneous detection of interfering ions without altering the sensing performance. However, different approaches are currently available to improve biosensing ability using 2D materials. It is expected that researcher will take a step forward in the future to deliver practical biosensors.

Acknowledgments Dr. C.R. thanks Science and Education Research Board (DST-SERB), Govt. of India for providing a National Postdoctoral Fellowship (PDF/2017/003016). Dr. R.T.R. acknowledges the financial support from Defence Research and Development Organization (DRDO), New Delhi, Govt of India (grant no. DLS/86/50011/DRDO-BU center/1748/D (R&D).

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8 Electrochemical Sensing Platform Based on Graphene-Metal/Metal Oxide Hybrids for Detection of Metal Ions Contaminants Swagatika Kamila1,2, Bishnupad Mohanty1,2, Sushanta K. Das1,2, Satyapriya Sahoo1,2 and Bikash Kumar Jena1,2 1

Colloids and Materials Chemistry Department, CSIR-Institute of Minerals and Materials Technology, Bhubaneswar, India; 2 Academy of Scientific & Innovative Research, New Delhi, India

8.1 INTRODUCTION 8.1.1 Importance of Detection of Metal Ion Contaminants The carcinogenic and hazardous nature of some metal contaminants including arsenic, mercury, chromium, lead, and so forth warrant much attention for detecting these heavy metals in the ecosystem. These toxic elements are present in the environment, taking different forms, coming from different sources, and causing serious health issues. Heavy metals are persistent, bioaccumulative, and toxic substances that do not readily break down by natural processes and are not readily metabolized. They may accumulate inside the body or affect ecological food chains through consumption and cause severe health and environment problems. These metals, once released into the environment, may have long-term toxic effects on human health, even with just a trace presence (Fig. 8.1). Fundamentals and Sensing Applications of 2D Materials DOI: https://doi.org/10.1016/B978-0-08-102577-2.00008-7

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FIGURE 8.1 Sketch of diseases caused by heavy metal ion pollution. Source: Reproduced from http://blog.associatie.kuleuven.be/danhuayao/.

Among the various elements, arsenic (As) is one of the most carcinogenic and toxic, causing skin cancers and kidney diseases. Arsenic is present in the earth crust and in groundwater in various forms such as arsenite, arsenates, etc. Direct exposure to this toxic form of arsenic is harmful to the whole ecosystem [1]. The anthropogenic or natural contamination of groundwater by arsenic has raised an alarm throughout the world and in particular by the World Health Organization (WHO), which has set 10 ppb as the maximum permissible contamination value [2]. Groundwater can contain arsenic in oxy anionic form with oxidation states of 3 and 5, that is, the trivalent As(III) and pentavalent As(V); As(III) is many fold more toxic than As(V) [3]. Both forms exist within a pH range of 6
9. In the environment of groundwater and geology either As(III) or As(V) is dominant, whereas As(V) is dominant in oxic water and As(III) in anoxic water; but both variants are reported to coexist in both types of water. In groundwater As can be found in the aquifer with varying dissolution and desorption processes releasing arsenic from the solid phase to liquid phase. Iron oxide reductive dissolution is considered to be the principal cause of As release from aquifer sediments.

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Similarly, mercury (Hg) is a toxic metal contaminant that exists in the Hg(II) state and is widely found in wastewater. Primary sources of contamination are modern industrial processes. Hg(II) is highly dangerous at a very low concentration level. Excess exposure creates serious health disorders such as brain damage, kidney failure, motion disorder, and various other cognitive diseases. WHO has standardized the permissible toxic value for Hg(II) as 1 ppb [4]. Chromium (Cr) is another toxic and carcinogenic element widely abundant in groundwater with Cr(III) and Cr(VI) as common oxidation states. Cr(VI) is more toxic than Cr(III). Chromium contamination leads to serious health problems including lung and sinonasal cancer [5,6]. The guideline value of chromium estimated by WHO is 50 μg L21 (50 ppb) [7]. Like other toxic metal contaminants, lead (Pb) is known to be carcinogenic and to cause adverse effects on the body. It can harm the kidneys, nervous system, and reproductive system. Lead is very harmful to fetuses and young children at the brain development stage. Extremely high levels of lead contamination may lead to seizures and coma and can be fatal. The toxic nature of these metal contaminants require precise monitoring of trace levels in water prior to use [8,9].

8.1.2 Methods of Detection Many industrial and analytical methods have been developed to determine the concentration level of heavy metal ions in samples. These conventional methods include atomic absorption spectroscopy (AAS), atomic emission spectroscopy (AES), atomic fluorescence spectroscopy (AFS), induced coupled plasma mass spectrometry (ICP-OES), neutron activation analysis (NAA), and X-ray fluorescence spectroscopy (XRF) [10]. These methods can simultaneously detect different metal ions and have good sensing parameters. However, these methods are expensive and require tedious sample preparation and preconcentration procedures [11,12]. Moreover they are not portable for on-site detection. Other procedures that are less expensive and are easy to use are therefore being developed to replace the traditional techniques. Current research in nanotechnology provides new opportunities for the development of various portable sensors for on-site monitoring of multiple metal contaminants. Nanomaterials used for sensing heavy metal ions include (1) optical sensors, (2) biological sensors, and (3) electrochemical sensors (Fig. 8.2) [13,14]. 8.1.2.1 Optical Sensor Optical sensors are the powerful tools that provide analyte information on the presence, concentration, and other physical properties of

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FIGURE 8.2 Scheme of different tools for heavy metal ion sensing.

metal ions contaminants. It consists of a molecular recognition element and a signal transducer. The sensor detects the optical signal that arises due to the interaction of a molecular recognition element with a target molecule. Optical sensors are based on spectroscopic measurement techniques. The detection of metal ions by a fluorescent sensor is based on the changes in physico-chemical properties of a fluorophore [15]. It is commonly used for the detection of toxic metal ions. Organic dyes are used as a fluorophore in Fo¨rster resonance energy transfer (FRET) sensors, as they are easy to label with small size minimizing possible steric hindrance. Typically in FRET the emission band spectrum of a donor overlaps with the absorption band spectrum of the acceptor [16]. Ono et al. established a FRET sensor for heavy metal ion sensing. The organic dye fluorophore and quencher are linked to two ends of a molecular system to complete the FRET process. The sensor detects Hg21with a limit of detection (LOD) of 40 nM [17]. Many fluorophore materials such as quantum dots, luminescent nanoparticles, metal clusters, carbon quantum dots, nanometallic organic framework, etc. have recently been developed [16,18,19]. Colorimetric sensing is an emerging technique for heavy metal ion sensing based on the principle of plasmonic nanoparticles. Plasmons can be excited into resonance on the surface of noble metal nanoparticles in the presence of electromagnetic radiation of suitable wavelength. The technique induces a collective charge of conduction band electrons in the metal nanoparticle [20]. Liu et al. reported colorimetric sensing of Hg21 using AuNP functionalized with oligonucleotides. The solution color changes from green to yellow, which was ascribed to AuNP aggregation due to Hg21 induced oligonucleotide hybridization [21]. Similarly, a small portable colorimetric sensor was developed with citrate stabilized AuNP. The shift in plasmonic resonance of the AuNP in response of Hg21 was measured [21].

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Surface-Enhanced Raman spectroscopy (SERS) sensors have rarely been used for heavy metal ion detection [22]. SERS is a spectroscopic technique that can fingerprint molecules but is limited for direct recognition of heavy metal ions. To avoid this issue, it is possible to functionalize the surface of plasmonic nanoparticles with an organic ligand which specifically binds the heavy metal ions. Li et al. established a SERS-based As31 sensor by glutathione-modified AgNPs using the above principle [23]. 8.1.2.2 Biological Sensor Biological sensing methods are based on the interaction between biomolecules and heavy metal ions. Biological samples can also be directly integrated into a device for biological sensing of metal contaminants [24,25]. Recently DNAzymes have shown promise for the detection of heavy metal ions because of their high metal binding affinity and good catalytic activity. DNAzymes sense heavy metal ions through fluorescent and electrochemical methods and have great advantages over other sensors because of their good response range, good selectivity, and ease of operation. Skotadis et al. developed platinum nanoparticle-modified DNAzymes as biosensing material for the detection of lead ions (Pb21) [26]. Liu and Lu made a Pb21 biosensor by using DNAzymes and a DNA substrate to assemble AuNPs into 3D aggregates. The enzyme substrate pair served as cross-linkers for AuNP aggregation and the substrate strand was cleaved by the DNAzyme strand with the addition of Pb21, resulting in the dissociation of AuNPs with a color change from purple to red [27]. Slocik et al. reported a 2-aminopurine-modified DNA homopolymer for the sensing of mercury ions [28]. Zhao et al. designed a colorimetric biosensor that uses enzymatic cleavage of DNA on welldispersed AuNPs for the detection of Pb21 [29]. Chen et al. demonstrated a FET sensor based on a protein-functionalized reduced graphene oxide (rGO) film to detect various metal ions [30]. 8.1.2.3 Electrochemical Sensor Extensive research has been going on to develop an electrochemical sensor focused on novel electrode materials [31]. In comparison to spectroscopic methods, these techniques are user friendly, selective, and have fast response times that help online sensing of the samples [11]. An electrochemical device has three electrodes: a working electrode (WE), a counter electrode, and a reference electrode as shown in Fig. 8.3. The WE can be modified with sensing material for the detection of specific metal ion contaminants [32,33]. Research is ongoing to use modified electrodes with various electrode materials including metal nanoparticles, polymer, metal oxide, carbon nanotube (CNT), and others.

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FIGURE 8.3 Scheme of electrochemical sensor.

Electrochemical techniques for heavy metal ion sensing include voltammetry, amperometry, potentiometry, impedimentary, and conductometry, etc. [33]. Voltammetry is the most useful and widespread technology for measuring the current response of an analyte to an applied potential and the magnitude of the peak current determines the concentration level of the analyte. Voltammetric techniques have a wide and dynamic range of detection, from sub-ppb to ppm. Higher concentrations of analytes (ppm or high ppb) can be measured by pulse or square-wave voltammetry techniques. Lower concentrations of analyte can be detected by the stripping voltammetry techniques [34]. This technique can simultaneously detect toxic metal ions. Xiong et al. demonstrated AuNP decorated carbon foam electrodes for electrochemical monitoring of Pb21, Cu21 [35]. Jena et al. reported simultaneous sensing of As31, Hg21, and Cu21 by AuNP decorated on thiol functionalized 3D silicate network modified polycrystalline gold electrodes with LOD of 0.02 ppb by anodic stripping voltammetry (ASV) (Fig. 8.4) [3]. Differential pulse anodic stripping voltammetry (DPASV) is another important technique for sensing heavy metal ions. Au-CF modified on gold electrode has been used for the simultaneous detection of Pb21and Cu21 by DPASV with LOD of 5.2 and 0.9 nM, respectively [35]. Gao et al. developed a composite of nafion with rod-like hydroxyapatite electrode material for simultaneous detection of Hg21, Cu21, Pb21, and Cd21 with LOD values of 30, 21, 49, and 35 nM, respectively [36].

8.2 MATERIALS FOR ELECTROCHEMICAL SENSING OF METAL ION CONTAMINANTS Nanomaterials have made revolutionary changes in analytical chemistry. Nanomaterials possess unique physiochemical and electronic properties and hence have found widespread use in electrochemical applications. The fabrication of nanomaterial-based electrodes help with

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FIGURE 8.4 (A) Scheme of gold nanoelectrode ensembles (GNEE). (B) FESEM images of GNEE on the sol
gel network, and (C) Square wave anodic stripping voltammogram (SWASV) curve of GNEE toward simultaneous detection of As(II), Hg(II), and Cu(II). Source: Reproduced with permission from B. Kumar Jena, C. Retna Raj, Gold nanoelectrode ensembles for the simultaneous electrochemical detection of ultratrace arsenic, mercury, and copper, Anal. Chem. 80 (2008) 4836
4844.

sensitive and selective detection of heavy metal ions. Until now many electrode materials have been developed for electrochemical sensing of heavy metal ions. Inorganic nanoparticles have a regular structure, high electrocatalytic efficiency, high surface activity, chemical and thermal stability, and strong adsorption ability [33]. Inorganic nanomaterials such as metal nanoparticles, metal oxides, and carbon-based nanomaterials (i.e., graphene, CNT, etc.) are commonly used in the field of electrochemical sensing of heavy metal contaminants. Functionalization and composite formation of metal nanoparticles with any substrate are very attractive techniques and have gained tremendous attention for their sensing application. Initially, metal ion contaminants were detected using mercury drops and mercury-film electrodes because of their reproducibility, wide potential range, high sensitivity, repeatability, and excellent performance with stripping voltammetry [37]. But high toxicity prevents their use. Therefore, mercury-free electrodes were developed. Recently bismuth, tin, and antimony nanoparticles were used for highly sensitive and

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selective detection of heavy metal ions, and these nanoparticles are less toxic than mercury. Reduced graphene oxide
bismuth (rGO/Bi) nanoparticle nanocomposites show sensitive and selective detection of heavy metals such as Cd12, Pb12, Zn12, and Cu12. rGO/Bi nanocomposites have detection limits of 2.8, 0.55, 17, and 26 μgL21 for Cd21, Pb21, Zn21, and Cu21, respectively [38,39,40]. However Bi-, Sn-, and Sb-modified electrodes require activation to improve sensing reproducibility and easily hydrolyze to form insoluble compounds. In recent years noble metal nanoparticles, transition metal oxide, bimetallic nanoparticles, carbonaceous materials (CNT, graphene), and composites have also been used for electrochemical analysis of heavy metals. A nanoelectrode assembly of gold nanoparticles has been used for monitoring As(III), Hg(II), and Cu(II) with good sensitivity. Gold nanoelectrode ensembles (GNEEs) can detect As(III), Hg(II), and Cu(II) [41] at well below the level set by WHO [3]. A single-walled carbon nanotubes (SWCNT) electrode was used for the detection of Cd(II) and Pb(II) ions by square-wave stripping voltammetry (SWSV) with LOD of 2.2 and 0.6 ppb for Cd(II) and Pb(II) respectively [41]. Bimetallic nanoparticles like AuPt, FePt, FeAu, and FePd were used for As(III) detection. Among these nanoparticles FePt exhibits the lowest detection limit with a sensitivity of 1.2 ppb and 1.23 μA ppb21 [42]. Nanostructured transition metal oxides such as TiO2, MgO, SnO2, ZrO4, Fe3O4, ZnO, NiO, CeO2, NiWO4, and MnO2 are usually used for heavy metal sensing. Transition metal oxides are generally low cost and nontoxic, with high stability, high conductivity, and strong adsorption. For example, dimercaptosuccinic acid-functionalized Fe3O4 nanoparticles modified carbon electrodes were used to sense Cu21, Pb21, Cd21, and Ag1 in natural water and Pb21 in urine [43]. A porous MgO nanoflowermodified electrode was used for the detection of Pb(II) and Cd(II) [44]. DNA-based carbon@TiO2 hybridized electrode (DNA/C-TiO2 NTs) was used for the detection of the Hg21 ion [45]. CNTs including SWNTs and multiwalled carbon nanotubes (MWNTs) are gaining significant attention due to their high electrical conductivity, low electrode fouling, large surface area, high stability, and wide potential window [46
48]. A bare CNT electrode, grown on a nonmetallic silicon carbide (SiC) substrate had a detection limit for Pb, Cd, and Zn on the nanomolar scale [49]. CNT electrodes have been compared to conventional electrodes, but CNT electrodes can detect multiple ions simultaneously with narrower linear ranges. More importantly, CNT electrodes are much friendlier to the environment. Covalent and noncovalent functionalized CNTs are also used for sensing heavy metal ions [50,51]. Organic molecules (OMs) have been used for metal ion sensing because specific OMs have the ability to recognize specific metal ions [52]. The Pearson acid
base concept or hard and soft acids and bases

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(HSAB) is used to predict the interactions between heavy metals and OMs, in which hard acids tend to react with a hard base and form stronger bonds. The opposite is true where a soft acid tends to react with soft base form a more stable complex [53]. Adhering to this principle, soft acids such as Hg21, Cd21, and Ag1 combine with a soft base like thiolate (RS2) or thiol (S), whereas a hard ligand like Cr31or Al31 tends to combine with hard ligands like alkoxide (RO2 ) or hydroxide (OH2), etc. Similarly ligands like Zn21, Cu21, Co21, Pb21, etc. which are borderline tend to combine with amino groups. Based on this theory Cu21 or Cd21 ions are detected by using L-cysteine and glutathione. OMs modified with inorganic nanomaterial are also used for metal ion sensing [54]. Some conducting and chelating organic polymers are used for the sensing of heavy metal ions. These polymers are generally combined with inorganic nanomaterials. Nanomaterials and nanoparticle composites are also used for sensing heavy metal like Cd21 and Pb21 [55]. A variety of biomaterials that includes enzymes, amino acids, peptides, and proteins are used to detect specific heavy metal ions [56]. The biomolecule contains a heteroatom like sulfur, nitrogen, and oxygen, which helps with easy attachment of heavy metal ions via metal
ligand interaction [57]. Thiolated amino acid
capped AuNPs are used for sensing of Hg21. Mercury ions interact with amino acids through cooperative metal
ligand interaction to form a stable complex. Heavy metal ions form complex with nucleic acid bases, structures, and functional nucleic acids. Further, they are also used for heavy metal ion sensors [58].

8.3 GRAPHENE-BASED MATERIALS FOR METAL CONTAMINATE SENSING: AN OVERVIEW The discovery of graphene has led to new research on 2D layered materials. Research on 2D layered materials like graphene, hexagonal BN, transition metal dichalcogenides, black phosphorous, and layered double hydroxides materials has been extensively collected in the field of electronics, sensor, biomedicine, energy storage, and catalysis [59]. These 2D materials are promising in the field of metal ion sensing application. Graphene consists of a single-layer sp2 carbon framework in which atoms are arranged in hexagons. The extended honeycomb network along with long-range π- conjugation yields extraordinary and unique properties such as the quantum Hall effect (QHE), good conductivity [60], large surface area (2630 m2 g21) [61], good optical transparency [62], high mechanical strength [63], and excellent thermal conductivity (3000
5000 Wm21 K21) [64]. Due to these unique properties, graphene could prove to be a remedy for many technological issues. To date a

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myriad of graphene-based composites have been successfully prepared with organic crystals [65,66], inorganic nanostructures [67
69], metalorganic frameworks [67
69], polymers [70
73], and biomaterials [74
76] which are explored in such applications as batteries [77
80], fuel cells [81,82], supercapacitors [83
86], photovoltaic devices [87
89], photocatalysis [90
93], Raman enhancement [94,95], and sensing [96] platforms. Nanomaterials always tend to agglomerate in solutions due to their high surface energy and due to this they lose active sites and subsequently their catalytic ability [97]. When composites of graphene and nanomaterials are prepared, graphene provides a large specific surface area to accumulate all the nanoparticles and reduce their surface tension [98]. In this way the nanoparticles are properly dispersed on graphene surface with intact active sites for catalysis. From both theoretical and experimental observations, it is reported that the properties of graphene are dependent on its geometric structures. Specifically, the presence of oxygen functional groups on exfoliated graphene oxide (GO) and rGO surface helps further functionalization with the added advantage of tuning the properties of the graphene sheets [99]. Therefore explicit control over the synthesis of graphene is crucial to their fundamental physical and chemical properties. Significant research has been carried out to develop synthetic methods for preparing high-quality graphene as well as high production yield. In 1975 Lang first attempted to synthesize mono and few-layered graphene by thermal decomposition of carbon on single-crystal Pt substrate [100]. The drawback of that process was the number of graphene sheets deposited on different crystal planes of Pt. There was no consistency between the properties of graphene sheets. Later attempts were made to produce graphene in 1999 [101,102]; however, Novoselov et al. succeeded in separating graphene from graphite stacks by micromechanical cleavage in 2004 and won the Nobel Prize in Physics in 2010 [103]. Since then, the technology has been developed for synthesizing graphene with high yield. Graphene can be synthesized using two approaches: (1) bottom-up and (2) top-down. The two categories are building graphene sheets from molecular precursor and breaking the van der Waals force of attraction between graphite flakes. Under the bottom-up approach, various techniques have been used to make graphene sheets by adding carbon precursors step by step, for example, (1) chemical vapor deposition (CVD), (2) epitaxial growth on SiC, (3) arc discharge, or (4) chemical synthesis. For the top-down approach, procedures are based on breaking the van der Waals force of attraction between graphite sheets, called exfoliation. Under this category there comes (1) mechanical exfoliation, (2) reduction of GO, and (3) liquid phase exfoliation. The quality of graphene obtained by any means is a significant issue that defines which procedure is more facile and trustworthy. The synthesis procedure

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FIGURE 8.5 Scheme of graphene synthesis by various approaches. Source: Reproduced with permission from R. Raccichini, A. Varzi, S. Passerini, B. Scrosati, The role of graphene for electrochemical energy storage, Nat. Mater. 14 (2014) 271.

defines graphene quality (G) and cost (C). The scalability (S), purity (P), and yield (Y) can readily be determined and the liquid phase exfoliation and reduction of GO are the best procedures (Fig. 8.5) [104].

8.3.1 Graphene for Sensing of Metal Contaminants Graphene has extraordinary properties like large surface area, high electron mobility, and thermal conductivity making graphene a good candidate for sensing [105,106]. Biomolecules adhere to the high surface area of graphene, and the small bandgap of graphene helps electron transport process between biomolecules and the electrode surface. Schedin et al. were pioneers who fabricated a microscopic sensor based on graphene [107]. The sensor responds as soon as the gas molecules attach or detach from the surface of the graphene. To date, many graphene-based sensors for detecting heavy metals have been proposed.

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Zhao et al. described a polypyrrole rGO composite for sensing Hg(II) ions with a detection limit of 1.5 3 1028 mol L21 [108]. Ion and coworkers presented an electrochemical sensor for the detection of Pb21 with a detection limit of 1 3 1029 mol L21 using amino-functionalized exfoliated graphite nanoplatelet
electrode matrix [109]. Xuan et al. prepared a micropattern solvothermal-assisted reduced graphene oxide on Au electrode (TRGO/Au) for the electrochemical detection of Pb and Cd with a linear detection range of 1
12 μgL21 and detection limits of 0.4
1 μg L21 is reported (Fig. 8.6) [110]. Sadhukhan and Barman developed 2D flat carbon nitride material analogs of graphene for simultaneous detection of Cu1, Pb21, and Hg21. The LOD for Cu1 and Pb21 was 7 3 1028 M and for Hg21 9.1 3 10211 [111]. Some inorganic layered transition metal oxides are electrochemically

FIGURE 8.6 (A) Scheme for the fabrication sequence of micropatterned TRGO/Au, (B) scanning electron microscope (SEM) image of TRGO/Au, and (C) SWASV of different concentration of Cd and Pb ions with the in situ-plated Bi/TRGO/Au in 0.1 mol L21 of acetate buffer containing 600 μg L21 Bi ions. Source: Reproduced with permission from X. Xuan, M.F. Hossain, J.Y. Park, A fully integrated and miniaturized heavy-metal-detection sensor based on micro-patterned reduced graphene oxide, Sci. Rep. 6 (2016) 33125.

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FIGURE 8.7 Scheme showing the electrochemical process for sensing of metal ions on the metal or metal oxide
reinforced graphene hybrids.

active for sensing toxic metal ions. Yuan et al. synthesized nanostructure Na-TNS for successfully detecting Hg21 ion by stripping voltammetry [112]. Graphene produced by the reduction of graphene oxides usually has some oxygen functional groups on its surface which can form a complex with metal ions. But the drawback is restacking properties of graphene sheets due to van der Waals forces (π
π interaction) that inhibits its sensing performance. So to avoid the agglomeration of graphene, some metal nanoparticles, metal oxides, and conducting polymer-decorated graphene have been developed for electrochemical heavy metal ion sensing [113,114]. The metal or metal oxide nanoparticle-reinforced graphene hybrids were modified on a conducting substrate for sensing metal ions by anodic stripping or electrochemical reduction process (Fig. 8.7).

8.3.2 Graphene-Metal Hybrids for Sensing of Metal Contaminants Noble metal nanoparticles such as gold, palladium, platinum, or silver are widely used as heavy metal ion sensors owing to their excellent electrical conductivity, large surface area, and good adsorption capability. These nanomaterials are helpful for decreasing the overpotential of electroanalytical reactions and maintaining the reversibility of redox reaction. The combination of noble nanomaterials with various substrates may open new avenues for utilizing graphene nanosheet and hybrid material for electrochemical sensing platforms with high

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performance. Santhosh et al. developed graphene
AuNP electrodes by an electrochemical deposition process [115]. This nanostructured composite electrode facilitates the easy electron transfer process and the electrochemical sensing behavior of Cr(VI) detection with remarkably good sensitivity and selectivity. Zhang et al. fabricated an electrode by electrochemical deposition of graphene and Au on a glassy carbon electrode (GCE) surface. They electrochemically detected Hg21 with a detection limit of 0.001 aM (Fig. 8.8) [116]. The Chen group reported electrochemical detection of Hg21 by changing rGO with a thioglycolic acid-functionalized AuNPs electrode. The lowest concentration range of Hg21 was 2.5 3 1028 M [117]. Kempegowda et al. synthesized graphene
Pt nanocomposites by reducing H2PtCl6 6H2O and GO in the presence of ethylene glycol which acts as a mild reducing agent. The chemically modified graphene
Pt nanocomposite was used for electroanalytical application for selective sensing of As by square-wave anodic stripping voltammetry with a detection limit of 1.1 nM [118]. Sahoo et al. developed AuNP and rGO nanocomposite-modified carbon paste by electro-depositing AuNp on an rGO-modified carbon paste electrode. This electrode was used for sensing As(III) by anodic stripping voltammetry with an LOD of 0.13 μgL21 [119]. Ibrahim et al. synthesized Au-graphene-selenocystine modified glassy carbon electrodes for the detection of Pb21 by square-wave anodic stripping voltammetry along with measurements of cadmium and lead with a detection limit of 0.08 and 0.05 ppb [120]. Song et al. prepared naturefriendly graphene (Gr)/L-cysteine/gold electrodes for sensing cadmium (Cd) by DPSV technique. The sensitivity and detection limit were



FIGURE 8.8 Graphene EAu
modified electrode for Hg21 sensing. Source: Reproduced with permission from Y. Zhang, G.M. Zeng, L. Tang, J. Chen, Y. Zhu, X.X. He, et al. Electrochemical sensor based on electrodeposited graphene-Au modified electrode and nanoAu carrier amplified signal strategy for attomolar mercury detection, Anal. Chem. 87 (2015) 989
996.

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152 nA mm22 μg21 L and 1.42 μg L21 respectively [121]. Sang et al. successfully synthesized rGO and Ag nanoparticles (AgNPs/rGO) hybrid by a simple in situ method. The synthesized hybrid was used for detection of Pb(II), Cd(II), Cu(II), and Hg(II) with LOD of 0.141, 0.254, 0.178, and 0.285 μM, respectively. The AgNPs/rGO-modified electrode displays good antiinterference properties and superior stability [122]. Ting et al. synthesized a graphene quantum dot (GQD)
functionalized gold nanoparticles (AuNPs) hybrid for detection of Hg21 and Cu21. The GQDs
AuNPs hybrid senses Hg21 and Cu1 with an ultralow detection limit of 0.02 nM and 0.05 nM, respectively (Fig. 8.9) [123]. Han et al. synthesized Ag nanoparticles on 3D graphene foam by a one-step in situ method. The composite electrode was used for the detection of heavy metal ions like Hg21 and has detection limits of 8.0 μA μM21 and 0.11 μM, respectively. This composite has a good cycle life, long-term durability,

FIGURE 8.9 (A) Scanning electron microscope (SEM) image of GQD
AuNPs on GCE. (B) ASV curves of GQD
AuNP electrode with various concentration of Hg21, and (C) schematic illustration of Hg21 ion detection. Source: Reproduced with permission from S.L. Ting, S.J. Ee, A. Ananthanarayanan, K.C. Leong and P. Chen, Graphene quantum dots functionalized gold nanoparticles for sensitive electrochemical detection of heavy metal ions, Electrochim. Acta 172 (2015) 7
11.

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and reproducibility [124]. Liu et al. synthesized Fe3O4
TiO2
NG
Au
ETBD/GCE electrodes to electrochemically detect Pb21 with square-wave voltammetry method with a detection limit of 7.5 3 10213 mol L21 [125]. In 2000 Wang and Lu replaced a mercury electrode with a bismuth film electrode for detection of heavy metal ions. A bismuth film electrode is attractive in electrochemical sensing because of its low toxicity, high selectivity, and resistance to dissolved oxygen in the analyte. The main drawback is interference and fouling of the film electrode. rGO
Bi nanocomposite was the best choice for sensing heavy metal ions using stripping voltammetry. The rGO/Bi nanocomposite was prepared by in situ syntheses, with ethylene glycol as a solvent Bi(NO3)3, and 5H2O as Bi precursor and hydrazine hydrate as a reducing agent. The rGO/Bi nanocomposite electrode easily detects Cd21, Pb21, Cu21, and Zn21 in aqueous medium due to better binding activity over the Bi film electrode. This nanocomposite electrode is highly sensitive for Cd21, Pb21, Zn21, and Cu21with detection limits of 2.8, 0.55, 17, and 26 μg L21, respectively. The exploration of various graphene-metal nanoparticle hybrids toward heavy metal ion detection is summarized and presented in Table 8.1. TABLE 8.1 Graphene-Metal Nanoparticle Hybrids for Heavy Metal Ion Detection Materials

Heavy metal ions and detection methods LOD

Graphene
Au-NPs

Cr (VI), CV




[115]

Graphene
Au-NPs

Hg(II), CV

0.001 aM

[116]

rGO
Au-NPs

Hg(II), voltammetry

2.5 3 10 2 8 M

[117]

Graphene
Pt

As(III), ASV

1.1 nM

[118]

rGO
Au-NPs

As(III), ASV

0.13 μg L21

[119]

Graphene
Au-selenocystine

Pb(II), CV

0.08 and 0.05 ppb

[120]

References

Fe3O4
TiO2
NG
Au
ETBD Pb(II), SWASV

7.5 3 10 2 13 mol L21 [125]

GR/L-cysteine /AuNPs

Cd(II), DPSV

1.42 μg L21

[121]

rGO/AgNPs

Pb(II), Cd(II), Hg(II), and Cu(II), SWASV

0.141, 0.254, 0.258, 0.178 μM

[122]

Graphene/Au quantum dot

Hg(II) and Cu(II), CV

0.02, 0.05 nM

[123]

AgNPs/rGO

Hg(II), SWASV

0.11 μM

rGO/Bi nanocomposite

Cd(II), Pb(II), Cu(II), and Zn(II), stripping voltammetry

[124] 21

2.8, 0.55, 26, 17 μg L

[38]

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8.3.3 Graphene and Metal Oxide Hybrids for Sensing of Metal Contaminants Metal oxide and graphene hybrids are promising materials for heavy metal ion sensing. Wei et al. developed SnO2
rGO composites by wet chemical method. This metal oxide graphene nanocomposite simultaneously detects Cd(II), Pb(II), Cu(II), and Hg(II) by square-wave anodic stripping voltammetry techniques with a detection limit of 1.015 3 10210 M, 1.839 3 10210 M, 2.269 3 10210 M and 2.789 3 10210 M, respectively (Fig. 8.10) [126]. Sharma developed rGO/NiWO4 by facile hydrothermal synthesis to electrochemically detect Cd(II), Pb(II), Cu(II), and Hg(II) by SWSV. The LOD of Cd(II), Pb(II), Cu(II), and Hg(II) were found to be 4.7 3 10210 M, 3.8 3 10210 M, 4.4 3 10210 M, and 2.8 3 10210 M for individual detection and 1.0 3 10210 M, 1.8 3 10210 M, 2.3 3 10210 M, and 2.8 3 10210 M for simultaneous detection respectively [127]. Xie et al. synthesized CeO2 nanoparticle-decorated graphene hybrid by a hydrothermal method. The rGO/CeO2 nanocomposite electrochemically detects Cd(II), Pb(II), Cu(II), and Hg(II) with an LOD of 2. 3.41 3 10210 M, 1.046 3 10210 M, 1.124 3 10210 M, and 2.187 3 10211 M, respectively [128]. Xiong et al. reported rGO/Fe2O3 nanocomposite as a good candidate for simultaneous detection of heavy metal ions (Cd21, Pb21, and Hg21) by anodic stripping voltammetry technique [129]. For individual detection the LOD was 8, 6, and 4 nM for Cd21, Pb21, and Hg21, respectively. 32 Hg(II)

28

Increasing conc.

Current (μA)

24 20

Cd(II) Pb(II) Cu(II)

16 12 8 4 0 –1.2

–0.8

–0.4 0.0 Potential (V)

0.4

0.8

FIGURE 8.10 (A) SWASV response of SnO2/rGO for simultaneous detection of Cd(II), Pb(II), Cu(II), and Hg(II). Source: Reproduced with permission from Y. Wei, C. Gao, F.-L. Meng, H.-H. Li, L. Wang, J.-H. Liu, et al. SnO2/reduced graphene oxide nanocomposite for the simultaneous electrochemical detection of cadmium(II), lead(II), copper(II), and mercury(II): an interesting favorable mutual interference, J. Phys. Chem. C 116 (2012) 1034
1041.

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FIGURE 8.11 (A) TEM images of rGO
AlOOH composite, (B) SWASV response of the AlOOH
rGO nanocomposite-modified GCE for the simultaneous detection of Cd(II) and Pb(II) over a concentration range of 0.2
0.8 μM. Source: Reproduced with permission from C. Gao, X.-Y. Yu, R.-X. Xu, J.-H. Liu, X.-J. Huang, AlOOH-reduced graphene oxide nanocomposites: one-pot hydrothermal synthesis and their enhanced electrochemical activity for heavy metal ions, ACS Appl. Mater. Interfaces 4 (2012) 4672
4682.

For simultaneous detection, the LOD values were calculated to be 28, 8, and 17 nM for Cd21, Pb21, and Hg21, respectively. Similarly Sun et al. developed Fe3O4/rGO electrode for detection of Cd(II), with LOD of 0.056 μmol L21 [130]. Fan et al. developed γAlOOH/rGO composites by hydrothermal methods and used this composite for electrochemical sensing of Pb(II) by stripping voltammetry with an LOD of 1.5 3 10
11 M [131]. Gao et al. synthesized AlOOH
rGO nanocomposites having both excellent electrical conductivity of graphene and good adsorbability of AlOOH; a promising candidate for heavy metal ion sensing. These nanocomposites have a detection limit of 9.3 3 10211 M and 3.5 3 10211 M for Pb(II) and Cd(II) ions respectively as shown in Fig. 8.11 [132]. Prakash et al. synthesized Fe3O4 and rGO hybrid by chemical reduction and electrochemically detected Cr(III) ions by cyclic voltammetry [133]. Investigation of various graphene
metal oxide hybrids toward heavy metal ion detection is summarized and presented in Table 8.2.

8.4 CONCLUSION AND FUTURE PERSPECTIVE Graphene is an outstanding material that has attracted significant research interest from the scientific community over the past decades. The 2D layered structure and unique properties of graphene make it useful for a wide variety of applications including electronic, energy, sensing, and biomedical. This review summarized the current literature regarding the synthesis of graphene and graphene-based nanocomposite material and its use for electrochemical heavy metal ion sensing.

FUNDAMENTALS AND SENSING APPLICATIONS OF 2D MATERIALS

319

8.4 CONCLUSION AND FUTURE PERSPECTIVE

TABLE 8.2

Graphene
Metal Oxide Hybrids for Heavy Metal Ion Detection

Materials

Heavy metal ions and detection methods

LOD

References 210

SnO2
rGO

Cd(II), Pb(II), Cu(II), and Hg(II), SWASV

1.015 3 10 M, 1.839 3 10210 M, 2.269 3 10210 M, and 2.789 3 10210 M

[126]

rGO/NiWO4

Cd(II), Pb(II), Cu(II), and Hg(II), SWASV

4.7 3 10210 M, 3.8 3 10210 M, 4.4 3 10210 M, and 2.8 3 10210 M

[127]

rGO/CeO2

Cd(II), Pb(II), Cu(II), and Hg(II), SWASV

2.3441 3 10210 M, 1.046 3 10210 M, 1.124 3 10210 M, and 2.187 3 10211 M

[128]

rGO/Fe2O3

Cd(II), Pb(II), and Hg(II), SWASV

8 nM, 6 nM, and 4 nM

[129]

Fe3O4/rGO

Cd(II), SWASV

0.056 μmol/L

[130]

AlOOH/rGO

Pb(II), SWASV

9.3 3 10
11 M, 3.5 3 10
11 M

[132]

γ-AlOOH
rGO

Pb(II), Cd(II), SWASV

1.5 3 10
11 M

[131]

Fe3O4
rGO

Cr(VI), CV




[133]

The future perspective of graphene and its nanocomposite is bright, and numerous avenues have opened for the shape and size control synthesis of nanomaterials decorated on graphene nanosheet by in situ and ex situ processes. The growth of metal nanoparticles on the graphene surface increases both the surface area and electrical conductivity as well as conferring excellent electrochemical catalytic activity toward heavy metal ions. Among the sensing methods, electrochemical detection is an active area of research. This method offers advantages such as high sensitivity, low detection limit, rapid response, low cost, and portability. The electrochemical technique coupled with the use of various nanomaterials is useful for online detection of heavy metal ions with fast response. Such sensors offer a high degree of specificity and facilitate the design of integrated systems for environmental studies. Progress in the field of graphene and graphene-based composite will further extend the platform for dependable techniques for toxic heavy metal sensing. In the future, it is crucial to develop graphene-based nanocomposite electrode materials and to explore their sensing application.

8.5 Acknowledgment The work is financially supported by CSIR-HRDG Project (YSP-02, P-81-113), New Delhi, India.

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C H A P T E R

9 2D Materials for Field-Effect Transistor
Based Biosensors Debalina Sarkar Massachusetts Institute of Technology (MIT), Cambridge, MA, United States

9.1 INTRODUCTION Sensors, especially biosensors, are indispensable for modern society due to their wide applications (Fig. 9.1) in public health care, national and homeland security, forensic industries, as well as environmental protection. There is a great demand for ultrasensitive biosensors that can detect biomolecules with high reliability and specificity in complex environments such as whole blood. Moreover, detection ability at low biomolecule concentration is necessary for screening many cancers [1], neurological disorders [2,3], and early stage infections [4] such as HIV. Currently, enzyme-linked immunosorbent assay (ELISA) based on optical sensing technology is widely used as a medical diagnostic tool as well as a quality control check in various industries. ELISA requires the labeling of biomolecules, which might alter the target
receptor interaction by conformation change. Moreover, ELISA requires the use of bulky, expensive optical instruments and as such is not suitable for fast point-of-care clinical applications. On the other hand, biosensors based on field-effect transistors (FETs) [5
9] are highly attractive as they promise real-time label-free electrical detection, scalability, inexpensive mass production, and the possibility of on-chip integration of both sensor and measurement systems (Fig. 9.2). In a conventional FET used for digital computational applications, two electrodes (source and drain) are used to connect a semiconductor material (channel). Current flowing through the channel between the source and drain is modulated

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Genomics & proteomics research

Clinical diagnostics

Drug discovery

Cell probing

Forensic applications

Security against bio-terrorism

FIGURE 9.1 The various applications of a biosensor emphasizing its significance to modern society.

Point-of-care testing Better treatment opportunities

Field–effect -transistor biosensor Low-cost mass production

Label-free electrical detection

Improved survival rates Improved security

Small size and weight

On-chip integration of both sensor and measurement systems

FIGURE 9.2 Schematic showing the potential of biosensors based on field-effect transistors.

by a third electrode called the gate, which is capacitively coupled through a dielectric layer covering the channel region. In the case of an FET biosensor, the physical gate present in a logic transistor is removed and the dielectric layer is functionalized with specific receptors for the selective capture of the desired target biomolecules. When captured, the charged biomolecules produce a gating effect, which is transduced into a readable signal. This signal takes the form of change in the electrical characteristics of FET, such as drain-to-source current or channel conductance. In this chapter we first demonstrate the huge potential of 2D semiconducting materials for sensing [9] and then show that the use of steep turn-on characteristics can make possible the realization of ultrasensitive and fast sensors [10].

9.2 2D MATERIAL FOR SENSING 9.2.1 Field-Effect Transistor-Based Biosensor Given the importance of FET biosensors, there has been a lot of work in identifying an appropriate channel material for the same. Among the FUNDAMENTALS AND SENSING APPLICATIONS OF 2D MATERIALS

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various materials reported, nanostructured materials carbon nanotubes (CNTs) and Si nanowires (NWs) have been found to be most attractive due to their size compatibility and ability to provide high sensitivity [8,11,12]. However, the same 1D nature that leads to efficient electrostatics and hence higher sensitivity also leads to difficulty in fabrication, thereby creating a major challenge for the success of 1D technologies. While the top-down fabrication technique for 1D structures suffers from high cost and slow production rate [13], the bottom-up method faces severe integrability issue [11,13], thus hampering the practical usability of such structures. The 2D materials, on the other hand, are highly promising as they cannot only provide excellent electrostatics due to their atomically thin structures but also possess planar nature, which is amenable to large-scale integrated device processing and fabrication. The thinning down of 3D materials such as Si into 2D structures is not only fabrication-wise expensive but would also suffer severely due to interface defects and uncontrollable bandgap variation when scaled down. Hence naturally 2D layered materials are desirable leading to interest in graphene-based FET biosensors [11,14
17]. However, we show here that the lack of a bandgap in graphene fundamentally limits its sensitivity. Here we suggest the tremendous potential of MoS2, which is a biocompatible material [18], as the channel material in labelfree FET biosensors (Fig. 9.3A) [9]. MoS2 belongs to the class of transition metal dichalcogenides (TMDs), which consist of 2D stacked layers of covalently bonded transition metal and dichalcogenide atoms arranged in a hexagonal lattice where adjacent layers are held together by relatively weak van der Waals forces. Due to this weak interlayer bonding in TMDs, it is possible to obtain atomically thin films with pristine interfaces, a monolayer of MoS2 being only around 0.65 nm thick. While MoS2-based photodetectors [19], fluorogenic nanoprobes [20], gas detectors [21], chemical sensors [22], and electrodes for electrochemical sensing [23] have been reported in the literature, Sarkar et al. [9] represents the first demonstration of MoS2- (or, for that matter, any TMD-) based FET biosensors working in aqueous environment and in subthreshold region, which is capable of ultrasensitive and specific detection of biomolecules. We demonstrate that the proposed biosensor achieves excellent sensitivity for pH sensing as well as biomolecule detection. Also, MoS2 has pristine surfaces (without out-of-plane dangling bonds), which reduces surface roughness scattering and interface traps. This results in low density of interface states on the semiconductor
dielectric interface, which cannot only lead to better electrostatic control but also reduce low frequency (flicker) noise, which is one of the main sources of noise in FET biosensors [24]. In addition, we show through rigorous theoretical calculations that MoS2-based biosensors can achieve ultimate scaling limits while retaining high

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Electrolyte Receptor molecules

Reference electrode Target biomolecules

Mo S

(A)

Electrolyte

(B)

(C)

Macro-fluidic channel

(D)

FIGURE 9.3 (A) Schematic diagram of MoS2-based field-effect transistor (FET) biosensor. For biosensing, the dielectric layer covering the MoS2 channel is functionalized with receptors for specifically capturing the target biomolecules. The charged biomolecules after being captured induce gating effect, modulating the device current. An electrolyte gate in the form of Ag/AgCl reference electrode is used for applying bias to the electrolyte. The source and drain contacts are also covered with a dielectric layer to protect them from the electrolyte (not shown in this figure). (B) Optical image of MoS2 flake on 270 nm SiO2 grown on degenerately doped Si substrates. Scale bar, 10 μm. (C) Optical image of the MoS2 FET biosensor device showing the extended electrodes made of Ti/Au. Scale bar, 10 μm. (D) Image and schematic diagram (inset figure) of the chip with the biosensor device and macrofluidic channel for containing the electrolyte. Inlet and outlet pipe for transferring the fluid and the reference electrode is not shown in the figure.

sensitivity, which is useful for detection at low biomolecular concentrations as well as reduction in power and space requirements, crucial for achieving dense integrated structures. Furthermore, ultrathin MoS2 possesses transparency [25] as well as high flexibility and mechanical strength [26]. MoS2 devices fabricated on transparent and flexible substrates can adapt to the curvilinear surfaces of the human body and thereby hold great promise for wearable and implantable biosensor devices.

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9.2.1.1 Note on Transduction Mechanism and Sensor Performance It is well known that if the bare FET channel is in contact with the electrolyte, direct absorption of biomolecules can lead to nonspecificity [13,27]. To achieve specificity, so that the nonspecific biomolecules cannot directly get absorbed on the channel, the channel should have an effective layer, that is, it should be either directly functionalized with specific groups/linker/receptors [28
31] or covered with dielectric/lipid/ polymer [8,32,33] etc., which enable functionalization. This is necessary even for pH sensing in the case of graphene, CNTs, or TMDs, as pH sensing on bare sensor surface is dependent on defects. This will lead to a large variation in response depending on the presence or absence or density of defect sites [34]. Moreover, a defect-less surface is desirable to avoid unwanted effects such as scattering. Furthermore, the transduction mechanism is complicated in case of bare channel surface and can be a combination of different effects including (1) electrostatic gating, (2) direct charge transfer, and (3) mobility modulation [27,35]. It is worthwhile to mention here that the source/drain metal electrodes should be passivated to avoid the issue of the biomolecules getting adsorbed directly on the electrodes changing the local work function (WF) of the metal and hence the contact resistance [35,36]. Now an unambiguous transduction mechanism is desirable for advancement of FET biosensor technology. The essence of biosensors based on FETs, as the name suggests, is that transduction occurs through field effect, that is, through electrostatic control of the channel through the charge induced either by biomolecules or pH change. As long as an unambiguous transduction mechanism of field effect is achieved (the effect can take place through any effective layer be it oxide, lipids, polymer, linker/receptor layers, or any other groups), the parameters that will dictate the sensitivity of the FET biosensor are the shift of potential on the effective layer (which mainly depends on the properties of effective layer like density of sites, type and concentration of biomolecules, ionic concentration and pH of solution, etc.) and how the shift of potential can change the current (which depends on the effective layer as well as the channel material). Thus while the biosensing will be affected by various factors (properties of effective layer, biomolecules, and solution), this work focuses on the way channel material will affect the biosensor performance. In this work, we suggest the potential of MoS2 as channel material and note the use of one of the possible effective layers, that is, covering the MoS2 using HfO2 demonstrating excellent sensitivity. HfO2 layer also helps to provide near-ideal change in surface potential with change in pH for a wide pH range. Moreover, using HfO2 as an effective layer allows

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taking leverage of the vast body of literature that exists on the functionalization of oxides. Nevertheless, other effective layers and functionalization methods (such as direct functionalization on MoS2 surface using chelating agents [37], different dielectric layers, or functionalization using polymers, etc.) can also be tried in the future. While using different effective layers can lead to different numerical values of sensitivity, but as long as the transduction mechanism occurs unambiguously through field effect (which is desirable, and the essence of FETs), this does not affect the fundamental conclusions of the chapter with regard to evaluation of the channel material. 9.2.1.2 pH Sensing The operation of the MoS2 biosensor is first demonstrated for the case of detection of pH changes of the electrolytic solution. The pH sensing is based on the protonation/deprotonation of the OH groups on the gate dielectric (Fig. 9.4A) depending on the pH value of the electrolyte, thereby changing the dielectric surface charge. This pH-dependent surface charge together with the electrolyte gate voltage applied through the reference electrode determines the effective surface potential of the dielectric. The drain current as a function of the electrolyte gate voltage for different pH values of electrolyte is shown in Fig. 9.4B. A significant increase in current is obtained at a particular applied bias with decreasing pH (or higher positive charge on the dielectric surface that causes lowering of the threshold voltage of the FET) leading to the successful demonstration of MoS2 pH sensor. The shift in threshold voltage, which has been calculated using the extrapolation in the saturation region (ESR) method [38], is found be 59 mV/pH. This threshold voltage shift can be understood from the following discussion. The change in surface potential on dielectric with change in pH of the electrolyte can be written as [39] dϕs kB T 5 2 2:3 α q dpH  21 kB T CS α 5 2:3 2 11 ; q βS

(9.1a) (9.1b)

where φs is surface potential on the gate dielectric, kB is Boltzmann constant, T is temperature, q is electronic charge, CS is electrical surface capacitance, and β S is intrinsic buffer capacity, which depends on the number of sites on the dielectric per unit area (which can be either protonated, deprotonated, or neutral) as well as the dissociation constants. The ideal change in surface potential that can be obtained is 59.6 mV pH21 at 300K when α approaches 1. When the intrinsic buffer

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FIGURE 9.4 (A) Illustration of the principle of pH sensing. At lower pH (higher concentration of H1 ions), the OH group on the dielectric surface gets protonated to form OH21 leading to a positive surface charge on the dielectric while at higher pH, the OH group gets deprotonated to form O2 leading to a negative surface charge on the dielectric. (B) Drain current for an n-type MoS2 field-effect transistor (FET) based pH sensor is plotted as a function of electrolyte gate voltage for three different pH values of the solution. The thickness of the MoS2 used is around 2 nm and the subthreshold swing (SS) obtained is around 78 mV dec21. A decrease in pH values leads to an increase in device current consistent with higher positive charge at lower pH and n-type behavior of the FET biosensor. (C) Comparison of sensitivity in subthreshold, saturation, and linear region for a pH change of 4
5 of the electrolyte solution derived from the Id-Vg curves shown in (B). Subthreshold region shows substantially higher sensitivity of 713 while the saturation and linear regions exhibit much lower sensitivities of 53.69 and 12.96, respectively.

capacity of the dielectric surface is high, α can reduce to 1 leading to an almost ideal response, which has been shown to be the case for HfO2 [40]. This has also been confirmed by our investigations. Sensitivity for pH sensing (defined as Sn_pH 5 (IpH2
IpH1)/IpH1*100, where IpH1 and IpH2 are the values of current at two different pHs of the electrolyte, pH1 and pH2, respectively, where pH1 . pH2) is deduced from the curves in subthreshold, saturation, and linear regions. Fig. 9.4C shows the comparison of pH sensitivity in these three different regions. In the subthreshold region the drain current has exponential dependence on the gate dielectric surface potential, while in saturation and linear region the relationship is quadratic and linear, respectively. Hence the

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sensitivity in the subthreshold region is much higher compared to those in the saturation and linear region [10,41]. Sensitivity values as high as 697 and 713 are obtained for pH change from 3 to 4 and 4 to 5, respectively. The critical parameter of an FET, which gives indication of the efficiency of gating effect and hence, the sensitivity of the biosensor is the subthreshold swing (SS) [10]. SS is given by the inverse of the slope of their log10(ID) 2 VGS curve where ID and VGS are the drain-to-source current and gate-to-source voltage, respectively. Therefore, the SS of a device essentially indicates the change in gate voltage required to change the subthreshold current by one decade (SS 5 dVGS/d[log10{ID}]). Thus the smaller the SS, the higher will be the change in current for a particular change in the dielectric surface potential due to gating effect produced by the pH change or attachment of biomolecules and hence, higher the sensitivity. The dependence of sensitivity (Sn ) on SS is given by [10] i hÐ ϕf 1 dϕ SS 2 1; (9.2) Sn 5 10 ϕi which depicts the exponential dependence of sensitivity on SS where φi and φf denote the initial and final surface potential on the gate dielectric before and after attachment of the target biomolecules. The ultrathin nature of MoS2 and its pristine interfaces lead to excellent SS and hence high sensitivity of the proposed device in spite of presence of very thick gate dielectric (30 nm). 9.2.1.3 Specific Detection of Biomolecules Next, the specific sensing of biomolecules using the MoS2 biosensor is investigated through the well-known biotin-streptavidin interaction where the biotin and streptavidin act as models for receptor and target molecules, respectively. Fig. 9.5A shows that a device functionalized with biotin exhibited substantial decrease in current on addition of streptavidin solution compared to that measured in pure buffer without streptavidin. This is in agreement with the negative charge of the streptavidin, as the pH of the solution (0.01X PBS) used is greater than the isoelectric point (abbreviated as pI and defined for a particular molecule as the pH at which that molecule is neutral) of streptavidin. Addition of pure buffer again caused negligible change in current consistent with the strong binding between biotin and streptavidin. To rule out the possibility of nonspecific interactions and false signals, a number of control experiments are carried out. First, an unfunctionalized device exhibited similar current levels in a pure buffer and streptavidin solution (Fig. 9.5B) indicating the absence of false signals. Second, a lower value of pH (,pI of streptavidin and thereby attributing positive charge to it) was used, and it was observed that the addition of streptavidin solution

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FIGURE 9.5 (A) A device functionalized with biotin (as shown by schematic diagram) was first measured in pure buffer (0.01X PBS) as shown by the green curve. Addition of streptavidin solution (10 μM in 0.01X PBS) leads to decrease in current (red curve) due to the negative charge of the protein as pH of solution is more than the pI of streptavidin. The device is then measured again in pure buffer leading to no significant change (black curve). (B) An unfunctionalized device (device with only 3-aminopropyl triethoxysilane [APTES] attached to it but not functionalized with biotin), as shown by the schematic diagram exhibit similar current in pure buffer and streptavidin solution (solutions used are same as in [A]) confirming that there are no false signals. (C) Addition of streptavidin solution (10 μM) at a pH of 4.75, which is less than the pI of streptavidin, leads to an increase in current consistent with the positive charge of the protein. (D) Addition of IgG (56 μg mL21) at pH of 4.75 (which is smaller than the pI of IgG), leads to negligible change in device current as IgG is not specific for biotin.

to a device functionalized with biotin leads to an increase in current compared to that in pure buffer (Fig. 9.5C). In yet another experiment, a device functionalized with biotin did not result in current change (Fig. 9.5C) on addition of immuno-globulin G (IgG), which is not specific for biotin, confirming the absence of nonspecific bindings. 9.2.1.4 Scalability and Single Molecule Detection Analysis It is of particular interest in biosensing to detect biomolecules at very low concentrations, especially a biosensor which can detect down to a

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single molecule is highly desirable. Low concentration detection (LCD) is dependent on various factors including the site density on effective layer, type of biomolecule, sensor area, analyte mass transport in the solution, etc. Since in this section we focus on the channel material, we discuss the way in which the channel material will impact the LCD. The channel material will be evaluated in terms of LCD through the criteria whether a few or in the most desired case a single biomolecule attached to the effective layer can cause measurable change in the current. In case of graphene it has been shown that single gas molecules can be detected [42]. However, in this case the gas molecules dope the graphene by getting directly absorbed into it which, as discussed earlier, leads to nonspecificity and is not desirable for biosensors. To achieve specificity an effective layer is necessary (as discussed above) and the fundamental limitations of graphene for electrostatic modulation of current through effective layer is discussed in the next section. Single biomolecule detection has been reported using CNTs where the transduction mechanisms involve bridging gap in the channel [43] or modulation of scattering [44] and not electrostatic field effect. The limitations of CNTs for practical usability has been discussed earlier and in addition to creating gaps in CNTs requires complex processing and leads to low yield [43], which upset the advantages of using FET platform. Detection of single virus with a Si NW FET [45] and single bacterium using graphene [46] have been demonstrated. Viruses (around 100 nm) and bacteria (several micrometers) are typically of larger size. For detection of a single entity of smaller biomolecules such as DNA or small proteins (,10 nm) with high sensitivity using a FET biosensor, aggressive downscaling of device dimensions is required as shown through theoretical analysis presented below. A simple yet effective theoretical model is developed to understand the effect of dimensionality on sensitivity. In the model we consider a small biomolecule with impact dimension of 5 nm placed at the center of the channel on top of the gate dielectric of an silicon on insulator (SOI) FET (without the physical gate). For the simulations, this biomolecule is represented by a uniform surface charge distribution over the impact dimension. This representation has been validated through the numerical solution of Poisson
Boltzmann [47]. As observed from Fig. 9.6, with the decrease in channel length, the sensitivity first increases. This is because the ratio of region that is influenced by the biomolecule to the total channel length increases. However, with further decrease in the channel length (beyond B250 nm), the sensitivity begins to decrease. This is because the electric field from the source/drain region begins to influence the channel potential at shorter channel lengths decreasing the effective influence of the biomolecule and hence the sensitivity. In order to maximize the

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FIGURE 9.6 Sensitivity as a function of channel length for a biomolecule with impact dimension of 5 nm placed in the middle of the channel. The Si channel thickness is taken to be 20 nm. The dielectric used is 5 nm thick HfO2.

sensitivity, we make the channel length similar to that in which charge due to a biomolecule affects (we call it the impact-dimension) and at the same time the influence of the source/drain electric fields on the channel must be minimal so that the channel is mainly controlled through the gating effect. A parameter that indicates the efficiency of gate control is the natural length scale (λ) [48], which is a function of the permittivity and thickness of the gate dielectric as well as those of the semiconducting channel. In general, to ensure that the channel is controlled mainly by the gate, λ should be 5 to 10 times smaller than the channel length (Lg) [49]. Expressions for λ for SOI structure and nanowire are given by Eqs. (9.3a) and (9.3b), respectively. rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi εs λSOI 5 td ts (9.3a) εd sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi   εs r2NW ln 1 1 td =rNW ; (9.3b) λNW 5 2εd where εd and εs are the permittivity of the gate dielectric and the semiconducting channel respectively. td and ts are the thickness of the gate dielectric and the semiconducting channel (for the SOI structure) respectively. rNW is the radius of the nanowire. Now, for example, taking the parameters of silicon as the semiconducting channel and 3 nm HfO2 as the gate dielectric, and considering λ 5 Lg/5, in order to maximize sensitivity for a single biomolecule with 5 nm impact dimension, an SOI structure with silicon channel thickness of 0.69 nm, or alternatively a nanowire structure with radius of about 2.2 nm, is required. To achieve

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such small dimensions with Si, highly complex processing techniques will be required. Moreover, the interface roughness at such ultrathin dimensions may be a major issue. Taking the parameters of MoS2 and the same dielectric, the thickness of MoS2 required is 1.225 nm, which is close to that of bilayer MoS2. In fact, with MoS2, even smaller thickness can be achieved easily since the thickness of a monolayer flake is B0.65 nm. Due to the layered structure of MoS2, ultrathin layers can be easily obtained. In addition, the layered structure also leads to pristine interfaces. Thus MoS2 can achieve the ultimate scaling limits, which cannot only maximize the sensitivity for detection of single biomolecules but also lead to reduction in power and space requirements. Currently TMD technology is at an early phase and is still undergoing development. Scaling down the gate dielectric thickness remains to be achieved. To realistically determine the true potential of MoS2 for single molecule detection, such that its performance is not screened by the thick dielectric layer, rigorous quantum mechanical simulations based on nonequilibrium Green’s function formalism [50] show that even at 5 nm channel length, MoS2 can maintain excellent gate control over the channel leading to near-ideal SS [9], which is critical for obtaining high sensitivity. Thus MoS2 is highly advantageous for scaling down of FET biosensor devices, which cannot only lead to higher sensitivity for detection of single quanta of biomolecular element especially when the entity is of smaller size but can also highly facilitate lowpower (due to lower OFF-currents at low FET supply voltages) and high-density biosensor device architectures. The increase in response time associated with the analyte transport at low concentration (not associated with the channel material) could be addressed by methods for increasing total flux toward the sensor, for example, through application of electrostatic or magnetic fields [51,52]. 9.2.1.5 Comparison With Graphene In the previous sections we discussed the superiority of MoS2 with 2D layered structure for FET-based biosensing compared to other conventional materials or 1D structures. It might be expected that graphene, which is also a 2D layered material will share the same virtues as MoS2. However, as discussed below, graphene suffers from fundamental constraints in sensitivity as well as detection limits. The zero bandgap of graphene leads to very high SS even though excellent electrostatics can be achieved due to its ultrathin nature. As is evident from Eq. (9.2), the high SS leads to low sensitivity. (Note that in case of low bandgap materials, the term SS is used to denote the mathematical factor dVGS/d (log10[ID]) and not the swing in subthreshold region since the threshold voltage is not well defined due to high leakage.)

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TABLE 9.1 Sensitivity of graphene-based FET biosensors measured in wet environment from previous literature. For sensing similar and even higher concentration of molecules or pH change, sensitivity of graphene-based FET biosensors are much lower than that based on MoS2 pH/molecule detected

Concentration

Sensitivity

Jnl. Am. Chem. Soc. 130, 14392
14393 (2008)

pH

2
4

 3.77

Nano Lett. 9, 3318
3322 (2009)

pH

4
5.1

 12

Nano Lett.10, 1864
1868 (2010)

pH

6
7

 14

Nano Lett. 9, 3318
3322 (2009)

BSA

300 nM

 0.363

Jnl. Am. Chem. Soc. 132, 18012
18013 (2010)

IgE

290 pM

 0.436

Adv. Mater. 22, 1649
1653 (2010)

DNA

10 pM

 15

Nanoscale 4, 293 (2012)

DNA

100 nM

2

Nano Lett. 12, 5082 (2012)

amyl butyrate

400 fM

 10

ACS Nano 4 (6), 3201
3208 (2010)

dopamine

2 mM

 0.75

Graphene FET biosensors

ACS Nano 5 (3), 1990
1994 (2011)

Ca

10 μM

 11

ACS Nano 6 (2),14 86
1493 (2012)

VEGF

100 fM

 0.3

21

A rough comparison of the sensitivity of an FET biosensor based on graphene and MoS2 can also be drawn by taking example of previous papers published in literature. While the sensitivity in the case of MoS2 for a pH change of 3
4 is 193 and that for 100 fM streptavidin solution is 196, the sensitivity in case of graphene for similar or even higher pHchange/concentration of biomolecules is always much lower than that of MoS2 as shown in Table 9.1. For proper comparison, it is necessary to compare graphene and MoS2 for the same type of biomolecules or the same pH change. Hence we have performed experiments with FET biosensors based on graphene and MoS2 having similar channel thickness, length, and top dielectric thickness and measured under similar conditions in the model case of pH sensing for the same pH change. The experimental findings are in line with the theoretical predictions as graphene biosensor exhibits of SS of more than 5000 mV dec21 even though it has ultrathin body (Fig. 9.7A). MoS2, on the other hand, due to its ultrathin nature and pristine interfaces cannot only achieve excellent electrostatics but at the same time due to its sizable bandgap (the bandgap of MoS2 varies in the range of 1.8 eV [for monolayer] to 1.2 eV [for bulk]) can also

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FIGURE 9.7 (A) Graphene exhibits very little modulation of current and very high subthreshold swing (SS) with the variation of electrolyte gate voltage. This is due to the lack of bandgap in graphene as illustrated through the band structure of graphene in the inset figure. Change in pH of the solution from 3 to 4 leads to significantly lower change in current compared to that in MoS2-based pH sensor. The thickness of the graphene used in this experiment is around 7 nm. (B) Comparison of sensitivity of graphene and MoS2based field-effect transistor (FET) biosensors for the same change in pH from 3 to 4. Sensitivity of MoS2-based FET biosensor is 74-fold higher compared to that based on graphene.

suppress tunneling leakage currents, which degrades SS at bandgaps below around 0.4 eV. A combination of both of these factors leads to excellent SS in case of MoS2. Even with a very thick (35 nm) dielectric, MoS2 with similar thickness as graphene provides an excellent SS of 150 mV dec21. Hence graphene provides much lower sensitivity of 2.6 compared to that (193) achieved by MoS2 (Fig. 9.7B) for the same change in pH of the electrolyte (from pH of 3 to pH of 4). Graphene-based pH sensor reported in literature [15] has shown a sensitivity of about 12 for a pH change of 1.1. In Ref. [15], the electrolyte is directly in contact with the graphene channel (without any dielectric layer) in which case the sensing mechanism is complicated and, in addition, for the detection of biomolecules there is the possibility of nonspecific interactions. Moreover, since we measured the MoS2 and graphene devices with same dielectric thickness and under similar conditions, our values provide a more appropriate comparison. Note that opening a bandgap in graphene remains extremely challenging [53
56]. On the other hand, while the other popular form of carbon—CNTs—possess bandgap, their application as biosensors is severely limited due to the issues with integrability as well as chirality [11]. The minimum detection limit of graphene is also fundamentally constrained due to its limitation of SS as explained below. Considering the minimum change in current (with respect to the initial current) required for detection ability as Ξ, we can derive that the change in surface

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FIGURE 9.8 Minimum detectable change in the surface potential of the dielectric for a field-effect transistor (FET) biosensor as a function of its subthreshold swing (SS). Minimum detection limit degrades substantially with increase in SS.

potential (Δφmin) that is required to produce this required current change is given by: Δϕmin 5 SSlog10 ðΞ 1 1Þ:

(9.4)

This is the minimum change in surface potential needed for the detectable signal and, as is clear from Eq. (9.4), is directly proportional to the SS. Therefore, an FET biosensor with lower SS requires a lower Δφmin (Fig. 9.8) and hence a lower change in pH (or lower number of biomolecule conjugations) for detectable signal and thereby a lower detection limit. With an MoS2-based FET biosensor it is possible to obtain the ideal SS of 60 mV dec21, while for graphene, due its lack of bandgap, SS will be around 1000 or higher. Thus as per the discussion above on the relation of SS and detection limit, the minimum detection limit of graphene is also much worse than that of MoS2 (Fig. 9.9). In the case of reduced graphene oxide (rGO), variability, lack of precise control of bandgap, and presence of defects are major issues [57], which severely limit the performance. In-depth understanding of the surface modification reactions for bandgap modulation of rGO is still lacking. The uncontrollable bandgap together with the low purity of rGO leads to very low sensitivity of rGO-based biosensors as is evident from Table 9.2. Note that MoS2 not only possesses ultrathin nature and considerable bandgap but also has pristine interfaces free of dangling bonds, which in combination, leads to its ultrahigh sensitivity.

9.2.2 Work-Function Modulated Gas Sensor TMDs are not only highly promising for biosensing, their large surface-to-volume ratio make them a potential candidate as gas sensor

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FIGURE 9.9 Minimum change in surface potential that is detectable by MoS2 fieldeffect transistor (FET) biosensor and graphene FET biosensor.

TABLE 9.2 Sensitivity of rGO-based FET biosensors from previous literature. For sensing similar and even higher concentration of molecules or pH change, sensitivity of graphene (rGO) based FET biosensors are much lower than that of MoS2 Reduced graphene oxide(rGO-) based FET biosensors

Molecule detected

Molar concentration

Sensitivity

Adv. Mater. 22, 3521
3526 (2010)

IgG

12

 11

ACS Nano 4 (6), 3201
3208 (2010)

Dopamine

2 mM

 0.75

ACS Nano 5 (3), 1990
1994 (2011)

Ca21

10 μM

 11

ACS Nano 6 (2), 1486
1493 (2012)

Vascular endothelial growth factor (VEGF)

100 fM

 0.3

Nanoscale 4, 293 (2012)

DNA

100 nM

2

material. Moreover, functionalization using noble metallic nanoparticles (NPs) can also open up new avenues for gas-sensing [58
60] applications, as has been demonstrated in the case of nanotubes/nanowires or graphene oxide. Metallic NPs can be used to adjust the threshold voltage of TMD-based FETs, which can be useful for digital applications. Apart from digital applications, the modulation of work-function of metallic NPs can also be employed as the transduction mechanism for building effective gas sensors. As an example, we configured an MoS2based FET functionalized with Pd NPs as a gas sensor for sensing hydrogen gas. Pd can adsorb hydrogen, which leads to a change in its

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FIGURE 9.10 (A)Real-time measurement of current of MoS2 field-effect transistor (FET) without any nanoparticles (NPs). Negligible change in current was observed when the device was exposed to hydrogen (at time 5 20 min). (B) Real-time measurement of current of the same MoS2 FET after incorporation of Pd NPs. Current increases substantially upon exposure to hydrogen (3 ppm at time 5 45 min) from 0.2 μA to about 1 μA.

WF [61]. The doping effect and hence the electrical characteristics of the MoS2 FETs are dependent on the WF of the metallic NPs. Hence we can detect change in real time in the WF of Pd NPs due to adsorption of hydrogen through the change in current in a Pd NP functionalized MoS2 FET. Fig. 9.10A shows the current in an MoS2 FET before the incorporation of any NPs. We observed negligible change in device current upon exposure to hydrogen (3 ppm). The same device was then incorporated with Pd NPs and measured again in real time (Fig. 9.10B). In both the cases, the gate voltage was chosen to make the FET operate in the subthreshold region for obtaining maximum sensitivity [9,10,62
64]. The current level was seen to increase substantially upon exposure to the same hydrogen level as before. This is because adsorption of hydrogen led to a decrease in the Pd WF [63], thus a decrease in the p-type doping and hence an increase in the current level of the ntype MoS2 transistor. Previously, sensitivity (defined as the ratio of change in conductivity/current to the initial conductivity/current) of much less than 1 was obtained at room temperature for 3 ppm of hydrogen gas exposure by using bulk MoS2 [65]. In this work, for the same hydrogen concentration, sensitivity of about 5 is obtained at room temperature. This improvement is due to the use of few-layer MoS2 (8-nm thick) and biasing the device in subthreshold region.

9.3 FUNDAMENTAL LIMITATION OF ELECTRICAL SENSORS AND THE SOLUTIONS In previous sections we discuss the advantages of 2D TMDs as a channel material in sensing applications. The dependence of sensitivity

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on SS is also derived (Eq. 9.2). From this equation, it is clear that the fundamental limitation on the minimum achievable SS also poses a fundamental limitation on the sensitivity of conventional FET- (CFET-) based biosensors, irrespective of the channel material. While the relation between sensitivity and SS can be understood easily from intuition, it is not quite intuitive that the thermionic SS also limits the response time of the biosensors; we will discuss this effect below. In the following sections, we propose and theoretically demonstrate that these limitations can be overcome by employing steep transistors with subthermionic SS, focusing on comparatively less disruptive technologies namely, tunnel FET (TFET) and impact-ionization MOS (I-MOS).

9.3.1 Tunnel Field-Effect Transistor
Based Biosensor The working mechanism of TFET-based biosensor (Figs. 9.11) is similar to that of TFETs used for digital applications. Before the attachment of biomolecules to the sensor surface, the tunneling barrier between source and channel is high (width of the barrier is depicted by the length of the blue arrow) and hence the current in TFET is low. After biomolecule-receptor conjugation, due to the charges present in the biomolecules (positive charge is assumed here), the bands in the channel bend down, leading to a decrease in the tunneling barrier (width of the barrier is depicted by the length of the brown arrow) and hence, increase in the tunneling current. Thus the biomolecules can be detected by monitoring the change in current through the TFET biosensor device. Here we establish the supremacy of a TFET biosensor compared to that based on CFETs. We present extensive numerical simulations based on nonequilibrium Green’s function formalism for accurate results as well as analytical solutions with the aim of providing easy physical insights. The modeling scheme can be divided into two major parts. The

Energy

Before biomolecule conjugation After biomolecule conjugation

Conduction band Valence band

Source Channel

Drain

FIGURE 9.11

Schematic diagram depicting the working mechanism of a tunnel fieldeffect transistor (TFET) based biosensor.

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first part deals with the kinetics of biomolecules within the electrolyte, their capture by the receptors, and thereby the development of surface potential (φbio) on the oxide in the presence of electrostatic screening by the ions present in the electrolyte. The second part deals with the electrical response of the TFET or the change in tunneling current with the change in the surface potential developed due to attachment of the biomolecules. Here we focus on 1D structures for computational efficiency. However, the conclusions derived are general and can also be applied to 2D or 3D structures. First, we deal with the first part of the modeling scheme, that is, derivation of surface potential (φbio) due to the binding of charged biomolecules by receptors. The biomolecule-receptor conjugation occurs through two processes [66]. The first process involves the diffusion of the biomolecules to the oxide surface, which has been functionalized with specific receptors and is described by the equation dρ 5 Dr2 ρ; dt

(9.5)

where ρ is the concentration and D is the diffusion coefficient of the biomolecules in the solution. Second process involves the capture of the biomolecules by the receptors and is described by the equation dNbio 5 kF ðN0 2 Nbio Þρs 2 kR Nbio ; dt

(9.6)

where Nbio is the surface density of conjugated receptors or in other words, surface density of the captured biomolecules, N0 is the surface density of receptors used to functionalize the surface of the oxide, ρs is the concentration of the biomolecules on the surface of the oxide, kF is the capture constant and kR is the dissociation constant. Using the above two equations, the surface density of charge due to attached biomolecules on the sensor surface can be calculated [66]. Now the surface charge formed on the sensor surface attracts ions within the electrolyte, which forms a second layer of charge (of opposite polarity). This second layer electrostatically screens the first layer and hence decreases the effective potential developed on the oxide surface. This double-layer charge density can be calculated using the nonlinear Poisson
Boltzmann equation, which for a 1-1 electrolyte is given by [67]  KB T qϕ q X 2 sinh 2r ϕðrÞ 1 zi δðr 2 ri Þ: (9.7) 5 KB T εw i λDH 2 q Here λDH denotes the Debye
Huckel screening length and is given by

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qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi εw KB T=2q2 I0 Navo ; where εw is the dielectric constant, I0 is the ion concentration of the electrolyte, Navo denotes the Avagadro’s number, zi is the partial charge, and ri is the location of the atoms within the biomolecule. Finally, φbio can be found by equating the surface charge on the oxide due to the conjugated receptors to the sum of the charge in the electrolyte double layer and the charge developed within the semiconductor 1D structure [41,66]. Next, we discuss the second part of modeling scheme and it is this part that dictates the critical differences between the CFET- and TFETbased biosensors. The analytical formula for band-to-band tunneling (BTBT) current (IBTBT) can be derived and used to obtain the sensitivity of the biosensor, which is defined as

 (9.8) Sn 5 IBTBT ðϕ0 1 ϕbio Þ 2 IBTBT ðϕ0 Þ =IBTBT ðϕ0 Þ; where φ0 denotes the initial surface potential on the oxide before the attachment of biomolecules. In above equation, it is implicit that φ0 is tuned such that the current is dominated by the source-channel BTBT and the energy window ΔE $ 0. The TFETs exhibit ambipolarity and for ΔE , 0 the current is mainly dominated by channel-drain tunneling. Hence to avoid undesirable ambipolar effects, it is required to tune φ0 such that the operational mode of the biosensor always remains in the regime where source-channel current dominates. Using Eqs. (3.28) and (9.8), the analytical formula for sensitivity can be derived as ! pffiffiffiffiffi π 2q m1=2 EG 3=2 λϕbio ϕbio   Sn 5 exp  11 2 1: (9.9) ϕ 0 2 EG ¯h 2ϕ0 2 EG 2ϕ0 1 2ϕbio 2 EG The above analytical formula provides important insights regarding the dependence of sensitivity on the initial surface potential φ0. We can see that the sensitivity increases as φ0 is decreased (keeping ΔE $ 0). This is because, for TFETs, the rate of increase in current with gate voltage is higher for smaller values of ΔE (and thus for smaller values of φ0) giving rise to increased sensitivity at lower values of φ0. Note that the small value of ΔE indicates TFET operation in the subthreshold region. Thus Eq. (9.9) indicates that in order to achieve high sensitivity, the TFET biosensor should be operated in the subthreshold regime. Eq. (9.9) also provides direct physical insights regarding the dependence of the sensitivity on the bandgap of the material. As is evident from the equation, sensitivity increases with an increase in bandgap. This is because of the decrease in the current before the capture of biomolecules, that is, IBTBT(φ0) with an increase in bandgap.

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349

For TFETs with a relatively large bandgap materials or employing asymmetric design techniques at source and drain to reduce ambipolarity, φ0 may be tuned so that the current is mainly dominated by the relatively smaller reverse-biased P-I-N junction current (Irev) and the sensitivity will be given by   (9.10) Sn 5 IBTBT ðϕ0 1 ϕbio Þ 2 Irev ðϕ0 Þ =Irev ðϕ0 Þ: In this case, the sensitivity will increase with decreasing bandgap at the source-channel junction due to the exponential increase in IBTBT(φ0 1 φbio). The whole modeling scheme is represented through a diagram in Fig. 9.12. The subthreshold regime forms the optimal sensing domain not only for TFET biosensors as discussed above but also for the conventional FET biosensors [41]. CFETs suffer from a fundamental limitation on the minimum achievable SS of [KBT/q ln(10)] due to the Boltzmann tyranny effect where KB is the Boltzmann constant and T is the temperature. However, the TFETs overcome this limitation due to the Fermi-tail cutting by the bandgap of the semiconductor. The charged biomolecules essentially produce a gating effect on the semiconductor channel. Hence the change in current in TFET biosensors, because of their smaller SS, is substantially higher than that for CFET biosensors in the subthreshold region for the same surface potential developed due to attachment of biomolecules (φbio) as illustrated in Fig. 9.13A. Fig. 9.13B shows the current as a function of the drain voltage for both CFET and TFET biosensors before and after the biomolecule conjugation. We can observe that for similar currents in both biosensors before biomolecule conjugation, the current in TFET biosensors can be more than two orders of magnitude higher than that in CFETs after the attachment of the biomolecules, which obviously indicates significant increase in the sensitivity. Comparison of the performance of CFET and TFET biosensors, for biomolecule as well as for pH sensing, clearly shows that the sensitivity of TFET biosensors can surpass that of CFET biosensors by several orders of magnitude (Fig. 9.14A and B). The dependence of Sn on SS is depicted by Eq. (9.2), which depicts the strong relation between the two. Thus TFETs can harness the benefits of the substantial increase in sensitivity (up to more than four orders of magnitude) with decreasing SS and lead to ultrasensitive biosensors while CFET biosensors are strictly restricted to a higher limit on the maximum achievable sensitivity as highlighted in Fig. 9.15. In the following discussions, we show that TFET biosensors not only lead to a substantial increase in sensitivity, but also provide significant improvement in terms of the response time, which is defined as the

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FIGURE 9.12 Diagram representing the modeling scheme for tunnel field-effect transistor (TFET) based biosensors.

time required to obtain a desired sensitivity (more specifically the time needed to capture a certain number of biomolecules in order to achieve a desired change in electrical signal). First, we derive an analytical formula for the surface density of biomolecules (Nbio) that is required to be captured in order to obtain a particular sensitivity. Nbio can be related to φbio as ((1/Cox 1 1/CNW)21 1 CDL)φbio [41] where Cox, CNW, and CDL represent the oxide, quantum, and electrolyte double-layer capacitance respectively. From Eq. (9.2), we can write φbio as SSavg 3 log10(Sn 1 1) where SSavg denotes the average value of SS over the range φ0 to

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FIGURE 9.13

(A) Current as a function of surface potential developed due to biomolecule conjugation (φbio) at a drain voltage (VD) of 0.3 V. Due to the smaller SS in tunnel field-effect transistors (TFETs), they can lead to higher change in current compared to CFETs for the same change in surface potential. (B) Current as a function of drain voltage before and after biomolecule conjugation for φbio 5 0.1 eV.

FIGURE 9.14

(A) Sensitivity for sensing of biomolecules as a function of biomolecule concentration. (B) Sensitivity for pH sensing for different pH values. φ0 is tuned for tunnel field-effect transistor (TFET) and CFET so they operate in the subthreshold regime. The bandgap and the effective masses used in the simulations are 0.4 eV and 0.15 m0, respectively (where m0 denotes the mass of a free electron) and the diameter of nanowire is taken as 5 nm.

(φ0 1 φbio). In the subthreshold region (1/Cox 1 1/CNW)21 1 CDL  CDL and hence, Nbio can be written as   πεw RNW K1 RNW =λDH SSavg   Nbio 5 (9.11) log10 ðSn 1 1Þ: λDH K0 RNW =λDH In the above equation we have used the expression for CDL as πεwRNW/λDH 3 K1(RNW/λDH)/K0(RNW/λDH) [41]. Here, λDH denotes the Debye
Huckel screening length, εw is the dielectric constant of the electrolyte, RNW is the radius of the 1D structure, and K0 and K1 are the

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FIGURE 9.15

Sensitivity as a function of subthreshold swing (SS) averaged over four orders of magnitude of current for both CFET- and tunnel field-effect transistor (TFET) based biosensors. Surface potential change due to attachment of biomolecules (φbio) is taken to be 0.1 V. Sensitivity increases substantially with the decrease in SS. The shaded region shows the sensitivity values for CFET biosensors indicating that there is a restriction on the maximum achievable sensitivity since the SS in CFETs cannot be minimized below 60 mV dec21 at room temperature. All simulations in this figure are performed through self-consistent solutions of Poisson’s and Schrodinger’s equations within the framework of nonequilibrium Green’s function (NEGF) formalism.

FIGURE 9.16 (A) Surface density of biomolecules (Nbio) required to be attached to the sensor surface for both CFET and tunnel field-effect transistor (TFET) biosensors in order to achieve the same sensitivity value in both, as a function of subthreshold swing (SS). It is observed that Nbio decreases significantly with a decrease in the SS. (B) 2D color map showing the response time (in seconds) of the biosensor as a function of the SS and the molar concentration of the biomolecules in the solution.

zero- and first-order modified Bessel functions of the second kind. It is clear that Nbio decreases with decreasing values of the swing (Fig. 9.16A). This can easily be explained by the fact that for a better response of the sensor to the gating effect, the lower would be the

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required change in oxide surface potential (φbio) and hence in the required Nbio to achieve the same sensitivity. The response time (tr) can be related to Nbio [68]. Now, using Eq. (9.11), the dependence of response time to the SS is derived as   πεw RNW 2 K1 RNW =λDH log10 ðSn 1 1Þ SSavg   tr 5 3 ; (9.12) ρ0 λDH K0 RNW =λDH D Navo where ρ0 is the concentration of biomolecules, Navo denotes the Avagadro’s number, and D is the diffusion coefficient of the biomolecules in the solution. Since the CFETs are plagued by the Boltzmann tyranny effect, there are fundamental limitations to the minimum response time that can be obtained from biosensors based on them. This lower limit in CFETs can be derived using (9.12) as   πεw RNW 2 K1 RNW =λDH lnðSn 1 1ÞKB T   tr2min 5 : (9.13) λDH K0 RNW =λDH D Navo ρ0 In Fig. 9.16B, the response time is plotted as a function of both the SS and the biomolecule concentration in the electrolyte. Since TFET biosensors are not bound by a lower limit on the SS, they can be highly advantageous for reducing response time (up to more than an order of magnitude) and detecting biomolecules at low concentrations. Note that in this work we have presented the results for n-TFET assuming a positive charge of the biomolecules. In general biomolecules such as DNA possess negative charge. However, this sign change does not affect the general discussion and results presented here.

9.3.2 Impact-Ionization-MOS-Based Biosensor Here we show that the phenomenon of impact ionization can be leveraged to beat the limits of conventional FET biosensors, thereby leading to an ultrasensitive and fast electrical biosensor [64]. The structure of the proposed nanowire-based impact-ionization MOSFET biosensor for detecting positively charged biomolecules is shown in Fig. 9.17. While the most commonly used acronym for impactionization MOSFET is I-MOS, we will use impact-ionization field effect transistor (IFET) as the acronym here, in conjunction with CFET and TFET. The ends of the nanowire are doped to form a P1-I-N1 diode, which is operated in the reversed-bias mode. Portion of the I-region toward P1 source is covered with thick oxide to prevent the influence of biomolecules in that region, which we call the protected region (PR). This region is needed due to the requirement of a threshold length for impact ionization to occur as well as to prevent band-to-band tunneling

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FIGURE 9.17 Schematic diagram of a nanowire-based IFET biosensor for the detection of positively charged biomolecules. A nanowire structure is chosen to provide high electrostatic control and large surface-to-volume ratio. The inset figure shows the source/drain and channel doping scheme, the protected region (PR) and the sensing region (SR) in the channel. For detection of negatively charged biomolecules, the position of PR and SR should be interchanged.

from valence band of source to conduction band of I-region. The rest of the I-region is covered with a thin oxide for effective gating effect through charged biomolecules, and we call it the sensing region (SR). If the source is biased at a negative voltage such that the reverse bias is below the avalanche breakdown voltage, no impact ionization occurs before biomolecule conjugation (Fig. 9.18A). The attachment of charged biomolecules in SR increases the effective electric field in PR activating impact ionization (Fig. 9.18B). Occurrence of impact ionization leads to a sharp increase in current or in other words, to ultralow SS as shown in Fig. 9.19. By altering the source voltage this sharp increase in current can be made to occur at very small values of surface potential in SR developed due to biomolecule conjugation (Fig. 9.19). While accurate results can be obtained through numerical simulations using TCAD tools, analytical formalism is necessary to gain a physical insight. Hence in the following discussion we focus on deriving analytical formula for sensitivity using a simplified 1D model. The modified 1D Poisson equation for the PR and SR can be written as Eqs. (9.14a) and (9.14b), respectively: d2 ψp ðxÞ dx2

50

(9.14a)

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(B)

Energy

PR (Lp) Source

Conduction band SR (Ls) Drain

Valence band x Before biomolecule conjugation

e1

Electron-hole pairs generation

e3 Energy

(A)

355

e1 e2

h3

SR

h1 h2 h1

After biomolecule conjugation

FIGURE 9.18 Band diagram (A) before and (B) after biomolecule conjugation in IFET biosensor. The source is biased at a negative voltage, slightly below the breakdown voltage. Hence no impact ionization occurs before biomolecule conjugation. Attachment of the biomolecules in the SR leads to increase in electric field in PR. (B) Now an electron e1 can gain enough energy from the electric field to knock out an electron from the valence band creating an electron (e2) and hole (h2). Similarly a hole h1 can lead to generation of an electron (e3) and hole (h3). Thus carriers get multiplied leading to impact ionization.

FIGURE 9.19 The black curve (involving left and top axis) shows the current as a function of source voltage (Vs) before biomolecule conjugation. Breakdown occurs when absolute value of Vs is little higher than 5.2 V (marked as VBD in the top axis) leading to a sharp increase in current. If the Vs is kept # 5.2 V, the breakdown can be made to occur through the surface potential on the oxide developed due to biomolecule conjugation (φbio) as shown by the blue, red, and green curves (involving left and bottom axis). The value of φbio at which the breakdown occurs depends on the applied Vs and are shown in the figure as φbio1, φbio2, and φbio3 for Vs equal to 5.2, 5.15, and 5.1 V, respectively. It is clear that the φbio required for breakdown decreases as source is biased closer to the breakdown point.

d2 ψs ðxÞ ψs ðxÞ 1 ϕ 2 5 0: dx2 λ2

(9.14b)

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interface in the SR, x is the direction from source to drain as shown in Fig. 9.18A and is taken to be 0 at the source-PR junction. λ is defined as the natural length scale [48]. The band-bending in the source/drain regions are neglected, which is a valid assumption for highly doped regions. The semiconductor-oxide interface potential at the drain-SR junction is taken as the reference point and hence set to 0, and that at source-PR junction is defined as Usrc. Thus ψs(Lp 1 Ls) 5 0 and ψp(0) 5 Usrc where Lp and Ls are the lengths of the PR and SR, respectively, as shown in Fig. 9.18A. The other two boundary conditions for solving the Poisson equations given by Eqs. (9.14a) and (9.14b) are obtained from the continuity equations between ψp and ψs  and their derivatives as  ψp(Lp) 5 ψs(Lp) and dψp ðxÞ=dxx5Lp 5 dψs ðxÞ=dxx5Lp . The solution of the potentials are given by

ψs ðxÞ

   ðUsrc 1 ϕÞcosh Ls =λ 2 ϕ x     1 Usrc (9.15a) ψp ðxÞ 5 2 λ sinh Ls =λ 1 Lp cosh Ls =λ       λðUsrc 2 ϕÞsinh Lp 1 Ls 2 x =λ 1 Lp ϕ cosh x 2 Lp =λ     5 λ sinh Ls =λ 1 Lp cosh Ls =λ    : λϕLp ϕ sinh x 2 Lp =λ     2ϕ 1 λ sinh Ls =λ 1 Lp cosh Ls =λ (9.15b)

Since impact ionization occurs in the PR, it is necessary to simplify the equation of semiconductor-oxide interface potential in that region given by Eq. (9.15a) in order to obtain a simplified equation for the impact ionization current. Note that the impact of SR has been intrinsically incorporated in Eq. (9.15a) through the factors φ, λ, and Ls. Using the condition Ls .. λ, which is the case in an electrostatically wellcontrolled device, Eq. (9.15a) can be simplified as ψp ðxÞ 5 2

ðUsrc 1 ϕÞx 1 Usrc : λ 1 Lp

(9.16)

Using Eq. (9.16), the electric field in the PR can be derived as Fp 5

ðUsrc 1 ϕÞ : λ 1 Lp

(9.17)

This electric field can be used to calculate the impact ionization coefficient α, which is defined as the number of electron-hole pairs generated by a carrier per unit distance traveled and is given by α 5 αN e2Fcrit =jFp j where αN is an empirical parameter and Fcrit is the critical electric field. For deriving a simplified analytical solution, αN and Fcrit for electron and

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holes is assumed Ð L to be similar, and thus the ionization integral M can be written as M 5 0 p α dx. Using Eq. (9.17) M can be derived as   src1ϕÞ  2Fcrit =ðUλ1L p : (9.18) M 5 Lp αN e The avalanche breakdown occurs when M reaches the value of 1. Now the potential φ can be divided into two parts: the initial potential φ0 which can be adjusted using the electrolyte reference electrode and the potential developed due to biomolecule conjugation φbio. The threshold value of the potential due to biomolecule attachment that is required for avalanche breakdown (φbio_th) can be derived by equating M to 1, and thus we obtain   Fcrit λ 1 Lp   2 Usrc 2 ϕ0 : (9.19) ϕbio th 5 ln α Lp For φbio , φbio_th, the current (Ii) is given by Irev/(1 2 M) where Irev is the reverse-biased P-I-N junction current. Thus we can write ðU 1ϕ 1ϕ Þ    src 0 bio  2Fcrit = λ1Lp Ii 5 Irev for ϕbio , ϕbio th : (9.20) 1 2 Lp αN e After avalanche breakdown the current in the IFET biosensor will behave like the conventional FET with an effective drain-to-source voltage (Vds_eff) equal to the potential at x 5 Lp at φbio 5 φbio_th. Using Eqs. (9.16) and (9.19), Vds_eff can be derived as Vds

eff

52

Fcrit Lp   1 Usrc : ln α Lp

(9.21)

Now the current (Ic) for φbio . φbio_th can be written as

 2 for ϕbio 2 ϕbio Ic 5 μW=ð2LÞC ϕ2 ϕ0 1ϕbio th 

 Ic 5 μW=Ls C ϕ 2 ϕ0 1 ϕbio for ϕbio 2 ϕbio

th

. Vds

 th

Vds

eff

2 Vds

eff

2

=2

th

, Vds

eff

(9.22a)



eff :

(9.22b)

Using the equations of current given by Eqs. (9.20) and (9.22a and 9.22b), analytical formulas for sensitivity can be derived where sensitivity is defined as the ratio of the change in current due to biomolecule conjugation to the initial current before conjugation. For φbio , φbio_th, Sn is derived as ðUsrc1ϕ Þ  ðUsrc1ϕ 1ϕ Þ   0 bio  2Fcrit = λ1Lp 0  2Fcrit = λ1Lp Sn 5 1 2 Lp αN e 1 2 Lp αN e 2 1: (9.23A)

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For φbio . φbio_th, Sn can be written as Sn 5

Ic 2 Ii : Ii

(9.23b)

From the analytical derivations we can see that the sensitivity is dependent on the initial condition of the IFET, which can be tuned by source and reference gate bias, thus modulating Usrc and φ0, respectively. These two knobs should be adjusted in such a way that the IFET biosensor is always below the breakdown potential before biomolecule conjugation. Also, it is clear from (9.19) that by proper tuning of Usrc and φ0, the threshold value of potential due to biomolecule conjugation can be reduced and thus a fewer number of biomolecules attaching to the biosensor surface will be able to cause a substantial increase in the current. Fig. 9.20A shows the results of source bias sweep calculated through TCAD simulations in IFET biosensor before and after the biomolecule conjugation for different values of biomolecule concentration (ρ0) in the electrolyte. The results of drain voltage sweep for a CFET biosensor is shown in Fig. 9.20B. Because of the sharp increase in current due to impact ionization in IFET, the current curves after biomolecule conjugation are distinctly distinguishable from the one before the conjugation even at very small values of ρ0. For CFET biosensors, on the other hand, there is very small change in current after conjugation as ρ0 is decreased. In Fig. 9.21A, the sensitivity is plotted as a function of ρ0 for CFET and two different bias points of IFET. When Vs and φ0 in IFET are

FIGURE 9.20 The current as a function of source bias in (A) IFET and (B) CFET biosensor before and after biomolecule conjugation for different values of biomolecule concentration (ρ0) in the electrolyte. Unless mentioned otherwise, all simulations for IFET biosensor are done for a silicon nanowire with a diameter of 30 nm with an enclosing oxide thickness of 3 nm, and the ionic concentration I0 is taken as 1025 M.

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FIGURE 9.21 (A) Sensitivity of both IFET and CFET biosensors as a function of biomolecule concentration (ρ0). Bias pt1 refers to the condition of IFET where Vs and φ0 are adjusted to obtain the minimum SS while bias pt2 refers to the condition when Vs is 0.05 V below that in bias pt1. The average SS of the IFET over three orders of magnitude of drain current is around 1.7 mV dec21. (B) Sensitivity as a function of ionic concentration (I0). While sensitivity decreases for both IFET and CFET biosensors with the increase in I0 due to electrostatic screening, the IFET biosensor still exhibits substantially higher sensitivity. In this case, IFET is at bias pt1.

adjusted to obtain the minimum SS (bias pt1), even low ρ0 can lead to sharp increase in current and hence very high sensitivity (around four orders of magnitude higher compared to CFET). However, if IFET is biased at lower Vs (bias pt2) high ρ0 is required in order for breakdown to occur. Once sufficient ρ0 is reached, current and hence sensitivity increases sharply. For higher values of ρ0, sensitivity at bias pt2 is higher (around six orders of magnitude higher compared to CFET) than that at bias pt1 (around 2.5 orders of magnitude higher compared to CFET) because of the lower initial current before biomolecule conjugation at bias pt1. Since high sensitivity at low biomolecule concentration is desirable, bias pt1 is preferable for IFET biosensor operation. As is clear from Fig. 9.21B, advantage of IFET biosensor over CFET is retained even when the ionic concentration of the electrolyte is increased (which increases the electrostatic screening by the ions). Fig. 9.22 shows the sensitivity comparison between IFET and CFET biosensors for pH sensing. The pH sensing is based on the change in surface charge due to protonation/deprotonation of the OH groups on the enclosing oxide surface, which depends on the concentration of H1 ions and hence on the pH value. The IFET biosensor can lead to around four orders of magnitude increase in sensitivity compared to a CFET for pH detection. Apart from sensitivity, another critical parameter for gauging the performance of the biosensors is the response time. Response time (tr) is defined as the time required to obtain a desired sensitivity. Before a target analyte molecule can bind at the sensor surface and electrostatically

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FIGURE 9.22 Sensitivity comparison between IFET and CFET biosensors for pH sensing as a function of pH values. Here, IFET is at bias pt1.

modulate the channel conductance, the molecule must diffuse from the bulk solution to the sensor surface. This diffusion process takes time and sets lower limits on achievable detection times at a given analyte concentration [69]. Hence a more specific definition of response time is the time needed to capture a certain surface density of biomolecules (Nbio) [68] to achieve a desired change in electrical signal. Fig. 9.23A illustrates that tr is directly proportional to the required Nbio and inversely proportional to ρ0. Fig. 9.23B shows that the IFET can lead to significant reduction in response time compared to CFET. This effect can be understood in the following way. Extremely low SS of IFET implies that to obtain the same change in current and hence the same sensitivity, the required change in surface potential (φbio) is much lower in IFET compared to that in CFET. Since tr is directly proportional to Nbio, which is again directly proportional to φbio, decrease in φbio leads to decrease in Nbio and consequently to reduction in tr. From Fig. 9.23B we can also conclude that within a same desired response time, IFET can detect biomolecules at substantially lower biomolecule concentration. Note that TFETs employing interband tunneling can also lead to a sharper increase in current or lower SS compared to CFETs as discussed in the previous section, and hence is attractive as a sensor for biomolecules [10,62] as well as gaseous species [63]. The best reported average SS value over four decades of current at a low voltage of 0.1 V for TFETs is 36.5 mV dec21 [70] and further improvement is expected. The phenomenon of impact ionization has been shown to lead to SS as low as 72 μV dec21 [71]. The IFETs based on silicon, however, have a very high breakdown voltage and application of novel technology is required to lower the operating bias [72]. From an ultralow power perspective,

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FIGURE 9.23 (A) The color map showing the average response time (tr) in seconds of the biosensor as a function of the required surface density of biomolecules (Nbio) on the oxide surface to achieve the desired sensitivity, and the concentration (ρ0) of the biomolecules in the solution. tr increases as ρ0 is decreased as it takes more time to capture the biomolecules when its concentration in the solution is low. tr increases with increase in Nbio. This is because if the required surface density of biomolecules is more, it will take more time to reach that value. (B) Average response time as a function of the biomolecule concentration (ρ0). For the same value of ρ0, IFET can lead to significant reduction in tr. If the response time is kept constant, then within the same desired tr, IFET can lead to detection at much lower biomolecule concentrations.

the TFET biosensors remain attractive. Since IFETs suffer from reliability issues, they may be more promising as dispensable sensors. Here though the results are presented for silicon nanowire
based IFETs, and the general discussion is valid for other materials and structures as well.

9.3.3 Tunnel Field-Effect Transistor
Based Gas Sensor In this section, we propose a gas sensor that leverages the band-toband-tunneling current-injection mechanism of TFET to achieve a much superior performance under ambient conditions compared to conventional FETs [63]. The results are discussed in terms of two important sensing elements: metal (Pd/Pt) and conducting polymers as the gate material (Fig. 9.24). First, we discuss the gas sensors with metallic gate as sensing element. Thick, continuous metallic gate can be used to sense hydrogen [73,74]. The transduction mechanism involves dissociation and adsorption of hydrogen molecules at the metal surface and thereby diffusion of some atomic hydrogen into metal which form dipoles at the interface changing the gate work function (WFG) (Fig. 9.25A). This process pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi depends on the flux of gas Ψ given by Ψ 5 P= 2π mg KB T, heat of

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FIGURE 9.24 The schematic diagram of a field-effect transistor gas sensor
based on SOI structure with metallic gate (A) and organic conducting polymer gate (B) as the sensing element. For a conventional n-type MOSFET-based sensor, the doping in source, channel, and drain are N 1 , P, and N 1 , respectively, while for that based on n-type tunnel field-effect transistor (TFET), the sequence is P 1 , I, and N 1 . Continuous Pd and Pt film is explored for metallic gates, while polyaniline and polypyrrole are discussed for polymer gates. Semiconductor material is taken to be silicon. Note that we use the term MOSFETs generically to specify conventional FETs even in case of a polymer gate.

FIGURE 9.25 (A) The schematic diagram of dipoles at the interface of metal and oxide layer. (B) Change in work-function of Pd and Pt metal gate as a function of hydrogen gas pressure. Interface concentration of hydrogen sites and sticking coefficient is lower for Pt [75] compared to Pd leading to lower values of ΔWFG for same hydrogen pressure. Note that ΔWFG is negative and absolute values have been plotted here.

adsorption at surface ΔH  s, and interface ΔHi, which follows the Temkin isotherm as ΔHi 5 ΔHi0 1 2 αξ i where α 5 qμNi =ðε ΔHi0 Þ, P is gas pressure, mg is gas molecular mass, KB is Boltzmann constant, T is temperature, ΔHi0 is initial heat of adsorption, ξi is interface coverage of

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hydrogen, Ni is concentration of interface hydrogen sites, q is elementary charge of an electron, μ is effective dipole constant, ε is permittivity. The change in WF is given by [74]   ΔWFG 5 2 ξ i Ni μ =ε:

(9.24)

Fig. 9.25B shows the ΔWFG of metal gate as a function of hydrogen gas pressure. The ΔWFG can be employed to bend the bands in a TFET and hence modulate its current. A nonlocal 2D model based on selfconsistent solutions of Poisson’s and Schrodinger’s equations including the effect of phonons is used for detailed device simulations. At the same time, to provide better physical insights, an analytical expression for sensitivity (Sn) is derived using a simplified 1D model. The 1D Poisson’s Eq. (9.25) is solved to obtain the potential in the channel assuming high-source doping and hence neglecting band bending in source. d2 ϕi ðxÞ ϕi ðxÞ 1 VG 2 WFG 1 WFS 2 5 0: dx2 λ2

(9.25)

Here, φi is potential at semiconductor-oxide interface, WFS is semiconductor work function, VG is gate voltage, and λ is natural length scale [48]. Then, calculating tunneling probability using the WKB approximation and the two-band approximation, the band-toband-tunneling current (IBTBT) is derived using Landauer’s formula [10,50] as  pffiffiffi  pffiffiffi IBTBT ðWFG Þ 5 2q2 =h exp 2π qm1=2 EG 3=2 λ= 2¯hð2ðVG 2 WFG 1 WFS Þ 2 EG Þ :  Ð V -WF 1WFS 2EG  fS 2 fD dE 3 0G G (9.26)

Here, h is Plank’s constant, m* is carrier effective mass, EG is bandgap of semiconductor, fs is Fermi function at source, fd is Fermi function at drain. Sensitivity (Sn) is defined as the ratio of change in current after gas adsorption to the initial current before gas adsorption and is given by

 Sn 5 IBTBT ððVG 2 WFG0 Þ 2 ΔWFG Þ 2 IBTBT ðVG 2 WFG0 Þ =IBTBT ðVG 2 WFG0 Þ: (9.27) Using Eqs. (9.24), (9.26), and (9.27), Sn of TFET gas sensor is derived as

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FIGURE 9.26 (A) Color map showing the importance of gate bias and operation regime of field-effect transistor (FET) on performance of MOSFET (A) and tunnel fieldeffect transistor (TFET) (B) gas sensor. Pd is used as a gate material. For convenience of plotting and understanding, the effective gate voltage is offset from the actual gate voltage such that for MOSFET, VGeff is taken to be 0 at 0.35 eV lower than the threshold voltage. For TFET VGeff 5 0 where the SS is minimum. At any value of gas pressure, sensitivity of MOSFET gas sensor remains almost constant for lower values of VGeff due to the almost constant SS of MOSFET. However, with increase in VGeff, the effect of gate decreases as MOS crosses the subthreshold region and hence the sensitivity degrades. For TFET gas sensor, sensitivity keeps on increasing as VGeff is decreased due to the improvement in SS. By effective tuning of gate bias, sufficient improvement in sensitivity can be achieved through TFET gas sensor compared to that of MOSFET gas sensors. 0

1 pffiffiffiffiffi 1=2 3=2   π 2q m E λ ξ N μ =ε G i i  A   Sn 5 exp@ ¯hð2ðVG 2 WFG0 1 WFS Þ 2 EG Þ 2ðVG 2 WFG0 1 WFS Þ 1 2 ξ i Ni μ =ε 2 EG 0 1 :   ξ N μ =ε i i @ A 3 11 21 VG 2 WFG0 1 WFS 2 EG (9.28)

In Fig. 9.26 A and B, Sn is plotted as a function of the gate bias and the gas pressure for MOSFET and TFET respectively. It is observed that for both, maximum Sn is obtained in the subthreshold region. This can be understood since the highest effect of gate occurs in the subthreshold region. In MOSFETs the SS is limited by the Boltzmann tyranny effect to [KBT/q ln(10)], which also puts severe limitations on the achievable sensitivity. TFETs overcome this limitation due to Fermi-tail cutting by the bandgap and hence can lead to significantly higher Sn for gas sensing as shown in Fig. 9.27. Now, we will focus on organic conducting polymer gate as a sensing element. Organic conducting polymers can be used to obtain selective detection of specific target gas molecules. The gas molecules form a charge transfer complex with the polymer matrix through the exchange of a fractional charge δ [76,77]. Depending on the sign (positive/ negative) of δ, the gas molecules behave as a secondary dopant

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FIGURE 9.27 Comparison of sensitivity of MOSFET and tunnel field-effect transistor (TFET) gas sensor both biased at VGeff 5 0.

(acceptor/ donor) and change the Fermi level and hence the bulk component of work function of the polymer (WFP). ΔWFP is given by [77] ! X ΔWFP 5 KB T=2δ ln P= k i Pi 1 1 ; (9.29) 



i

where δ 5 γ EF 2 χg , EF is Fermi level of polymer, χg is Mulliken electronegativity coefficient of gas, Pi is pressure of background nonspecific gases, ki is selectivity coefficients, WFP0 is work function before gas absorption, and γ is proportionality constant [76]. Using Eqs. (9.26), (9.27), and (9.29) analytical formula for sensitivity of TFET gas sensor is derived as 0 1 pffiffiffiffiffi 1=2 3=2 π 2q m EG λjΔWFP j A Sn 5 exp@ ¯hð2ðVG 2 WFPS0 Þ 2 EG Þð2ðVG 2 WFPS0 Þ 1 2jΔWFP j 2 EG Þ 0 1 ; j j ΔWF P A21 3 @1 1 VG 2 WFPS0 2 EG (9.30) where ΔWFPS0 is an initial work function difference between polymer and semiconductor. From Fig. 9.28A and B, it is clear that TFETs provide substantial increase in Sn compared to conventional FETs as shown for detection of NH3 and CH3OH with Polyaniline (PANI) and poly-pyrrole-tetrafluoroborate (PPTFB), respectively. The change in WF of polymers depends on its initial WF (WFP0), which varies with varying growth conditions [76]. If EF equals χg, no charge transfer takes place and ΔWFP50. We call this point the neutrality point (NP).

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FIGURE 9.28 (A) Change in work-function of polymer gate (B) and sensitivity comparison of tunnel field-effect transistor (TFET) and MOSFET sensors (C) as a function of gas pressure. Results are shown for sensing of ammonia with polyaniline (PANI) and methanol with poly-pyrrole-tetrafluor-oborate (PPTFB). The effect of nonspecific background P gases is captured through the term kiPi and is taken to be 10211 in both cases. Sensitivity of MOSFET and TFET gas sensor with polymer gate based on polypyrrole and p-polyphenylene for sensing hexane and chloroform as a function of initial work function of the polymer (versus the work function of the Au reference grid). TFET gas sensors lead to much higher sensitivity compared to those based on MOSFETs even in the region near the neutrality point.

tΔWFPt increases as WFP0 moves away from NP. For MOSFETs reasonable Sn is obtained only further away from NP (Fig. 9.28C) and thus requires high control of polymer growth conditions to achieve specific WFP0. In TFETs high Sn can be achieved even in regions near NP (Fig. 9.28C) In Fig. 9.29, Sn is plotted as a function of SS taking an example each from metallic gate as well as polymer gate as sensing element. We can observe that in both cases Sn increases substantially with the decrease in SS. This figure has important technological implications and shows that extremely high Sn can be achieved through implementation of low SS devices like TFETs in gas-sensor technology.

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FIGURE 9.29 Sensitivity as a function of average SS for both metallic gate (Pd for sensing hydrogen at pressure of 1029 Torr) as well as polymer gate (polyaniline (PANI) for sensing ammonia at pressure of 10212 Torr) as the sensing element.

To be applicable for practical applications, a sensor should possess high efficiency under ambient conditions in the presence of nonspecific background gases. P In the case of polymer film, it is clear from Eq. (9.29) that increase in kiPi term (which represents the effect of background gases) decreases ΔWFP. In the case of metallic gate, the presence of oxygen in air sufficiently reduces the surface and interface coverage of hydrogen (Fig. 9.30A) and hence the tΔWFGt(Fig. 9.30B) due to water formation and subsequent desorption reactions [78]. From Fig. 9.30C we can see that if the SS is reduced, the tΔWFGt required to achieve a desired sensitivity can be more than an order lower. Hence TFETs with low SS can be highly beneficial in detecting target gas molecules under atmospheric conditions where presence of nonspecific gases screen the change in gate WF. For stable operation, the influence of temperature (T) variations on the sensor performance should be minimal. T affects the sensor performance by influencing (1) interaction between gas and sensing element and (2) properties of semiconductor. Here, the effect of T is discussed taking the metallic gate as an example and it is seen that the increase in T leads to the reduction in hydrogen coverage and hence the tΔWFGt (Fig. 9.31A). We define the T affectability (AT) as

 AT 5 I ðT 1 ΔT Þ 2 I ðTÞ =I ðT Þ:

(9.31)

Analytical equations of T affectability are derived for both MOSFET (AT-MOS) and TFET (AT-TFET)-based gas sensors and are given by

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FIGURE 9.30 (A) The surface (ξs) and interface (ξi) coverage of hydrogen in Pd without and in the presence of oxygen (152 Torr) as a function of hydrogen pressure. (B) Effect of varying oxygen pressures (1026
152 Torr) ontΔWFGt. ξ s, ξ i andtΔWFGt decrease in the presence of oxygen. (C) The change in gate work function that is required to obtain a desired sensitivity (here the desired value is taken to be 0.05) is plotted as a function of the average subthreshold swing. The required tΔWFGt decreases strongly with a decrease in SS. Hence, the same sensitivity can be achieved at much lower values of tΔWFGt for low SS devices like tunnel field-effect transistor (TFET).

0

1   =ε 1 Δφ 2 N μ Δξ 2 ΔE =2 2 ΔEA i G i b A AT2MOS 5 exp@ 2 KB ðT 1 ΔTÞ=q 0  1 ΔT=T V 2 WF 2 φ 1 E =2 1 EA 2 qNa t =C G G G Si ox b A21 3 exp@ 2 KB ðT 1 ΔTÞ=q (9.32)



 pffiffiffi pffiffiffi AT-TFET ðWFG Þ 5 exp 2π qm1=2 λ= 2¯h 3 ΔFnc ðTÞ 2 1

(9.33a)

Fnc ðTÞ 5 EG ðTÞ3=2 =ð2ðVG 2 WFG ðT Þ 1 WFS ðTÞÞ 2 EG ðTÞÞ:

(9.33b)

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FIGURE 9.31 Effect of temperature (T) variations on gas-sensor performance. tΔWFGt decreases with T due to decrease in the surface (ξs) and interface (ξ i) coverage of hydrogen. (A) AT-MOS is dominated by change in threshold voltage (Vth) through modification in interaction term between gas and sensing element (SE) (μNiξi/ε) and semiconductor properties (ϕb, EG, and EA) and the change in SS. (B) AT-TFET is dominated by change in tunneling probability through modification of WFG (due to gas-SE interaction) and semiconductor bandgap (C).

Here ϕb is the energy difference between Fermi level of semiconductor and its intrinsic Fermi level, EA is electron affinity of semiconductor, Na is channel doping concentration, tsi is silicon thickness, and Cox is gate capacitance. T influence MOSFET subthreshold current mainly through change in the threshold voltage (Vth) and SS. It is well known that in MOSFETs, Vth decreases as T increases. However, the MOSFET gas sensor exhibits a counterintuitive opposite trend (Fig. 9.31B). This behavior can be explained by the increase in WFG(5WFG0 2 tΔWFGt) with T due to decrease in gas adsorption and hence tΔWFGt. The SS of MOSFET increases linearly with T. For a silicon TFET for digital applications, it has been experimentally demonstrated [79] that the dominant factor through which T influences the current is through increase in the tunneling probability (PBTBT) due to the decrease in bandgap (EG) (phonon emission is nearly independent of T). For TFET gas sensors, the situation is different and the factor through which T dominantly affects the current is given by Fnc(T) (Eq. 9.33b). We can

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FIGURE 9.32 AT is plotted for MOSFET (A) and tunnel field-effect transistor (TFET) (B) with T 5 250K and ΔT varying from 10K to 100K. AT is positive in MOSFET (current increases with T) due to the increase in SS compensated to some extent due to increase in increase in Vth. AT is negative in TFET due to the decrease in tunneling probability with T. Overall AT-TFET is much smaller than AT-MOSFET. Note that the isothermal point (IP) of MOSFET occurs in the linear region, which will lead to degraded sensitivity and IP will vary with gas adsorption. Hence a TFET-based gas sensor can offer much more stable operation under T variations compared to that based on MOSFETs.

observe that the increase in PBTBT due to the decrease in EG is offset by the increase in WFG term that occurs in the denominator of Fnc(T). Hence, PBTBT of TFET gas sensor decreases with T (Fig. 9.31C). From Fig. 9.32A and B, it is clear that the TFET has substantially lower AT than that of MOSFETs and hence it is much less vulnerable to T variations.

9.3.4 2D Materials for Steep Transistors In previous sections we discussed that low SS devices are promising for achieving ultrasensitive biosensors. In this section, we show that employing 2D materials as channel can be very beneficial for developing low SS TFETs. Recently, we developed a TFET with atomically thin channel which achieved subthermionic SS over four decades up to a low VDS of 0.1 V. We built this TFET by engineering the substrate, portions of which are configured as a highly doped semiconductor source and other portions are etched and filled with a dielectric for hosting the drain and gate metal contacts, while ultrathin 2D TMD forms the channel (Fig. 9.33A). This TFET structure offers several unique advantages as explained below. First, the use of 2D TMD material as channel attributes excellent electrostatics and thus, this TFET is promising for simultaneous scaling of device dimensions and supply voltage. Note that using 3D material as the source does not hamper device electrostatics,

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FIGURE 9.33 (A) Schematic diagram illustrating the cross-sectional view of the Atomically thin and Layered Semiconducting channel- Tunnel Field Effect Transistor (ATLAS-TFET) with ultrathin bilayer MoS2 (1.3 nm) as channel and degenerately doped ptype Ge as the source. Path for electron transport is shown by the red arrows that run vertically from the Ge source to the MoS2 and then laterally through the MoS2 layers to the drain. As the Ge is highly doped, the tunneling barrier height is mainly determined by the effective bandgap of MoS2 (including van der Waals [vdW] gap) while the tunneling width is determined by the MoS2 thickness. (B) Band alignment of Ge and bilayer MoS2 showing their electron affinities (EA) and bandgaps (EG) and thus, illustrating the formation of a staggered vertical heterojunction. The crystal structure of both the materials are shown below while the band structures are shown on both sides. Band diagrams along vertical dashed line in (A), in both OFF (C) and ON (D) states. The white regions represent the forbidden gaps (zero density of states). While the effective bandgap of bilayer MoS2 has been illustrated in (B), here the bands for the two layers are shown separately with the van der Waals gap between them for better visual interpretation of current flow. Note that the drain contact is located perpendicular to the plane of the figure and is not shown in it. In the OFF state, electrons from the valence band of Ge, cannot transport to MoS2 due to the nonavailability of density of states (DOS) in MoS2 (black arrow and cross sign). At higher energies, empty DOS is available in MoS2, but no DOS is available in Ge, again forbidding electron flow (orange arrow and cross mark). With further increase in energy reaching above the conduction band of Ge, DOS is available in both Ge and MoS2. However, the number of electrons available in the conduction band of Ge source is negligible due to the exponential decrease in electron concentration with increase in energy above the Fermi level according to Boltzmann distribution. Thus very few electrons can flow to the MoS2 (purple arrow), leading to very low OFF-state current. With the increase in gate voltage (D), when the conduction band of MoS2 at the dielectric interface is lowered below the valence band of the Ge source, electrons start to flow (green arrow), resulting in an abrupt (subthermionic) increase in BTBT current.

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as the channel region is the one that needs to get modulated by the gate and it is atomically thin in our case. Second, the 2D channel also allows extremely small tunneling distance (which is determined by the channel thickness), needed for increasing the BTBT current. Third, combining 3D and 2D materials opens up unprecedented opportunities for designing custom-built heterostructures. We have chosen germanium as the 3D material since it has low EA and bandgap among the commonly used group IV and III
V semiconductors, while MoS2 is chosen as the 2D material since it has relatively high EA among the most commonly explored TMDs. Thus Ge-MoS2 forms a staggered heterojunction (Fig. 9.33B), with a small band overlap at the interface, which is necessary for increasing the BTBT current. Fourth, the heterojunction is formed with van der Waals bond and thus has strain-free interfaces. Fifth, while methodologies for obtaining stable as well as high doping in TMDs is very challenging and still under investigation, 3D materials already enjoy well-developed doping technologies that have been leveraged in this work, for forming highly doped source. This enables the creation of an ultrasharp doping profile and hence, high electric field at the source-channel interface as there is a negligible chance of diffusion of dopant atoms across the heterojunction due to the presence of van der Waals gap. Sixth, since MoS2 is placed on top of Ge forming a vertical source-channel junction, BTBT can take place across the entire area of MoS2-Ge overlap, which leads to higher ON-current than that in case of line overlap obtained in lateral junctions. Last but not the least, we have achieved this TFET on a planar platform, which is easily manufacturable compared to 1D structures such as nanowires and nanotubes. Fig. 9.33C and D demonstrates the operation of the ATLAS-TFET through the band diagrams along vertical dashed line in Fig. 9.33A, in OFF (Fig. 9.33C) and ON (Fig. 9.33D) states. In this paper, n-type transistor is achieved where positive voltage is applied to the drain electrode contacting the MoS2 layers with respect to the highly p-doped Ge source. Hence, electrons tend to move from the Ge to the MoS2 and this electron transport can be modulated by the gate to turn the device ON or OFF. In the OFF state, only electrons above the conduction band of Ge can transport to MoS2 (purple arrow), leading to ultralow current due to the scarcity of available electrons at high energies above the Fermi level. At lower energies, no electrons can flow either due to the nonavailability of density of states (DOS) in Ge source (orange arrow) or in MoS2 channel (black arrow). Hence, the OFF current is very low. As the gate voltage is increased, the conduction band of MoS2 is lowered below the valence band of the Ge source (ON state), and hence filled DOS in the source gets aligned with empty DOS in the channel, leading to an abrupt increase in electron flow (green arrow) and hence,

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9.4 SUMMARY

373

current, which can lead to subthermionic SS. Electrons after tunneling from the Ge source to the MoS2, are sucked in laterally by the drain contact as shown by the red arrows in Fig. 9.1A. Note that bilayer MoS2 is used instead of a monolayer. Though the thickness of bilayer MoS2 is higher by 0.65 nm compared to that of monolayer, bilayer MoS2 still offers excellent electrostatics and ultralow tunneling barrier width and at the same time, has smaller bandgap and is more robust to surface scattering.

9.4 SUMMARY In this chapter we show that while 2D semiconductors and steep transistors have evolved with the aim of obtaining ultra-scalability and reduction of power consumption in digital electronics, they also hold great promise in a completely diverse arena of bio/gas-sensor technology. Here we demonstrated FET biosensors based on molybdenum disulfide (MoS2), which provides extremely high sensitivity and at the same time offer easy patternability and device fabrication, thanks to its 2D atomically layered structure. While graphene is also a 2D material, we show here that it cannot compete with MoS2-based FET biosensor, which surpasses the sensitivity of that based on graphene by more than 74-fold. Moreover, we establish through theoretical analysis that MoS2 is greatly advantageous for biosensor device scaling without compromising its sensitivity. Furthermore, MoS2 with its highly flexible and transparent nature can offer new opportunities in advanced diagnostics and medical prosthesis. This unique fusion of desirable properties makes MoS2 a highly potential candidate for next-generation low-cost biosensors. Apart from biosensing, it is also shown that MoS2 can also be used for gas-sensing applications through functionalization of its surface by noble metallic nanoparticles. While MoS2 has these excellent properties as a channel material, we elucidate that conventional FET-based sensors suffer from fundamental limitations on the maximum sensitivity and minimum detection time achievable due to their fundamental limitations in SS. We proposed and theoretically demonstrated that sensors based on steep transistors can overcome such limitations and lead to substantially higher sensitivity (over four orders of magnitude) and over an order of magnitude lower response time and hence, are highly desirable for sensing applications. Finally, we demonstrated that 2D materials are promising candidates as channel material for steep transistors.

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Further Reading Iler, 1979R.K. Iler, The Chemistry of Silica, Wiley, New York, 1979. Liu et al., 2013W. Liu, J. Kang, D. Sarkar, Y. Khatami, D. Jena, K. Banerjee, Role of metal contacts in designing high-performance monolayer n-type WSe2 field effect transistors, Nano Lett. 13 (5) (2013) 1983
1990. Radisavljevic et al., 2011B. Radisavljevic, A. Radenovic, J. Brivio, V. Giacometti, A. Kis, Single-layer MoS2 transistors, Nat. Nanotechnol. 6 (3) (2011) 147
150. Seker et al., 2000F. Seker, K. Meeker, T.F. Kuech, A.B. Ellis, Surface chemistry of prototypical bulk II 2 VI and III 2 V semiconductors and implications for chemical sensing, Chem. Rev. 100 (7) (2000) 2505
2536. Stern et al., 2007E. Stern, et al., Label-free immunodetection with CMOS-compatible semiconducting nanowires, Nature 445 (7127) (2007) 519
522. Stern et al., 2010E. Stern, et al., Label-free biomarker detection from whole blood, Nat. Nanotechnol. 5 (2) (2010) 138
142.

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C H A P T E R

10 Optical Biochemical Sensors Based on 2D Materials B.N. Shivananju1,2, Hui Ying Hoh3, Wenzhi Yu2 and Qiaoliang Bao2 1

State Key Laboratory of Applied Optics, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, Jilin, P.R. China 2Department of Materials Science and Engineering, ARC Centre of Excellence in Future Low-Energy Electronics Technologies (FLEET), Monash University, Clayton, VIC, Australia 3College of Electronic Science and Technology and Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, Shenzhen University, Shenzhen, P.R. China

10.1 INTRODUCTION An optical sensor is a device that converts a physical stimulus (strain, pressure, thermal, electrical, magnetism, light, ultrasonic, touch, motion, sound, or biochemical molecules) into an optical output for reading or further processing [1,2]. However, compared to electrical sensing devices [3], optical sensing devices (based on prisms, microscope, spectroscopy, optical interferometers, waveguides, fibers, photonic crystal fibers, and optofluidic) have many desirable advantages, such as ultra-sensitivity, real-time monitoring, no electrical interferences, multiwavelength analysis (i.e., simultaneous response to different analytes), multifunctionality, long-term stability, lightweight, cost-effectiveness, remote measuring capability, lab-on-fiber capability, and in-vivo biochemical sensing applications [4
9]. In optical sensing, critical improvements involve increasing the specificity of label-free sensing and lowering the limit of detection (LOD) [7,8]. Researchers have

Fundamentals and Sensing Applications of 2D Materials DOI: https://doi.org/10.1016/B978-0-08-102577-2.00010-5

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explored optical sensors for various sensing applications such as refractive index [7], temperature [10], light [11
13], strain [14], pH [15], biochemical [16], gas [17], and humidity [18] using a range of nanomaterials [19]. In the last decades, studies on utilizing nanomaterials, including nanowires, nanoparticles, quantum dots, carbon nanotubes (CNTs), metal oxides, and polymers for optical sensing applications have been reported [20]. In comparison, 2D nanomaterials such as graphene, transition metal dichalcogenides (TMDs), topological insulators (TIs), boron nitride (BN), perovskite, and black phosphorus (BP) have attracted significant attention for optoelectronic and photonic devices [21
25]. The richness of the optoelectronic properties of 2D materials has encouraged the development of many optoelectronic and photonic devices, such as ultrafast lasers [26
28], photodetectors [29
32], modulators [33], polarizers [34], light-emitting diodes [35], and plasmonic devices [36,37]. However, we believe that the true potential of 2D materials lies in optical sensors, especially for biochemical sensing in the health-care sector, which could address some of the drawbacks of current sensor technology [38
48]. This book chapter comprehensively and critically reviews emerging optical biochemical sensors based on 2D materials. We first elaborate their optical sensing properties, followed by fabrication of 2D materialcoated optical sensors, biomolecule sensing applications (single cell detection, DNA sensing, and protein sensing), chemical sensing applications (gas sensing, humidity sensing, and ions sensing) and health-care applications (cancer diagnosis, optogenetics, and ophthalmology).

10.2 BIOCHEMICAL OPTICAL SENSING PROPERTIES OF 2D MATERIALS 2D materials have exceptional biochemical optical sensing properties. First, due to the atomic-thin layer structure and large surface area, they are excellent substrates for adsorption of biomolecules via π
π stacking. Second, the large surface-to-volume ratio allows high-energy transfer efficiency and fast response time due to ultrafast carrier mobility. Moreover, the 2D materials of interest possess excellent biocompatibility, exceptional fluorescence-quenching ability, broadband light absorption, high chemical stability, outstanding robustness and flexibility [1,48]. Therefore, 2D materials have become widespread in biochemical sensing, diagnostics, and health-care applications [1,48]. The most striking features of optical biochemical sensors based on 2D materials are ultra-sensitivity and ultrafast response, thereby posing the potential to replace some of the current electrical sensors used for biochemical sensing applications [1,46].

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10.3 FABRICATION OF 2D MATERIALS OPTICAL SENSORS

FIGURE 10.1

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Biochemical sensing principle of 2D materials coated optical sensors.

Fig. 10.1 shows the basic mechanism of biochemical sensing based on 2D material-coated optical sensor. When biochemical molecules come into contact with the 2D material, the Fermi level of the 2D material will shift either to p-type or n-type, changing its optoelectronic properties [1]. Thus the conductivity of the 2D material can be easily varied by biochemical doping, which is a very important attribute for any biochemical sensing using optoelectronic and photonic devices. The interaction between the biochemical molecules and the surface of the 2D material results in a change in the electron-hole carrier density of the 2D material, which in turn changes the local refractive index of 2D materials as shown in Eq. (10.1) [1]:  σg;i iσg;r 1=2 1 n2DM 5 2 ; (10.1) ωΔ ωΔ where σg,r and σg,i are the real and imaginary parts of the conductivity of a 2D material, ω is the frequency of light, and Δ is the thickness of 2D material. The optical output (wavelength, intensity) of 2D materialcoated optical device (neff) is determined by the refractive index of 2D material (n2DM), which in turn depends on the interaction of biochemical molecules on the surface of the 2D material [1].

10.3 FABRICATION OF 2D MATERIALS OPTICAL SENSORS 2D material
based optical sensors can be fabricated in three ways: (1) transferring chemical vapor deposition (CVD) grown 2D materials onto optical sensors, (2) drop-casting 2D materials solution onto optical sensors, and (3) direct CVD growth of 2D materials onto optical sensors.

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10.3.1 Transferring CVD-Grown 2D Materials Onto Optical Sensors Fig. 10.2 shows the various steps involved in transferring CVDgrown 2D material (graphene) onto optical sensors (microfiber) [49]. First, using CVD a single layer of graphene is grown on the top surface of copper (Cu) foil. Then, poly(methyl methacrylate) (PMMA) polymer is spin-coated at 4000 revolutions per second for 40 s onto the top surface of the graphene/Cu foil, forming a PMMA/graphene/Cu sandwich-like structure. The PMMA polymer film acts as a shielding layer to avoid cracking of the graphene film until it is safely transferred onto the optical sensors [49]. The bottom layer of Cu foil is completely etched from the PMMA/graphene/Cu heterostructure by exposure to 1 M ferric chloride (FeCl3) solution for 60 min. The PMMA/graphene film is washed three to four times with deionized (DI) water to remove any Cu foil residues, then transferred carefully onto the optical sensors, such as optical prisms, waveguides, fibers, etched fibers, D-shaped fibers, tip of fiber connectors, microfibers, photonic crystal fibers, and fiber Bragg gratings (FBGs) [1]. The top layer of the PMMA polymer is next removed by treating it with acetone [49]. Finally, a nanosecond (ns) laser beam through a tapered fiber tip is employed to cut the extra graphene. Upon lifting the optical fiber, the graphene film is wrapped over the optical fiber to form a graphene-coated optical sensor [49].

FIGURE 10.2 The fabrication process of graphene-based optical sensors. Steps depict the transfer of CVD-grown graphene onto optical sensor [49].

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10.3.2 Solution Method of Coating 2D Materials Onto Optical Sensors The drop-casting method of coating 2D material solutions onto optical sensors (tilted fiber Bragg grating [TFBG]) is shown in Fig. 10.3 [50]. The liquid-phase exfoliation method is used to prepare black phosphorous (BP) nanosheets solution and these sheets are then coated onto the optical sensor [50]. The synthesis of BP nanosheets solution is as follows: (1) 25 mg bulk BP crystal was cut and ground into small pieces, which were then added to 25 mL absolute ethanol; (2) the BP dispersion was sonicated by a cell crasher at 25 kHz and 1200 W for 3 h to break the weak van der Waals stack of BP, where the temperature of solution is kept below 277 K with an ice bath; (3) the as-prepared BP dispersion is centrifuged at 5000 rpm for 15 min; (4) the supernatant containing few-layered BP nanosheets is decanted gently [50]. Finally, a BP nanosheets solution is coated onto the optical TFBG sensor using in situ layer-by-layer (i-LbL) technique, which is actually a chemical surface modification [50]. The sequence of the i-LbL technique is as follows: (1) An optical TFBG sensor is rinsed with acetone to remove any impurities from the sensing surface. (2) An alkaline treatment is carried out by dipping TFBG sensor in 1.0 M NaOH solution for one hour to enrich the number of 2 OH groups on the TFBG sensing surface. (3) The sensor is rinsed with DI water and ethanol. (4) A silanization procedure is carried out by incubating the TFBG in freshly prepared 5% (3-aminopropyl) triethoxysilane (APTES) for 2 h to form Si-O-Si bonding. (5) The TFBG sensor is placed into a microfluidic channel where 30 μL BP nanosheet solution is carefully drop-cast onto it. (6) The negatively charged BP nanosheets adhere to positively charged amino groups on the APTES-silanized TFBG sensing surface due to

FIGURE 10.3 Schematic representation of the synthesis of BP nanosheets (A
D) and deposition process of BP nanosheets an optical TFBG sensor (E
G) [50].

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electrostatic force; (7) Once the ethanol solvent has fully evaporated, the TFBG sensor is dried and coated with BP nanosheets. (8) The above procedures were repeated until the desired thickness of BP nanosheets on the surface of optical TFBG sensor is obtained. (9) The real-time BP deposition on TFBG surface can be monitored by capturing the transmission spectra of BP-TFBG sensor. (10) The optical BP-TFBG sensor is dried and stored in vacuum before further use for biochemical sensing applications [50].

10.3.3 Direct CVD-Grown 2D Materials Onto Optical Sensors Until now there has been no report on the direct CVD growth of 2D materials on optical sensors or optical fibers. Recently, Shivananju et al. demonstrated the direct CVD growth of CNTs on optical FBGs sensors for various applications [11
13], and Jingyu et al. demonstrated the direct CVD growth of graphene on the solid glass [51]. The above two techniques demonstrated a straightforward method of coating optical sensors with 2D materials for various applications, including biochemical sensing. Fig. 10.4 shows a schematic diagram of the CVD approach for the direct growth of uniform and high-quality graphene film on optical FBG sensor. In this process, optical sensors or optical fibers are thoroughly cleaned with DI water, acetone, and ethanol before being loaded into a quartz tube which is placed inside a three-zone high-temperature furnace. The optical sensors inside a quartz tube are flushed with 500 sccm Ar to remove impurities before the temperature of the furnace is increased. The furnace is heated to the desired growth temperature of graphene and allowed to stabilize for approximately 10 min. Typical growth conditions in the quartz tube are optimized with a gas mixture of 500 sccm Ar, 50 sccm H2, and 15.5 2 26.5 sccm CH4, with growth temperatures of 1000 C 2 1100 C for 1 2 7 h [51]. Some optical components

FIGURE 10.4 Schematic diagram of direct CVD growth 2D material onto the optical sensor.

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or optical fibers cannot tolerate a temperature of 1000 C due to low melting points. In this case, we need to adopt low-temperature (400 C
500 C) CVD growth of graphene on optical sensors or optical fibers. The thickness and uniformity of graphene or 2D materials onto optical sensors can be controlled by varying the flow rate of gases, growth temperature, and growth time [1,51].

10.4 BIOMOLECULES SENSING APPLICATION 10.4.1 Single Cell Detection Single cell detection plays a very important role in diagnosis, where a single diseased cell, which differs from the normal cells, carries information about the illness [52]. So there is a need for accurate detection of single cells among a group of normal cells. Currently, flow cytometry and optofluidics methods are widely used for label-free real-time single cell detection. However, these methods suffer from inaccurate singlecell detection and they require high-energy laser source for detection, which may damage the cells [52]. Recently, Xing et al. demonstrated a graphene-based highly sensitive optical refractive index sensor (4.3 3 107 mV/RIU) with a high resolution of 1.7 3 1028, which was used for single cancer cell detection from a group of normal cells [52]. Fig. 10.5A shows the experimental setup used for the ultrasensitive flow sensing of a single cell detection, where a He-Ne laser (632.8 nm, 80 μW) is used as the input light source, adjusted to circularly polarized light by a polarizer and quarter-wave plate. This circularly polarized light is focused onto the graphene-based optical prism sensor (GOPS) platform at the center of a microfluidic channel using an objective lens. The inset of Fig. 10.5A shows a schematic diagram of the GOPS, which consists of a quartz sandwich structure on the prism, a polydimethylsiloxane (PDMS) microfluidic chip, 8.1 nm thick h-rGO, and cell flow. The width and height of the optical microfluidic channel are approximately 20 and 12.5 μm. The diaphragm is used to further improve the detection range by reducing the reflected polarized light beam spot to 1 μm, which passes through the polarization beam splitter (PBS) to separate the TM and TE modes, which are detected by a balanced photodetector [52]. In this single cell detection experiment, Jurkat cancer cells (1%) and normal lymphocytes taken from blood were detected by changes in voltage using the GOPS microfluidic device. The refractive index and size of the Jurkat cancer cells are significantly larger than normal lymphocytes cells. Fig. 10.5B demonstrates the distinct changes in the voltage amplitude signal with respect to time, as the Jurkat cancer cells and

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FIGURE 10.5

Graphene-coated optical prism for ultrasensitive flow sensing of a single cell. (A) Flow-sensing system for a single-cell setup. The inset provides the schematic of the graphene-coated optical single-cell sensor platform, which is a PDMS microfluidic chip/h-rGO/quartz sandwich structure on a prism for single-cell detection. (B) Discrete time-dependent changes in voltage corresponding to mixed lymphocytes and Jurkat cells as they roll across the h-rGO detection window. (C) Enlarged images of the panel for certain positions in which the voltage signals are clearly depicted. The high and low signals represent Jurkat cells and lymphocytes, respectively, as they roll across the optical detection window. (D) Microscopic images of Jurkat cells. (E) Microscopic images of lymphocytes; scale bar is 15 μm [52].

normal lymphocytes flow through the h-rGO optical microfluidic channel at a rate of B7 μL h
1. Fig. 10.5C clearly shows the high and low-voltage signals, which represent the Jurkat cancer cell and normal lymphocyte, respectively, as they pass over the h-rGO optical microfluidic channel, indicating the high resolution and sensitivity of the GOPS, with an ability to detect a single cancer cell among the group of normal cells. The high-voltage amplitude denotes the signal from the Jurkat

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cancer cell due to its larger refractive index and size, compared to the low-voltage amplitude signal from normal lymphocyte cells. Fig. 10.5D
E shows the microscopic images, where the Jurkat cancer cells (Fig. 10.5D) are on average larger than the normal lymphocyte cells (Fig. 10.5E). The average sizes of cancer and normal cells are translated into an average refractive index that is accurately detected by the graphene-based optical refractive index sensor [52].

10.4.2 Deoxyribonucleic Acid Sensing Deoxyribonucleic acid (DNA) sensing is very important in diagnostics. The selective and fast detection of DNA molecules at ultralow concentration is vital in numerous fields, such as gene therapy, disease diagnostics, biomedical applications, point-of-care clinical analysis, food quality, safety, and environmental monitoring. Currently, polymerase chain reaction (PCR) is widely used for DNA target and signal amplification [53]. Many new technologies such as silicon-nanowire sensors
based field-effect transistor (FET) and surface-enhanced Raman scattering have been shown capable of direct detection of DNA at low concentrations [53]. Recently, 2D materials are being explored for DNA sensing applications, due to their large surface areas and unique optoelectronics properties. Researchers have already demonstrated that graphene and its derivative graphene oxide (GO) can be used for highly sensitive DNA hybridization detection down to a concentration level of few tens pM [53]. In 2014, Loan et al. reported a graphene-MoS2 heterostructure for ultrasensitive detection of DNA hybridization with the concentration level of attomolar (aM) based on optical photoluminescence (PL) [53]. A single layer of MoS2 exhibits high fluorescence-quenching ability, a desirable quality when using PL. The single layer graphene acts as a shielding layer from the surrounding environment to prevent the degradation of MoS2 and serves as a biocompatible interface layer to host DNA molecules on its surface [53]. Fig. 10.6A shows the schematic diagram of a PL experimental setup used for ultrasensitive detection of DNA hybridization based on graphene-MoS2 heterostructure [53]. A confocal optical microscope equipped with 473 nm laser source was used to probe the PL signal of the DNA molecules as they interact with the graphene-MoS2 heterostructure. The spatial PL mappings for the graphene-MoS2 heterostructure immobilized with the DNA solution (40 μL; 10 μM) and hybridized with the complementary DNA solutions (40 μL with various concentrations from 1 to 100 aM) is shown in Fig. 10.6B. The PL mapping intensity increases in the graphene-MoS2 heterostructure with the increase in the concentration of the added target DNA. The PL measurements for

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FIGURE 10.6 (A) The schematic illustration of the DNA detection method using an optical microscope and the graphene/MoS2 heterostructure sensor. (B) The photoluminescence (PL) peak area mappings of the graphene/MoS2 heterostructure hybridized with the complementary target DNA. (C) The PL spectra and integrated PL peak area in the presence of target DNA (1, 10, 100, and 1000 aM) [53].

the graphene-MoS2 heterostructure is carried out in a dry state, where the heterostructure was rinsed with DI water and dried after each probe DNA immobilization and target DNA addition. Fig. 10.6C summarized the integrated PL peak area over 1.7
1.95 eV for each condition, where we can clearly see a positive correlation between integrated PL peak area (in an arbitrary unit) and the concentration of the added complementary DNA from 1 aM to 1 fM. The PL color mapping clearly demonstrates graphene-MoS2 heterostructure responds to the target DNA with the detection limit of aM concentration [53].

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10.4.3 Protein Sensing The biomedical industry needs highly sensitive, ultrafast and compact biosensors that allow protein sensing to be monitored in real time. Methods such as enzyme-linked immunosorbent assays (ELISA), mass spectrometry, radial immunodiffusion, and western blotting have been reported for protein sensing applications [54]. Currently, there is a significant interest in the development of label-free protein sensing based on optical fiber methods. Fig. 10.7A shows a schematic of an optical fiber sensor for C-reactive protein (CRP) sensing, in which anti-CRP antibody (aCRP)-GO is coated on an etched fiber Bragg grating (EFBG) [54]. CRP is an indicator for cardiovascular diseases and chronic inflammations and plays a major role in diagnosing a patient. Currently, various techniques are used to detect CRP, such as the standard ELISA (0.3
10 mg L
1), quartz crystal microbalance (0.13 μg L
1
5.01 mg L
1), electrochemistry (0.1
50 mg L
1), or surface plasmon resonance (SPR, 2
5 mg L
1) [54]. A GO-coated EFBG device operates in the clinical range of 0.01
100 mg L
1 and work by monitoring the shift in the Bragg wavelength (ΔλB 5 2neffΛ, where neff is a function of ncore  1.47 and nclad 5 nGO  1.7, and Λ  532 nm, which is the grating period) as a function of concentration of CRP, as shown in Fig. 10.7B. It was demonstrated that the shift in the Bragg wavelength for aCRP-GO-coated EFBG is Bfive times higher than for aCRP without GO. This result verifies the sensitivity enhancement due to the presence of GO, which increases the binding between aCRP molecules and the surface of EFBG. Cross-sensitivity was evaluated, along with CRP detection, by introducing different interfering compounds, such as glucose (4000 mg L
1), urea (2000 mg L
1), and creatinine (6000 mg L
1), showing there is no significant shift in the Bragg wavelength. This work demonstrates the specificity of aCRP-GO complex-coated EFBG for CRP detection (0.01
100 mg L
1) in the presence of other compounds (glucose, urea, and creatinine), which is comparable to other standard methods reported previously [54]. The above results show the potential of 2D material
based optical sensors for protein sensing applications.

10.5 CHEMICAL SENSING APPLICATIONS 10.5.1 Gas Sensing The sensing and measurement of gas molecules such as CO2, CO, NO2, NH3, H2, and O2 at room temperature are important for both understanding and monitoring a variety of occurrences such as

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FIGURE 10.7 (A) Schematic image of GO-coated EFBG sensor [45]. (B) The shift in the Bragg wavelength as a function of CRP concentration in (i) aCRP (purple open circles) and (ii) aCRP
GO complex (olive green squares) coated EFBG sensors. Inset: Shift in the Bragg wavelength as a function of the concentration of only interfering factors such as urea (blue open squares), glucose (magenta open circles), and creatinine (red open stars) [54].

industrial processes, environmental changes, and hospitals [1,17]. Over the past few decades, researchers have demonstrated gas sensing at low concentrations (ppm) using various nanomaterials, such as CNTs, nanoparticles, nanofibers, nanosheets, and quantum dots [17]. Recently, 2D materials
based gas sensors are showing superior performance in terms of ultralow concentration (ppm or ppb) detection and ultrafast

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sensing, arising from their atomic-thin layer structure, large surface area, large surface-to-volume ratio, large gas-adsorption capacity, fast response time to dynamic gas molecules due to its ultrafast carrier mobility. Fig. 10.8A shows a schematic diagram of the experimental setup of graphene-based 2D material-coated D-shaped optical fiber for multiple gas (NH3, H2O, and xylene) sensing applications [55]. A tunable laser (1510
1590 nm) with a power of 12 dBm was used as the input light

FIGURE 10.8 (A) Schematic diagram of the experimental setup of graphene-coated D-shaped fiber (GDF) for gas sensing. Spectral shifts of the GDF exposed in (B) NH3 gas, (C) H2O vapor, and (D) xylene gas. (E) GDF’s sensitivities for three types of gas molecules [55].

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source, which propagates through the graphene-coated D-shaped fiber (GDF) placed inside a sealed gas chamber, and the output transmitted light is collected by using an optical spectrum analyzer (OSA). When gas molecules (NH3, H2O, and xylene) come into contact with graphene, the permittivity of the graphene film will vary, causing a change in the refractive index of the sensing region of the optical D-shaped fiber, which in turn results in a shift in the output transmission spectrum. Fig. 10.8B
D shows the transmission spectrum shift of GDF for different gas molecules (NH3, H2O, and xylene). The gas molecules were injected into the gas chamber with varying concentrations of 0, 200, 500, and 1000 ppm. GDF shows a maximum sensitivity of B3 pm ppm
1 for NH3, B1 pm ppm
1 for H2O, and B0.6 pm ppm
1 for xylene gas molecules, respectively (Fig. 10.8E). It is found that the sensitivity of GDF is 10 times better than without graphene [55]. These results forecast the potential of 2D materials coated optical sensors for ultrasensitive and highly selective gas molecules sensing applications.

10.5.2 Humidity Sensing Humidity sensors are used to measure and monitor the amount of water present in the surrounding air. These sensors are widely used in industries such as semiconductor, biomedical, textiles, food processing, pharmaceuticals, meteorology, microelectronics, agriculture, structural health monitoring, and environment monitoring [18]. Materials such as metal oxides, polymers, hydrogel, nanoparticles, and CNTs are explored for humidity sensing [18]. Recently, researchers are trying to explore 2D materials such as graphene, BP, and TMDs for humidity sensing applications [56
58]. These 2D materials have a high surface-to-volume ratio which supports a high sensitivity to variation in humidity. Various sensing techniques such as electronic (resistive, capacitive), thermally conductive, gravimetric, and optical methods are available for humidity sensing. However, optical sensors for humidity sensing gain popularity due to many advantages such as compact size, lightweight, inexpensive, real-time monitoring of humidity in hazardous environments, and remote humidity sensing capability [56]. Luo et al. have demonstrated a novel 2D Tungsten disulfide (WS2) material-coated optical side-polished fiber (WS2CSPF) for use as a highly sensitive and fast response humidity sensor [56]. Fig. 10.9A shows the experimental setup which consists of a 1550 nm laser source, 1 3 2 coupler, humidity chamber (humidity adjusting range: 35%RH to 95%RH and temperature adjusting range: 210 C to 100 C), and optical power meters or OSA. A commercial humidity/temperature meter is inserted into the chamber to monitor the actual humidity and

FUNDAMENTALS AND SENSING APPLICATIONS OF 2D MATERIALS

FIGURE 10.9 (A) Experimental setup for humidity sensing based on 2D tungsten disulfide (WS2) material-coated optical side-polished fiber (WS2CSPF). (B) Variation of relative output optical power through WS2CSPF. (C) Relative output optical power of WS2CSPF as a function of relative humidity and SPF’s data [56].

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temperature. An input laser (1550 nm) is coupled into an unpolished single-mode fiber to monitor the power fluctuation of the laser. The output laser power of SPF (control measurements) and WS2CSPF (humidity sensing) are monitored using an optical power meter or OSA. The relative humidity (RH) inside the chamber was first increased from 35%RH to 85%RH in steps of 5%RH and later decreased in similar steps. The response of the WS2CSPF optical sensor has been investigated by exposing the sensor sequentially to a range of different humidity conditions at a constant room temperature (25 C). Fig. 10.9B shows the performance of the WS2CSPF optical humidity sensor to varying humidity from 35%RH to 85%RH in steps of 5%RH, both increasing and decreasing order. The basic mechanism of humidity sensing based on WS2CSPF optical sensor is as follows: whenever humidity is increased in the chamber, the concentration of H2O molecules increases. These molecules interact with the 2D WS2 material and charge transfer takes place from WS2 to H2O molecules. According to orbital mixing theory, the conductivity of WS2 material decreases due to the reduction of major conducting electrons following a humidity increase, which in turn decreases light absorption. The transmitted loss of an SPF coated with WS2 film decreases while the transmitted optical output power increases. Hence the humidity sensing function enhancement can be achieved by using an SPF coated with a 2D WS2 material, as seen in Fig. 10.9C, which shows the relative output power of WS2CSPF optical humidity sensor as a function of RH in comparison with SPF optical sensor without the 2D WS2 material. This novel WS2CSPF optical humidity sensor can achieve a linear correlation coefficient of 99.39%, a sensitivity of 0.1213 dB/%RH, and a humidity resolution of 0.475%RH [56]. It is clear from these data that the sensitivity of a WS2CSPF optical humidity sensor is enhanced about 15 times compared to that of the bare SPF without 2D WS2 material. Therefore, we can see that the presence of the 2D WS2 material greatly improves the sensitivity and stability of the optical SPF humidity sensor [56].

10.5.3 Heavy Metal Ion Sensing Heavy metals are metallic chemical elements (mercury, arsenic, thallium, chromium, cadmium, and lead) with a relatively high density and are usually toxic and carcinogenic. They are natural components of the earth’s crust which cannot be destroyed and degraded and enters into our bodies via air, drinking water, and food. Heavy metal (e.g., lead [Pb]) at a high concentration in drinking water can lead to toxic biochemical effects in humans which in turn cause problems in the kidneys, heart, nervous system, gastrointestinal tract, joints, and

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reproductive system. Cell functions such as the synthesis of hemoglobin can also be affected. According to guidelines of the World Health Organization (WHO), the maximum permissible limit of lead in drinking water is 10 ppb; exceeding this concentration will affect the human health. Heavy metal ion detection in drinking water is therefore of vital importance [50]. Recently, Liu et al. demonstrated ultrasensitive 2D material (BP) integrated tilted fiber grating (TFG) optical sensor for detecting heavy metal (Pb) ions with an LOD of 0.25 ppb [50]. Fig. 10.10A shows a schematic diagram of the experimental setup used for heavy metal sensing, where a broadband light source with TM polarized resonance was directed onto the optical BP-TFG microfluidic sensor and the output signal was monitored by using an OSA. Solutions with different concentrations of Pb21 (0.1, 1.0, 10, 100, 1000, 1 3 104, 1 3 105, 1 3 106 and 1.5 3 107 ppb) solutions were injected into the BP-TFG microfluidic channel for 120 s and then the solution was carefully withdrawn from the microfluidic channel, such that the BP-TFG optical sensor was exposed for 180 s. The

FIGURE 10.10 (A) Experimental setup for heavy metal chemical sensing. (B) Transmission spectra of BP-TFG showing a clear upshift as Pb21 ions concentration increases. (C) The resonant intensity of BP-TFG change with increasing Pb21 ions concentration [50].

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transmission spectrum was captured by using an OSA for each Pb21 concentrations. Before using the BP-TFG optical sensor for the next measurement (different concentration), it was completely washed with ethanol to remove the adsorbed Pb21 ions [50]. Fig. 10.10B shows the transmission spectrum shift of the BP-TFG optical sensor at different Pb21 concentrations. We can clearly see that by increasing Pb21 concentration, the transmission peak intensity decreases along with wavelength red-shift, indicating that strong optical absorption occurs between BP 2D material and Pb21 ions, which in turn changes the effective refractive index of TFG cladding. Fig. 10.10C shows the transmission spectrum intensity of BP-TFG optical sensor as a function of Pb21 concentration, revealing a nonlinear relationship, with ultra-sensitivity of 0.5 3 1023 dB ppb
1, 7.7 3 1027 dB ppb
1, and 2.3 3 1028 dB ppb
1 for Pb21 concentration ranges of 0B100 ppb, 103B105 ppb, and 106B107 ppb, respectively. These results show the potential of BP-TFG optical sensor for ultrasensitive detection of Pb21 over a wide range of concentrations, from 0.1 ppb to 1.5 3 107 ppb, which is a few orders of magnitude larger than that of other electrical based lead sensors [50].

10.6 HEALTH-CARE APPLICATIONS 10.6.1 Photothermal and Chemotherapy for Cancer Diagnosis Cancer is currently the deadliest disease among humans. Cancer cells grow and reproduce uncontrollably, creating tumors in a specific part of the body and spreading to other areas, where they attach to and destroy surrounding healthy tissues and organs and ultimately cause death. The spread of cancer from one part of the body to another is called metastasis. Today, we can identify distinct types of cancers based on where they start growing, such as breast cancer, lung cancer, kidney cancer, liver cancer, and each cancer requires different approaches for diagnosis and treatment [59,60]. One common diagnostic methods is optical imaging (fluorescence, molecular, and Raman spectroscopy) or biopsies, which give information on cancer initiation, progression, and metastasis. Treatments can include radiotherapy, chemotherapy, biological therapy, hormone therapy, stem cell transplantation, and surgery. However, most of these diagnosis and treatment methods are expensive, invasive, and inaccurate. Hence there is a need for cost-effective, minimally invasive, and accurate methods of detecting cancer. New approaches are possible only with the recent developments in the field of biosensors, especially using nanomaterials such as graphene and its derivatives [59]. GO and graphene-quantum dots (GQDs) can be used as fluorescence probes in

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PL imaging due to their biocompatibility, photostability, nontoxicity, and chemical inertness, which are desirable attributes for designing fluorescence-based biosensors. Moreover, graphene-based fluorescence probes have two vital functionalities in PL: specific detection of cancer cells and as therapeutic agents. Light-induced thermal therapy has been validated clinically for treating and curing cancer tumors. In photothermal therapy, near-infrared light penetrates the target human tissues with limited depth and provides safety delivery of drugs. However, simply using phototherapy treatment can result in the incomplete killing of cancer cells. Therefore, the combination of photothermal and chemotherapy is a more effective treatment. Chemotherapy can enhance the efficiency of photothermal therapy by targeting surviving cancer cells or by inhibiting regrowth of damaged tumor blood vessels. More recently, light-activated nanoparticles that release their payload in response to light irradiation have been developed, achieving improved drug bioavailability with superior efficiency [59,60]. Recently Liu et al. demonstrated effective drug delivery with PEGylated MoS2 2D nanosheets for combined photothermal and chemotherapy targeting cancer cells [59]. 2D MoS2-PEG nanosheets have very strong NIR wavelength absorption and are a promising candidate for photothermal therapy. The nanosheets also possess high surface area to mass ratio, enabling efficient loadings of therapeutic molecules, such as chemotherapy drugs doxorubicin (DOX), 7-Ethyl-10hydroxycamptothecin (SN38), and a photodynamic agent chlorin e6 (Ce6). Fig. 10.11A shows the schematic diagram of the fabrication of MoS2-PEG which is subsequently used for drug loading. 2D MoS2 nanosheets were synthesized by chemical exfoliation and then functionalized with lipoic acid modified PEG (LA-PEG) using a thiol reaction to increase their physiological stability and biocompatibility for drug loading. Fig. 10.11B
F shows the MoS2-PEG nanosheets after loading with DOX, which can be used for combined photothermal and chemotherapy for in vivo cancer treatment. First, 1 3 106 murine breast cancer 4T1 cells (40 μL PBS) were injected into the back of Balb/C female mice. When the cancer tumor volume reached B50 mm3, these mice were randomly separated into five groups and intratumorally injected with 20 μL of PBS, DOX, MoS2
PEG, and MoS2-PEG/DOX ([DOX] 5 0.5 mg kg
1, [MoS2-PEG] 5 0.34 mg kg
1). Next, the infected mice were irradiated with NIR (808 nm, 0.35 W cm
2) for 20 min and temperature changes of these mice were monitored by using an IR thermal camera. In Fig. 10.11D, we can clearly see a significant change in temperature, from room temperature to 44 C
45 C in the case of mice injected with MoS2-PEG/DOX as compared to PBS and DOX. Mice with intratumoral injection of MoS2-PEG/DOX but without laser irradiation were also studied as the control experiment. After various treatments, the length

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FIGURE 10.11 (A) A scheme showing the fabrication process of MoS2-PEG and the subsequent drug loading. (B) A scheme showing in vitro targeted combination therapy with MoS2-PEG-FA/DOX. (C) Scheme of combination therapy based on intratumorally injected MoS2-PEG/DOX. (D) Infrared (IR) thermal images of 4T1 tumor-bearing mice recorded by an IR camera. The doses of DOX and MoS2-PEG were 0.5 mg kg
1 and 0.34 mg kg
1, respectively. Laser irradiation on the tumors was conducted by using 808-nm near-infrared (NIR) laser at a power density of 0.35 W cm
2 for 20 min. (E) The temperature change of the tumors was monitored by an IR thermal camera in different groups during laser irradiation as indicated in (C). (F) Tumor volume growth curves of different groups of mice after various treatments (five mice for each group) [59].

and width of the tumors were monitored every two days for the next 21 days with a digital caliper. The tumors injected with either PBS or free DOX grew quickly within 21 days, suggesting that free DOX at this low dose was not effective in inhibiting the tumor growth. However, the tumor growth in the group treated with MoS2
PEG/DOX and exposed to the NIR irradiation was dramatically inhibited after the combined photothermal therapy and chemotherapy. The above results show that DOX-loaded MoS2
PEG nanosheets used in the combined photothermal and chemotherapy can achieve an outstanding synergistic effect in inhibiting tumor growth in animal model experiments [59].

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10.6.2 Optogenetics Neuroscience is a multidisciplinary science that focuses on the study of the structure and function of the nervous or brain system. Optogenetics is a tool in which individual neuronal circuits in the brain are manipulated by light [61,62]. These circuits relate to different aspects of our behavior and personality as demonstrated in an animal module (Fig. 10.12A). Optogenetic tools play a vital role in health-care applications to resolve some of challenging neural disorders such as Parkinson’s, depression, addiction, autism, anxiety, schizophrenia, drug abuse, memory loss, spinal cord injury, eyesight loss, and stroke. Microelectrocorticography (micro-ECoG) neural interfaces are widely used to access the brain neurons externally to detect and record high-quality neuron signals from the brains of patients suffering from multiple neural disorders, to provide suitable therapy. The current micro-ECoG arrays are made up of indium-tin-oxide (ITO), which is not ideal for the neural interface due to many disadvantages such as brittleness, high-temperature processing required, inability to conform to the cortical surface, and most importantly limited light transmission at the ultraviolet (UV) and infrared wavelengths, which is a critical consideration in optogenetics and neural imaging. graphene- based micro-ECoG neural interfaces can overcome all these drawbacks in addition to its many advantages, such as biocompatibility, flexibility, high mechanical strength, broadband wavelength transmission, high thermal and electrical conductivity, and tunable optoelectronic properties [62]. Fig. 10.12B shows a graphene-based carbon-layered electrode array (CLEAR) device used for optogenetics and neural imaging. The CLEAR device consists of four-layer graphene with a minimum sheet resistance of 76 Ω per square, retaining broadband wavelength (300
1500 nm) with B90% transmission and also possessing mechanical strength superior to ITO and ultrathin metals. Fig. 10.12C shows a schematic diagram of an optogenetics experiment conducted on a mouse by placing a CLEAR device on the cerebral cortex of the mouse brain and stimulating the neurons by shining a 473 nm blue laser (100 mW) through a 200 μm optical fiber and recording in parallel the electrical output signal. From Fig. 10.12D we can clearly see the blue light stimulus being delivered through a CLEAR device embedded on the cortex of a Thy1:: ChR2 mouse. The basic mechanism of optogenetics is as follows, first, a light-sensitive protein (channelrhodopsin-2) is extracted from archaebacteria and algae; this protein produces an electrical current in the form of ions in response to blue (473 nm) light. The DNA extracted from this light-sensitive protein (channelrhodopsin-2) is inserted into specific neurons in the (Thy1::ChR2 mice) brain, and these neurons communicate by “firing,” that is, an electrical signal is created by opening and closing

FUNDAMENTALS AND SENSING APPLICATIONS OF 2D MATERIALS

FIGURE 10.12 Graphene-based carbon-layered electrode array (CLEAR) device for neural imaging and optogenetics applications. (A) Optogenetics implemented in the animal module [61]. (B) The fabrication process of the CLEAR device. (C) Schematic drawing of the experimental setup, showing the CLEAR device implemented on the cerebral cortex of a mouse, with an optical fiber delivering blue light stimuli to the neural cells. (D) Image of a blue light stimulus being delivered via an optical fiber, through the CLEAR device implanted on the cortex of a Thy1::ChR2 mouse. (E) Optical evoked potentials recorded by the CLEAR device; x-scale bars: 50 ms, y-scale bars: 100 μV. (F) Maximum intensity projection (MIP) of OCT angiogram showing cortical vasculature visible through the CLEAR micro-ECoG device (FOV 2.8 3 2.8 mm2 and 1.1 3 1.1 mm2, respectively). (G) Doppler blood flow velocity image showing the directionality of blood flowing through the vasculature below the CLEAR device (FOV 2.8 3 2.8 mm2 and 1.1 3 1.1 mm2). Red represents blood flowing towards the lens, and green represents blood flowing away [62].

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ion channels. Finally, light-induced responses from the cerebral cortex of the mouse brain are recorded by the CLEAR device for three different light intensities with a stimulus time of 3 ms (Fig. 10.12E). Because of the high transparency of the CLEAR device in the infrared wavelength region, we can also clearly see the structure of the cerebral vascular 3D optical coherence tomography (OCT) angiogram image of the mouse brain (Fig. 10.12F). The typical velocity profile of the blood flow in the brain vessels is easily visible through the CLEAR device as shown in Fig. 10.12G. This study confirms that a 2D materials-based CLEAR device can be easily embedded on the brain surface for superior performance in neural imaging and optogenetics applications [62].

10.6.3 Ophthalmology In the last few years, many wearable devices have been developed based on flexible and stretchable nanomaterials, as well as advances in micro/nanofabrication, smart electronics, and information technology. Currently, researchers are showing extensive interest in wearable devices for biomedical applications such as smart contact lens [63
65]. These electronic wearable devices need flexible, biocompatible, and environmentally stable electrode materials for various sensing and display applications. The recent innovation in 2D materials such as graphene can play a vital role in wearable biomedical devices. Wearable eye contact lenses have been used for diagnosis of glaucoma and diabetes by measuring intraocular pressure and glucose composition of tears [63]. During this process, RF technologies can be employed for signal or power supply; however, electromagnetic wave interference (EMI) from surrounding wireless devices can affect the signal collected. Wearing contact lenses for a long period of time may also cause dry eye syndrome. Therefore there is a need to protect the eye from EMI and retain moisture using a diffusion barrier [63]. Recently, Lee et al. demonstrated a smart eye contact lens
based on a CVD-grown graphene 2D material which acts as dehydration protection and electromagnetic interference shielding to prevent eye diseases such as cataracts [63]. Fig. 10.13A and B shows the schematic diagram of working principle of the EMI shielding graphene-based smart eye contact lenses. When the contact lens is not covered with graphene (Fig. 10.13A), the EM wave can easily pass through the contact lens and be directly absorbed by the eyeballs, which may cause thermal damage in the eyeballs leading to cataracts. Once the contact lens is covered by the graphene layer (Fig. 10.13B), the EM wave is partially absorbed by the graphene layer, avoiding thermal damage to the inner eyeballs. Fig. 10.13C shows experimental validation by exposing normal and

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FIGURE 10.13 The schematic and working principle of a graphene-coated contact lens. (A) Electromagnetic (EM) wave passes through contact lens and absorbed by an eyeball, possibly causing heat damage inside. (B) EM energy is absorbed by graphene and dissipated as heat before reaching the interior of the eye. (C) Sample preparation for the microwave oven test. Egg whites on an Si wafer are covered with normal contact lenses and lenses with a graphene coating, respectively. (D) Infrared (IR) camera images showing the elevated temperature of the graphene-coated lens inside a microwave oven, indicating the EM energy is efficiently absorbed and dissipated as heat. (E, F) Dehydration of a contact lens can be reduced due to gas-impermeability of graphene. (G) Schematic of the experimental setup to measure the water evaporation rate through contact lenses. (H) Weight loss measured with time on a hot plate at 38 C [63].

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graphene-coated contact lens to strong EM radiation (120 W for 50 s). This was done by placing them inside a microwave oven, which emits EM of similar wavelength as those from 4 G LTE and Bluetooth (2.45 GHz). The results clearly show that the thermal denaturalization, i.e., the color change of the egg white, shielded by a graphene-coated lens is considerably less than the case with the normal lens. When the graphene layer is exposed to EM radiation, the charge carriers (electrons) in orbital motion induce oscillating magnetic moments in response to the external magnetic field, which efficiently absorbs the EM energy and dissipates it as thermal energy. From Fig. 10.13D, we can clearly see the temperature change (captured by IR camera) from room temperature (B27 C) to above B45 C when strong EM (120 W for 20 s) irradiated the graphene-coated lens, while the temperature of the contact lens without graphene remains almost unchanged [63]. Fig. 10.13E
F shows a schematic diagram of how prolonged wearing of contact lenses without a graphene layer leads to dehydration of the eyeballs, which may cause xerophthalmia (Fig. 10.13E), whereas graphene lenses offer protection from dehydration (Fig. 10.13F). The dehydration protection of the graphene-coated contact lens is verified by placing the normal and graphene lens on water-filled vials. The vials are next placed on a hot plate maintained at 38 C (Fig. 10.13G). After one week, the weight of the vial covered by graphene lens decreased by 0.5535 g, while the weight of the vial covered by normal lens decreased by 0.8268 g, showing that graphene acts as a dehydration protection material (Fig. 10.13H). Thus graphene-coated eye contact lens can provide dehydration protection and electromagnetic interference shielding for eyeballs and this serves as a bionic platform for wearable biomedical technologies in the future health-care applications [63].

Acknowledgment We acknowledge support from the National Natural Science Foundation of China (61875139), Shenzhen Nanshan District Pilotage Team Program (LHTD20170006) and Australian Research Council (ARC, FT150100450, IH150100006 and CE170100039). Q. Bao acknowledges support from the Australian Research Council (ARC) Centre of Excellence in Future Low-Energy Electronics Technologies (FLEET).

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C H A P T E R

11 Recent Developments in Graphene-Based TwoDimensional Heterostructures for Sensing Applications Pratik V. Shinde, Manav Saxena and Manoj Kumar Singh Centre for Nano and Material Sciences, Jain University, Jain Global Campus, Bengaluru, India

11.1 INTRODUCTION In a world of technologies, progress is determined to a great extent by the growth of material chemistry. These advanced materials play a crucial role in our daily lives. When a new material with unusual dimensionality and properties is created new avenues will open. Historically, people have explored material synthesis, composition, and structures to improve their properties. Since the 1980s low-dimensional materials have led material science because of their unique properties compared to bulk materials [1]. Low-dimensional materials fall into the following classes: two dimensional (2D), one dimensional (1D), and zero dimensional (0D). Due to their unique properties 2D materials have attracted attention in condensed matter physics and in chemistry. The current 2D material library is shown in Table 11.1. This library increases every day as research progresses. 2D materials that include metals, semiconductors, and insulators show a broad range of electronic properties. 2D materials have properties such as high surface area, surface state free nature,

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TABLE 11.1 Graphene Family

Current 2D Library. Graphene

Hexagonal Boron Nitride (h-BN)

Boron-CarbonNitrogen (BCN)

Fluorographene

Graphene Oxide (GO)

Metallic Dichalcogenides: 2D MoS2 , WS2 , MoSe2, WSe2 Chalcogenides

Semiconducting Dichalcogenides:

NbS 2, NbSe 2, TaS 2, TiS 2, NiSe 2 and so on

ZrS2 , ZrSe 2 , MoTe 2, WTe 2 and so on

Layered Semiconductors: GaSe, GaTe, InSe, Bi2Se3 and so on Hydroxides:

Micas, BSCCO

MoO3 , WO3 Perovskite Type:

2D Oxides

Layered Cu Oxides

TiO2, MnO2, V 2O5 , TaO3, RuO2 and so on

Ni(OH)2, Eu(OH)2 and so on

LaNb2O7, Bi4Ti3O12, (Ca,Sr)2Nb3O10

and so on

Others

Yellow shaded area shows monolayer materials that are stable under ambient temperatures. Orange shaded area is stable in air. Pink shaded area shows materials unstable in air but stable in inert atmosphere. Green shading indicates 3D compounds that have been successfully exfoliated. “Others” indicates 2D crystals—including borides, carbides, nitrides, and so on.

distinctive optical bandgap, quantum spin Hall effect, and strong light matter interactions [1,2]. Physical properties such as magnetism, superconductivity, charge density wave (CDW), and crystal structure (2 H, 1 T) of 2D materials and 2D transition metal dichalcogenides (TMDCs) is shown in Fig. 11.1. There are two approaches to the synthesis of 2D materials. First is the top-down-bottom exfoliation approach while the other is a bottom-up-based approach. In general the first approach includes mechanical exfoliation and liquid exfoliation, while the latter approach includes chemical vapor deposition (CVD), physical vapor deposition, or vapor phase transport. Geim and coworkers isolated graphene by mechanical exfoliation using Scotch tape in 2004 [3,4]. This method gives highly pure 2D material, but the number of layers, their size, orientation, and phase are not controlled [5]. For large-scale production this method is not useful. Liquid exfoliation is useful for the low-cost production of 2D materials, but for some applications the size and quality of 2D materials are major issues. In this regard, CVD is more efficient because high-quality and large-area 2D materials can be prepared at a reasonable cost [6
8]. Graphene is just one example of the large 2D materials family. A new class has emerged of atomically thin 2D materials known as TMDCs. These have the formula MX or MX2 where M is a transition metal like Mo, W, Pd, etc. and X is a chalcogenide like S, Se, and Te, for example, MoS2, WS2, MoSe2. Many stable combinations are possible with transitions metals such as hafnium, zirconium, titanium, nickel,

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FIGURE 11.1

Physical properties of 2D materials and 2D transition metal dichalcogenides (TMDCs) such as magnetism [ferromagnetic (F)/antiferromagnetic (AF)], superconductivity, charge density wave (CDW), and crystal structure (2 H, 1 T).

and with nontransition metals like gallium, indium, bismuth, and tin. 2D TMDCs attract attention for capacitive energy storage [9,10] and sensing applications [11,12] due to their large specific area and van der Waals (vdWs) gap between each neighboring layer. The isolation of new 2D materials motivates researchers to fabricate vdWs heterostructures using atomically thin 2D materials as the building blocks. Each block has its own optical, electrical, and thermal properties. By stacking these blocks it should be possible to achieve new structures with different properties [13
15]. These 2D materials create heterostructures by adding layer-on-layer in a perpendicular direction to the atomic layer. 2D material-based heterostructures have novel electron-electron coupling and electron-phonon coupling, generated due to layer-layer interactions [16,17]. 2D material heterostructures may have improved device performance compared to single crystal, for example, by providing stronger mechanical flexibility [18,19]. Tuning of the energy band alignment and charge carrier mobility is possible by changing components of the heterostructures. Unlimited combinations

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of heterostructures are possible because a number of 2D materials are available with different properties. But it remains a challenge to fabricate the entire 2D material family and to utilize these for fabrication of heterostructures on a large scale. These vdWs heterostructures have numerous novel physical phenomena and novel applications such as ultrahigh-speed photodetectors [15], correlated light emitters [20], newgeneration field-effect transistors [21], high-sensitivity sensors [22], and memory devices [23].

11.2 GRAPHENE AND TWO DIMENSIONAL TRANSITION METAL DICHALCOGENIDES Graphene has generated great interest over the past decade. In the late 1930s researchers studied the thermal stability of a single-atomthick sheet. They predicted that, due to the minimization of surface energy it is impossible to isolate free-standing graphene at room temperature [24,25]. But in 2004 Geim and colleagues successfully isolated monolayer graphene by mechanical exfoliation. Graphene has a honeycomb lattice structure with a D6h point group [26,27]. The nonequivalent carbon is inverted onto each other by inverse symmetry operation from D6h point group. Graphene has exciting properties like a large theoretical specific surface area (2630 m2 g21) [28], high thermal conductivity (B5000 Wm21 K21) [29], high Young’s modulus (B1.0 TPa) [30], high intrinsic mobility (200000 cm2 v21 s21) [31,32], and good optical transmittance (B97.7%) [33]. These extraordinary features of graphene come from a combination of its dimensionality and a very peculiar band structure. Graphene, while being an interesting material for many applications, is chemically inert, and so another molecule with the desired properties is required to functionalize it, which in turn results in the loss of some of its exotic properties. Another increasing material is black phosphorous (BP), which is also known as phosphorene [34
37]. BP has a vertically staggered hexagonal lattice geometry with highly anisotropic properties. Monolayer BP has a bandgap 1.5 eV [35] and good carrier mobility up to 1000 cm2 V21 s21 [37]. BP may be referred to as a bridge between graphene and TMDCs. Although graphene has many enchanting properties, its lack of electronic bandgap stimulated research in other 2D inorganic materials like MoS2, WS2, MoSe2, TiS2, with a semiconducting nature. These emerging metal dichalcogenides have a sandwich structure of transition metal (Mo, W, Ti, Nb) between two chalcogen layers (S, Se, Te). In this structure, between the layers, weak noncovalent bonding takes place, while covalent bonds give them in-plane stability [38]. TMDCs have a variety of composition and properties but despite this there is one interesting common feature present in all monolayer TMDCs that is they all grow FUNDAMENTALS AND SENSING APPLICATIONS OF 2D MATERIALS

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in triangular shapes varying with edge composition [39,40]. Depending on the arrangement of atoms, 2D TMDCs can form two crystal structures: a trigonal prismatic (2 H) phase and an octahedral (1 T) phase. At room temperature, a 2 H phase of MX2 material is the most stable phase, while the 1 T phase can be acquired by Li-intercalation or electron beam irradiation [41]. TMDCs provide access to a range of properties related with their atomic-scale thickness, direct bandgap strong spin-orbit coupling and glowing electronic and mechanical properties [42]. These properties are in many ways unusual from their bulk counterparts. The properties of TMDCs can be tailored because of different crystalline structure and stacking sequences of their layers properties. TMDCs are used in many devices like photodetectors, photovoltaic devices, transistors, memory devices, and sensing devices [5]. Depending on composition TMDCs may be classified as semiconductor (MoS2, WS2), semimetals (WTe2, TiSe2), true metals (NbS2, VSe2) and superconductors (TaS2, NbSe2) [43]. The most frequently studied member of the family is monolayer MoS2; its production was firstly achieved in 1986 [44]. MoS2 not only has good chemical stability and mechanical flexibility but also superior optical and electrical properties [43,45]. The semiconducting nature of MoS2 is because of its ultrathin direct bandgap, between 1.3 and 1.9 eV (B1.8 eV) due to quantum confinement effect [46,47]. It has a much higher optical absorption coefficient (107 m21 in visible range) which can be used to fabricate ultrasensitive photodetectors [48,49]. Also it has high current on/ off ratio (B107 2 108) and larger work function (5.1 eV) [48]. The larger bandgap, work function and larger absorption efficiency have given MoS2 an edge over graphene which is why MoS2 is referred to as a “beyond graphene” material. The library of TMDCs opens up the possibility of fabricating novel heterostructures with new physics and material science.

11.3 FABRICATION OF HETEROSTRUCTURES FROM TWO DIMENSIONAL CRYSTALS Since the first fabrication of a graphene/h-BN heterostructures by exfoliation [50], research on 2D materials heterostructures has exploded. The fabrication of heterostructures with tuned properties depends on the interaction strength of the layers of materials. Owing to the layer structure of 2D crystals, the fabrication of heterostructures can be lateral or vertical. Lateral 2D heterostructures can be integrated by arranging 2D materials into monolayer or in-plane designs, while vertical 2D heterostructures are integrated by vertical stacking of the 2D materials. Covalent bonds present between the layers and vdWs bonding play a key role in 2D crystal heterostructures. Weak vdWs forces bind layers together and a strong covalent bond gives in-plane stability to the FUNDAMENTALS AND SENSING APPLICATIONS OF 2D MATERIALS

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heterostructures [51]. Such anisotropic bonding enables control of heterostructures material properties in distinctive ways. Since weak vdWs forces play a prime role in interlayer coupling, without dangling bond on the surface, the lattice-matching requirement is relaxed in heterostructures [52]. When stacking of different crystals takes place, synergetic effects becomes indispensable [53]. The reality is that the size of the 2D crystal heterostructures is key for a wide range of applications. There is a need to increase the size of 2D crystal-based heterostructures to make them compatible with 2D crystals. Recently researchers found out that franckeite minerals naturally show almost pristine alignment in crystal lattices with the absence of tapped residues between layers, which is more advantageous than humanmade vdWs heterostructures [54]. The fabrication of vertical stacking and lateral stitching of 2D material-based heterostructures with controllable growth is still a challenge. According to the design of heterostructures, fabrication methods also vary. In this chapter we present various fabrication methods of heterostructures such as mechanical exfoliation, molecular beam epitaxy (MBE) method, hydrothermal synthesis, and CVD.

11.3.1 Mechanical Exfoliation After monolayer graphene was isolated by mechanical exfoliation, researchers devoted great attention to 2D materials [55
58]. These materials are stacked or stitched together to form 2D material heterostructures. In this method, Scotch tape was used to manually produce 2D material flakes [55]. Vertical heterostructures are possible by stacking arbitrary stable 2D materials layers with vdWs forces. However, for lateral heterostructures fabrication is much more difficult because atoms from different 2D materials need to bond together to form effective lateral heterojunctions [59]. Depending on the interaction strength of the two layers of crystals, physical properties of heterostructures also vary. For example, scattering from charge impurities, substrate surface roughness, and optical phonons of SiO2 limits the carrier mobility of graphene on SiO2 while atomically flat and free charge trapping h-BN layers are superb substrates for graphene [2]. Although this method is a quick and convenient way to fabricate vertical heterostructures, layer-by-layer stacking remains unmanageable and layers become randomly placed. With the use of adhesive tape, it is possible to create high-quality monolayer material for fundamental study, but this method is impractical for large sheet production [2]. The exfoliation and restacking approach offers a remarkable deal of flexibility for fabrication of various 2D material heterostructures. Fig. 11.2A and B shows optical microscope images of monolayer MoS2 and monolayer MoSe2 respectively, exfoliated by mechanical exfoliation from bulk crystals [60]. On Si-SiO2 substrate monolayer MoS2

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FIGURE 11.2 (A) Optical microscope images of monolayer MoS2. (B) Optical microscope images of monolayer MoSe2. (C) Fabricated MoS2/MoSe2 heterostructure. Source: Images reprinted (adapted) with permission from F. Ceballos, M.Z. Bellus, H.Y. Chiu, H. Zhao, Ultrafast charge separation and indirect exciton formation in a MoS2
MoSe2 van der Waals heterostructure. ACS Nano 8 (12) (2014) 12717
12724. Copyright (2014) American Chemical Society.

FIGURE 11.3 (A) Scanning electron microscopy (SEM) images of the flowerlike MoS2/ CdS heterostructures. (B) Transmission electron microscopy (TEM) images of MoS2/CdS heterostructures.

was transferred on monolayer MoSe2 for the fabrication of MoS2/MoSe2 heterostructures. This fabricated MoS2/MoSe2 heterostructures as shown in Fig. 11.2C [60].

11.3.2 Hydrothermal Synthesis Hydrothermal synthesis is considered to be the most promising method for heterostructures synthesis. It is relatively inexpensive, shows high efficiency, and gives good crystallized products [61,62]. Typically, a homogeneous solution is added into a Teflon-lined stainless-steel autoclave and then heated at different temperatures. Zhang successfully synthesized the flowerlike MoS2/CdS heterostructures by a one-step hydrothermal route [62]. Hydrothermally synthesized MoS2/ CdS heterostructures scanning electron microscopy (SEM) images are shown in Fig. 11.3A. Approximately 800 nm size flowerlike MoS2/CdS

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heterostructures are seen in the image. Transmission electron microscopy (TEM) images of heterostructures are shown in Fig. 11.3B. TEM confirms the flowerlike morphology of the heterostructures [62].

11.3.3 Molecular Beam Epitaxy The MBE method is widely used for fabrication of high quality and homogeneous wafer-scale epitaxial layers. In this method, high-purity material is heated by electron beam evaporators until they begin to sublimate slowly in an ultrahigh vacuum. The gaseous elements react with each other and condense on a substrate. This epitaxial growth of films depends on the deposition rate [63]. This method has advantages like instant introduction and control over multiple sources, easy doping of materials, and controllability of atomic layers [64]. This fabrication approach provides better crystalline and ultrathin heterostructures. The method is highly reproducible [65] with control over atomic layers and thickness. Graphene/h-BN heterostructures have been fabricated on copper substrates by an MBE method [64]. Raman spectra of graphene/ h-BN stacked heterostructures is shown in Fig. 11.4A. G and 2D peaks show the existence of graphene while 1364 cm21 peak confirms evidence of h-BN. SEM images of the growth of high-quality graphene/ h-BN stacked heterostructures are shown in Fig. 11.4B. The inset shows a large triangular h-BN flake of about 20 μm.

11.3.4 Chemical Vapor Deposition In recent years the CVD technique has been used for the fabrication of 2D material heterostructures. CVD is a useful technique for the

FIGURE 11.4 (A) Raman spectra of graphene/h-BN heterostructures. (B) Scanning electron microscopy (SEM) images of graphene/h-BN heterostructures.

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FIGURE 11.5

Schematic diagram of thermodynamics and kinetic functionality of chemical vapor deposition (CVD).

controllable synthesis of 2D materials [66
68]. CVD not only provides large-area 2D materials for mechanical assembly but also direct growth of various stacking structures. Generally CVD requires a very high temperature to activate the gaseous precursors and successive gas
solid phase reactions on the substrate. Even if the input of precursors is the same, the structure, composition, and properties of formed products vary depending on various factors. Temperature triggering, substrate engineering, precursor design, gas flow or pressure growth time, and cooling rate are factors that play a crucial role in the CVD mechanism [1]. Fig. 11.5 is a schematic representation of how parameters like precursor, substrate, temperature, and pressure affect mass and heat transfer, growth of materials, and their interface reactions. In CVD, high-purity precursors are needed to avoid undesirable contamination and unwanted side reactions. In the vapor deposition process materials are deposited on the substrate such as Si/SiO2 [69], mica [70], copper [71], polyimide [72], and nickel [73]. Temperature mainly decides the composition and uniformity of products because it affects mass transport of species and their reaction at the vapor-solid interface. When nucleation at the vapor-solid interface takes place, high temperatures lead to a thermodynamic process while low temperatures lead to a kinetic process. At lower pressure, reactions are more controllable because volume flow and velocity of gas are greatly increased, but the concentration of precursor decreases. By tuning these factors, the controlled growth of heterostructures is possible. Fig. 11.6 shows the formation of vertical and lateral heterostructures. In the first step one layer of 2D material is deposited on the substrate. In the second step another material is deposited or transferred onto the first layer. This is the most important step because it

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FIGURE 11.6 Schematic diagram of the formation of vertical and lateral heterostructure.

FIGURE 11.7 One-step vapor phase growth mechanism of heterostructure.

decides the type of heterostructures. If two layers stack one on top of the other, vertical heterostructures are formed, and if they connect to one plane, this is known as lateral heterostructures. Recent developments in CVD provide high controllability over size, number of layers, as well as stacking or stitching modes in fabrication of heterostructures. 2D crystal heterostructures growth by CVD may be one or two steps. A one-step vapor phase growth of heterostructures is shown schematically in Fig. 11.7. Ajayan’s group showed a one-step CVD growth of WS2-MoS2 lateral and vertical heterostructures [7]. Chen’s group first showed the two-step CVD growth of MoS2-MoSe2 lateral heterostructures [74]. To date many 2D material heterostructures are synthesized by a CVD method such as graphene/h-BN [75], MoS2/h-BN [76], WS2/h-BN [77], WS2/MoS2 [78], MoS2/WSe2 [79], and SnS2/MoS2 [80]. MoS2/graphene heterostructures are also fabricated by CVD method. Raman spectra and optical image of the MoS2/graphene heterostructures fabricated by a two-step mechanism is shown in Fig. 11.8A and B [81]. The optical image clearly shows the MoS2/graphene film on the top of the side. Raman spectra of MoS2/graphene heterostructures are shown (top, red star) and graphene Raman spectra (bottom, black star) [81].

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FIGURE 11.8 (A) Raman spectra of MoS2/graphene heterostructures. (B) Optical image of the MoS2/graphene heterostructures. Source: Images reprinted (adapted) with permission from Q. Liu, B. Cook, M. Gong, Y. Gong, D. Ewing, M. Casper, et al., Printable transferfree and wafer-size MoS2/graphene van der Waals heterostructures for high-performance photodetection. ACS Appl. Mater. Interfaces 9 (14) (2017) 12728
12733. Copyright (2017) American Chemical Society.

11.4 TWO DIMENSIONAL CRYSTAL-BASED HETEROSTRUCTURES SENSORS The massive demand for highly sensitive, selective, reliable, and portable, low-power-consuming sensors has triggered research into new sensing materials based on 2D crystals. These materials have attracted attention due to such properties as large surface-to-volume ratio, planar crystalline structure, many active sites, and low electronic noise. Combining these properties to fabricate lateral and vertical heterostructures for a wide range of applications is the most important task. A chemical sensor is a device that provides information about the chemical composition of its environment. Here we briefly discuss different types of sensors such as humidity sensors, gas sensors, and surface plasmon resonance (SPR) sensor fabricated from heterostructures materials.

11.4.1 Humidity Sensor The humidity sensor is a device that senses, measures, and reports the relative humidity (RH) of air or determines the amount of water vapor present in gas mixture (air) or pure gas. Humidity sensing is related to a water adsorption and desorption process [82]. Humidity sensors are used for monitoring industrial as well agricultural products [83]. Humidity sensors are used in equipment such as incubators, sterilizers, and pharmaceutical processing equipment [83].

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Various types of humidity sensors are available, and these operate on different principles such as capacitive [84], resistive [85], semiconductor [85], optical [86], and surface acoustic waves [87]. Some advantages to capacitive sensors include low power consumption, good linearity, and wide range RH detection, but a complicated fabrication process is the major drawback [88
90]. Resistive sensors overcome these problems with their ease of fabrication, high sensitivity, low cost, and low power consumption [91
96]. Metal oxide [97], polymers [98], and carbonbased [99] materials are the most frequently used materials for the fabrication of humidity sensors. These materials have low cost and high sensitivity as well as good compatibility. But metal oxide sensors degrade in humidity [100] and polymers have poor stability in highly humid environments [101]. Slow response/recovery time and high operating temperatures remain design challenges for these sensors. Recently researchers have constructed heterostructures-based humidity sensors. These show a high performance with fast response time and selectivity. Molybdenum disulfide (MoS2) is one of the most studied materials for sensing and it has a high on/off ratio [102] and a low power consumption [103]. Sensors based on MoS2/Si nanowire heterojunctions show high sensitivity, a fast response time, and excellent stability at different RH values [101]. These sensors show better performance with both forward as well as reverse voltage. Fig. 11.9A shows current-voltage (I
V) curves of sensors at different RH values ranging from 11% to 95% under forward voltages where the current increases with an increase in RH values. The graph of increase in sensitivity with respect to an increase in RH values is shown in Fig. 11.9B. The highest sensitivity obtained is 392% at 95% RH. Fig. 11.9C shows the current response of MoS2/Si nanowires heterojunction in switching between dry air and different RH values at a bias voltage 15 V. When the device comes into contact with dry air or humid gas it behaves as a reversible switch between high and low conductance with high stability and repeatability. Fig. 11.9D shows a single-cycle response time of the sensor at different RH. At the highest RH value the response and recovery time is 26.4 and 15.1 s respectively. At reverse voltage these MoS2/ Si nanowires heterojunction sensor shows promising results like a forward voltage [101].

11.4.2 Gas Sensor There is a need to detect hazardous gases with highly sensitive and efficient sensors [104]. Sensors play an important role in environmental monitoring and industrial product monitoring and tracking. Semiconductor-based metal oxide has attracted attention for gas sensors

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FIGURE 11.9 (A) I
V curves of MoS2/SiNWA heterojunction at voltage under varied relative humidity (RH) values. (B) The dependence relation between sensitivity and relative humidity. (C) Current response of MoS2/Si nanowires heterojunction to dynamic switches between dry air and varied RH values at Vbias 5 5 V. (D) Single-cycle response with different RH values.

because of its properties such as simplicity of fabrication, simplicity of operation, high sensitivity, extensive detection range, compact size, and low cost [105
107]. However, semiconductor metal oxide sensors operate at high temperature (100 C
400 C) [108]. Issues include high power consumption and ignition of flammable and explosive gases, and due to growth of oxide grains they have low reliability [108,109]. To lower the temperature and improve sensitivity, these obstacles can be removed using such techniques as doping of novel metals [110
112], designing unique nanostructures as sensing material [113,114], ultraviolet illumination [115
119], and formation of heterostructures [120
122]. In gas sensing, sensitivity of the material depends on the adsorption and desorption processes, while the speed of these processes is responsible for the response and recovery time. 11.4.2.1 Nitrogen Dioxide Sensor Nitrogen dioxide (NO2) is one of the most common, and pungent reddish-brown oxidizing air pollutant forms in fuel engines, chemical factories, and power plants by combustion-emission processes [123].

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Even a few ppm exposures of NO2 can cause inflammation of lung tissue, irritation to the throat, bronchiolitis fibrosa obliterans, and silofiller’s disease in humans. The increased amount of gas in the environment causes acid rain and photochemical smog [124,125], contributing to the atmospheric reactions that form ground-level ozone. There is therefore an urgent need to expedite the development of highly sensitive, selective, and highly responsive NO2 gas sensor devices. Jung et al. [126] fabricated a highly transparent, flexible, and sensitive NO2 gas sensor based on an MoS2/rGO composite. The sensor detects concentrations as low as 0.15 ppm of NO2. Also, they have successfully improved the sensitivity to NO2 gas by at least 300% compared to pure rGO thin film gas sensors. Wang et al. [127] reported WO3 nanorods and sulfonated reduced graphene oxide-(S-rGO-) based NO2 gas sensors working at room temperature. The optimized sensor exhibits excellent reproducibility, selectivity, and extremely fast recovery kinetics and it possesses a high response toward 20 ppm. NO2 is 149% in 6 s, which is 100 times faster than that of the corresponding WO3/rGO sensors. 11.4.2.2 Hydrogen Sensor Hydrogen is a lighter gas than air and is a colorless, odorless gas at atmospheric conditions. It is a very clean energy source with use in fuel cells as well as in internal combustion engines. However, the reaction of hydrogen with an oxidizing agent (nitrous oxide), halogens (fluorine and chlorine), and unsaturated hydrocarbons (acetylene) is extremely exothermic [128]. So there is a strong need to develop “alarm” sensors that will detect hydrogen at a concentration well below the lower explosion limit in air. As shown in Fig. 11.10A, a CeO2/SnO2 heterostructure shows good response to hydrogen gas without going into saturation with increasing gas concentration [129]. Compared to pure CeO2 and SnO2 sensors, CeO2/SnO2 heterostructures are more stable. The CeO2/SnO2 heterostructures sensor shows a good response to even low H2 concentration. Fig. 11.10B shows the response and recovery time to hydrogen gas at different concentrations (5
10 ppm), which indicates that the sensor has a high reproducibility. Fig. 11.10C and D shows the response and recovery time plots of the sensor at 300 C, which are 17 and 24 s respectively. The CeO2/SnO2 sensor shows a sensitivity of 19.2 ppm21 due to the higher surface area and large amount of oxygen adsorption [129]. 11.4.2.3 Hydrogen Sulfide Sensor Hydrogen sulfide (H2S) is a toxic gas with an unpleasant smell that is like rotten eggs. It is colorless and flammable. It naturally occurs in natural gas, crude petroleum, volcanic gases, and hot springs. It is also produced from the decomposition of organic matter or waste produced by

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FIGURE 11.10 (A) Response curves of the CeO2, SnO2, and CeO2-SnO2 nanostructures exposed to different concentrations of H2 gas at 300 C. (B) Response curves of CeO2-SnO2 sensor at different concentrations and temperatures versus time. (C) Response times of CeO2-SnO2 toward H2 gas at 300 C. (D) Recovery curves of CeO2-SnO2 sensor at different concentrations and temperatures versus time.

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FIGURE 11.11 (A) The adsorption and reaction model on the surface of MoO3/rGO hybrid. (B) The responses of MoO3/rGO hybrids with different amount of graphene at 40ppm H2S gas in 70 C
220 C temperature range.

humans and animals. If the concentration of H2S increases to greater than 250 ppm, then it is a risk for human health. Sometimes a high concentration may cause death [130]. Therefore H2S sensors for instantaneous monitoring and control of H2S gas are needed. Recently MoO3 has been used for H2S gas sensors due to structural advantages like anisotropic structure, high electron mobility, and large surface-to-volume ratio [131]. But lower sensitivity and higher operating temperatures restrict its use as a sensor. MoO3 sensing properties may improve with graphene by fabrication of MoO3/rGO hybrids. Compared to MoO3, the MoO3/rGO hybrids show higher response and lower operating temperatures [132]. The adsorption and reaction model on the surface of the MoO3/rGO hybrid is shown in Fig. 11.11A while Fig. 11.11B shows the responses of MoO3/rGO hybrids with a different amount of graphene at 40 ppm H2S gas in the 70 C
220 C temperature range. A MoO3/5 wt% rGO hybrid has the highest response of 59.7 to 40 ppm H2S gas, which is three to four times higher than pure α-MoO3. Fig. 11.12 shows the transient response of pure α-MoO3 and MoO3/5 wt % rGO hybrid at the same operating temperature as H2S gas of different concentrations. The response time and recovery time of the sensor is about 9 and 17 s respectively. As Fig. 11.13 shows, the sensor based on the MoO3/5 wt% rGO hybrid not only has good stability but also has better reproducibility [132].

11.4.3 Surface Plasmon Resonance Sensor SPR is widely used for biological and environmental sensing. SPR sensors also play a role in medical imaging and remote sensing applications. Kretschmann [133] and Otto [134] are the two types of

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(A) 140 110ºC MoO3/5 wt% rGO

Sensor response (Ra/Rg)

120

MoO3

100

20

80 ppm H2S

40

40 ppm H2S

5 ppm H2S

10 ppm H2S

60

20 ppm H2S

80

0 0

Sensor response (Ra/Rg)

(B)

100 360 540 720 900 1080 1260 1440 1620 1800 1980 Time (s)

140 110ºC MoO3/5 wt% rGO

120

MoO3

100 80 60 40 20 0 0

10

20

30 40 50 60 Concentration (ppm)

70

80

90

FIGURE 11.12 (A) Transient response. (B) Responses of pure α-MoO3 and MoO3/5 wt % rGO hybrid at the same operating temperature as H2S gas with different concentrations. (B) 70

70 60 50 40

110ºC 40 ppm H2S

MoO3/5 wt% rGO

30 20 10

Sensor response (Ra/Rg)

Sensor response (Ra/Rg)

(A)

60 110ºC 40 ppm H2S

50

MoO3/5 wt% rGO 40 30

0 –10 –5 0 5 10 15 20 25 30 35 40 45 50 55 60 65 Time (day)

20 0

2

4

6 8 Frequency

10

12

14

FIGURE 11.13 (A) Stability of MoO3/5 wt% rGO hybrid sensor. (B) Cycling response of MoO3/5 wt% rGO hybrid sensor.

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configuration on which SPR biosensors are based. The conventional SPR sensor is based on the Kretschmann structure, in which a thin film of gold (Au) is coated on the prism to stimulate surface plasmons. The Kretschmann configuration is widely used because it does not require a thin air gap between the prism base and metal film. The SPR phenomenon measures the change in optical reflectivity of thin metal film like gold or silver, which arises due to changes in refractive index at the metal surface [135]. That means resonance occurs at the surface only when incident rays of p-polarized light that fall on the interface match with the wave vector of the surface plasmon. Silver has good sensitivity [136,137], but its poor stability [138] makes it unsuitable for an SPR sensor. However thermal and chemical stability as well as good optical performance of gold make it an ideal metal for an SPR sensor [138,139]. SPR sensors are known for superior accuracy, quick response, and label-free detection [140,141]. SPR technology is also easy to multiplex, is compact, and reliable for DNA hybridization [142
144]. Recent studies have shown that graphene can be used as a substrate in enhanced SPR sensors. Due to its high adsorption surface area [145
150], graphene is an important material in sensing fields. The high surface area offers a better contact with analyte as well as extensive field enhancement at the substrate interface [49]. For more than 10 layers of graphene deposited on a metallic SPR sensing substrate (50 nm) the graphene-based SPR sensor shows higher sensitivity [49]. Compared to graphene sensors, heterostructures based on graphene show higher sensitivity [49,151
153]. The advantage of heterostructures is that adsorption increases at the surface due to vdWs force of attraction and enhancement of field at the interface. Recently the 2D material molybdenum disulfide (MoS2) has attracted attention because of its analogous structure to graphene. MoS2 is a semiconductor material with ultrathin direct bandgap. It has a higher optical absorption coefficiency and larger work function. Therefore MoS2 is considered a promising resource for SPR biosensors with higher sensitivity. A MoS2-graphene hybrid nanostructure sensor shows sensitivity enhancement in the SPR biosensors. A five-layer Au-MoS2-graphene hybrid-based SPR biosensor is shown in Fig. 11.14 [154]. The prism is first coated with Au, then MoS2 followed by graphene coated onto the MoS2. This SPR sensor has a high sensitivity of 87.8 degree/RIU, and it can detect DNA hybridization. At 633 nm operating wavelength with 50 nm gold layer thickness it has a detection accuracy of 1.28 and a quality factor of 17.56 [154]. These sensors are used for medical diagnostics [155,156], enzyme detection [157,158], food safety testing [159,160], and environmental monitoring [161,162]. Another 2D material called blue phosphorene (blueP) can be used in SPR sensors. A blueP/MoS2 heterostructure fabricated on an Ag layer sensor shows high sensitivity [163]. Sensing performance at λ 5 662 nm

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FIGURE 11.14 The five-layer Au-MoS2-graphene hybrid-based surface plasmon resonance (SPR) biosensor.

is listed in Table 11.2, showing that Ag 1 blueP/MoS2 heterostructures have the highest sensitivity and detection accuracy, higher than the graphene SPR sensor [163].

11.4.4 Nitrite Sensor Nitrite exists in natural waters, soil, and physiological systems [164]. It is an essential part of beverages and food products preservatives [165]. For humans and animals, the high concentration of nitrite can be poisonous. Nitrite is a carcinogenic material and decreases the ability of hemoglobin to carry oxygen in the human body [166,167]. Thus there is a requirement for sensitive and rapid nitrite sensors for public health and environmental security. Electrochemical sensors based on rGO/MoS2 can be used for nitrite detection. rGO/MoS2/GCE sensors show high sensitivity, high stability, wide linear concentration ranges, and low detection limits [168]. Fig. 11.15A shows CV of electrodes in 0.1 M phosphate buffer solution (PBS) toward 500 μM nitrite showing an rGO/MoS2/GCE sensor oxidation current value of 45 μA with a potential of B0.85 V. The rGO/ MoS2/GCE sensor shows better catalytic activity than pure rGO and

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TABLE 11.2

Comparison of Sensing Performance at λ 5 662 nm.

Configuration (CaF2 1 Prism)

Change in resonance angle ΔθSPR (degree)

Sensitivity (degree/ RIU)

Full width at half maximum, (FWHM) (degree)

Detection accuracy (DA) (1/degree)

Ag

0.859

186.83

0.945

1.058

Ag 1 Graphene

0.877

190.570

1.52

0.658

Ag 1 BlueP/MoS2

0.905

196.798

1.45

0.690

FIGURE 11.15

(A) CV curves of the as-prepared electrodes in 0.1 M phosphate buffer solution (PBS) (pH 5 7.0) with 500 μM nitrite. (B) CV curves of the rGO-MoS2/GCE electrode in 0.1 M PBS (pH 5 7.0) under different concentrations of nitrite: 100, 300, 500, 700, and 1000 μM (scan rate: 50 mV s21).

MoS2 sensors. Fig. 11.15B shows that as the concentration of NO2 2 increases from 100 to 1000 μM the oxidation peak also increases. That means rGO/MoS2 heterostructures show excellent electro catalytic properties [168]. At 0.80 V and 0.2
4800 μM nitrite concentration, the step amperometric current (I
t) graph of rGO/MoS2/GCE sensors and the inset image show amperometric current at lower NO2 2 concentration as shown in Fig. 11.16A. The rGO/MoS2/GCE sensor reaches 95% of the steady state value within 3 s after the incorporation of palpable nitrite concentration [168]. Fig. 11.16B is the current (μA) versus concentration (μM) graph of the amperometric response with stirring rate of 200 rpm. The sensor shows superior linear response in the range of 0.2 to 4800 μM. The rGO/MoS2 heterostructures sensor shows an improved response for nitrite detection [168].

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FIGURE 11.16 (A) The amperometric current responses of rGO-MoS2/GCE for successive addition of nitrite range from 0.2 to 4800 μM in 0.1MPBS (pH 5 7.0). Inset image (i): amperometric current response of 0.2 to 50 μM. (B) The linear plot of oxidation current plateau value versus nitrite concentration.

11.5 CONCLUSION In recent years we have witnessed the growth of 2D material-based van der Waals heterostructures, where it is possible to control layer structure, arrange the atoms as wanted, and synthesize the new materials with innovative properties. The choice of vdWs heterostructures is limited only by the imagination, and the extensive availability of 2D materials and parameters opens infinite probabilities that will lead to the design of new generations of heterostructure materials. The 2D features and stability of building blocks makes heterostructures an ideal material for the electronic devices. This chapter provides information about heterostructure-based sensors and also briefly covers recent development in material chemistry as well as fabrication methods of 2D crystal-based heterostructures. It summarized recent progresses on 2D materials, their properties, and applications. We discussed the properties of heterostructures with fabrication methods. We showed how heterostructures have been fabricated by methods such as mechanical exfoliation, MBE, hydrothermal, and CVD. Compared to other methods, CVD shows advantages such as large area and high quality sheets of heterostructures. We further discussed application of heterostructures for sensors. Heterostructure materials have high sensitivity, fast response times, and excellent stability compared to the individual atoms. This brief review is an attempt to show the importance of vdW heterostructures and their use for sensing. Recent findings in this field show that their influence will be noticeable in the near future. The emergence of diverse structural properties in vdWs heterostructures opens new avenues for fundamental scientific studies and device

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C H A P T E R

12 Wearable and Flexible Sensors Based on 2D and Nanomaterials Rutuparna Samal and Chandra Sekhar Rout Centre for Nano and Material Sciences, Jain University, Bengaluru, India

12.1 INTRODUCTION Wearable and flexible sensors are devices designed to be worn on various parts of the human body that can be adjusted easily to respond to altered circumstances, monitoring the health status of the person. Wearable sensing elements are imperative to the field of health management as they improve on conventional methodologies for early identification of disease. Over the past decade these sensing devices have been made to process essential physiological information continuously and precisely in real time. The major advantage of wearable and flexible sensors [1] over traditional ones is that they are small, lightweight, transportable, inexpensive, noninvasive [2
4], and convenient. The invention of these sensors has simplified human life leading to better healthcare options [4]. Wearable and flexible sensors have been deployed in diverse sectors including gas sensing [5], environmental monitoring [6], security [7], and smart vehicles [8], but in this chapter we discuss the applications of such sensors for human body monitoring. The broad area classification of human sensors is shown in Fig. 12.1. The sensing mechanisms can be broadly classified based on mechanical, electrical, electrochemical, electronic, biochemical, and thermal properties. The diverse materials used for wearable electronics include onedimensional (1D), two-dimensional (2D), and three-dimensional (3D) structured organic, inorganic, composite semiconductors, polymers, graphene-related compounds, transition metal oxides, conductors, and hybrid materials.

Fundamentals and Sensing Applications of 2D Materials DOI: https://doi.org/10.1016/B978-0-08-102577-2.00012-9

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Conformal Non conformal

Integrated with body or apparel

Self-generated

Configuration Power

Portable devices (rigid, conventional)

Integrated External

Wearable sensors Active

Sensing method

Passive

Integrated Data Sensing function

Transmitted External

Physical Chemical

FIGURE 12.1 Systematic compartmentalization of wearable sensors [8].

The developments of wearable devices have helped to improve the quality of healthcare in a fascinating way. The physical human parameters such as heart rate, body temperature, bodily motion, strain, respiratory rate, blood pressure, muscle activity cardiovascular diseases, and neurological activity can be measured using these dynamic wearable devices. Examination of chemicals such as volatile organic compounds (VOCs) and metabolites [9,10], electrolytes (including heavy metal ions [11,12], calcium [13], sodium, bicarbonates, chlorine, potassium), toxic gases [14], glucose [15,16], amino acid, urea, lactate [17] associated with body biofluids like saliva, sweat [9,18,19], tears, urine, and blood, holds promise for the modern healthcare system. The wearable technology ensures that patients not only in hospital settings but also in nonclinical settings can be more involved in their health in a very simple and convenient way. In recent years several forms of wearable sensors have emerged including ring [20], gloves, shirt [21], bedsheet, badge, jacket [22], shoes, watches, belts, wristlet [23], arm band, and eyeglasses. In the late 1940s the first ever wearable sensor, the Holter monitor, was used for monitoring abnormal heart rhythm and was clinically important in the late 1960s. Sensors quantifying the oxygen saturation and glucose levels in blood emerged in the 1970s [24] and 1980s [25], respectively. The data from wearable sensors have been beneficial for estimating treatment efficacy and the premature detection of health disorders. Fig. 12.2 shows different types of flexible sensors based on their working principles.

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FIGURE 12.2

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Types of wearable and flexible sensors.

Flexible sensors generally comprise a pliable substrate [26], a functional material for disseminating vital electric, optical, thermal signals [27], and a well-defined sensing component to identify the chemical constituent [28]. The new materials with excellent levels of deformability, sensibility, malleability (enduring the original property), squeezability, and resiliency are predominantly appropriate for flexible sensor applications.

12.2 COMMENDABLE CONSIDERATIONS FOR WEARABLE AND FLEXIBLE SENSOR Progress in the area of wearable and flexible electronics has focused researchers on emerging new materials possessing key attributes of flexibility and wearability. The typical sensor design must provide for the comfort of both the medical practitioner and the user. Dimension, weight, cost, portability, performance, and user-friendliness play a great role in sensor configuration. The miniaturization of the sensor allows reducing the size of the sensor device. The development of smaller sensors led to the need for less space. Portable sensors provide an advantage as it can be transferred from one patient to another speedily and effortlessly. The low cost of wearable sensing devices makes them affordable for users. The device should be lightweight with minimal

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FIGURE 12.3 Sensor design and application of flexible 2D materials.

hardware elements and negligible noise levels. High performance and comfort should make it user-friendly in workplaces. Fig. 12.3 summarizes the use and importance of 2D flexible materials for sensor device fabrication. The materials for modern wearable sensors should be selected scrupulously with certain basic criteria. Compatibility, sensitivity, stability, and flexibility are some of the key properties of wearable sensor materials. Materials having a strong mechanical property, high susceptivity, and a good detection limit are the most desirable for flexible device applications. The material should be stable and adjustable to the shape of human skin. Favorable material selection should facilitate the smooth and stable collection of data/detection of signals from the epidermal skin without obstructing natural human body processes.

12.2.1 Materials for Wearable and Flexible Sensors The literature describes a wide range of sensing mechanisms. The materials that have the required characteristics for sensing applications include piezoelectric materials or piezoresistive material for strain

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sensors; thermocouple or resistance temperature detectors (RTDs), VOC sensors, etc. The integration of these heterogeneous materials to form the proper working sensor requires a great effort. The significant materials used for skin sensors include flexible metal, semiconductors, flexible polymers, 1D, 2D materials, inorganic materials, and silicon-based elastomers, etc.

12.3 WEARABLE AND FLEXIBLE TEMPERATURE SENSOR Accurate and precise measurement of fluctuations in physiological parameters like temperature can lead to the prevention of many heatrelated health problems. Body temperature will change depending on environmental conditions and transfer of heat from other sources. Body temperature sensors can be attached to the epidermis for both spatial and temporal heat measurement. Efficient sensitivity, reusability, longrange stability, high reliability, and fast response are essential requirements of wearable temperature sensors.

12.3.1 Classes of Wearable Temperature Sensors Numerous temperature sensors based on flexible substrates have been developed including thermocouple sensors, thermistors, semiconductor-based temperature sensors (field-effect transistors, FETs), resistive temperature detectors, conductive composite-based temperature sensors, conductor-based temperature sensors, heat flux sensors, flexible brain core temperature sensors, near body ambient temperature sensors, silicon sensors, infrared (IR) temperature sensors, thermal radiation sensor, thermometer and temperature sensor chip, etc. Some of these wearable and flexible temperature sensors are discussed here. 12.3.1.1 Thermally Sensitive Resistor (Thermistor) Thermistors are solid-state devices that exhibit precise temperature variations when exposed to a change in resistance. Thermistors are thermally sensitive resistors. The electrical resistance versus temperature graph depends on temperature with a slope corresponding to the temperature coefficient of resistance (TCR), denoted as α [29]. Thermistors generally have a negative temperature coefficient of resistance (NTC). These have high accuracy (0.1 C
1.5 C), but the operating temperature range is limited. The temperature coefficient of resistance is given by

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  α 5 Rx 2 Ry =Ry ΔT;



and the sensitivity of the sensor is Sensitivity 5 ðRx 2 Ry Þ=ðx 2 yÞ; where Rx and Ry are the resistances of the composites at x C, y C, respectively and ΔT is the temperature deviation from x C to y C [30]. Most NTC thermistors are composed of semiconductor and metal oxide
based materials. An NTC thermistor gives low resistance values for high temperature ranges (highly nonlinear curve). Wei Chen’s group reported a noninvasive wearable NTC sensor (Mon-A-Therm 90045) for measuring the body temperature of a newborn infant [31]. Interestingly, Yan et al. reported a flexible and tunable thermistor inspired by silver nanowire (AgNW) electrodes and graphene sensing channels. A polydimethylsiloxane- (PDMS-) based graphene thermistor has NTC, which is clearly observed from the device voltage and current variation, that is, the current in the device shows increased value from 0.79 to 1.34 μA in the temperature range of 30 C
100 C at a fixed voltage of 10 V [32]. With an increase in temperature from 30 C to 100 C the current of the device has an increment from 0.79 to 1.34 μA. The NTC behavior was confirmed from the nonlinear variation of the temperature with resistance. In a separate literature by Wu et al., the resistance change in accordance with TCR of polycrystalline thin film was studied as shown in Fig. 12.4 [33]. In Neella et al., an RGO-Au nanocomposite showed NTC, excellent bending ability, good sensitivity, and fast response time (476 ms). The NTC value of flexible Kapton supported RGO-Au composite was found to have 10 times less value than that of graphene-based sensors [34]. Highly flexible and compressible skin-worn bi-sheath-core structures having NTC with good tunability was also reported [35]. Solar exfoliated reduced graphene oxide (SrGO) and graphene flakes on polyimide substrate have been used as temperature sensors over a temperature range of 35 C
45 C holding NTC value as of 241.30 3 1024 C21 and 274.29 3 1024 C21 respectively [36] (Figs. 12.5 and 12.6). 12.3.1.2 Resistance Temperature Detector Resistance thermometer detectors (RTDs) measure temperature corresponding to the resistance change and are similar to the working principle of thermistor. But RTDs are more precise, accurate, expensive, and stable than that of thermistors. RTDs generally possess positive temperature coefficients (PTCs) of resistance, that is, the resistance changes linearly with temperature. These are primarily found to be made up of high-purity conductive metal such as platinum, copper, nickel, etc.

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FIGURE 12.4 (A) Representative diagram of graphene thermistor embedded into PDMS (polydimethylsiloxane) and its resistance versus temperature nonlinear relationship graph, I
V curves (Source: Adapted with permission from C. Yan, J. Wang, P.S. Lee, Stretchable graphene thermistor with tunable thermal index, ACS Nano 9 (2) (2015) 2130
2137, Copyright (2014) American Chemical Society); (B, C) a schematic representation of integrated pH and temperature sensor, and comparison of sensor property with the commercially available sensors (Source: Reprinted with permission from S. Nakata, T. Arie, S. Akita, K. Takei, Wearable, flexible, and multifunctional healthcare device with an ISFET chemical sensor for simultaneous sweat pH and skin temperature monitoring, ACS Sens. 2 (3) (2017) 443
448. Copyright (2017) American Chemical Society). (D) Image of a temperature sensor attached on human wrist [36].

FIGURE 12.5 Sensing mechanisms of piezoresistive, piezocapacitive, and piezoelectric sensors [46].

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FIGURE 12.6 (A) Schematic of flexible SWNTs/PDMS for wrist pulse detection [50]. (B) Gauge factor versus strain percentage in flexible nanopaper, stretchable nanopaper, CNTs and AgNW electrodes, stretchable nanopaper (Source: Adapted from C. Yan, J. Wang, W. Kang, M. Cui, X. Wang, C.Y. Foo, K.J. Chee, P.S. Lee, Highly stretchable piezoresistive graphene
nanocellulose nanopaper for strain sensors, Adv. Mater. 26 (13) (2014) 2022
2027 with permission); nanohybrid structure of sandwiched SWCNT in between PU-PEDOT:PSS for piezoresistive strain sensing (Source: Reprinted with permission from E. Roh, B.U. Hwang, D. Kim, B.Y. Kim, N.E. Lee, Stretchable, transparent, ultrasensitive, and patchable strain sensor for human
machine interfaces comprising a nanohybrid of carbon nanotubes and conductive elastomers, ACS Nano 9 (6) (2015) 6252
6261). (C) Stepwise fabrication procedure of AgNW/PUU/PDMS-based piezocapacitive sensor [64] and diagram of CNT-Ecoflex nanocomposite/microporous Ecoflex dielectric/CNT-Ecoflex nanocomposite flexible piezocapacitive pressure sensor [65].

Wearable and motion-free Ag nano-crystal- (NC-) based thin film with PTC up to 1.34 3 1023K21 with a temperature range of 85K
370K has recently been reported. Ligands like tetra butyl ammonium bromide (TBAB) were introduced into the highly conductive Au NC, which resulted in high PTC values [37]. Graphite composites having operational temperature at intervals 30 C
110 C have been investigated. The PTC value for graphite-polydimethylsiloxane composite on flexible polyimide films (0.042K21 and 0.286K21) was found to be better than that of a platinum-based temperature sensor (0.0055K21) [38]. Platinum thin film embedded with polyimide films was reported with highest sensitivity of 15.59 Ω C21 and TCR value ranging

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between 0.0027 and 0.0030 C21 having varying thicknesses used for temperature monitoring applications [35]. Polymer-assisted graphene nanowalls (GNWs)/PDMS facile biocompatible sensor possesses a TCR as high as 0.214 C21 with a large thermal coefficient of PDMS and exhibits a more efficient and faster response in monitoring the real body temperature [39]. 12.3.1.3 Thermocouple Sensors In 1822 a German physicist named Thomas Johann Seebeck discovered that a temperature difference between two electrical conductors (or semiconductors) induces a voltage across the junction as a function of temperature giving rise to the “Seebeck effect.” An electromotive force (emf) is produced due to the generation of a temperature gradient. Thermocouples rely on this Seebeck effect. The pair of metals forming the junction is known as “thermocouple.” The performance of the thermocouple is determined by the Seebeck coefficient of the two respective metals forming the thermocouple junction; in practice it is impossible to list the Seebeck coefficient of all possible pairs. A Seebeck coefficient of metals is given with respect to platinum whose Seebeck coefficient is defined as zero. Operating over a wide temperature range is the main advantage of thermocouple-based temperature sensors, but on the other hand they provide poor accuracy (errors up to 2 C). The voltage difference or the emf introduced by the Seebeck effect is given by V 5 ðα1 2 α2 ÞΔT 5 α12 ΔT; where α12 is the Seebeck coefficient for the material pair (1 and 2). A wearable microstructure-frame-supported organic thermoelectric (MFSOTE) device composed of poly (3, 4-ethylenedioxythiophene): poly (styrenesulfonate) (PEDOT: PSS) layer, with a thermocouple residing on its bottom electrode was used to measure the temperature of the human skin in the 0 C
100 C range [40]. Li et al. investigated cascades of thermocouples up to a micrometer wide made up of nickel films of thickness 100 nm made on flexible substrates such as PET and parylene for temperature sensing applications. With a different number of cascades, the change in resistance and sensitivity was realized. Thermocouples consisting of 64 cascades show a better Seebeck coefficient value of 55.69 μV K21 compared to the commercially available K-type thermocouple (39.6 μV K21) [41]. 12.3.1.4 Semiconductor-Based Sensors Semiconductor-based sensors comprise organic and inorganic semiconductor materials. Diodes, transistors, light-emitting diodes (LEDs),

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and FETs are the active components of semiconductor-based sensors. These temperature sensors can be subdivided into different categories based on output current, output voltage, output resistance, analog output, and digital output, etc. They have high linearity, high accuracy, and operate well in intermediate temperature ranges. A spin-coated layer of RGO/polyvinylidene fluoride (PVDF)-TrFE on an FET structure was reported with highly responsive, flexible, stable, and transparent features, operating well in both high- and lowtemperature regimes, that is, 30 C
40 C and 40 C
80 C respectively [42]. Organic semiconductor-based pentacene thin film transistor (TFT) temperature sensors deposited on polyvinyl phenol dielectrics with bottom contact structure measures the variation of drain currents with respect to temperature in the subthreshold regime [43]. An ion sensitive FET integrated with Ag ink printed on polyethylene terephthalate (PET) film is used to monitor skin temperature [44]. Two organic semiconductor double layers composed of copper phthalocyanine (CuPc) and 3, 4, 9, 10-perylenetetracarboxylic-dimide (PTCDI) forming thermal sensor array was also reported to work well in the wide temperature ranges [45]. The flexible thermal sensor composed of an amorphous indiumgallium-zinc-oxide (a-IGZO) thin film transistor shows high sensitivity as well as a seven-times increase in the output current with the increase of temperature from 20 C to 100 C [2]. The sensing element is composed of reduced graphene oxide (RGO) nanosheets into an elastomeric polyurethane (PU), which enables a highly responsive and accurate detection of minute changes in temperature with sensitivity  1.34% over the temperature range of 30 C
80 C [46].

12.4 WEARABLE STRAIN/PRESSURE SENSORS Wearable strain sensors can be directly affixed to the skin for measurement of pressure or movement based on signals such as heart rate, respiration/breathing rate, joint movements, and muscle activities. These sensors possess maximum compatibility with the mechanical property of human skin and have certain criteria such as high sensitivity at low pressure system, stretchability, weightlessness, low hysteresis, and mechanical reliability.

12.4.1 Sensing Mechanisms Up to the present date various wearable tactile sensing devices have been developed, but they are broadly summed up on the basis of their

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sensing capabilities and transduction mechanism. In these types of sensors, the electrical signal commonly includes a piezoresistive, piezocapacitive, or piezoelectric type, which is converted to various types of essential information. Here we briefly review the three types of sensing mechanism in Fig. 12.5. 12.4.1.1 Piezoresistive-Based Wearable Strain Sensor Mechanical deformation caused by changes in human physiological movement results in the change in electrical properties of the piezoresistive-based strain sensors. The change in the resistivity is proportional to the applied external stimulus. The stretching and bending of a conductive material may produce electrical faults, effecting the resistance change in the sensing element. For a simple geometry, the resistance R of a material is given as: R 5 (ρ L)/A, where ρ, L, and A represent resistivity, length, and cross-sectional area, respectively. The resistance change depends more generally on geometry and resistivity. The resistance change in case of a strain sensor can be expressed as ΔR=R 5 ð1 1 2ν Þε 1 Δρ=ρ; where ν and ε are Poisson’s ratio and strain, respectively. The strain sensitivity can be defined by gauge factor (GF), denoted as, GF 5 ΔR=ðR0 3 εÞ; where ΔR 5 R 2 R0, R0, ε are the change in resistance, unstrained resistance, and strain respectively. Metal, conductive polymer, and graphene-based sensors are widely used. On the basis of sensing mechanism, materials, and size, the sensitivity or GF of flexible strain devices ranges from 1 to 100. Furthermore, the band structure observed in graphene [47] and carbon nanotubes (CNTs) [48] is actually caused by the piezoresistivity because the stress modifies the bandgap and consequently the mobility of charge carriers [49]. Piezoresistive effects develop in elastic components like silicone elastomer: PDMS consisting of conductive thin film filler mainly due to the intrinsic piezoresistivity of the filler and the change in contact setup (Eg—stretching, bending, contact area variation, and breaking of contact, etc.) [50]. In order to improve the mechanical properties as well as the electric properties, many fillers have been investigated recently such as CNTs [51], graphene [52,53], elastomers [54,55], and metal particles [56]. Conducting polymers with innate piezoresistive properties can be used as pressure-sensitive devices [57]. Due to the weak electrical conductivity of graphene caused by structural deformation, the piezoresistive sensitivity of graphene is low compared to other elastomeric materials.

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Zhao et al. investigated an ultrasensitive sensor constituting nanographene films having a GF over 300, with the piezoresistive behavior well explained by the charge tunneling model [58]. Highly improved stretchability is found in carbon nanomaterials, such as CNTs. Transparent (62% optical transparency) and stretchable (100% stretchable) piezoresistive single-walled carbon nanotubes (SWCNTs) with conductive composite of PU-poly (3, 4-ethylenedioxythiophene polystyrenesulfonate) (PEDOT: PSS) sensing minute strain were realized by Hwang et al. [59]. The study reported material that was novel, low-cost, and highly sensitive (sensitivity 2.2 kPa21 in the range of 35
2500 Pa), that featured ultra-conformal contact for uneven body areas, ultra-low energy consumption (1026 W), and a wearable sensor composed of SWCNT/tissue paper mounted at polyimide [60]. Gong et al. reported on highly sensitive, highly stable (450,000 loading
unloading cycles), and responsive (response time ,17 ms) wearable sensors based on piezoresistivity with the interdigitated PDMS over a wide pressure range of 13 to 50,000 Pa [61]. Up to 70% strain withstands and GFs ranging from 2 to 14 has also been reported by silver nanowire (AgNW) [62]. Graphene-woven fabric (GWF) resulted in the GF value over 500, fewer than 2% strain, and indicating good stretchability in oblique angle direction determines the human motion and strain in terms of resistance [63]. 12.4.1.2 Piezocapacitive-Based Wearable Strain Sensor Traditional capacitive sensors work on the mechanism of capacitive coupling. A piezocapacitive strain sensor is constructed of soft dielectric and two flexible, stretchable electrodes. The dielectric material is sandwiched between the two electrodes. Piezocapacitive materials [64] are used for the production of strain sensors, in which the distance between the electrodes is reduced by the application of pressure, causing an increase in the capacitance according to C 5 ε0 εr A=d; where ε0 is the free space permittivity (constant), εr is the relative permittivity of the dielectric layer between the plates, A is the area of the overlap of the two plates, and d is the distance between the two plates. A systematic fabrication details of AgNW/PUU/PDMS-based piezocapacitive sensor has shown in Fig. 12.6. The AgNWs/PUU/ PDMS piezocapacitive sensor detects the change in capacitance with applied pressure. To compare the pressure change with capacitance variations different cover materials such as PET, and thin/thick glass have been used [65]. Piezocapacitive strain devices show low sensitivity

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compared to that of piezoresistive and piezoelectric sensors. Recently Kwon et al. has overcome the sensitivity challenge by replacing the normal dielectric with microporous dielectric material. The micropores of the elastomer are very useful in detecting minute pressure changes because of its highly deformable property, hence improving the sensitivity of the devices. The presence of microporous dielectric elastomer also showed giant piezocapacitive effect, thereby improving the performance of the sensors [66]. 12.4.1.3 Piezoelectric-Based Wearable Strain Sensors The application of external mechanical forces, pressure or strain on piezoelectric material produces electric charges thereby showing the change in the electric dipole moment, which is responsible for the resulting change in polarization. The conversion of the changes in pressure is measured in terms of electric signals (voltage). The electromechanical reflex is referred to as piezoelectric effect. Piezoelectric-based strain sensors constitute conductive sensing element/thin film braced with flexible substrates. The energy conversion efficiency of the piezoelectric material is defined by a physical quantity, the piezoelectric coefficient (d33). Piezoelectric polymers exhibit higher flexibility than piezoelectric inorganics but possess low d33 value compared to the inorganic materials. The piezoelectric material for wearable application includes inorganic material/polymer [67], crystals, polymer/ceramic [68], zinc oxide (ZnO) nanowires (NWs) [69,70], etc. Wang et al. demonstrated ZnO nanowire for artificial skin technology due to its high piezoelectric, high sensitivity, high spatial resolution, and mechanical stability [71]. Self-powered piezoelectric-based strain sensors constituting piezoelectric nanogenerators (NGs) and coplanar-gate graphene transistors (GTs) maintain their output drain current values even after the continuous application of strains, thereby implying excellent performance in the field of real-time sensing [72]. PVDF is a polymer material with a high piezoelectric factor, which makes it a suitable candidate for flexible strain sensors. Arrays of nanofibers of PVDF-cotrifluoroethylene as shown in Fig. 12.7 have ultrahigh sensitivity at extremely low pressure (0.1 Pa) [73]. Lead zirconate titanate (PZT) is an ultrathin inorganic piezoelectric material reported by Roger et al. with sensitivity (0.005 Pa) and fast response times of up to 0.1 ms [74]. High sensitivity and quick response are the main advantages of piezoelectric sensors, but this limits their ability to detect low-frequency mechanical stimuli. These sensors have the capability of achieving self-powered detection in the field of flexible and wearable electronics.

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FIGURE 12.7 (A) Fabrication procedure for the strain sensing device. (B, C) Schematic underlying the sensing mechanism [71].

12.5 WEARABLE SENSORS ANALYZING THE SWEAT METABOLITES Sweat is a biological fluid, originating from the coiled-shaped eccrine gland which reach out to the skin surface through the dermal duct. Sweat is rich in chemical information which provides information on the wearer’s state of health. This chemical information can provide further insights by combining it with the physical sensor data. Several approaches have been taken forward in recent years to specify the chemical components in the sweat. Some of the multifunctional wearable sensors detect analytes including ions, glucose, and pH.

12.5.1 Glucose Detection Skin-worn glucose biosensors are leading the way in diabetes diagnoses comparable to the traditional needle-based blood tests. These biosensors offer an attractive and improved route for glucose determination and could be very promising for insulin control in the human body in the future [75,76]. Sweat-based glucose monitoring is a simple and noninvasive way for management of diabetes. The advantages of glucose testing from sweat are the ease of access, continuous availability, and rich composition of electrolytes (Na1, K1, Ca1, and Mg1) and various trace elements like zinc, copper, iron, nickel, and lead, etc. [77]. The concentration of blood glucose (BG) and perspiration-based glucose varies between 10 μM and 0.7 mM for diabetic patients. Moyer et al. analyzed sweat glucose (SG) and BG values over a broad range of sweats and concluded that the harvested sweat is a great

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source for detecting the BG in human body [78]. The research in glucose biosensors started with patch-type biosensors. Yeon et al. developed a CVD-grown graphene-based FET sensor that can detect 3.3
10.9 mM levels of BG in individuals [79]. They incorporated glucose oxidase (GOD) in a graphene-based sensor. The use of GOD over glucose dehydrogenase (GDH) rules out the possibility of error in detection of glucose by increasing glucose concentration from nonglucose sugars present in human sweat. Then Lee et al. [80] produced a gold-doped CVD-grown graphene biosensor in which they used solid-state Ag/ AgCl as counter electrodes which greatly enhance the electrochemical activity and the ability to detect glucose level even at low concentrations. This area is advanced through miniaturizing the biosensor, producing ultrathin, stretchable devices that make it more comfortable to access the sweat. Human epidermis is subject to constant bending and stretching during normal physical activity period. Very recently Abella´n et al. reported a stretchable screen-printed electrochemical sensor to determine glucose in human sweat [14]. Amay et al. described a tattoobased noninvasive glucose sensor which is very easy to wear, but the long-term stability could be a limitation of this type of stretchable device [81]. An emerging topic in this area is the self-extraction of sweat for glucose analysis. Sometimes it’s difficult to get enough sweat for the detection process so a widely used process for sweat excretion, known as iontophoresis, which was initially developed for cystic fibrosis detection may be used [82]. In this method chemical agonists contact the epidermal gland with the help of electrical current. The regular process of SG determination is a collaborative process of stimulation, collection, and analysis of the sweat sample which is time consuming and involves delicate procedures. To overcome these Sam et al. designed a wearable device based on an iontophoresis interface for periodic sweat extraction and in situ analysis. The device had two major portions constituting an electrode array containing sweat induction and sensing electrodes, which are interconnected with a wireless flexible printed circuit board. The iontophoresis process and sweat-sensing process are decoupled. The electrochemical device can also control the volume of extracted sweat without any electrode corrosion for robust SG analysis [83]. An MoS2/AuNPs/glucose oxidase (GOx) hybrid bioelectrode detects glucose with sensitivity 13.80 μA μM21 cm22 with detection limit of 0.042 μM [84]. The sensing response of the MoS2 layered on gold substrate is shown in Fig. 12.8. CNT-based nanoelectrode assemblies are also reported to act as glucose sensors for the detection of SG [17]. Despite the improved results for glucose monitoring through wearable biosensors there are still issues like device interface, skin physiology, sensing mechanism, electrochemical reaction of used materials. These

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FIGURE 12.8 (A) Schematic of the electron transfer process on the MoS2-layered Au substrate. (B, C) Comparative glucose sensing response for MoS2, MoS2/AuNPs, and MoS2/AuNPs/Glucose oxidase (GOx) hybrid and their corresponding calibration graph ˙ (Source: Reprinted from O. Parlak, A. Incel, L. Uzun, A.P. Turner, A. Tiwari, Structuring Au nanoparticles on two-dimensional MoS2 nanosheets for electrochemical glucose biosensors, Biosens. Bioelectron. 89 (2017) 545
550, with permission from Elsevier). (D) Full integrated eye glasses loaded with sensors at the nose bridge and printed circuit boards (PCB) at the hinges (Source: Adapted from J.R. Sempionatto, T. Nakagawa, A. Pavinatto, S.T. Mensah, S. Imani, P. Mercier, J. Wang, Eyeglasses based wireless electrolyte and metabolite sensor platform, Lab Chip 17 (10) (2017) 1834
1842, with permission of the Royal Society of Chemistry). (E) Graphical representation of Interstitial fluid and sweat sensor (Source: Reprinted from J. Kim, A.S. Campbell, J. Wang, Wearable non-invasive epidermal glucose sensors: a review, Talanta 177 (2018) 163
170, with permission from Elsevier).

have to be resolved for any future application on the human body. Moreover, the built devices need to be extensively evaluated locally in human skin before practical application.

12.5.2 pH Detection Real-time monitoring of sweat pH is a challenging task. Sweat pH testing is used for general wellness and is very much needed by athletes. Sweat pH does not remain constant but varies due to a number of biological factors [85]. A slightly acidic pH favors nonresident pathogens and bacteria. In an early attempt, Weber et al. fabricated novel pH and lactate biosensors using CNTs as electrodes [86]. The CNT

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electrodes are functionalized with carboxylic acid to provide accurate pH readings. Although this CNT-COOH electrochemical sensor is able to detect pH in 1
10 range, it’s neither wearable nor suitable for real-time sweat analysis. Decades later, a simple flexible sensor for sweat pH detection technique was developed by Huang et al. using iridium oxide film formed by a sol
gel method [87]. They used lithography to develop this novel sensor. First, layers of chromium (Cr) and gold (Au) were deposited on polyimide surface using electron beam lithography and iridium oxide film developed on top by traditional sol
gel technique. The chemical reaction which governs the pH detection is given below. [88] Ir2 O3 1 6H1 1 6e2 22Ir 1 3H2 O IrO2 1 4H1 1 4e2 2Ir 1 2H2 O 2IrO2 1 2H1 1 2e2 2Ir2 O3 1 H2 O The redox potential of the above reaction is given by: E 5 E0 2 2:303

RT pH 5 E0 2 0:05916pH; F

where E0 is potential of the standard electrode. They tested this flexible sensor for a range between 1.5 and 12 of pH. Many more reports on these kinds of flexible biosensors for sweat pH detection are available [12,89
91], but these need to be further modified to meet miniaturization, stability, sensitivity, and reversibility targets for practical application in human body diagnosis.

12.5.3 Electrolytes/Ions and Metabolite Detection Human sweat is home to a number of chemical constituents with different concentrations. Biomarkers including ions of sodium, chlorine, calcium, and potassium are at the top of the list with large concentrations [85]. The variations of these sweat metabolites play a dominant role in human physiology. Most importantly for athletes the variation of sodium and chlorine ions in sweat indicate the level of dehydration, so real-time monitoring can help the athletes maintain proper body fluid balance and preventing hyponatremia [92]. Recently, an advanced wireless and interchangeable “Lab-on-a-Glass” fully integrated system determining lactate, potassium, and glucose from sweat was reported— the first of this kind of biosensor platform. Nose pads of eyeglasses were stickered with printed sensors patterned on PET. They attached amperometric and potentiometric sensors on each side of the eyeglass nose-bridge pads, embedded with an electronic circuit [93]. Sonner et al. developed microfluidic models for partitioning the biomarkers/electrolytes

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and observed variations of the ions (Na1, Cl2, K1, NH41) with respect to sweat rate. A number of attempts have been made to develop miniature devices for Na1 detection, but here the discussion will be limited to flexible bio-sensors [94]. Schazmann et al. devised a new kind of sodium sensor belt using ion selective electrodes. Their device uses a pump for automatic sweat collection. They used an acrylonitrile butadiene styrene, a polymer-based impermeable plastic platform which holds the integrated ion selective electrodes on it [95]. The sweat selection is based on a capillary collection method. In addition a potentiometer is incorporated to calibrate the Na1 concentration. There are more reports on this kind of flexible sensors for detection of other constituents of human sweat like chloride, ammonium [96], and potassium [96]. Real-time monitoring of heavy metals was detected using a zinc-based stretchable and strain-resistant tattoo sensor [97]. In addition to the above discussions on various types of reported wearable flexible biosensors, there are still more different types of chemical sensors which are produced to detect other sweat metabolites like lactate, uric acid [98], concentration, etc. Sweat lactate is produced by the eccrine gland metabolism, which usually increases during physical exercise [99]. Khodagholy et al. made a patch-type transistor device that could directly measure sweat lactate concentration from human perspiration [100]. Later Wenzhao et al. produced flexible tattoo sensors by a conventional screen-printing technique, which are basically functionalized with lactate oxidase. This type of tattoo sensor is easy to wear and is noninvasive compared to the usual blood draw method [101]. A lightweight graphene nanosensor, prone to bending and stretching, has a sensitivity from 0.08 to 20 μM for lactate content on skin [102].

12.6 WEARABLE SENSORS FOR VOLATILE BIOMARKERS DETECTION Tracking the level of volatile biomarkers from human skin through an epidermal electrochemical sensor can provide insight into the early diagnosis of several human health risks. At room temperature organic compounds with high vapor pressure (volatility) go under the name of VOCs. Extraction of VOCs from human skin or breath [103,104] should help in the identification of health risks. Wearable electronics or e-skin provides an ideal approach for the prognosis of VOCs in a noninvasive way outside of any clinical settings. The presence of volatile biomarkers like aniline, acetone, mono methylated alkenes, and 2-propanol indicate the possibility of lung cancer and breast cancer respectively [105,106]. Several detected VOCs from human skin include alcohols (ethanol, 2-propanol etc.), aldehydes (n-heptanal, benzaldehyde, n-hexanal, etc.),

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hydrocarbons (1-octene, isoprene, 1-nonene, etc.), ketones (2-butanone, 2-heptanone, etc.), esters (ethyl acetate, isopropyl acetate, etc.), terpenes (DL-limonene), heterocycles (1, 3-dioxolane), etc. [107]. Materials that are used in the detection of VOCs include nanomaterial-based metal oxides, conductive polymers, silicon NWs, responsive dyes, graphene-based material. Responsive dye is a special kind of material that changes color when exposed to VOCs. Sensors based on responsive dyes utilize Cu(II), Zn(II), Co(III)-based porphyrins, bromophenol blue, chlorophenol red, etc. as a sensing element [108]. A sweat-secreting drug named pilocarpine was used to stimulate the secretion of sweat soon after the rise of alcohol level. In this process the alcohol content of the person’s body can be measured by examining the excess sweat caused by the drug pilocarpine. Iontophoretic operation measures the alcohol content in the sweat. Ethanol can be detected easily in sweat due to its water solubility and hydrophilic-lipophilic nature that helps it to permeate through pores in the human skin. The ethanol concentration in blood also can be measured by monitoring the alcohol concentration of sweat as well [109]. Methanol, ethanol, and isopropyl alcohol was measured using hydrothermally synthesized SnS2 nano flake-based sensors [110]. Fig. 12.9 shows the structural

FIGURE 12.9 (A) Sensor responses graph with the concentration of various volatile compounds. (B) Performance of SnS2 nano flakes-based sensor with recovery time of the alcohol [110]. (C) Schematic depiction of processes of ethanol sensing; (D) iontophoretic sensing tattoo device with the iontophoretic electrodes (IEs; anode and cathode), working electrode (WE), reference electrode (RE), and counter electrodes (CE). Source: Reprinted with permission from [109], Copyright (2016) American Chemical Society.

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arrangement of SnS2 nano flakes and their sensing behavior. For the extraction of biomarkers from human skin Jin et al. reported an array of ligand functionalized gold nanoparticles (GNP) films based on flexible polymer substrates [111].

12.7 CONCLUSION AND PROMISING OUTLOOK The accelerating development of material science, electronics, and engineering is having an enormous impact on quick disease diagnosis and real-time monitoring by wearable sensors. Wearable and flexible sensor devices provide an alternative to massive and rigid sensors. These sensors deliver improved comfort as well as yield real-time measurement of physical parameters such as temperature and pressure, etc. The flexible sensors are composed of a sensing element, data processing unit, and the interconnecting connections in the system; all the constituents play the same level of importance in the sensor design and working principle. To summarize, the characteristics of wearable and flexible sensors for practical applications must include following vital points: • • • • •

excellent level of conductivity remarkable skin compatibility cheap, durable, and easily washable/cleanable lightweight, portable, and producible in large scale extraordinarily sensitive and flexible

The sensing mechanism can be improved by best selection of material, characteristics, and good connection schemes. Substrates like PDMS, textile, and paper are used for flexibility and stretchability. Biomaterial-based substrates show improved biological biocompatibility over polymeric elastomers. Replacement of the polymer with the biomaterial can help to eliminate such problems as side effects and itching, etc. Despite various advances in the field of wearable and flexible skin sensors for real practical applications, many challenges remain like miniaturization, complexity of the system, symmetrical investigation of skin sweat, large-scale production, appropriate structural design, longterm stability, period of use, accuracy, packaging of the sensor network, reproducible sensing response, selection of commendable material, effective integration of sensors with electric power, stretchability, biocompatibility, data collection and transmission, and signal detection. For consideration of the wearer’s comfort optimized sensor design and integration of innovative self-powered, multifunctional, ultra-modern smart wearable and flexible electronics is required. This could provide new information for delivering health status in the near future.

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C H A P T E R

13 Photo Sensor Based on 2D Materials Dattatray J. Late1, Anha Bhat1,2 and Chandra Sekhar Rout3 1

Physical and Materials Chemistry Division, CSIR-National Chemical Laboratory, Dr. HomiBhabha Road, Pune, India 2Department of Metallurgical and Materials Engineering, National Institute of Technology, Srinagar, India 3Centre for Nano and Material Sciences, Jain University, Jain Global Campus, Bangalore, India

13.1 INTRODUCTION In the frontiers of device electronics are emerging applications in the fields of medical diagnosis, optical communications, radiation and smoke detection, flame detection, barcode detection, process control, environmental sensing, motion sensing, thermography, night vision, and astronomy. The most important development over the past decade includes the integration of optics into electronic systems, which has paved the way for multifunctionality and enhanced performance. One such development is the harnessing of light into electric signals with enhanced speed, efficiency, and flexibility over the range of wavelengths packaged as photodetectors. A photo detection process can operate through various ways like phototconductive effect [1], photo-thermoelectric effect (PTE) [2], and photovoltaic effect [3] fulfilling the consensus of absorbing the photons to generate an electric response. When a photon with sufficient energy enters the junction, it strikes an atom to release an electron from the atomic structure. This mechanism is known as the inner photoelectric effect. During this mechanism, free electron and hole pairs are created.

Fundamentals and Sensing Applications of 2D Materials DOI: https://doi.org/10.1016/B978-0-08-102577-2.00013-0

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These free electron and hole pairs are either combined or they remain free. Movement of free electrons and hole pairs from the depletion layer due to external electric field produces a photocurrent. The photocurrent is proportional to the amount of light entering the intrinsic region. The chapter deals with different two-dimensional (2D) material architectures ranging from transition-metal dichalcogenides (TMDCs) to black phosphorous (BP) and graphene/TMDC heterostructures with a general comparison of photoresponse for all of them. The heterostructures have proven to be quite efficient material as photodetectors and sensors due to their optimized properties and good charge trapping efficiency.

13.2 CHARACTERISTICS OF PHOTOSENSORS BASED ON THE 2D MATERIALS Bulk silicon has an indirect bandgap of 1.1 eV, which limits its photon absorption capacity to the visible and near infrared (IR) region of the electromagnetic spectrum, and is a major factor contributing to its low efficiency as a photodetector. Many alternatives such as 3D structures of Gallium Arsenide (GaAs) have been proposed to detect IR wavelengths, but there are industrializing drawbacks such as packaging, an increase in the size of the device, and many additive fabrication steps. With the advent of graphene and few-layered TMDCs, alternative materials have been found to provide higher absorption efficiency in the visible range with a wide operational wavelength [4
6]. Features including increased absorption efficiency and transparency that are attributed to single atomic scale thickness make these materials a potential player in cutting-edge applications of photovoltaics, photodetection, and photonics. TMDs show out-of-plane quantum confinement effects and vertical confinement where the reduced thickness constricts the excitons and hence increases the absorption efficiency [7]. A characteristic feature of 2D semiconductor crystals are the Van Hove singularities leading to sharp peaks in the density of states at a particular energy attributed to the d orbital localization of electronic bands. In the case of 2D semiconductors, these singularities fall in the vicinity close to the conduction and valence band edges. Thereby a photon with energy close to the bandgap has an increased probability to excite an electron-hole pair, which makes them receptive to the incident light. This property, along with remarkable elastic modulus .10% has extended their applications to flexible sensors by tuning the optical properties on the basis of strain engineering [8,9]. Based on the different mechanisms of photons to electrical signal conversion, the photo-sensing effect can be achieved by the following three effects: photovoltaic effect, photoconductive effect, and PTE.

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13.3 PHOTOVOLTAIC EFFECT In the photovoltaic effect the electromagnetic radiation energy is converted into electric energy in certain semiconductor materials. The photovoltaic-based photodetector contains p-/n-type (PN) photodiodes formed by two semiconductors with opposite doping type. When light of a suitable wavelength is incident on these cells, energy from the photon is transferred to an atom of the semiconducting material in the p-n junction and Schottky barrier photodiodes are formed at the interface between a semiconductor and a metal. The built-in difference in electric field is necessary for the photovoltaic effect and is created either by local chemical doping [10] and electrostatic control through gates [11]. The configuration of photodetectors based on this approach is usually in the cadre of PN diodes used at zero bias, which is in coherence with photovoltaic mode or under reverse bias, which is the photoconductive mode. In the former, the dark current is the lowest, which is good for detectivity, but at the same time due to no internal gain, absolute responsivity is usually smaller than photoconducting. However the speed of a photodiode as shown in Fig. 13.1 is increased due to the reduction of the junction capacitance in case of reverse bias [13,14].

FIGURE 13.1 (A) p-n junction band alignment, where absorption of a photon with Eph . Ebg generates an electron-hole pair, which is then separated and accelerated by builtin electric field at the junction. (B) Ids 2 Vds, which results in a short-circuit current Isc and an open-circuit voltage Voc. With maximum power generation denoted by Pel max in the fourth quadrant [12]. Source: Printed with permission.

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13.4 PHOTOCONDUCTIVE EFFECT In a photoconductive effect after electromagnetic radiation absorption the electrical conductivity of nonmetallic solids is increased due to the generation of additional free electron carriers. A typical photoconductor consists of a semiconductor as a channel with two ohmic contacts affixed to opposite ends of the channel, which serves as source/drain electrodes [15]. In the absence of light is a zero current between two electrodes called the dark current. The electromagnetic radiation having higher energy than the bandgap is able to generate electronholes pairs which can be separated by applying a voltage. Such free electrons and holes move due to voltage drift to their respective majority sides (toward electrodes). Due to this a depletion layer is created on either side of the junction. The drifted electrons and holes increase the conductivity of the device. The current as depicted in Fig. 13.2 is generated due to the photoconductive effect and is dependent on the intensity of the electromagnetic radiation, length of the transistor channel, source
drain voltage, and the charge carrier mobility [16,17].

13.5 PHOTO-THERMOELECTRIC EFFECT In PTE a light-induced heating leads to a temperature gradient through a semiconductor channel. There is a temperature difference between the two ends of the semiconductor channel. Due to the Seebeck effect, as shown in Fig. 13.3, the temperature difference gets converted into a voltage difference ΔV whose magnitude is linearly proportional to the temperature gradient [16,18].

13.6 MOLYBEDNUM DISULFIDE-BASED PHOTOSENSING DEVICES The abundant availability of molybdenum disulfide (MoS2) has made it one of the most widely studied TMDCs. Bulk MoS2 is subjected to various methods like chemical exfoliation [18], ultrasonic treatment, and mechanical exfoliation [19,20] in order to yield a monolayer. For impurity-free studies, researchers would prefer to grow crystals by processes like chemical vapor deposition [21]. This method of homegrown crystals also allows for the induction of dopants to engineer the bandgaps [22]. Phototransistors of MoS2 have been reported with a photoresponsivity of about 7.5 3 1023A W21. A monolayer phototransistor

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FIGURE 13.2 (A) Band alignment with small current Idark under external bias for a semiconductor channel contacted with two metals (M) without illumination. (B) Band alignment under illumination with photons of energy (Eph) higher than the bandgap (Ebg). (C) Ids 2 Vg traces in the dark and under illumination. Illumination results in an increase in the conductivity (vertical shift) and a positive photocurrent across the entire gate voltage range. (D) Ids 2 Vds curves in the dark (black line) and under illumination (red line), which results in an increase of the conductivity and a positive photocurrent under illumination [16]. Source: Printed with permission.

fabrication has been reported which involved the deposition of MoS2 on Si/SiO2. The height of monolayer MoS2 measured by AFM is B0.8 nm, and the photoluminescence of a single-layer MoS2 sheet was observed at room temperature using the 488 nm laser, which showed a dominant PL peak at 676 nm. The dominant peak arises from the direct intraband recombination of the photogenerated electron-hole pairs in the singlelayer MoS2, while the weak peak at B623 nm is considered to be the

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FIGURE 13.3 (A) Schematic of a field-effect transistor whose metal contact is locally illuminated by a laser. The circuit is open and a thermoelectric voltage ΔVPTE develops across the contacts. (B) Thermal circuit of the device. (C) Ids 2 Vds characteristics in the dark (black line) and under illumination (red line) of a device whose photo-response is dominated by the photo-thermoelectric effect [16]. Source: Printed with permission.

contribution of the energy split (Fig. 13.4) of valence band spin-orbital coupling of MoS2 [20,23,24]. The device showed unprecedented characteristics of incident-light control, efficient photoresponsivity, and photoswitchability, which allows the fabrication of cheap and affordable optoelectronic devices. Further, the study in this device stated that the photocurrent was dependent on the optical power. When the device was subjected to energy above the excitation wavelength of 670 nm, the photocurrent was lower than after the expulsion of dark drain current. However, there was a significant increase in the photocurrent when the wavelength was lowered from 670 nm, satisfying the basic principle of incident photon energy being greater than the energy gap, which is about 1.83 eV. The electrons having large incident photon energy (hν . 1.83 eV) can generate more photoelectrons contributing to the increased photocurrent. Moreover this device also showed good stability and efficient photoswitchability. The switching behavior was observed when drain current shot up to high value under illumination and resumed to lower values under dark, which is an off state [25]. In other cases of chemical vapor deposition (CVD)-grown MoS2, a photosensor reported by Lopez et al. [25] was made of triangular structures of MoS2 grown on Si/SiO2 substrates by mild sulfurization of MoO3. Electron beam lithography was used to fabricate the metal contacts of reportedly 4 nm thick Ti and 100 nm thick Au at 10
7 Torr

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FIGURE 13.4 (A) Drain current (Ids) as a function of excitation wavelength of the illumination source at constant optical power. (Inset shows single-layer MoS2 AFM image and optical image of fabricated device). (B) Output characteristics of phototransistor at different illuminating optical powers. (C) Dependence of photocurrent on optical power at different Vds. (D) Photoswitching characteristics of single-layer MoS2 phototransistor at different optical power and drain voltage [20]. Source: Printed with permission.

pressure. The Raman spectrum obtained at a laser excitation of 488 nm revealed a PL spectrum with maximum intensity at 670 nm corresponding to 1.86 eV photon energy. The first order Raman modes are observed due to D3h point group symmetry which is the characteristic of noncentrosymmetric monolayer MoS2. The PL and Raman spectra were obtained in the region between cathode and anode confirming the presence of monolayer MoS2 and its direct bandgap. The sensor response was observed when two wavelengths of the light along the order of 488 and 514.5 nm were used to probe the photosensitivity. The laser excitation and bias voltage is observed between 0 and 90 s, which is complemented further with current versus time plot as the readings are accounted for every 200 ms. The applied bias voltage was increased from 1.5 to 2.0 V around tB52 s, barely producing a flicker in the dark current whereas for 5 s starting at t 5 14 s and t 5 64 s, the excitation laser illuminated the device. As shown in Fig. 26 noting the response to excitation in both cases, the photocurrent response was found to be around 50% larger in response to the bias voltage at 2.0 V (63 s , t , 68 s) as compared to V 5 1.5 V (14 s , t , 19 s) [25] (Fig. 13.5).

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FIGURE 13.5 (A) Bias voltage and laser excitation. (B) Total current as a function of time for the CVD-grown monolayered MoS2 device. (C) Effect on photocurrent. IV plots of the device under darkness and two different wavelengths (514.5 and 488 nm) [25]. Source: Printed with permission.

FIGURE 13.6 Schematic of MoSe2 phototransistor fabrication by polymer transistor [27]. Source: Printed with permission.

13.7 MOLYBDENUM DISELENIDE-BASED PHOTOSENSOR The monolayer MoSe2 has properties like direct bandgap (EgB1.6 eV) and large exciton binding energy similar to that of MoS2, which has made it one of the most sought-after choices for investigating electrical and optical properties. The argument is further strengthened by the observance of strong photoluminescence in MoSe2 [26]. It is expected to show a higher photoresponse in the solar spectrum range and has high antiphotocorrosion stability. A phototransistor was fabricated by depositing 50 nm titanium (Ti) electrodes by electron-beam evaporation on silicon substrates. As shown in Fig. 13.6 MoSe2 was transferred onto the Ti electrodes using polydimethylsiloxane (PDMS) transfer technique [27].

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FIGURE 13.7 (A) Drain-source voltage dependence of the drain current at different values of the gate voltage of an MoSe2-based phototransistor. (B) Gate voltage dependence of the drain current at different values of the drain-source voltage of an MoSe2 phototransistor [27]. Source: Printed with permission.

The higher saturation current is observed in the phototransistor structure compared to multilayered MoSe2. The variation of the drain current (Id) with drain-source voltage (Vds) at varied values of the gate voltage is shown in Fig. 13.6(B) while linear dependence was observed at low drain-source voltages along with transfer characteristics for a range of drain-source voltages. This shows the ohmic nature of the contacts. The phototransistor showed n-type behavior, accumulating the electrons at the interface between the gate oxide and MoSe2. The threshold voltage (Vth) and the current on/off ratio at Vds 5 10 V were observed to be about 225 V and 3 3 104 while the high threshold voltage is unsuitable for low power consumption applications. High κ-dielectrics material if used instead of SiO2 as the gate oxides will reduce the threshold voltage (Fig. 13.7) and enhance the current on/off ratio [27
29].

13.8 TUNGSTEN DISULFIDE-BASED PHOTOSENSOR Different observations of the photocurrent response of CVD grown WS2 on quartz substrate has been reported. The spectral responsivity was proportional to the wavelength of the excitation source with the limits for lower excitation (λ 5 647 nm; E 5 1.91 eV) and was found to be 2.0 μ A W21 while for the higher energy (λ 5 568 nm; E 5 2.71 eV) it was higher value of 21.2 μ A W21. The sensitivity with different excitation intensities was found to support the observation that the photocurrent, IP, is nonlinearly related to the power of the laser excitation [30]. Such a photosensitive material has good sensitivity at low intensities and higher sensitivity at higher intensities. The photocurrent

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measurements were carried out at room temperature using a vacuum chamber at 1 3 10
6 Torr pressure, and were coupled to a micro-Raman spectrometer. In all the photocurrent measurements, the photocurrent produced by switching the laser beam on and off is the time-varying component. The plots below show photocurrent versus applied voltage plots and the three different illumination intensities were at powers of 0.65, 3.25, and 6.25 mW. These I
V plots show a linear increase of photocurrent with applied voltage. A large photocurrent was observed by the spectral responsivity of the WS2 device as a function of the wavelength of the laser excitation. The photocurrent was monitored as a function of time with periodic laser beam illumination revealing the strong dependence on the wavelength and on the illumination power. The fast measured response resulted in 5.3 ms for response and recovery times with good responsivity, and stability as compared to other TMDs, demonstrating that WS2 is a good option for optoelectronics [31]. In the case of WS2, as shown in Fig. 13.8, Zheng et al. reported a unique

FIGURE 13.8 Device structure and photoresponse of photodetector. (A) Schematic representation of the photodetector consisting of a WS2 film and the Ti/Au contacts on quartz, and the laser sources were applied perpendicularly to the film. (B) Current-voltage plot obtained without illumination; the inset displays the microscope image of a pair of the Ti/Au electrodes. (C) I
V curves of the device obtained under different light intensities from 12.1 to 395 μ W cm
2 at a wavelength of 365 nm. (D) The corresponding responsivity vs. voltage plots acquired under various light intensities [32]. Source: Printed with permission.

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FIGURE 13.9 (A) Optical image of a BP photodetector. The ring-shaped structure shows the photocurrent collector where yellow lines are Ti/Au electrodes and the area enclosed by a white line is the BP flake. (B) Corresponding photocurrent microscopy image of the device with illumination at 1500 nm. (C) Polarization dependence of photoresponsivity with illumination from 400 to 1700 nm [34]. Source: Printed with permission.

photoelectrical conversion property with a high responsivity of 53.3 A W
1 and a high detectivity of 1.22 3 1011 Jones at 365 nm as well as a technique for large-scale growth of WS2 film, which can be transferred to be developed for such applications as photosensors, solar cells, and photo-electrochemical cells.

13.9 BLACK PHOSPHOROUS-BASED PHOTOSENSOR BP is the anisotropic material that exhibits direct bandgap of about B1.8 eV in single layer which is tunable with different thicknesses covering the visible to mid-IR spectral range, as compared to bandgap of B0.3 eV in its bulk form [33]. Photodetector-based on BP layer (Fig. 13.9) shows high responsivity of about 103 A W21 at 300 K and 7 3 106 A W21 at 20 K in the near-IR region (900 nm). The photogenerated carriers are effectively collected due to good ohmic contact to BP [34]. BP was also integrated with silicon waveguide to realize high responsivity photodetection. The photovoltaic current dominates the photocurrent at low doping where the intrinsic responsivity reaches 135 mA W21 for a thickness of about 11.5 nm and 657 mA W21 for a thickness of about 100 nm at room temperature and having high response bandwidth exceeding 3 GHz [35,36].

13.10 2D HETEROSTRUCTURES The heterostructures of 2D materials aim to develop the structures that have optimized emergent properties due to the in-proportion growth of constituent. Graphene/MoS2 structures aim at harnessing the high carrier mobility of graphene and photon absorption capability of

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MoS2. A high photo gain greater than 108 and a photoresponsivity value higher than 107 A W21 is exhibited in the photodetector based on this heterostructure. The high photo gain is due to a recirculation of electrons in graphene due to the enhanced lifetime of charge trapping of holes due to MoS2. The electric field of an external gate modulates the amplitude and polarity of the photocurrent in the gated vertical heterostructures with the maximum external quantum efficiency (EQE) of 55% and internal quantum efficiency up to 85% [37]. The p-type WSe2 and n-type MoS2 heterostructures have been reported to have tuned the photocurrent by the gate voltages leading to an interlayer tunneling recombination of majority carriers [38]. Similarly, MoS2-graphene-WSe2 heterostructures have the photoexcited electrons and holes which, due to the built-in electric field, are effectively separated. The depletion region of the p-n junction enables photodetection over a broad range with high sensitivity. Due to the photon energy being larger than the bandgaps of the TMDs materials in the visible range, the abundant photogenerated free. In case of IR wavelength, the photon energy is comparatively smaller than the bandgap of TMDs, which leads to the forbidden interband absorption of both monolayer MoS2 and WSe2, leaving graphene alone as a photon-absorbing medium. This has implications in relatively smaller photoresponse of the heterostructure in the IR range. The device exhibits unprecedented performance with photoresponsivity of 104 A W21 at 400 nm, and the specific detectivity shows up to 1015 Jones and 1011 Jones in the visible and near-IR region respectively, as carriers are produced by each constituent, resulting in a considerably higher photoresponse [39,40] (Fig. 13.10).

13.11 RECENT DEVELOPMENT AND APPLICATIONS The photosensor devices are marked by certain parameters like responsivity, EQE, internal quantum efficiency, time response, and wavelength and noise equivalent. The photodetectors based on different 2D materials like MoS2, MoSe2, WS2 even though grown on the same substrate show huge variability in responsivity. The photodetectors based on MoS2 present comparatively larger responsivity and response times with respect to MoSe2 or WS2 devices. One common factor about being receptive to environmental changes is what makes them a potent option for light-sensitive gas detectors. The semiconducting di- and tri-chalcogenides have been observed to not show photoresponse at telecommunication wavelengths which paved the way to investigate the BP and which demonstrates sizable responsivity (about 0.1 A W21) and response speed (f3dB B3 GHz) under λ 5 1550 nm excitation. BP has developed as a promising candidate for fast and broadband detection in

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FIGURE 13.10 (A) Schematic of the photodetector based on graphene/MoS2 heterostructure along with the graphs showing photoresponsivity (left) and photogain (right) for the graphene/MoS2 photodetectors. (B) Graphene-MoS2-graphene heterostructures laserilluminated device and its I
V characteristics. Inset represents the schematic illustration of the device. (C) Schematic diagram of a van der Waals-stacked MoS2/WSe2 heterojunction device with lateral metal contacts and the corresponding graph shows the measured and simulated photocurrent at Vds 5 0 V as a function of gate voltages with photocurrent map as inset of the device. (D) Cross-section model of MoS2-graphene-WSe2 heterostructurebased photodetector with photoresponsivity R (left) and specific detectivity D (right) for wavelengths ranging from 400 to 2400 nm measured in ambient air (bottom). Inset is the optical image the device [41]. Source: Printed with permission.

IR region which could also be exploited for light energy harvesting in the IR part of the spectrum. It is evident that photodetectors based on semiconducting layered materials display a large (about 10 orders of magnitude) variation in their responsivity. The limiting dark current and improved device performance have been the novel features of design variations of TMD-based photodetectors. In the case of 2D heterostructures, for the visible range, the photon energy is larger than the bandgaps of the individual TMDs materials and sufficient amount

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of photogenerated free carriers are produced by each layered materials, resulting in a considerably higher photoresponse. Besides these, the indium, gallium, and tin chalcogenides are fast catching up as Ga, In, and Sn compounds show responsivities that are comparable or larger than the one measured from TMDC-based photo-FETs. They can also be used as UV detectors as the operation can be extended to UV region. The superior performance of 2D materials are due to their emergent properties, which leads to ultrafast response time, generation of carriers, and better control on switchability. All of these factors have made them a better choice for optoelectronics and photonic devices.

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C H A P T E R

14 Future Prospects of 2D Materials for Sensing Applications Hywel Morgan1, Chandra S. Rout2 and Dattatray J. Late3 1

School of Electronics and Computer Science, University of Southampton, Southampton, United Kingdom 2Centre for Nano and Material Sciences, Jain University, Jain Global Campus, Ramanagaram, Bangalore, India 3 Physical and Materials Chemistry Division, CSIR-National Chemical Laboratory, Dr. HomiBhabha Road, Pashan, Pune, India

Over recent years research on two-dimensional (2D) materials has accelerated because of their exotic electrical and optical properties, rich physics, and novel chemical properties. They also have very interesting and outstanding mechanical properties due to their atomically thin dimensions. The isolation and large-area synthesis of a variety of novel 2D atomically thin materials provide new opportunities for layer-bylayer materials and hybrid/heterostructure device engineering that enable the investigation and exploitation of superior or currently unknown properties that promise a range of new device applications and technologies. The different properties of 2D materials need to be further explored, and while many technologies and applications are still in their infancy, their exceptional applications and potential should be explored further. This book covers cutting-edge research, unresolved problems, and stateof-the-art results on 2D material properties and sensor device applications. In this book, 2D materials include graphene, hexagonal boron nitride (h-BN), metal dichalcogenides, metal trichalcogenides, black phosphorous, metal oxides, MXenes (silicene, germanene, etc.), metal hallides, and metal carbides.

Fundamentals and Sensing Applications of 2D Materials DOI: https://doi.org/10.1016/B978-0-08-102577-2.00014-2

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There is significant scope for in-depth development of 2D materials sensors as presented in this book. Systematic, accurate, and extensive 2D materials characterization is one of the challenges that need to be extended to all inorganic 2D materials. This is highly applicable to new materials that are still in their infancy, including PtS2, PtSe2, HfS2, HfSe2, HfTe2, TiS2, GaTe, FeSe, InSe, In2Se3, MoTe2, WTe2, Bi2Te3, GaS, GaSe, GeS, GeSe, SnS2, SnSe2, TaS2, ZrS2, ZrSe2, Sb2Te2, and black phosphorous, for which no detail characterization data is available. Examples include Raman spectra as function of number of layers/thickness of the nanosheet, scanning tunneling microscopy, transmission electron microscopy, and electron energy loss spectroscopy (EELS). EELS is based on inelastic scattering of fast electrons in a thin specimen and can provide structural and chemical information about a thin nanosheet down to one atomic resolution. Currently, there is growing interest in 2D materials for various applications such as sensors that have the possibility of operating at room temperature. There is tremendous interest in furthering our understanding of the sensing mechanisms involving 2D materials by different spectroscopic techniques. It should come as no surprise if the next-generation sensing devices are replaced by 2D materials-based channel materials because of their unusual and extraordinary properties which are not found in either bulk materials or other oxide-based nanomaterials. The various evaluation and characterization techniques described in this book will guide future materials research and design of devices yet to be realized. The scope of future work includes addressing large-scale production and large area deposition of 2D materials or heterostructure with precise control of layer thickness. This needs to be combined with methods to understand, characterize, and determine optimal properties to attain best performance.

FUNDAMENTALS AND SENSING APPLICATIONS OF 2D MATERIALS

Index Note: Page numbers followed by “f” and “t” refer to figures and tables, respectively.

A Ab initio molecular dynamics (AIMD) simulations, 113
114 Accumulation time, 265 Acetylene linkage, influence, 167
169 Acid rain, 419
420 Acrylonitrile butadiene styrene, 453
454 Acute promyelocytic leukemia (APL) detection, 279 Added exchange, fraction of, 184
185 Adhesive tape, 27
28 Adsorbates, 58 Adsorption energy, 177
178, 215
217, 216f sensing elements, 182
189 electronic properties, 182
186 optical properties, 186
189 site, 177 Adsorption-induced charge transfer mechanism, 217 Ag doping on contact angle, 267
268, 268f Agglomeration, 68
69 Ag layer sensor, 424
425 “Alarm” sensors, 420 Al-doped graphene, 224 Amorphous indium gallium-zinc-oxide (aIGZO) thin film transistor, 446 Angle-resolved photoemission spectroscopy (ARPES), 115
116 Anisotropic bonding, 411
412 Anti-CRP antibody (aCRP)-GO, 389, 390f Antimonene, 118, 119f, 172
174 Antimonene material 2 based supercapacitors, 124
126 Aptamer-based sensors, 285 Arc discharge, 310
311 Arsenene, 118
120, 172
174 B-doped and N-doped, 172
174 Arsenic (As), 302 Ascorbic acid, 226 Asymmetric (hybrid) electrochemical supercapacitor, 71f

ATLAS-TFET with ultrathin bilayer MoS2, 371f, 372
373 Atomic absorption spectroscopy (AAS), 303 Atomic emission spectroscopy (AES), 303 Atomic fluorescence spectroscopy (AFS), 303 Atomic force microscopy (AFM), 8, 27
28, 36
38, 37f Atomic layer deposition (ALD), 96
97 Atomic orbitals, 146
147 Attomolar (aM) based on optical photoluminescence (PL), 387 Au-graphene-selenocystine modified glassy carbon electrodes, 314
316

B Bader charge analysis, 178
180 Ball milling method, 33, 64
65 Band alignment, 7
8 bandgap, 7
8 bilayer graphene, 7 crystal symmetry, 8 density functional theory, 8 2 H bulk, 8 2H-TMDs with surface doping, 9f octahedral (Oh) lattice, 7
8 trigonal prismatic (D3h) arrangement, 7
8 ultrathin lattice and interplanar interaction, 7 Band-bending, 355
356 Bandgap, 7
8, 164
165, 372
373 Bandgap modifications GGA 1 u method, 183
184 GW corrections, 185
186 hybrid functional, 184
185 Band-to-band tunneling (BTBT) current, 348 Bare electrodes, 264
265 B5 distorted bipyramid isomer, 187f Becke 2 Lee 2 Yang 2 Parr (BLYP), 153

483

484

INDEX

6-Benzylaminopurine (BAP) determination, 290 “Beyond graphene” material, 410
411 Bias-dependent molecular sensitivity, 213 Bilayer graphene, 7, 41 Bilayer (2L) MoS2 film on Si/SiO2, 239f transistor, 240f Bimetallic nanoparticles, 308 Binder jetting, 50 Biochemical optical sensing properties, 380
381 Biocompatibility, 396
397 Biological sensor, 305 Biomarkers, 453
454 Biomolecular-based devices/sensors, 290
291 Biomolecules conjugation, 354, 355f, 358 sensing application, 385
389 deoxyribonucleic acid (DNA) sensing, 387
388 protein sensing, 389 single cell detection, 385
387 specific detection of, 336
337 Biorecognition elements, 266 Biosensors, 21. See also Sensors characteristics, 260 chemical and, 1 classification according to material used, 264f defining, 259
260 development, 261t electrochemical, 262
263. See also Electrochemical biosensors enzymatic, 266 history, 260
262 nanostructured, 265
266 next-generation, 263 patch-type, 450
451 4s—of, 264
265 types, 259
260 Biotin, device functionalized with, 336
337, 337f Biotin H1 and 2, 285 Bipolar junction transistor (BJT), 122
123 Black phosphorene, 172 Black phosphorous (BP), 1
2, 379
380, 410, 465
466 nanosheets solution, 383, 383f optical image of, 475f Black phosphorous-based photosensor, 475

Black phosphorous 2 based twodimensional material for sensors, 246
248 Blood glucose (BG) monitoring, 450 Blue phosphorene (blueP), 424
425, 426t B3LYP, 155
156 Boltzmann tyranny effect, 349 Born 2 Oppenheimer approximation, 148
149 Boron family, graphene analogs, 93
94 Boron nitride (BN), 249, 379
380 Brillouin zone (BZ), 6
7, 157
158 Brunauer 2 Emmett 2 Teller (BET) model, 45 Buckled honeycomb hexagonal lattice, 172 Bulk silicon, 466

C Cancer diagnosis biomolecule detection for, 269
270 disposable screen printing electrode (SPE), 270 photothermal and chemotherapy, 396
398 Capacity retention, 64
65 Carbon allotropes, 25
26 atom, covalent in-plane bonding, 26 black, 64
65 forms of, 26f quantum dots, 303
304 Carbonaceous precursors, 27 Carbon-based bioelectrodes, 269
270 Carbon-layered electrode array (CLEAR) device, 399
401, 400f Carbon-LiFePO4 (LFP/C), rate capability, 66 Carbon nanotubes (CNTs), 30 Carbon paste electrodes (CPEs), 264
265 Carcinoembryonic antigen (CEA), 21, 270, 272f Carrier mobility, 5
6, 26 Catechol (CC), 280
281 Categorized library, 170f Cauliflower mosaic virus 35S (CaMV35S) gene detection, 281 Cavitation, 29 C-doped BN nanosheets, 249 Cell-based H2O2 sensor, 272
273, 273f Chalcogen, 410
411 atomic layers, 106
107 Channel conductance, 329
330

INDEX

Channel length, sensitivity, 339f Channelrhodopsin-2, 399
401 Charge carrier mobility, 468 Charge transfer mechanism, 162 and orbital interactions, 178
180 process and density difference, 179f Charge trapping efficiency, 465
466 Chemical adsorption, 262
263 Chemical and biosensors, 1 Chemical and physical sensing, 13
21 Chemical exfoliation method, 8
10, 233 Chemical inertness, 396
397 Chemically modified electrodes (CME), 264
265 Chemically RGO, 57 Chemical methods, graphene synthesis, 32
34 Chemical sensing applications gas sensing, 389
392 heavy metal ion sensing, 394
396 humidity sensing, 392
394 Chemical sensor, 220, 249 Chemical surface modification, 383
384 Chemical vapor deposition (CVD), 8, 10, 310
311 2D layered materials preparation, 12f fabrication, 414
416 graphene synthesis, 32, 74 thermodynamics and kinetic functionality, 415f Chemico-physical tunability, 57 Chemiresistive devices, 57 Chemiresistive sensors, 208
209, 208f Chemisorption, 215 Chemotherapy, 396
397 drugs doxorubicin (DOX), 397
398 Chirality, graphene, 40 Chitosan (CS) functionalized rGO sheets, 270 Chlorosulfonic acid, 31
32 Chromium (Cr), 303 Chronoamperometric (CA) response, CEA, 270
272 Coated micromachined resonators, 57 Coffee-ring effect, 53 Colorimetric sensing, 304 Columbic efficiency, 63 Combustion-emission processes, 419
420 Computer-aided modeling software, 54 Conducting polymer matrices, 57
58 Conducting polymers, EC biosensors, 266

485

Conductive composite-based temperature sensors, 441 Conductometric sensors, 209
210, 210f Conductor-based temperature sensors, 441 Contact angle analysis, 267
268 Contact printing, 50 Conventional FET- (CFET-) based biosensors, 345
346, 349 and TFET biosensors, 349, 351f Conventional nanolithography, 229 Coplanar-gate graphene transistors (GTs), 449 Copper phthalocyanine (CuPc), 446 CO2-pulsed laser deposition, 249 Core-level spectroscopy (CLS), 115
116 Correlated light emitters, 409
410 Coupled cluster, 147 Covalent bonding, 262
263, 270 Covalent bonds, 411
412 CP2K, hybrid methods, 146
147 C-reactive protein (CRP) sensing, 389 Cross linking, 262
263 Cross-sensitivity, protein sensing, 389 Current collector, graphene as, 66
67 Cycle life, 207 Cyclic voltammetry (CV) analysis, 277f, 278
279 Cycling performance, 64
65, 65f Cystamine-modified gold nanoparticles, 266, 266f Cysteine, 276 Cytochrome c78, 21

D Debye 2 Huckel screening length, 347
348 Defect(s) adsorption and desorption, 225
226 magnetism of graphene, 45 Defect-free lattice, 47 Defect-induced ferromagnetism, 45 Defective and functionalized graphenebased gas sensors, 224
229 Defective graphene, 215
217 Deionized (DI) water, 382 Density functional calculations, graphene, 33 Density functional theory (DFT), 8, 43, 113
114, 147, 150
153 corrections to dispersion forces, corrections for, 156 hybrid functionals, 154
156

486

INDEX

Density functional theory (DFT) (Continued) local density approximation 1 U, 154, 155f exchange-correlation functional, approximations to, 152
153 Hohenberg and Kohn theorems, 150
151 Kohn 2 Sham equations, 151
152 Density of states (DOS), 159, 372
373 Deoxyribonucleic acid (DNA) sensing, 387
388 Detection limit, 207 Dichalcogenides, 273
274 Differential pulse anodic stripping voltammetry (DPASV), 306 Differential pulsed voltammetry (DPV), 270 Diodes, 445
446 Dirac cone, 6
7, 112
113, 169
172 Dirac points, 40
42 Direct charge transfer, 333 Direct ink writing (DIW), 50 Disease diagnostics, DNA sensing, 387 Dispersion corrections Grimme’s Density Functional Theory-D2, 180
181 Grimme’s Density Functional Theory-D3, 181 optPBE 2 van der Waals, 181
182 van der Waals 2 DF, 181 Dispersion forces corrections for, 156 semiclassical treatments, 156 and ultrasonication, 8 Disposable graphite pencil electrode (GPE), 270 DNA hybridization, 424 graphene-MoS2 heterostructure, 387
388, 388f DNA target and signal amplification, 387 DNAzymes, 305 Doped silicone, 171f Doping, 41
42 of novel metals, 418
419 Double-layer charge density, 347 Drain-to-source current, 329
330 Drop-casting method, 208
209 2D material coating, 383 GO sheets, 226 Drying, 53 2D and 3D printing, graphene drying, 53 printing, 51
53

E E-beam lithographically patterned electrodes, 222 E-beam lithography, 239 Electrical conductivity, 50
51 Electrical sensors and solutions, limitation impact-ionization-MOS-based biosensor, 353
361 steep transistors, 2D materials for, 370
373 tunnel field-effect transistor 2 based biosensor, 346
353 tunnel field-effect transistor 2 based gas sensor, 361
370 Electric double-layer capacitor (EDLC), 67
68 Electrochemical biosensors electrochemical sensing and their sensing mechanism, parameters, 265 enzymatic biosensors, 262
263 molybdenum disulfide (MoS2) based, 274
293 tin (IV) sulfide- (SnS2-) based materials, 289
293 tungsten disulfide- (WS2-) based materials, 283
289 nonenzymatic electrochemical sensors, 263 nonenzymatic sensing, nanomaterials for, 265
268 two-dimensional materials-based, 268
274 graphene sheets synthesis, 269
273 nongraphene 2D materials for, 273
274 Electrochemical double-layer capacitors (EDLC), 124 Electrochemical heavy metal ion sensing, 312
313 Electrochemical impedance spectroscopy (EIS) measurement, 270 Electrochemical label free sensing, 21 Electrochemical lithiation method, 8
10 and exfoliation processes, 8
10 Electrochemical sensing and their sensing mechanism, parameters, 265 Electrochemical sensor, 305
306 Electrolytes/ions and metabolite detection, wearable sensors, 453
454 Electromagnetic wave interference (EMI), 401

INDEX

Electron acceptor and conductance, 209
210 gas, 161
162 Electron-beam evaporation, 472 Electron beam irradiation, 410
411 Electron diffraction intensities, 38 Electron (hole) doping, 58
59 Electron-electron coupling, 409
410 Electron energy loss spectroscopy (EELS), 38 measurement, 276
277 Electron-hole carrier density, 381 Electron occupation number, 183 Electron-phonon coupling, 409
410 Electron-phonon interactions, 147 Electron spin resonance measurements, 45 Electro-polymerized MoS2, 279
280 Electrospun polymer fibers, 230 Electrostatic control, 467 Electrostatic gating, 333 ELK, 146
147 Energy conversion efficiency, 449 Energy cutoff, 176 sensing application, 157
158 Energy dispersive x-ray spectroscopy (EDS), 38 Energy storage, graphene analogs, 121
122, 123f Entrapment, 262
263 Enzymatic biosensors, 262
263, 266 Enzymatic glucose sensor, 262
263 Enzyme-based blood glucose sensors, 260 Enzyme immobilization, 263 Enzyme-less glucose sensing, 266
267 Enzyme-linked immunosorbent assays (ELISA), 329
330 protein sensing, 389 Epitaxial growth on SiC, 310
311 E-skin, 454
455 Etched fiber Bragg grating (EFBG), 389 Ethylene di amine (EDA) modified multiwalled carbon nanotubes (MWCNTs), 267
268 Ethylenediamine-modified rGO (EDArGO), 228
229 7-Ethyl-10-hydroxycamptothecin (SN38), 397
398 Exchange-correlation energy, 153, 181 Exchange-correlation functional, 153 approximations to, 152
153 Exchange functional, 176

487

Exfoliated WS2 flake, 241f External quantum efficiency (EQE), 475
476 Extrapolation in the saturation region (ESR) method, 334
335

F Fabricated chemiresistive gas sensors, 222 Fabricated graphene sensor, 229
230 Fabricated nonenzymatic sensors, 287
289 Fabricated reagent-less biosensors, 290
291 Fabricated single-layer chemiresistive sensors, 208
209 Fabricated transistor-based gas sensors, 238
239 Fabricated transistor gas sensors, 235 Fabrication, 57, 411
416 chemical vapor deposition (CVD), 414
416 hydrothermal synthesis, 413
414 mechanical exfoliation, 412
413 molecular beam epitaxy, 414 procedure, graphene sensor, 14f Face-centered cubic (fcc) crystal, 159
160 Fe-doped mono- and tetravacant graphene, 224 Fermi-tail cutting, 349 Ferromagnetism, 42 Few-layers graphene (FLG), 25
26 Fiber Bragg gratings (FBGs), 382 Field-effect transistor 2 based biosensors applications, 330f biomolecules, specific detection of, 336
337 electrical sensors and solutions, limitation, 345
373 graphene and, 340
343 pH sensing, 334
336 potential, 330f scalability and single molecule detection analysis, 337
340 transduction mechanism and sensor performance, 333
334 work-function modulated gas sensor, 343
345 Field-effect transistors (FETs), 122
123, 441, 445
446 sensors, 209, 209f Field emission scanning electron microscope (FESEM), 99

488

INDEX

Finite element method (FEM) modeling, 49 Flexible brain core temperature sensors, 441 Flexible tattoo sensors, 454 Flow cytometry, 385 Fluorescence probes, 396
397 Fluorescence quenching, 21, 380, 387 Fluorophore, 303
304 Fo¨rster resonance energy transfer (FRET), 303
304 Free space permittivity, 448
449 Fullerenes, 25
26 Fused deposition modeling (FDM), 50, 55

G Gallium selenide (GaSe), 101 Gallium sulfide (GaS), 100
101 nanosheet device, 244f Gas chromatography-mass spectroscopy (GCMS), 56 Gases on 2D materials, interaction mechanism adsorption energy, 177
178 adsorption site, 177 charge transfer and orbital interactions, 178
180 dispersion corrections, 180
182 Gas-sensing mechanisms, 13, 161, 389
392 device fabrication, 2
3 in graphene, 215
217 graphene-based 2D materials charge transfer mechanism, 162 gas-sensing mechanism, 161 graphene oxides and reduced graphene oxides, modeling, 164
166 graphyne and graphdiyne, modeling, 167
169 modeling, 162
163 in metal oxides, 219
220 2D transition metal dichalcogenides, 217
219 Gas-sensing parameters, 176 Gas sensors device configurations types, 207
213 chemiresistive sensors, 208
209, 208f conductometric sensors, 209
210, 210f field-effect transistor sensors, 209 heterojunction semiconductor gas sensor, 213 impedance sensor, 210, 211f Schottky diodes sensors, 213 Si microcantilever, 213

surface acoustic wave sensors, 211
212, 212f SWF change transistor, 213 gas-sensing mechanisms, 215
220 graphene analogous two-dimensional materials, 237
249 graphene-based materials, 220
237 graphene for, 58
61 hydrogen sensor, 420 hydrogen sulfide sensor, 420
422 influential parameters of, 213
215 nitrogen dioxide sensor, 419
420 parameters, 206
207 Gate dielectric, 334
335 Gating effect, 329
330, 353
354 Gauge factor (GF), 447
448 vs. strain percentage, 444f Gaussian-centered orbitals, 146
147 G-code, 54 General-area detector diffraction system (GADDS), 95 Generalized gradient approximation (GGA), 152 Gene therapy, 387 Germanene, 1
2, 111
112, 112f, 169
172 Germanium, 112 Glassy carbon electrodes (GCEs), 264
265 Glucose amperometry sensing of, 292f biosensing, 290
291 detection, wearable sensors, 450
452 Glucose oxidase enzyme (GOx), 262
263 Glucose oxidase (GOD), graphene-based sensor, 450
451 Glucose oxidase immobilization, 290
291 Glucose sensing in 2D materials alpha glucose, DFT relaxed structure, 190f 2D materials, simulations, 190
191 glucose molecule, simulations, 189
190 interaction, 191
192 metal-doped transition metals oxides, 193
196 MoO3, total density of states for, 193f WO3, total density of states for, 194f Glutathione-modified AgNPs, 305 Gold nanoelectrode ensembles (GNEE), 307f, 308 Graphene, 6
7, 311
313 applications of 2D and 3D printing, 49
55 for gas sensors, 58
61

INDEX

in lithium ion batteries, 61
67 in supercapacitors, 67
71 volatile organic compounds, detection, 56
58 atomic structure of, 6f challenges, 74
75 characterization microscopy, 36
38 Raman spectroscopy, 34
36 composition, 25
26 energy bands in, 6f field-effect transistor 2 based biosensors and, 340
343 graphene aerogels (GAs), 71
74, 73f metal contaminate sensing, graphenebased materials, 311
313 and metal oxide hybrids, 317
318 orientations of edge atoms in, 44f preparation, 1
2 properties, 38
49 synthesis, 27
34 chemical methods, 32
34 chemical vapor deposition (CVD), 32 liquid-phase exfoliation, 28
32 micromechanical cleavage, 27
28 zero bandgap, 1
2 Graphene aerogels (GAs), 71
74, 73f Graphene analogous two-dimensional materials, 237
249 black phosphorous 2 based twodimensional material for sensors, 246
248 layered III 2 VI group materials 2 based sensors, 243 layered metal oxide 2 based sensors, 244
245 transition metal di-chalcogenide 2 based gas sensors, 238
243 two-dimensional materials, 249 Graphene analogs, 92
121 applications, 121
126 energy storage, 121
122 optoelectronics, 121 sensor, 125f, 126 supercapacitor, 123
126 transistor, 122
123 boron family, 93
94 2D family and their applications, 92f layered metal dichalcogenides, 102
106 metal halides (MH), 99 metal monochalcogenides, 100
102 MXenes, 109
111

489

transition-metal dichalcogenides, 106
108 transition-metal oxides (TMO), 94
99 Xenes, 111
121 Graphene-based composites, 309
310 Graphene-based fluorescence probes functionalities in PL, 396
397 Graphene-based gas sensors, 13 defective and functionalized, 224
229 Graphene-based hybrids, 63
64 Graphene-based immunosensors for protein molecules, 271f Graphene-based materials, 220
237 defective and functionalized graphenebased gas sensors, 224
229 graphene/metal nanocomposites 2 based gas sensors, 230
234 graphene/metal oxide nanocomposite 2 based gas sensors, 234
237 graphene/polymer-based gas sensors, 229
230 pristine graphene-based gas sensors, 220
224 Graphene-based optical prism sensor (GOPS) platform, 385 Graphene-based smart eye contact lenses, 401
403, 402f Graphene-based two-dimensional heterostructures 2D library, 408t fabrication, 411
416 chemical vapor deposition (CVD), 414
416 hydrothermal synthesis, 413
414 mechanical exfoliation, 412
413 molecular beam epitaxy, 414 one-step vapor phase growth mechanism of, 416f physical properties, 409f 2D crystal-based heterostructures sensors, 417
426 gas sensor, 418
422 humidity sensor, 417
418 nitrite sensor, 425
426 surface plasmon resonance sensor, 422
425 and two dimensional transition metal dichalcogenides, 410
411 vertical and lateral, formation, 416f Graphene-coated D-shaped fiber (GDF) for gas sensing, 391
392, 391f

490

INDEX

Graphene-coated optical prism, 386f Graphene EAu 2 modified electrode, 314f Graphene FET biosensor, 330
332, 344f Graphene for gas sensing, modeling antimonene, 172
174 arsenene, 172
174 germanene, 169
172 phosphorene, 172
174 silicene, 169
172 stanene, 169
172 transition metal dichalcogenides, 174
176 MoS2, 174
176 Graphene/h-BN heterostructures, 414 Raman spectra of, 414f Graphene-metal hybrids, 313
316 Graphene/metal nanocomposites 2 based gas sensors, 230
234 Graphene-metal nanoparticle hybrids, 316t Graphene 2 metal oxide hybrids, 319t Graphene/metal oxide nanocomposite 2 based gas sensors, 234
237 Graphene-modified GPE, 270 Graphene nanoelectronic heterodyne sensor, 222
223 Graphene nanoplatelets (GNP), 71
73 Graphene nanoribbon (GNR) array, 231
232 Graphene oxides (GOs), 32
33, 160
161 adsorption energy and bond length, 164
165, 165t antibacterial properties, 74
75 DFT-optimized structures of, 164f and reduced graphene oxides, modeling, 164
166 Graphene paper, 64 Graphene/polymer-based gas sensors, 229
230 Graphene 2 Pt nanocomposites, 314
316 Graphene-quantum dots (GQDs), 314
316, 396
397 Graphene sensing channels, 442 Graphene sheets, 232f synthesis, 269
273 Graphene/TMDC heterostructures, 465
466 Graphene-woven fabric (GWF), 447
448 Graphite, 25
26 Graphite oxide, 27 Graphite pencil electrode (GPE), 270

Graphyne and graphdiyne, modeling, 167
169 acetylene linkage, influence of, 167
169 binding energy, 167 charge density plots, 168f DFT-optimized structure, 167, 168f Green solvents, 31 Groundwater contamination, 302

H Hall geometry, 13 Hall resistivity, 221, 221f Hansen solubility parameters, 30
31 Hard and soft acids and bases (HSAB), 308
309 Hartree 2 Fock approximation (HFA), 185
186 Hartree 2 Fock (HF) method, 147, 149 H-BN, 1
2 2 H bulk, 8 Health-care applications, optical biochemical sensors, 396
403 cancer diagnosis, photothermal and chemotherapy, 396
398 ophthalmology, 401
403 optogenetics, 399
401 Heat flux sensors, 441 Heavy metal ions detection, 316t, 319t sensing, 394
396, 395f Heterojunction semiconductor gas sensor, 213 Hexagonal-boron nitride, 93
94, 94f Hexagonal hollow, 167
169 Hexagonal lattice, 40 Heyd 2 Scuseria 2 Ernzerhof (HSE06), 184
185 High-angle annular dark-field (HAADF), 38 Highly oriented pyrolytic graphite (HOPG), 27
28, 113
114 High-performance transistors, 1
2 High-sensitivity sensors, 409
410 Hohenberg and Kohn theorems, 150
151 Honeycomb-buckled lattice, 111 Honeycomb lattice, 6
7, 25
26 Horseradish peroxidase (HRP), 266 HSE06, 155
156 2H-TMDs with surface doping, band structure evolution, 9f Hubbard correction, 154 Hubbard model, 43

INDEX

Humidity gas sensor, 214 sensing, 392
394, 393f 2D crystal-based heterostructures sensors, 417
418 Hummers method, 32
33, 57 Hybrid capacitor, 67
68 Hybrid functionals, 154
156 Hybrid graphene, 70f Hybridization, 1
2 Hybridization chain reaction (HCR), 284
285 amplification, 286f DNA detection, 287f Hybrid supercapacitors, 68
69 Hydrazine-based reduction, 45 Hydrazine hydrate (HH), 226 Hydrogen patterning, 41 storage, 46 Hydrogenated nanographite, 45 Hydrogen sensor, gas sensor, 420 Hydrogen sulfide sensor, gas sensor, 420
422 Hydrophilic and hydrophobic graphene, 160
161 Hydrothermal synthesis, 96
97 fabrication, 413
414 MoS2 nanostructures, 274
275 Hydroxyl functionalized graphene, 165
166 Hysteresis, 12
13

I Imaging ellipsometry, 36 Immunosensor preparation, 270 Immunosensors, functionalization, 270 Impact-ionization field effect transistor (IFET), 353
354, 358
361, 359f Impact-ionization-MOS-based biosensor, 353
361 band-bending, 355
356 band diagram, 355f biomolecule conjugation, 354, 355f, 358 nanowire-based impact-ionization MOSFET biosensor, 353
354, 354f natural length scale, 355
356 protected region (PR), 353
354 response time, 359
360, 361f semiconductor-oxide interface potential, 355
356

491

sensing region (SR), 353
354 sensitivity, 360f source bias, 358, 358f source voltage function, 355f Impedance sensor, 210, 211f Incorporated graphene nanoplatelets, 57 Indium selenide (InSe), 102, 103f Indium tin oxide (ITO), 41, 399 Induced coupled plasma mass spectrometry (ICP-OES), 303 Infrared (IR) temperature sensors, 441 Ink formulation, 51, 51f, 52f Inkjet printing, 226 Inner photoelectric effect, 465
466 Inorganic nanomaterials, 272
273, 306
308 In-plane stability, 411
412 In situ layer-by-layer (i-LbL) technique, 383
384 Integrated tilted fiber grating (TFG) optical sensor, 395
396 Intercalation, 8 induced phase transformation, 8
10 Interdigitated electrode, 208
209 Interference-based techniques, 36 Interlayer coupling, 411
412 Intrinsically conducting polymers, VOCs detection, 56 Ionic liquids (ILs), 31 Ion-selective field-effect transistor (ISFET) sensors, 260 Iontophoresis, 451
452 Iron oxide reductive dissolution, 302 Irreducible Brillouin zone (IBZ), 159
160 Isopropanol (IPA), 8

J Jurkat cancer cells, 385
387

K Kinetic energy cutoff, 157
158 Kohn 2 Sham equations, 151
152 K-point grid, 176 sampling, 157
158 Kretschmann structure, 422
424

L Label-free protein sensing, 389 Label-free real-time single cell detection, 385

492

INDEX

Label-free sensing, 379
380 “Lab-on-a-Glass,” biosensor platform, 453
454 Langmuir adsorption model, 230 Langmuir 2 Blodgett (LB) method, 117
118 Laser diodes, 121 Laser excitation and bias voltage, 470
471 Lattice dynamics, 147 Lattice-matching requirement, 411
412 Layer-by-layer manufacturing technology, 50 Layered III 2 VI group materials 2 based sensors, 243 Layered metal dichalcogenides, 102
106 tin diselenide (SnSe2), 104
106 tin disulfide (SnS2), 104 Layered metal di-selenides, 242 Layered metal oxide 2 based sensors, 244
245 Lead (Pb), 303 Lead iodide (PbI2), 99, 100f Lead zirconate titanate (PZT), 449 Light-emitting diodes (LEDs), 121, 445
446 Light-induced heating, 468 Light-induced thermal therapy, 396
397 Lightweight graphene nanosensor, 454 Li-intercalation, 410
411 Limit of detection (LOD), 13, 303
304, 379
380 Linearity, 12
13 “Linear response” approach, 183
184 Lipoic acid modified PEG (LA-PEG), 397
398 Liquid-phase exfoliation, 8, 28
29, 30f, 274
275, 310
311, 383, 407
408 graphene synthesis, 28
32 Lithium intercalation, 274
275 Lithium ion batteries (LIBs) cathodes, graphene in, 64
66 current collector, graphene as, 66
67 graphene in, 63
64 schematic, 62f working of, 61
63 Lithium nickel manganese oxides, 64
65 Local chemical doping, 467 Local density approximation (LDA), 152 Local refractive index, 381 Low concentration detection (LCD), 337
338 Low-energy electron diffraction (LEED) pattern, 112

Low-energy electron microscope (LEEM), 118 Low-voltage amplitude signal, 385
387 Luminescent nanoparticles, 303
304

M Magnetic carbon, 42 Magnetism, 147 Many-Body Hamiltonian, 147
148 Massless Dirac fermions, 6
7 Mass spectrometry, protien sensing, 389 Mechanical exfoliation, 8, 269
270, 310
311, 407
408, 411
412 fabrication, 412
413 Memory devices, 409
410 MEMS-based microgas chromatography, 57 Mercury (Hg), 303 Mercury-free electrodes, 306
308 Metal clusters, 303
304 Metal contaminate sensing, graphene-based materials graphene, 311
313 graphene and metal oxide hybrids, 317
318 graphene-metal hybrids, 313
316 Metal-decorated graphene/rGO, 230
231 Metal-doped transition metals oxides, 193
196 Metal 2 graphene interface, 231 Metal halides (MH), 99 lead iodide (PbI2), 99 Metal 2 insulator transition (MIT), 95
96 Metal ion contaminants detection, 301
306 biological sensor, 305 electrochemical sensor, 305
306 optical sensor, 303
305 diseases, heavy metal ion pollution, 302f electrochemical sensing, materials for, 306
309 metal contaminate sensing, graphenebased materials, 309
318 Metal monochalcogenides, 100
102 gallium selenide (GaSe), 101 gallium sulfide (GaS), 100
101 indium selenide (InSe), 102 Metal nanoparticles (MNPs), 265
266, 306
308, 343
345 Metal organic framework sensors, VOCs detection, 56 Metal oxide 2 based gas sensors, 161 Metal oxides, 56

INDEX

VOCs detection, 56 Microelectrocorticography (micro-ECoG) neural interfaces, 399 Microelectromechanical microsystems (MEMS), VOCs detection, 56 Microemulsion, 64
65 Micromechanical cleavage, 274
275 graphene synthesis, 27
28 Micro-mechanical exfoliation, 1
2 Microribbon graphene to dichlorobenzene (DCB), 225f Microscopy atomic force microscopy, 36
38 optical microscopy, 36 transmission electron microscopy, 38 Microstructure-frame-supported organic thermoelectric (MFSOTE) device, 445 Microturbulence, 29 Microwave-assisted exfoliation graphene, 31 Microwave plasma-enhanced CVD (MWPECVD) method, 235
236 Miniaturization, 57 Mobility modulation, 333 Mocouple-based temperature sensors, 445 Molecular beam epitaxy fabrication, 414 Molecular beam epitaxy (MBE), 101, 411
412 Molecularly imprinted- (MIP-) based sensor, 290 Molybdenum diselenide (MoSe2), 5
6 Raman spectra of, 16f single-layer, 15
16, 17f Molybdenum diselenide-based photosensor, 472
473 Molybdenum disulfide (MoS2), 5
6, 107
108, 174
176, 418, 424 based electrochemical biosensors, 274
293, 275f field-effect transistor (FET) biosensor, 344f field-effect transistors (FETs), 15, 22f gas adsorption on, 175f humidity sensor, 19f, 20f optical microscopy image, 15, 15f real-time measurement of current of, 345f screen printing electrode, 277f Molybdenum disulfide (MoS2) based electrochemical biosensors tin (IV) sulfide- (SnS2-) based materials, 289
293

493

tungsten disulfide- (WS2-) based materials, 283
289 Molybdenum trioxide (MoO3), 95 Molybednum disulfide-based photosensing devices, 468
471 Monolayers, 47
49 BP, 410 graphene, 38 of molybdenum disulfide (ML-MoS2), 18
19 WSe2, 242f MoO3-based nonenzymatic glucose sensor, 267
268 MoS2-based field-effect transistor (FET) biosensor, 332f MoSe2 phototransistor fabrication, 472f gate voltage of, 473f MoS2/graphene heterostructures Raman spectra, 417f MoS2-PANI preparation, 281
283, 282f Motion-free Ag nano-crystal- (NC-) based thin film, 444
445 MRSDCI, post HF-based methods, 186
187 Multilayer graphene, 223f Multilayer phosphorene-based FET sensor, 247 Multireference configuration interaction, 147 MXenes, 1
2, 109
111, 249

N Nafion/HRP/Mg-TND/Ti-based H2O2 sensor, 266
267, 267f Nanocomposites, 46 Nanodiamond-derived graphene (DG), 46 Nanogenerators (NGs), 449 Nanomaterials, 1, 263, 306
308 Nanometallic organic framework, 303
304 Nanostructured biosensors, 265
266 Nanostructured transition metal oxides, 308 Nanotubes, 25
26 Nanowire-based impact-ionization MOSFET biosensor, 353
354, 354f Natural graphite flakes, 31 Nb1.33C, 110
111 N-doped graphene oxide, 272
273 Near body ambient temperature sensors, 441 Neutron activation analysis (NAA), 303 Next-generation biosensors, 263

494

INDEX

Nitrite sensor 2D crystal-based heterostructures sensors, 425
426 Nitrogen dioxide sensor, gas sensor, 419
420 Nitrogen-doped graphene, 70
71 Nitrogen doping, 70
71 N-methyl-pyrrolidone (NMP), 8, 30
31 Noble-metal nanostructure-decorated graphene, 230
231 Noise frequency range, 221
222 Noncollinearity, 147 Noncontact printing techniques, 50 Noncontact processes, 50 Nonenzymatic electrochemical sensors (nEECSs), 263 Nonenzymatic sensing, nanomaterials for, 265
268 Non-equilibrium Green’s function (NEGF) calculations, 172 Nongraphene 2D materials for electrochemical biosensors, 273
274, 274f Noninvasive wearable NTC sensor, 442 Nonlocal density-based dispersion corrections, 156 Nonmetallic silicon carbide (SiC) substrate, 308 Nontoxicity, 396
397 Nose pads of eyeglasses, printed sensors, 453
454 Nuclear repulsion, 149 Nucleic-acid aptamers, 21 Nucleic acids detection, 21, 270

O Octahedral (Oh) lattice, 7
8 OCTOPUIS code, 187 Oligomeric ionic liquids, 31 Oligonucleotide hybridization, 304 On/off ratio, 5
6 On-site Coulomb interaction, 154, 183
184 Open energy bandgap, 269
270 Ophthalmology, 401
403 Optical absorption spectroscopy, 8 Optical amplifiers, 121 Optical biochemical sensors biochemical optical sensing properties, 380
381, 381f biomolecules sensing application, 385
389

deoxyribonucleic acid (DNA) sensing, 387
388 protein sensing, 389 single cell detection, 385
387 chemical sensing applications gas sensing, 389
392 heavy metal ion sensing, 394
396 humidity sensing, 392
394, 393f fabrication, 381
385, 382f direct CVD-grown, 384
385 solution method, 383
384 transferring CVD-grown, 382 health-care applications, 396
403 cancer diagnosis, photothermal and chemotherapy, 396
398 ophthalmology, 401
403 optogenetics, 399
401 Optical coherence tomography (OCT) angiogram, 399
401 Optical microscopy, 8, 36 Optical modulators, 121 Optical refractive index sensor, 385 Optical sensing technology, 329
330 Optical sensor, 303
305 Optical spectrum analyzer (OSA), 391
392 Optoelectronics graphene analogs, 121 Optofluidics methods, 385 Optogenetics, 399
401 Orbital-dependent potential, 154 Organic conducting polymers, 364
366 Organic molecules (OMs), metal ion sensing, 308
309 Organic semiconductor-based pentacene, 446 Orthorhombic vanadium pentoxide (V2O5), 95
96 morphologies, 96
97 Out-of-plane quantum confinement effects, 466 Oxide-assisted chemical vapor deposition (OCVD), 93
94 Ozone-treated graphene sensors, 225

P PANI-decorated graphene, 230 Parathyroid hormone concentration (PTH), immunoassay biosensors, 276 Parts per billion (ppb) levels, 1 Paste viscosity, 50 Patch-type biosensors, 450
451 Patch-type transistor device, 454

INDEX

Pauli exclusion principle, 151
152 Pd-decorated GNR sensors, 231
232 Pearson acid 2 base concept, 308
309 Perdew 2 Burke 2 Ernzerhof (PBE), 153 Perovskite, 379
380 Perovskite materials, 266
267 3, 4, 9, 10-Perylenetetracarboxylic-dimide (PTCDI), 446 PH detection, wearable sensors, 452
453 Phonon emission, 369
370 Phosphorene, 116
118, 172
174, 410 Photocatalysis, 46 Photochemical smog, 419
420 Photoconductive effect, 468 Photocurrent response, 470
471 Photo detection process, 465
466 Photodetectors, 121 Photodynamic agent chlorin e6 (Ce6), 397
398 Photoluminescence (PL) spectra, 218
219, 218f Photoresponse of photodetector device structure and, 474f Photo-response time, 243 Photoresponsivity, 243 Photosensitivity, 470
471 Photo sensor based on 2D materials black phosphorous-based photosensor, 475 characteristics, 466 development and applications, 476
478 molybdenum diselenide-based photosensor, 472
473 molybednum disulfide-based photosensing devices, 468
471 photoconductive effect, 468 photo-thermoelectric effect, 468 photovoltaic effect, 467 tungsten disulfide-based photosensor, 473
475 2D heterostructures, 475
476 Photostability, 396
397 Phototherapy treatment, 396
397 Photo-thermoelectric effect, 468 Photovoltaic effect, 467 PH sensing, 334
336 principle, 335f sensitivity, 335
336 subthreshold swing (SS), 335
336 Physical adsorption, 262
263 Physical vapor deposition (PVD), 99 Physisorption, 176, 215

495

Piezocapacitive-based wearable strain sensor, 448
449 Piezoelectric-based wearable strain sensors, 449 Piezoelectric polymers, 449 Piezoresistive-based wearable strain sensor, 447
448 Pilocarpine, 455
456 Planar substrates, 50 Plasmonic nanoparticles, 304 Plasmons, 304 Platinum nanoparticle-modified DNAzymes, 305 Platinum thin film embedded with polyimide films, 444
445 Point-of-care clinical analysis, DNA sensing, 387 Polarization beam splitter (PBS), 385 Poly(methyl methacrylate) (PMMA), 14
15, 222 polymer, 382 Polycrystalline chemical vapor deposition (CVD) graphene, 225f Polycrystalline graphene (PCG), 224
225, 225f Polydiacetylenes, 58 Polydimethylsiloxane (PDMS) based graphene thermistor, 442, 443f microfluidic chip, 385 transfer technique, 472 Polyethylene terephthalate (PET), 446 substrates, 41 Poly(methyl methacrylate) (PMMA)mediated technique, 32 Polymerase chain reaction (PCR), DNA sensing, 387 Polymer-assisted graphene nanowalls, 444
445 Polymer-based composites, 266 Polymeric binders, 61
62 Polymer nanocomposites, 266 Polymer photoresist PMMA, 229 Polymer sensors, 56 Poly(3,4-ethylenedioxythiophene)-poly (styrenesulfonate) (PEDOTPSS), 57 Poly (3,4-ethylenedioxythiophene): poly (styrene sulfonate) (PEDOT:PSS), 270
272, 272f Polytetrafluoroethylene (PTFE), 61
62 Polyvinyl alcohol (PVA), 49 Polyvinylidene difluoride (PVDF), 61
62

496

INDEX

Portable gas detection systems, gas sensing, 160 Portable sensors, 439
440 Positive temperature coefficients (PTCs), 442 Postprint treatment, 53
54 P-phenylenediamine (PPD), 226 Pressure-sensitive devices, 447
448 Printing, 51
53 Printing matrix, 50 Printing nanocomposites, 50 Printing paste, 55 Pristine, 171f Pristine antimonene, 172
174 Pristine graphene, 74
75 Pristine graphene (PG), 225f Pristine graphene-based gas sensors, 220
224 Pristine pyrolytic graphite, 42 Prostate specific antigen (PSA), 21 Protected region (PR), impact-ionizationMOS-based biosensor, 353
354 Protein sensing, 389 Protein sensing applications, biosensor, 389 Pseudo-capacitance, 67
68 Pseudo-capacitive material, 69 Pseudo-capacitors, 67
69, 124 Pseudopotential density functional theory (DFT), 146
147 Pulsed laser deposition (PLD), 96
97

Q Quantum dots, 303
304 Quantum Espresso, 146
147 Quantum Hall effect (QHE), 91
92, 309
310 Quantum Monte Carlo, 147 Quantum resistive sensors (QRS), 57
58 Quantum simulations method Born 2 Oppenheimer approximation, 148
149 density functional theory, 150
153 exchange-correlation functional, approximations to, 152
153 Hohenberg and Kohn theorems, 150
151 Kohn 2 Sham equations, 151
152 Hartree 2 Fock Method, 149 Many-Body Hamiltonian, 147
148 Quartz crystal microbalance, protien sensing, 389

R Radial immunodiffusion, protien sensing, 389 Radio frequency 2 enhanced plasma, 32 Receptor-ligand interactions, 285 Recovery time, 12
13 Reduced graphene oxide (RGO), 27, 160
161, 164
166 films, 305 nanosheets, 446 Reference electrode (RE), 265 Relative humidity (RH), 392
394 sensing, 18
19 Resistance thermometer detectors (RTDs), 442
445 Resistive temperature detectors, 441 Resolution, 207 Response and recovery time, 12
13, 207 Response time, 10
12, 359
360, 361f Responsive dye, wearable sensors, 455
456 Retinoic acid receptor alpha (RARA), 279 Reversibility, 228 RGO-based transparent conductors, 41 RGO-tetra ethylene pentamine (TEPA), 270 Room temperature ballistic conduction, 26

S Sandwich-type immunosensor, 21 Scalability and single molecule detection analysis, 337
340 Scanning probe microscopy (SPM), 36 Scanning transmission electron microscope (STEM), 38, 101 Scanning tunneling microscopy (STM), 8 Scanning tunneling spectroscopy (STS), 113
114 Schottky barrier height (SBH), 213 Schottky barrier photodiodes, 467 Schottky diodes sensors, 213, 249 Scotch tape, 407
408 Screened Coulomb potential, 184
186 Screening length, 184
185 Screening radius length, 184
185 Screen printing electrode (SPE), 270 Seebeck coefficient, 445 Seebeck effect, 445 Selected area electron diffraction (SAED), 38 pattern, 104 Selective laser melting, 50 Selective laser sintering, 50 Selectivity, 10
13, 207

INDEX

Self-activated transparent gas sensor, 14
15 Self-consistent cycle, Kohn 2 Sham equations, 153f Self-consistent procedure, 152 Self-interaction correction, 154 Self-powered piezoelectric-based strain sensors, 449 Semiconducting-based metal sulfides, 290 Semiconducting di- and tri-chalcogenides, 476
478 Semiconducting transition metal dichalcogenides (STMD), 188
189 Semiconductor-based metal oxide, 418
419 Semiconductor-based sensors, 445
446 Semiconductor-based temperature sensors, 441 Semiconductor-oxide interface potential, 355
356 Sensing, 46 Sensing application adsorption of sensing elements, change in properties due to, 182
189 electronic properties, 182
186 optical properties, 186
189 density functional theory, corrections to, 154
156 gases on 2D materials, interaction mechanism, 176
182 gas sensing, graphene-based 2D materials, 160
169 glucose sensing in 2D materials, 189
196 graphene for gas sensing, modeling, 169
176 quantum simulation methods, 147
153 quantum simulations, 146
147 simulation results, sensitivity of, 156
160 energy cutoff, 157
158 k-point grid, convergence, 159
160 theoretical modeling and simulations for, 145
146 Sensing elements, adsorption of, 182
189 electronic properties, 182
186 optical properties, 186
189 Sensing material nanostructures as, 418
419 Sensing region (SR), 353
354 Sensitivity, 12
13, 207, 222, 343
345, 348, 360f, 418
419 graphene, 57 Sensors biosensors, 259
260

497

graphene analogs, 125f, 126 performance, parameters of, 10
13 response, 10
12, 207 Shear mixers, 29 Shelf life, 263 Silicene, 112
114, 114f, 115f, 169
172, 249 Silicon-based anodes and graphene, 63
64 Silicon carbide (SiC), 269
270 Silicone, 1
2 Silicone-based sensor, 169
172 Silicon-nanowire sensors 2 based fieldeffect transistor (FET), 387 Silicon on insulator (SOI) FET, 337
338 Silicon sensors, 441 Silver doping on contact angle, 268f Silver layer sensor, 424
425 Silver nanowire (AgNW) electrodes, 442 Si microcantilever, 213 Single cell detection, 385
387 Single-crystal Pt substrate, 310 Single-crystal X-ray diffractometer (SCXRD), 95 Single graphene film, 269
270 Single-layer 2D crystals, 8 Single-layer graphene, 91 electronic band structure, 39f Single-layer graphene (SLG), 25
27, 221
222 sheet, 269
270 Single-molecule-detection limit, 13, 14f Single-walled carbon nanotubes (SWCNTs), 447
448 electrodes, 308 Skin-worn glucose biosensors, 450 Slater determinant, 149 Sodium sensor belt, 453
454 4s—of biosensors, 264
265 Solar exfoliated reduced graphene oxide (SrGO), 442 Solution-based reduction, graphene, 47
49 Solvothermal, 96
97 Source bias, impact-ionization-MOS-based biosensor, 358, 358f Source 2 drain voltage, 468 Spectroscopic measurement techniques, 303
304 Spin coating, 2D material, 208
209 Spin-orbit coupling (SOC), 7, 112
113 Spin-resolved density, 45 Squamous cell carcinoma antigen (SCCA), 270

498

INDEX

Square-wave anodic stripping voltammetry, 314
316 Square-wave stripping voltammetry (SWSV), 308 Stability, 12
13, 207 Standard Tessellation Language (STL) file, 54 Stanene, 114
116, 116f, 169
172 Steep transistors, 2D materials for, 370
373 Stereolithography, 50 Strain-resistant/compliance properties, 5
6 Streptavidin, 336
337 Stretchable screen-printed electrochemical sensor, 450
451 Stripping voltammetry, 306
308 Strontium palladium perovskites (Sr2PdO3), 266
267 Subthreshold swing (SS), 342f, 343f sensitivity, 352f Sulfonated reduced graphene oxide-(SrGO-) based NO2 gas sensors, 419
420 Sulfonated rGO (S-rGO), 236
237 Supercapacitors (SC), graphene in analogs, 123
126 doping and surface modifications, 70
71 graphene-based, 72t graphene nanocomposites with various materials, 69
70 introduction and working of, 67
69, 67f Superconductivity, 147 Surface acoustic waves devices, VOCs detection, 56 sensors, 211
212, 212f Surface-adsorbed oxygen ions mechanism, 161 Surface and interface physics, 6
7 Surface area graphene, 45
46, 46t Surface-enhanced Raman scattering, 387 Surface-Enhanced Raman spectroscopy (SERS) sensors, 305 Surface plasmon resonance (SPR) biosensor, 126 protien sensing, 389 Surface plasmon resonance sensor Ag layer sensor, 424
425 blue phosphorene (blueP), 424
425, 426t configuration types, 422
424 DNA hybridization, 424 heterostructures advantage, 424 Kretschmann structure, 422
424

molybdenum disulfide (MoS2), 424 role, 422
424 sensing performance, 426t uses, 424 Suspended bilayer graphene, 60
61 Suspended graphene membrane, schematic of nanoindentation, 48f Sweat-based glucose monitoring, 450
452 Sweat glucose (SG) monitoring, 450
451 Sweat lactate, 454 Sweat metabolites analysis, wearable sensors electrolytes/ions and metabolite detection, 453
454 glucose detection, 450
452 pH detection, 452
453 Sweat-secreting drug, 455
456 SWF change transistor, 213 Synchrotron radiation, 115
116

T Tattoo-based noninvasive glucose sensor, 450
451 Tattoo sensor, 454 Tellurene, 120
121 Temperature coefficient of resistance (TCR), 441
442 Temperature, gas sensor, 214 Tetra butyl ammonium bromide (TBAB), 444
445 Theophylline, 287 Thermal fluctuations, 25 Thermally sensitive resistor (thermistor), 441
442, 443f Thermal or microwave-assisted exfoliation, 27 Thermal radiation sensor, 441 Thermal vapor transport (TVT), 95 Thermistors, 441
442, 443f Thermocouple sensors, 441, 445 Thermodynamic instability, 25 Thermometer and temperature sensor chip, 441 Thin film transistor (TFT), 446 Thiolated amino acid 2 capped AuNPs, 309 Thiol-tailored aptamer, 285 Threshold voltage, 334
335 Ti3C2Tx, MXene, 109 Tilted fiber Bragg grating (TFBG), 383
384 Time-dependent density functional theory (TDDFT), 186
187 Tin diselenide (SnSe2), 104
106, 106f

INDEX

Tin disulfide (SnS2), 104, 105f Tin (IV) sulfide- (SnS2-) based materials, 289
293 Titanium dioxide (TiO2), 97
99, 98f Titanium oxide, 97
99 TMD monolayer, 7 Topological insulators (TIs), 379
380 Total energy convergence, 158, 158f Transduction mechanism and sensor performance, 333
334 Transistors, 445
446 graphene analogs, 122
123 Transition metal di-chalcogenide 2 based gas sensors, 238
243 Transition metal dichalcogenides (TMDCs), 1
2, 5
6, 106
108, 174
176, 379
380, 407
408, 465
466 classification, 410
411 molybdenum disulfide (MoS2), 107
108, 174
176 monolayers of, 10f tungsten disulfide (WS2), 108 Transition-metal oxides (TMO), 61
62, 94
99, 308 molybdenum trioxide (MoO3), 95 titanium dioxide (TiO2), 97
99 vanadium oxide, 95
97 Transition metal substrates, 32 Transmission electron microscopy (TEM), 8, 38 Triangle hollow, 167
169 Trigonal prismatic (D3h) arrangement, 7
8 Tumor biomarkers, 270 Tungsten disulfide (WS2), 5
6, 108 mechanical exfoliation technique, 16, 18f TEM analysis, 284f Tungsten disulfide- (WS2-) based materials, 283
289 Tungsten disulfide-based photosensor, 473
475 Tungsten disulfide (WS2) material-coated optical side-polished fiber (WS2CSPF), 392
394, 393f Tunnel FET (TFET), 345
346 Tunnel field-effect transistor 2 based biosensor, 346
353 biomolecule-receptor conjugation, 347 modeling scheme, 346
347, 350f working mechanism, 346f Tunnel field-effect transistor 2 based gas sensor, 361
370 affectability, 367

499

band-to-band-tunneling current, 363 gas pressure, function of, 366f gate bias and operation regime, 364, 364f gate work function (WFG), 361
363 metal and oxide layer, 362f metallic gate, 361
363 MOSFET sensitivity, 365f organic conducting polymer gate, 364
366 polymer gate, work-function, 366f schematic diagram, 362f semiconductor-oxide interface, 363 sensitivity, 363, 366, 367f tunneling probability, 363 Tunneling probability, 369
370 Two-beam-laser interference (TBLI) method, 227
228 2D and 3D printing, graphene advantages, 49
50 coffee-ring effect, 53 computer-aided modeling software, 54 contact printing, 50 fused deposition modeling, 55 G-code, 54 layer-by-layer manufacturing technology, 50 manufacturing techniques, 50 materials, 54 noncontact printing techniques, 50 noncontact processes, 50 planar substrates, 50 printing paste, 55 Standard Tessellation Language (STL) file, 54 steps drying, 53 ink formulation, 51, 51f, 52f postprint treatment, 53
54 printing, 51
53 2D blue phosphorus monolayer, 172 2D buckled hexagonal honeycomb structure, 249 2D crystal-based heterostructures sensors, 417
426 gas sensor, 418
422 humidity sensor, 417
418 nitrite sensor, 425
426 surface plasmon resonance sensor, 422
425 2D and 3D printing, graphene additives, 55

500

INDEX

Two-dimensional materials-based electrochemical biosensors, 268
274 graphene sheets synthesis, 269
273 nongraphene 2D materials for, 273
274 Two dimensional transition metal dichalcogenides, 410
411 2D library, 408t “2D material beyond graphene”, 169 2D material for sensing. See Field-effect transistor 2 based biosensors 2D materials, 1
2 band alignment, 7
8 chemical and physical sensing, 13
21 graphene analogous, 249 preparation, 8
10 sensor performance, parameters of, 10
13 surface and interface physics, 6
7 2D silicene, 112
113

U Ultrahigh-speed photodetectors, 409
410 Ultrahigh vacuum (UHV) system, 112 Ultrasensitive sensor, 447
448 Ultrasonication, 29, 33 Ultra-strong Coulomb interaction, 7 Ultrathin lattice and interplanar interaction, 7 Ultraviolet illumination, 418
419

V Vacancy-induced defected graphene, 224 Vanadium dioxide (VO2), 95
96 Vanadium oxide, 95
97 Van der Waals (vdWs) bonding, 91
92 density functionals, 156 interactions, 162
163 Van der Waals gap, 370
372 Van der Waals interaction, 262
263 Van Hove singularities, 466 VASP code, 146
147, 186
187 V4C3Tx, MXene, 109
110 Vertical graphene (VG), 235
236 VESTA, 178
180 Volatile organic compounds (VOCs), 438 detection, 56
58 Voltammetry, 306

W Wearable and flexible sensors advantage, 437 challenges, 456

characteristics, 456 chemicals examination, 438 developments, 438 Holter monitor, 438 materials for, 440
441 sensing mechanisms, 437 sensor design and application, 440f strain/pressure sensors, 446
449 sweat metabolites analysis, 450
454 systematic compartmentalization of, 438f temperature sensors, classes, 441
446 types of, 439f for volatile biomarkers detection, 454
456 Wearable strain/pressure sensors sensing mechanisms, 446
449 piezocapacitive-based wearable strain sensor, 448
449 piezoelectric-based wearable strain sensors, 449 piezoresistive-based wearable strain sensor, 447
448 Wearable temperature sensors, classes resistance thermometer detectors (RTDs), 442
445 semiconductor-based sensors, 445
446 thermally sensitive resistor (thermistor), 441
442 thermocouple sensors, 445 Western blotting, protien sensing, 389 WIEN2K, 146
147 Wire-bonded multilayer device, 223f Wire-bonded single-layer device, 223f Work-function modulated gas sensor, 343
345

X XCRYSDEN, 178
180 Xenes, 111
121 X-ray diffraction (XRD) patterns, 96
97 X-ray fluorescence spectroscopy (XRF), 303

Z Zero current, 468 Zero-dimensional graphene quantum dots, 272
273 Zero-gap semiconductor, 112
113 Zero-order Laue zone, 38 Zigzag-edged graphene sublattices, 43
44 Zinc oxide (ZnO) nanosheets, 279, 279f nanowires, 449