Semiconducting Metal Oxides for Gas Sensing [2nd ed. 2023] 9819926203, 9789819926206

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Semiconducting Metal Oxides for Gas Sensing

Yonghui Deng

Semiconducting Metal Oxides for Gas Sensing Second Edition

Yonghui Deng Department of Chemistry Fudan University Shanghai, China

ISBN 978-981-99-2620-6 ISBN 978-981-99-2621-3 (eBook) https://doi.org/10.1007/978-981-99-2621-3 1st edition: © Springer Nature Singapore Pte Ltd. 2019 2nd edition: © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

This book is dedicated To my wife, Ming and my sons Tao and Jing. It is because of your love and patience that support my academic pursuit along with enjoying time with you. To my parents for giving me lasting love and education that make me be brave, industrial, sympathetic and be able to love and understand the world. To my brothers for giving me endless support and sharing much more responsibility in taking good care of our parents.

Foreword

The past decades have witnessed the fast advance in science, technology, and engineering of small, smart, and highly sensitive semiconducting metal oxides (SMOs) gas sensors, which have found a broad range of applications including detection of toxic and combustible gases, breath analysis in medical diagnosis, food safety monitoring, and other industrial areas. Rapid developments in high-performance SMO gas sensor requires semiconducting metal oxides nanomaterials with precise control of composition, morphology, surface-to-volume ratio, and electrical properties. From the viewpoint of chemical synthesis and materials applications, how to achieve a good control over the composition, micro- or nanostructures, and interface properties and how to understand the relationships of these factors and gas sensing performance of SMOs are key to the development of modern intelligent gas sensors. In this book, the author summarizes the characteristics of SMO gas sensors, with an emphasis on basic properties, their principles of gas sensors, the progress that has been ongoing in refining their operation, and the trends defining where progress is likely to take us in the future. In particular, the book highlights the state-of-the-art progress in SMO gas sensors based on different semiconducting metal oxides, strategies to improve the performance, and various application fields. Moreover, it presents an outlook on the future development of SMO gas sensors, including materials design and gas sensing mechanism, nanodevice and structure design, and the development of applications. This book also offers broad examples of recent developments in semiconducting metal oxides gas sensors and an excellent introduction to applied physics, material science, nanoelectronics, and their various applications. March 2023

Dongyuan Zhao Laboratory of Advanced Materials, Department of Chemistry Fudan University Shanghai, China

vii

Preface

The rapid development of Internet of Things (IoT), big data technology, and artificial intelligence has boosted the gas sensor applications in a wide range fields covering industrial processes, environmental monitoring, medical diagnosis, virtual reality, etc. Among various types of gas sensors, the semiconducting metal oxide (SMOs)based sensors have gained particular attention due to their good sensitivity and low cost, which are highly desired in both technological and market demands. The current research about SMO-based gas sensors focuses on the development of high-quality sensing materials and design of optimal sensing devices to further improve the sensing performance to meet the increasing standard in practical applications. The second edition of this book focuses on semiconducting metal oxides as gas sensing materials, especially the recent advances in nanosized SMO materials with high specific surface area, tunable morphology chemically micro-/nanostructure, and crystal facet effect. Various factors that have influence on the sensing performances of SMO sensors, such as chemical composition, nanostructure and morphology, and surface properties of SMO materials, are thoroughly discussed and analyzed in this book, along with the in-depth elucidation on the gas sensing mechanism and enhanced sensing behavior. The applications of gas sensors and some new interdisciplinary techniques, such as electronic-nose (e-nose) devices consisting of multisensor arrays, are also highlighted in this second edition. The second edition of this book offers researchers in the field of metal oxide nanomaterials and gas sensor with relevant frontier theories and concepts. Engineers working on research and development about semiconductor gas sensor can obtain new ideas in sensor design and fabrication. And also, this book can serve as a valuable guidance for new researchers in gas sensing area, providing them with the basics of metal oxide nanomaterials and the principle of gas sensors. Thanks to many people who have helped with this book, including my graduate students Xinran Zhou, Kaiping Yuan, Yongheng Zhu, Lingxiao Xue, Junhao Ma, and Yidong Zou. Particularly, Xinran Zhou and Yongheng Zhu contributed a lot for their conscientious assistance in organizing and proofreading with book. Thanks to Dr. Mengchu Huang for his valuable suggestions on writing this book. Thanks to my colleagues throughout the gas sensing community, who have guided me in this ix

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Preface

field. Thanks to my collaborators, including Prof. Jiaqiang Xu, Prof. Xinxin Li, Prof. Pengcheng Xu, and Prof. Wei Luo for their kind assistance, support, and inspiration. Shanghai, China March 2023

Yonghui Deng

Contents

1

2

Understanding Semiconducting Metal Oxide Gas Sensors . . . . . . . . . 1.1 Development of Semiconducting Metal Oxide Gas Sensors . . . . . 1.1.1 Kinds of Metal Oxides Used in Gas Sensors . . . . . . . . . . 1.2 Application of Semiconducting Metal Oxide Gas Sensors . . . . . . 1.2.1 Use of Semiconductor Metal Oxide (SMO) Sensors in Outdoor Air Quality Assessment . . . . . . . . . . 1.2.2 Use of Semiconductor Metal Oxide (SMO) Sensors in Indoor Air Quality Assessment . . . . . . . . . . . . 1.2.3 Use of Semiconductor Metal Oxide (SMO) Sensors in Disease Diagnosis . . . . . . . . . . . . . . . . . . . . . . . 1.2.4 Use of Semiconductor Metal Oxide (SMO) Sensors in Food Safety . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.5 Use of Semiconductor Metal Oxide (SMO) Sensors in Agricultural Production . . . . . . . . . . . . . . . . . . 1.3 Physicochemical Properties of Semiconducting Metal Oxides . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3.1 Definition of Semiconducting Metal Oxides . . . . . . . . . . 1.3.2 Potential Performances . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3.3 Physical Fundamental of Semiconducting Metal Oxides . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sensing Mechanism and Evaluation Criteria of Semiconducting Metal Oxides Gas Sensors . . . . . . . . . . . . . . . . . . . . 2.1 Pure Metal Oxides Semiconductors . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.1 N-type Metal Oxides . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.2 P-type Metal Oxides . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Metal Oxide Heterojunctions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.1 n–n Heterojunctions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.2 p-p Heterojunctions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.3 p-n Heterojunctions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 1 3 4 5 6 9 11 14 16 17 17 18 25 33 34 36 37 39 39 43 44

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2.3

Doped Metal Oxides Semiconductors . . . . . . . . . . . . . . . . . . . . . . . 2.3.1 The Doping of the Main Group Metal Elements . . . . . . . 2.3.2 The Doping of the Transition Metal Elements . . . . . . . . . 2.4 Noble Metal Sensitized Metal Oxides . . . . . . . . . . . . . . . . . . . . . . . 2.5 The Effect of the Crystallite Size . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6 Gas Sensor Evaluation Criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6.1 Sensitivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6.2 Operating Temperature . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6.3 Selectivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6.4 Stability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6.5 Response–Recovery Time . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6.6 Limit of Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

4

Semiconducting Metal Oxides: Morphology and Sensing Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 The Effect of Morphology and Structure on Gas Sensing . . . . . . . 3.1.1 Grain Size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.2 Grain Phase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.3 Surface Geometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.4 Grain Networks, Porosity, and the Area of Intergrain Contacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.5 Agglomeration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Synthesizing Approaches to Metal Oxides Sensing Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.1 Sol–Gel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.2 Hydro- and Solvothermal Methods . . . . . . . . . . . . . . . . . . 3.2.3 Self-assembly Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.4 Microemulsion-Mediated Synthesis . . . . . . . . . . . . . . . . . 3.2.5 Chemical Vapor Deposition (CVD) . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Semiconducting Metal Oxides: Composition and Sensing Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Binary Oxides Heterojunctions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.1 p–n Heterojunctions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.2 n–n Heterojunctions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.3 p–p Heterojunctions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Noble Metal Modification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Doping with Heteroatom . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.1 Doping with Non-metallic Elements . . . . . . . . . . . . . . . . . 4.3.2 Doping with Metallic Elements . . . . . . . . . . . . . . . . . . . . . 4.3.3 Doping with Rare Earth Elements . . . . . . . . . . . . . . . . . . . 4.4 Composite with Carbon Materials (Graphene, Carbon Nanotubes) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

46 46 48 51 60 60 61 63 66 68 70 70 70 75 75 75 78 79 80 81 82 82 84 88 92 95 97 105 106 106 113 118 118 129 129 132 134 137 141

Contents

5

6

Semiconducting Metal Oxides: Microstructure and Sensing Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Potential Features of Semiconducting Metal Oxides . . . . . . . . . . . 5.2 Structure Type and Typical Architectures . . . . . . . . . . . . . . . . . . . . 5.3 Grain Size and Porous Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4 Surface Area and Heterogeneous Interface . . . . . . . . . . . . . . . . . . . 5.5 Crystal Structure and Internal Defects . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Interfacial Interaction Model Between Gas Molecules and Semiconducting Metal Oxides . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1 Adsorption and Desorption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1.1 Classical Adsorption and Desorption Theory . . . . . . . . . . 6.1.2 Effects of Adsorption and Desorption on Gas Sensing of MOS Materials . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Gas Diffusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.1 Classical Diffusion Theory and Model . . . . . . . . . . . . . . . 6.2.2 Diffusion Model of MOS Gas Sensor . . . . . . . . . . . . . . . . 6.3 Conclusions and Prospects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

xiii

149 149 150 160 168 173 180 189 190 190 193 213 213 226 239 242

7

New Approaches Toward High-Performance Gas Sensing . . . . . . . . . 7.1 Optical Gas Sensing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2 Surface Plasmon Resonance (SPR) Enhanced Gas Sensing . . . . . 7.3 Pulse-Driven Gas Sensing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4 Field-Effect Transistor Gas Sensors . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

253 253 258 263 266 269

8

Sensing Devices of Semiconducting Metal Oxide Gas Sensors . . . . . . 8.1 Resistor-Type Sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1.1 Device Structure and Fabrication of Resistor-Type Gas Sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1.2 Sensing Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2 MEMS Platforms Gas Sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2.1 Device Structure and Fabrication of MEMS Gas Sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2.2 Sensing Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3 Field-Effect Transistor-Type Gas Sensors . . . . . . . . . . . . . . . . . . . . 8.3.1 Structure and Fabrication of Nanowire FET Gas Sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3.2 Sensing Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

271 272 273 274 275 276 277 282 284 286 290 291

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9

Contents

Integration Technologies in Gas Sensor Application . . . . . . . . . . . . . . 9.1 E-nose: Sensors’ Array . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2 Statistical Analysis Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3 Temperature-Gradient Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.4 Integrated Manufacturing of Electronic Nose . . . . . . . . . . . . . . . . . 9.5 Electronic-Nose Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.5.1 Applications in Food Industry . . . . . . . . . . . . . . . . . . . . . . 9.5.2 Applications in Environmental Monitoring . . . . . . . . . . . 9.5.3 Applications in Respiratory Diseases . . . . . . . . . . . . . . . . 9.6 Conclusions and Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

299 299 302 306 308 309 310 313 315 319 320

10 Applications of Semiconducting Metal Oxide Gas Sensors . . . . . . . . . 10.1 Sensors for Volatile Organic Compounds (VOCs) Gas . . . . . . . . . 10.1.1 Sensors for Ethanol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.1.2 Sensors for Acetone . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.1.3 Sensors for Formaldehyde . . . . . . . . . . . . . . . . . . . . . . . . . 10.1.4 Sensors for Benzene, Toluene, Xylene (BTX) . . . . . . . . . 10.2 Sensors for Environmental Gases . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.2.1 Sensors for CO2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.2.2 Sensors for O2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.2.3 Sensors for SO2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.2.4 Sensors for O3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.2.5 Sensors for NH3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.3 Sensors for Highly Toxic Gases . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.3.1 Sensors for CO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.3.2 Sensors for H2 S . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.3.3 Sensors for NO2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.4 Sensors for Combustible Gas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.4.1 Sensors for CH4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.4.2 Sensors for H2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.4.3 Sensors for LPG . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.5 Sensors for Other Gases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

325 325 326 331 334 339 344 344 346 348 349 351 353 353 354 357 361 361 363 366 367 375

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 387

Abbreviations

ANN BET BTX gases CNTs COPD CP CTAB CVD DFA DFT EDL EDS FET GC-MS HAL HCB HCHO HRTEM IDLH IR KLE LDA LOD LPG MDA MEMS MIS MVOCs NPs NWs OMMO

Artificial neural network Brunauer–Emmett–Teller Benzene, toluene, and xylene Carbon nanotubes Chronic obstructive pulmonary disease Conducting polymer Cetyltrimethylammonium bromide Chemical vapor deposition Discriminant factor analysis Density function theory Electron depletion layer Energy-dispersive X-ray spectroscopy Field-effect transistor Gas chromatography and mass spectrometry Hole accumulation layer Hexachlorobenzene Formaldehyde High-resolution transmission electron microscopy Immediately dangerous to life and health Infrared radiation Poly(ethylene-co-butylene) Linear discriminant analysis Limit of detection Liquid petroleum gas Multivariate data analysis Micro-electro-mechanical System Metal–insulator–semiconductor Microbial volatile organic compounds Nanoparticles Nanowires Ordered mesoporous metal oxides xv

xvi

P2VP P4VP PEO-b-PS PL PMMA PPy QCM QDA rGO SAED SAW SEM SMO SPR SWCNTs TEA TEM TLV TMA TRPL TSB UV VOCs XPS XRD

Abbreviations

Poly(2-vinylpyridine)-b-polystyrene Poly(4-vinylpyridine)-b-polystyrene Polyethylene oxide-b-polystyrene Photoluminescence Poly(methyl methacrylate) Polypyrrole Quartz crystal microbalance Quadratic discriminant analysis Reduced graphene oxide Selected area electron diffraction Surface acoustic waves Scanning electron microscopy Semiconductor metal oxide Surface plasmon resonance Single-wall carbon nanotubes Trimethylamine Transmission electron microscopy Threshold limit value Trimethylamine Time-resolved photoluminescence Tryptone soy broth medium Ultraviolet Volatile organic compounds X-ray photoelectron spectroscopy X-ray diffraction

Chapter 1

Understanding Semiconducting Metal Oxide Gas Sensors

1.1 Development of Semiconducting Metal Oxide Gas Sensors Ever since they were first proposed in the 1950s, semiconductor metal oxides have been used as conductive sensitive materials for gas sensors. At that time, it was realized that the near-surface electrical properties of semiconductor materials could be significantly affected by changing the composition of the adjacent atmosphere [1– 7]. Subsequently, investigations on resistive gas sensors have received a great deal of attentions because such sensors have low cost, simple operation, online monitoring, and good reliability for real-time control systems, and all these functions are highly desired in diverse practical applications [8]. Semiconductor-based gas sensors were firstly employed as efficient sensors in 1968 for domestic gas leak detectors, and the first commercialized semiconductor metal oxide sensor was Figaro TGS (Taguchi Gas Sensor). It was quickly recognized that gas-sensitive resistors based on highsurface area artifacts of metal oxides operated within the range of 300–450 °C [9, 10], lacking of complete selectivity for single gas. However, they could provide early warning of natural gas leaks in homes, and such simple but effective units were produced for this purpose during this period. With the increasing of requirement in practical applications, much effort has been devoted to improve the performance of semiconductor gas sensors and reduce their essential power drain. In addition, enormous researches have been also made to enhance both the selectivity and sensitivity of such devices and to reduce their operational power requirements [11–13]. This development has involved to exploring the response mechanisms [14], selecting the most appropriate oxides, fabricating multiphase “heterostructures” [15], introducing catalytic metallic particles, and optimizing the manner in which the materials contact the gas [16]. Research and development programs around the world aimed at understanding the mechanisms behind the gas responses and identifying the optimum compositions and forms of the sensing elements [17]. In recent years, more researches have been employed to understanding © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Deng, Semiconducting Metal Oxides for Gas Sensing, https://doi.org/10.1007/978-981-99-2621-3_1

1

2 Table 1.1 Resistance responses of near-surface layers (that display n-type or p-type character) when reducing or oxidizing gases are introduced into air

1 Understanding Semiconducting Metal Oxide Gas Sensors

Characteristic of near-surface layer

Reducing gases

Reducing gases

n-type

Resistance decrease

Resistance increase

p-type

Resistance increase

Resistance decrease

the properties of metal oxides in gas sensing, and new material combinations have been identified that may pave the way to improve sensor performance [18]. The theory of surface adsorption/desorption and catalysis [19, 20] are based on the semiconductor surface physics (space-charge layers, surface states) that provide the basis for the scientific background of gas sensors. Adsorption isotherms describe the surface coverage as a function of partial pressure of targeted gas component. The adsorbed ions give rise to a typical shift of work function, surface charge, or dipole layer, which results in great changes of the space charge in the underlying semiconductor domain. Thus, the surface reactions at sensing temperatures below 500 °C generally involve to the changes of the concentration of surface oxygen species, such as O2 − , O− , or O2− , which are stable over different temperature ranges, respectively [21]. The formation of such ions from adsorbed oxygen at the gas/solid interface involves to capturing electrons from the metal oxide, and the oxygen ions can be viewed as traps for electrons [22]. In the case of p-type oxide, oxygen adsorbed from air acts as surface acceptor state. However, in this case, the process involves to transferring electrons from the valence band, which causes an increase in the charge carrier concentration in the near-surface region [23]. The responses to reducing and oxidizing gases in air are the reverse by n-type semiconducting materials. According to the mechanisms outlined above, materials can be classified as n-type or p-type semiconductors according to the sign of their resistance responses to reducing or oxidizing gases. As shown in Table 1.1, this mode of categorization is only applicable for the near-surface regions of materials [24]. In the case of n-type semiconductor, electrons are usually drawn from ionized donors via the conduction band, and the charge carrier density at intergranular surfaces is reduced and a potential barrier to charge transport is developed. Introducing even a low concentration of reducing gas (e.g., carbon monoxide) can lead to surface reaction with oxygen ions to release uncharged molecules (such as carbon dioxide), and return electrons to the conduction band. Within the normal operating temperature range of 300–450 °C, the predominant surface oxygen ion is O− , which results in an increase in the measured conductivity to an extent that is related to the local concentration of the reducing gas. When n-type semiconductor is exposed to oxidizing gas with low concentration instead of reducing gas, where a competitive adsorption of molecules such as NO2 can take place, and the overall results can cause the increase of the density of charge carriers trapped at the oxide surface and a decrease in the measured conductivity.

1.1 Development of Semiconducting Metal Oxide Gas Sensors

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1.1.1 Kinds of Metal Oxides Used in Gas Sensors Over the past 40 years, a large number of metal oxides have been investigated for resistance responses with respect to the introduction of trace reactive gases in air [25]. Several binary oxides with band gaps between 2 and 4 eV (e.g., TiO2 , Nb2 O5 , Ta2 O5 , ZnO, SnO2 , and WO3 ) are designed with high specific surface areas and can exhibit n-type semiconductor behavior toward minority gases in air [26]. In particular, SnO2 has been widely used as sensitive material in various gas sensors because of its high mobility of electrons and high stability. Lacking of SnO2 , advances in semiconductor gas sensors would have been limited [27]. In contrast, chemiresistor gas sensors fabricated by p-type oxide semiconductors (e.g., NiO, CuO, Co3 O4 , Cr2 O3 , and Mn3 O4 ) received relatively little attention, and the related research for fabricating such chemiresistors gas sensors was still in the early stage [28]. The perovskite structure formed from a wide variety of oxides with transport properties usually contains two typical conductive mechanisms, i.e., predominantly ionic conduction and predominantly electronic conduction. Interestingly, these perovskite oxides are particularly attractive for high-temperature applications because they often have high melting and/or decomposition temperatures and can also provide microstructural and morphological stability to improve reliability and long-term sensing performance. A number of oxides with perovskite (e.g., BaTiO3 , SrTiO3 , and LaFeO3 ) or pyrochlore crystal structures (e.g., Bi2 Sn2 O7 , Y2 Ti2 O7 , and Y2 TiFeO7 ) have also exhibited p-type semiconductor features in sensing responses [29]. Metal oxide-metal oxide-based nanocomposites play a very important role in various fields of gas sensing and are promising in the development of sensitive materials [30]. It has been established that the modifying of metal oxide (MeI O) by introducing another nanostructured metal oxide (MeII O), which can be either a catalytic or structure modifier, to develop non-homogeneous complex materials (i.e., nanocomposites MeI O–MeII O). At present, many researches are focusing on investigating the above-mentioned materials with different metal-to-metal ratios to develop sensors with acceptable response and selectivity relative to target gases [31]. Metal oxides complex based on stable conductive metal oxides (SnO2, In2 O3 , WO3 , Fe2 O3 , and ZnO) with high gas sensitivity have been also extensively studied, such as SnO2 –In2 O3 , SnO2 –ZnO, SnO2 –WO3 , and In2 O3 –ZnO [32].

1.1.1.1

Ways to Enhance Properties of Metal Oxide Semiconductors Gas Sensors

As the development of the technology in these individual areas, it becomes more challenging to develop sensors in terms of their reliability, detection limits, sensitivity, selectivity, and cost. Far more of the scientific literature has been devoted to seeking such improvements in metal oxide gas sensors than all other combined solid-state gas sensors. Emerging ways to make these improvements include reducing the size of the device, minimizing production expense, and improving the sensing performances

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in terms of rapid response and high sensitivity, selectivity, stability, and feasibility. These approaches have gained significant interest in the field of gas sensor technology. Moreover, exploring new materials, structures, and geometries for effective gas sensor applications are of extreme interest. These have included: (1) Morphological design of nanostructures have been developed to enhance the properties of metal oxide semiconductor gas sensors. In this approach, various metal oxide semiconductors with different geometrical structures (e.g., nanoplates, thin films, nanoparticles, nanorods, nanotubes, nanofibers, nanowires, and hollow spheres) are extensively used [33]. (2) n-type/p-type switching of sensing materials have been employed. To date, considerable efforts have been devoted to design high-performance gas sensors by using (a) longitudinal oxide p-n junctions, (b) nanocomposites sandwiched between p- and n-type semiconductor nanoparticles, and (c) one-dimensional n-type metal oxide semiconductors decorated with p-type metal oxide semiconductor nanoclusters [34]. (3) Sensing performance of materials has been enhanced through incorporating secondary components and taking advantage of driving sensors with alternating current rather than direct current. Over the years, several strategies have been used to demonstrate that incorporating metal nanoparticles such as Au, Pt, Pd, and Ag into the surface of metal oxides can effectively reduce the operating temperature and improve the lower limit of detection, sensor response, sensitivity, and selectivity [35]. (4) Combining metal oxide semiconductor with functional materials can elevate electrical conductivity and gas adsorption capacity, which is favorable for improving the sensitive properties and can effectively reduce the operating temperature. Nowadays, graphene, carbon nanotubes, conductive polymers and metal– organic framework (MOF) as functional materials are immensely studied to boost sensing performance of metal oxide semiconductor gas sensors. Such development has been driven mostly by research based on empirical or trial-and-error methods. However, for further development and innovation of semiconductor gas sensors, basic research on fundamental aspects is indispensable, such aspects include gas sensing mechanisms and sensor design principles, which have been largely ignored so far.

1.2 Application of Semiconducting Metal Oxide Gas Sensors Monitoring the concentration of gases is permanent requirement in many industrial fields as well as in daily life. The aim of this chapter is to summarize semiconductor gas sensor technology to satisfy a wider range of applications. With the development of industrial revolution, a tremendous amount of development has been achieved in the field of semiconducting metal oxide gas sensors, and fortunately, it appears that industry has been moved beyond the use of canaries. Because of their small size and low cost, semiconducting metal oxide gas sensors have been subjected to extensive

1.2 Application of Semiconducting Metal Oxide Gas Sensors Table 1.2 Application fields and the representative target gases of metal oxide semiconductor gas sensors

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Application fields

Target gases

Outdoor air quality assessment

SO2 , NOx , CO, hydrocarbons, VOCs

Indoor air quality assessment

Formaldehyde, CH4 , natural gas, liquefied petroleum gas

Disease diagnosis

H2 S, acetone, O2 , CO2 , 3-hydroxy-2-butanone,

Food safety

H2 S, SO2 , methanol, trimethanol, ethanol

Agricultural production

NH3 , Cl2 , H2 S, HF, SO2 , CO, VOCs

research and development as important devices for detecting the leaks of inflammable gases and toxic gases and are also found in various areas of technology, including air quality, combustion, agricultural, industrial processing, food industry, public safety, and medical diagnosis through analyzing exhaled breath [36]. The application fields and the representative target gases of metal oxide semiconductor gas sensors are summarized in Table 1.2.

1.2.1 Use of Semiconductor Metal Oxide (SMO) Sensors in Outdoor Air Quality Assessment Outdoor air pollution is a serious problem to restricting human health because of the inefficient combustion of fuels used in transportation and power generation. Children are particularly exposed at-risk environments because of the immaturity of their respiratory organs. Gas sensors play an important role in this process because they can provide real-time feedback for vehicular fuel and emissions management systems, and they can reduce the discrepancy between emissions observed in factory tests and “real-world” scenarios. In addition, the environment is polluted because of the huge number of toxic gases (such as CO and SO2 ) that are released in the atmosphere by the combustion of petroleum products like diesel, heating oil, and various fuels. In addition to an environmental impact, these gases have various harmful effects on humans. For example, CO can lead to chest pain and reduced mental alertness. Because of acid rain, the chemical composition of soil is altered and leads to the loss of minerals. In addition to the above-mentioned gases present in the atmosphere, the combination of NOx and CO in the presence of sunlight tends to produce O3 , which is harmful to both plants and humans. Because these gases are toxic and have adverse effects on the environment, scientists are engaged in designing safer substitutes for them, and a safe method for early detection of these gases in the atmosphere is needed in advance of their bad effects. Various sensors have been developed for detection

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of these gases, and metal oxide sensors in particular could be used to monitor the exposed trace gas [37]. Song et al. [38] designed a unique sensor with 3D tin oxide (SnO2 ) nanotube array as the sensing layer and the platinum (Pt) nanoclusters decorated on it as the catalytic layer (Fig. 1.1), which significantly improved the sensitivity and selectivity toward NO2 by enhancing the adsorption energy of target gas and reducing the activation energy. It not only exhibits extremely high sensitivity to NO2 with a detection limit of 107 ppb, but also can achieve selective NO2 detection while suppressing the response to interference gases. In addition, a wireless sensor system with integrated sensors, microcontrollers, and Bluetooth units was also presented, which enabled the practical application for indoor and on-road NO2 monitoring. Therefore, the delicate modulation and successful proof of the gas sensors lay the groundwork for the future real-time gas detection in smart home and intelligent city applications.

1.2.2 Use of Semiconductor Metal Oxide (SMO) Sensors in Indoor Air Quality Assessment Poor air quality has been linked to sick building syndrome, reduced productivity in offices, and impaired learning in schools. Indoor air quality has a major influence on the health, comfort, and well-being of building occupants. Most people in cities spend at least 90% of their time indoors, and thus, indoor air quality has a significant contribution on total exposure to air pollutants, including common volatile organic chemicals (VOCs) and other gases (such as formaldehyde (HCHO), methane (CH4 ), nitrogen dioxide (NO2 ), ozone (O3 ), and carbon monoxide (CO)). Sources of these pollutants include outdoor contaminants from traffic and industry, such pollutants enter buildings through ventilation systems, and indoor contaminants come from burning fuels, candles, and tobacco and from emissions of building materials, furnishings, cleaning products, electronic equipment, toiletries, people, and pets. New building products can be particularly important sources of pollution. In most cases, the concentrations at which these gases become a threat to health appear to be well within the detection limits that can be achieved with metal oxide sensor systems once interference problems can be avoided. Individual VOCs such as benzene or formaldehyde become a cause for concern at low concentration. However, in practice, it may be sufficient to measure the total concentration of all of the VOCs together to provide an indication of the total concentration of volatile organic compounds (TVOC), and this is a less demanding requirement [39]. Wang et al. [40] proposed the hierarchical SnO2 nanofibers/nanosheets via growing the nanosheet array uniformly on the surface of nanofibers, which were successfully prepared by hydrothermal method with hollow SnO2 nanofibers as the skeleton. The microstructure, morphology, chemical composition and oxidation state of SnO2 nanofibers, hierarchical SnO2 nanofibers/nanosheets and SnO2 nanosheets were compared and investigated through a series of characterization

1.2 Application of Semiconducting Metal Oxide Gas Sensors

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Fig. 1.1 Real-time indoor and on-road NO2 detection. a, b Wireless NO2 sensor system that can communicate with smartphone. Potential applications for c indoor and d on-road NO2 monitoring [38]. Copyright 2022, Wiley

methods, including XRD, FESEM, TEM, XPS, BET. And the growth process of hierarchical nanostructure SnO2 was thoroughly revealed, which indicated the successful synthetization of SnO2 nanofibers/nanosheets, and it was ascribed to the uniform modification of the SnO2 seeds on the nanofibers and the appropriate growth conditions of the nanosheets. The gas sensing performance of SnO2 -based nanomaterials was studied with HCHO as the target gas. The results showed that the gas sensor based on hierarchical SnO2 nanofibers/nanosheets exhibited the enhanced response (Ra /Rg = 57 toward 100 ppm HCHO at 120 °C), rapid response/recovery speeds (4.7/11.6 s to 100 ppm HCHO), improved selectivity as well as better transient response and trace detection ability, compared with SnO2 nanofibers, SnO2 nanosheets and the physical mixture of SnO2 nanofibers and nanosheets. Additionally, the gas sensing mechanism of SnO2 hierarchical nanostructures were deeply discussed, which claimed that the

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superior sensing properties were mainly due to the synergistic effect of nanofibers and nanosheets, the hierarchical structure, and the large specific surface area. Methane (CH4 ) as an extremely volatile and flammable gas with a lower explosive limit (LEL) of 4.4–5.0 vol% is commonly employed as a fuel in domestic life, posing a potential explosion risk. Therefore, designing novel sensing materials for selective identification of methane is significant for practical applications, especially under the interference of ethanol (C2 H5 OH) and NO2 . Cai et al. [41] developed a new core– shell ZnO/Pd@ZIF-8/Pt compounds, which possessed excellent selective sensing toward CH4 in the presence of interference gases (C2 H5 OH and NO2 ). This sensor demonstrated attractive CH4 selectivity (S C2H5OH = 1.3 and S NO2 = 38.3), ultrasensitive sensing response (304.6% to 5000 ppm CH4 at 230 °C), complete sensing reversibility and long-term stability. The further study revealed that the superior selectivity of CH4 was mainly dependent on the more superior catalytic activity of Pt toward ethanol compared with methane, and this led to the catalytic oxidation of C2 H5 OH in the material surface, thus inhibiting the diffusion of C2 H5 OH to the sensitive layer. Furthermore, owing to the abundant micropores (effective aperture pore size of 4.2 Å), ZIF-8 shows excellent filtering effect, which can hinder the diffusion of larger interfering gases, such as NO2 (4.5 Å), while smaller CH4 (3.8 Å) can pass through the pore channels. Thus, the synergistic effect of the selective catalysis of Pt and the sieving of ZIF-8 contribute to the superior CH4 selectivity (Fig. 1.2).

Fig. 1.2 Schematical illustration of CH4 selectivity improvement by ZIF-8 and Pt [41]. Copyright 2023, Elsevier

1.2 Application of Semiconducting Metal Oxide Gas Sensors

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1.2.3 Use of Semiconductor Metal Oxide (SMO) Sensors in Disease Diagnosis Although using exhaled breath analysis in disease diagnosis has a long history and it can trace back to ancient Greece, it is only very recently that the potential of modern analytical technology has enabled the potential convenience (non-invasive, real-time, low-cost analysis) of this approach to be pursued. In recent years, semiconductor metal oxide (SMO)-based chemiresistive sensors have been also explored for detecting sub-ppm concentrations of some VOCs in exhaled breath. Metal oxide sensors have been considered as possible diagnostic tools for specific conditions, including diabetes, halitosis, and lung cancer. For example, hydrogen sulfide (H2 S), acetone, toluene, ammonia, nitrogen monoxide, and pentane are known to possess strong relationships with diabetes, halitosis, lung cancer, kidney failure, asthma, and heart disease, respectively. In particular, nitrogen monoxide sensors have been successfully commercialized for monitoring asthma [42]. Typically, the indirect detection of halitosis by H2 S biomarkers via gas sensors evoke intensive research interest. However, such H2 S sensors require extremely high selectivity and sensitivity, as well as ppb level detection limits, which remain great technically challenging. The reported H2 S sensors show low sensitivity, poor selectivity, as well as high detection limits, which is difficult to satisfy the demands of clinical application. For instance, Feng et al. [43] fabricated NiO/WO3 nanoparticles (NPs) via first hydrolysis of WO3 NPs and subsequent modification with NiO NPs by facile hydrothermal method (Fig. 1.3), which exhibited superior H2 S sensing performance with highly sensitivity (Ra /Rg = 15,031 ± 1370 to 10 ppm H2 S at 100 °C), ppb-level detection limit (Ra /Rg = 4.95 ± 2.9 to 0.05 ppm H2 S at 100 °C), and high selectivity. Especially, a prototype sensor based on NiO/WO3 NPs has been served as monitor simulated exhaled halitosis and compared with gas chromatography, disclosing close H2 S concentrations, which laid the experimental foundation for future smart medical applications. In addition, NH3 produced by human metabolism can be also applied as a biomarker for kidney disease when the concentration of NH3 in exhalation exceeds to 800 ppb. The researchers are working on integrating exhaled-biomarker sensors with surgical masks, achieving the real-time detection of NH3 exhaled by renal patients. Typically, Du et al. [44] have developed bistable and water-resistant paper-based sensors through modifying ternary nanocomposites (ternary-NCs) on commercial filter paper (Fig. 1.4), which was integrated on surgical masks to detect NH3 at room temperature, showing ultra-sensitive response and excellent selectivity. The specially designed NCs were composed of a multiwalled carbon nanotube framework and a polypyrrole nanolayer, further supported with Pt nanodots. Notably, this paper-based sensors have been confirmed to possess superior resistance to bending from 0 to 1080°, attractive cuttability and foldability, excellent water tolerance even after immersion in water over 24 h, which allows it to be integrated into surgical

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Fig. 1.3 NiO/WO3 composite nanoparticles for highly sensitive and selective detection of H2 S biomarkers for halitosis [43]. Copyright 2021, American Chemical Society

masks for application in simulated exhalation diagnosis. And the NH3 sensing accuracy was further validated via ion chromatography measurement, confirming the effectiveness of the paper-based sensor.

Fig. 1.4 Nanocomposite-decorated filter paper as a twistable and water-tolerant sensor for selective detection of NH3 [44]. Copyright 2022, American Chemical Society

1.2 Application of Semiconducting Metal Oxide Gas Sensors

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Fig. 1.5 Smart portable sensing device of acetone detection and the readout concentration signal of the cellphone [45]. Copyright 2022, Wiley

As a typical volatile pollution gas, trace acetone can cause headache, fatigue and damage the central nerve system, thus posing a serious threat to air quality and human health. Therefore, accurate detection of trace acetone gas is very important. Inspired by the heteroatom doping engineering, Liu et al. [45] developed a unique acetone gas sensor based on erbium (Er)-doped mesoporous tungsten oxide (Er/mWO3 ) via one-step collaborative coassembly strategy, which possessed peculiarly hexagonal mesostructure (P63/mmc), controllable specific surface area (58.1–78.3 m2 g−1 ), uniform mesoporous size and highly crystallized framework with heteroatomic pore wall doping. The introduction of Er has been demonstrated to delicately adjust the micro/nanostructure and band properties of mWO3 , leading to outstanding acetone sensing performance, including ultra-sensitive response (Ra /Rg = 107 for 50 ppm), fast response and recovery speed (9/56 s), superior selectivity, low concentration detection (125 ppb), water tolerance property and excellent long-term stability. Such a charming sensing behavior is predominantly assigned to the unique interfacial catalytic sites induced by heteroatomic interstitial doping. This sensor has been integrated into portable smart sensors (Fig. 1.5), paving the way to smart gas sensors designed for practical environmental monitoring and industrial safety.

1.2.4 Use of Semiconductor Metal Oxide (SMO) Sensors in Food Safety The food safety and intelligent agriculture are extremely important to the economic growth in developing country, and the process control and monitoring quality of food and agriculture products have attracted increasing attention. With their multiplex and real-time sensing capabilities, SMO sensors have revolutionized sensing in the food and agriculture sectors with potential applications in detecting food contaminants such as preservatives, antibiotics, heavy metal ions, toxins, microbial load, and pathogens along with rapid monitoring of temperature, traceability, humidity, gas, and aroma of food stuffs. Among them, bacterial foodborne pathogens

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encompass various illnesses and continue to threaten public health all over the word. Therefore, various approaches have been proposed for detecting pathogens. It is well known that microorganisms (e.g., Listeria monocytogenes, LM) can produce specific species, such as microbial volatile organic compounds (MVOCs) that can be characterized as biomarkers [46]. In recent years, the detecting MVOCs, namely indicators of microbial contamination, have emerged as a novel and effective approach for revealing microbial contamination because it can be operated in a non-invasive and rapid way without the need for complex and expensive instruments and highly trained personnel. In this regard, chemiresistive gas sensors based on SMOs have attracted particular attention because of their unique advantages such as low-cost, convenient operation, fast response and recovery process, and tunable responsivity to target gaseous molecules, making it possible to monitor microbial contamination indirectly by measuring the concentration of relevant MVOCs [47]. Typically, 3-Hydroxy-2-butanone (3H-2B) has been confirmed to be a biomarker for LM exhalation. Thus, detecting 3H-2B is a convenient and efficient way to determine food safety. Wang et al. [48] constructed a 3H-2B gas sensor based on WO3 hollow spheres decorated with bimetallic Pt-Cu nanocrystals. It showed extremely sensitivity to 3H-2B, with a response value (Ra /Rg ) of 221.2 for 10 ppm 3H-2B at 110 °C, about 15 times higher than that of pure WO3 hollow spheres (Fig. 1.6). In addition, the PtCu/WO3 gas sensor displayed outstanding selectivity to 3H-2B, fast response and recovery rate (9 s/28 s), and low detection limit (LOD < 0.5 ppm). Sensing mechanism study proved that such outstanding gas sensing performance was mainly ascribed to the synergistic effect between Pt–Cu and WO3 , including the spillover effect of O2 molecules on the surface of Pt–Cu nanocrystals, the modulation of depletion layer via p-type Cux O to n-type WO3 , and the highly selective catalytic conversion of 3H-2B. This work provides a reasonable design and synthesis of efficient sensitive materials for detecting LM in food safety. Trimethylamine (TMA) is an organic amine gas, which is applied as an important index to evaluate the freshness of seafood. Zhu and coworkers [49] proposed an extremely efficient TMA sensor by loading gold nanoparticles (~4 nm) on WO3 nanosheets through ultrasonic processing, which possessed high response (Ra /Rg = 217.72 to 25 ppm TMA at 300 °C), fast response-recovery process (8 s/6 s), low detection limits (0.5 ppm), and excellent TMA selectivity (Fig. 1.7). In order to evaluate the practical application potential in the field of rapid non-destructive detection for seafood freshness, the composition of volatiles generated during the decay of Larimichthys crocea (0–15 days) was detected, which suggested that the freshness of L. crocea could be assessed through TMA detection by Au/WO3 sensor.

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Fig. 1.6 Bimetallic Pt–Cu nanocrystal sensitization WO3 hollow spheres for highly efficient 3H-2B biomarker detection [48]. Copyright 2020, American Chemical Society

Fig. 1.7 Ultra-efficient trimethylamine gas sensor based on Au nanoparticle-sensitized WO3 nanosheets for rapid assessment of seafood freshness [49]. Copyright 2022, Elsevier

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1.2.5 Use of Semiconductor Metal Oxide (SMO) Sensors in Agricultural Production Modern agriculture is a socialized and commercialized agriculture based on advanced technology and equipment [50]. Thus, the development of modern agriculture is a crucial measure to improve agricultural production efficiency and enhance disaster resistance. However, real-time monitoring the crop status is vital and challenge to adjust the environmental conditions of crop growth, so as to get rid of natural constraints. Thus, in the inevitable course of agricultural modernization, the advanced semiconducting metal oxide-based gas sensors are irreplaceable and indispensable to solve the above problem. For instance, the hormones, such as ethylene, can significantly affect the growth and development of plants, and some VOCs can be released when plants are attacked or hurt by pests or diseases. Thus, SMO-based sensors can be applied to real-time detecting the CO2 concentration in a greenhouse to determine whether it is below the optimal concentration for crop photosynthesis [51]. The CO2 concentration of the sealed poultry house also requires to be detected, in order to ensure it is lower than the maximum concentration that affects the growth and development of livestock; otherwise, it needs to be ventilated in time. Similarly, NH3 sensing technology is particularly urgent for the aquaculture industry and animal husbandry [52]. For example, in the chicken industry, the digestive system of chickens cannot fully digest the feed, resulting in a large amount of protein excreted through feces, which can be converted into NH3 via complex chemical reactions. However, NH3 is a key factor to influencing bird health and egg production [53]. Once the NH3 concentration exceeds the limit value, the egg production rate will drop significantly. For example, Chen et al. [54] developed a NH3 sensor operating at room temperature (RT) based on cobalt (Co)-doped TiO2 nanoparticles with a diameter of 40 nm, and studied the gas sensitivity under different cobalt doping contents (Fig. 1.8). The 20% Co/TiO2 showed the best sensing properties, with an enhanced response value of 14 toward 50 ppm NH3 at room temperature, short response and recovery time (25 s/48 s), good stability and selectivity. The working temperature is significantly reduced from 180 °C of pure TiO2 to room temperature, owing to the modification of Co species, which can be attributed to the optimization of electronic structure of Codoped TiO2 elucidated via structural simulation and mechanism analysis. Therefore, the development of MOS-based gas sensors plays a pivotal role in the modernization of agricultural production. Linalool has been claimed to play an irreplaceable role in the rice profile during the aging process, and the change of its concentration can be used as the index for the evaluation rice quality. Therefore, the detection and identification of linalool concentration has developed as a convenient and viable approach to assess rice quality. Xu et al. [55] realized the RT detection of linalool, a typical gas marker in the aging process of rice, by adjusting the surface chemical state of cerium oxide (CeO2 ) nanowires synthesized via a facile hydrothermal method. The oxygen vacancy engineering on CeO2 nanowires can be finely controlled by annealing under various atmospheres,

1.2 Application of Semiconducting Metal Oxide Gas Sensors

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Fig. 1.8 Co-doped TiO2 nanoparticles for NH3 detection at room temperatures [54]. Copyright 2022, Elsevier

and the samples calcined at 5% H2 + 95% Ar showed excellent linalool sensing properties at room temperature, including high response (Ra /Rg = 16.7 toward 20 ppm linalool), rapid response and recovery speed (16/121 s), as well as a low detection limit (0.54 ppm). The improved sensing characteristics are mainly dependent on the synergistic effect of the nanowire nanostructure, the large specific surface area (83.95 m2 ·g−1 ), and the formation of enormous oxygen vacancies accompanied by the increase of Ce3+ content. Furthermore, the practicability of this CeO2 sensor was confirmed through identifying two types of rice (Indica and Japonica rice) stored in various periods (1, 3, 5, 7, 15, and 30 days). Accordingly, the as-reported CeO2 offers a promising candidate material for the development of high-performance RT electronic nose devices for detecting rice quality. In the past, the rapid development of material science and semiconductor technology had a strong effect on gas sensor technology. Great strides have been made in developing metal oxide gas sensors, and there has been marked progress in the following areas: (i) The n-type/p-type equilibrium within sensor elements can be influenced by materials composition, the prevailing oxygen partial pressure, the concentration of some minority gases in the local atmosphere, and working temperature. Care must therefore be taken to avoid ambiguity in resistance measurements that are taken when the sensor material is close to the switching point. (ii) Incorporating a second phase component in the sensor material can improve sensitivity by restricting the particle size of the principal phase (and thus sustaining high surface area), adding a catalytic function, and exercising a favorable influence on the near-surface band structure.

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(iii) Close control of microstructure of both single oxides and material combinations can also greatly enhance sensitivity. However, a persistent challenge has been posed by the interference from and/or the degradation of gas responses by humidity. To date, most of the work on functionalized metal oxide sensors for more demanding sensing applications has focused on materials with n-type responses. Equivalent tests of materials that exhibit p-type response may be worthwhile. Progress toward the selectivity and sensitivity that are necessary for both air quality and medical applications can be further assisted by using arrays of metal oxide sensors from which the signals can be analyzed using pattern recognition techniques.

1.3 Physicochemical Properties of Semiconducting Metal Oxides With the rapid development of industrial production, a series of VOCs and toxic gases derived from industrial sources or automobile engines have produced and caused serious threat to environmental balance. Sensing technologies based on sensingactive materials along with the explosive growth has an essential value in the detection of gaseous molecules, especially for toxic gases, which can convert their with low content (i.e., ppm and ppb) into visual change in value, such as electrical signals, optical signals, and magnetic signals. It brings a huge hope for the rapid in-situ detection of food security and environmental gases. Although various sensing-active materials, such as metal oxide semiconductors, conducting polymers, metal oxide/polymer composites, are emerging and exhibiting excellent detection performance, the understanding of working mechanism and sensing behavior is also not clear [32]. Among these novel materials, SMO as a kind of special material for strong resistance change, can provide high sensitivity for target gases and show high response for low concentration [56]. In addition, SMO also possesses extra advantages for sensing applications, including low-cost, fast response-recovery speed, long-term stability, simple electronic interface, low maintenance, and so on, which is regarded as one of the most promising sensors for VOCs or other toxic gases [57–59]. However, the relative features and mechanism for SMO-based gas sensors are still unclear, and in this chapter, it will discuss and analyze the physicochemical properties as comprehensive as possible, and hopefully this chapter can beneficial for you to understand the basics of semiconducting metal oxides gas sensors.

1.3 Physicochemical Properties of Semiconducting Metal Oxides

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1.3.1 Definition of Semiconducting Metal Oxides In general, semiconducting metal oxides is a traditional non-stoichiometric oxide and sensitive to electrical conductivity. According to the view of chemistry, SMO is belonging to ionic solids, which consists of positive metallic and negative oxygen ions, combined with strong ionic bonds. Moreover, inside the SMO materials, the “s” electronic shell is usually inherently filled and possess superior thermal/ chemical stability compared with free-metal oxides. By contrast, the incomplete filling of the “d” shell can endow SMO special optical and electronic properties, involving adjustable energy bands, high dielectric constants, and novel electrical/optical response behavior [37, 58, 60, 61]. In terms of microstructure, the nanoarchitectures of SMO are flexible and controllable, such as zero-dimension, one-dimension, two-dimension, and three-dimension, also including porous, bulk, networks and other structures [62–64]. SMO-based materials possess a series of typical characteristics, for example, the conductive ability of SMO-based materials can increase with the increasing of temperature, and inducing resistivity drop. In addition, micro-impurity content, appropriate wavelength of light, electric and magnetic fields can also significantly change the conductivity of semiconductor.

1.3.2 Potential Performances In general, SMO-based materials have been confirmed with various advantages, including relatively high chemical activity, widespread availability, superior stability and potential environmental benignity. Above novel features dependences on the widely applications, including catalysis, sensor, energy storage, environmental remediation and solar cell, etc. [65–68]. Among these applications, SMO-based sensors have been developed several decades and mainly severed as chemiresistive semiconductor or catalytic/thermal conductor. The electrical conduction behavior of SMObased materials can easily adjust under various oxygen partial pressures (p(O2 )), and the interesting optical/electronic properties endow them available for integration into a diverse family of devices [58, 69]. From the view of materials, the application fields and performance will depend on the compositions, sizes, and shapes, where these factors can decide the maximum response in sensing process. In recent years, chemiresistive SMO sensors have aroused great attentions and been regarded as the most potential candidates for the detection of environmental pollutions [56, 70, 71]. During the practical applications, it will show high sensitivity, fast response/recovery time, simple electronic interface, low maintenance and ability to detect large number of gases. In fact, these chemiresistive SMO-based sensors produce resistance transformation once touching the reducing gases by the oxidative interactions with the negatively charged chemisorbed oxygen [72]. Lots of studies showed that mainly gas sensing features or parameters were adjusted and controlled by the potential performance of SMO materials, such as surface area, donor

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density, agglomeration, porosity, acid–base property, the presence of co-catalysts, and crystallinity. Thus, controllable adjust and design the potential performance of SMO materials will be the critical factor to restrict relative application for the sensing reaction.

1.3.3 Physical Fundamental of Semiconducting Metal Oxides As we know, the physical fundamentals of SMO materials play an important role in the adjusting the sensing performance, including crystalline structure, physical defects, energy band, impurity level, charge transportation, and p–n junction [73–75]. Especially for p–n junction, this factor will be discussed and analyzed in the following part in detail. As mentioned above, the major charge carriers in SMO materials can be easily manipulated through proper doping of donor or acceptors. However, the major charge carrier in metal oxides can be determined via doping aliovalent cations or oxygen non-stoichiometry. No matter doping or forming composites, the potential adjusting in physical level cannot separate from above physical parameters [76–78]. According to the conductive type, semiconductors can divide into n-type and p-type, and n-type semiconductor exhibits higher potential in various applications.

1.3.3.1

Crystalline Structure and Defects of Semiconducting Metal Oxides

Crystals usually possess a definite shape and fixed melting point, and these atoms or ions combine to a crystal in a regular way over a wide range (or long-range order) [69, 79]. According to the micro-arrangement, the crystalline structure of semiconducting can be divided into two classic types, including monocrystalline and polycrystalline, and the latter means that the whole crystal is mainly made of a regular arrangement of atoms. While polycrystalline is a whole piece of material that is randomly accumulated via a large number of tiny single crystals. On the contrary, non-crystals have irregular shapes and fixed melting points, and there is no long-term order in the internal structure, but there is structural short-range order in a small range within the space between several atoms [80–82]. For example, SnO2 as an important semiconductor, the surface of SnO2 crystal usually lacks one or more neighboring atoms, inducing weak or incomplete coordination of tin atoms (denoted as Sn*), which can produce abundant dangling bonds and unsaturated bonds. In addition, the surface of Sn* can exhibit various valences, enabling a variety of redox reactions and high chemical activity [83, 84]. The common crystal structures of SnO2 are rutile structure, and {110} plane corresponds to the most stable crystal face, considering as tetragonal or orthogonal crystal system with space group P42 /mnm (D4h 14 ), and the parameters of crystal cell is a = b = 0.4738 nm with c = 0.3188 nm [85, 86]. The unit cell is combined with two Sn atoms and four O atoms, forming a coordinate structure of 6:3. Especially, the energy gap of SnO2 is 3.6–4.0 eV, endowing typical

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n-type features based on the certain amount of intrinsic oxygen vacancy and tin interstitial atoms [87–89]. By contrast, ZnO materials, as a common multifunctional semiconducting metal oxide, possess the hexagonal wurtzite structure with space group P63 mc. In addition, the parameter of crystal cell is a = 0.3253 nm and c = 0.5213 nm [62, 90]. In the hexagonal crystal, O2− ions are arranged in a six-sided compact stack, and Zn2+ ions fill a half of the tetrahedron gap. The common planes is positively charged {0001} Zn* crystal face and negatively charged O* plane [87, 91]. These unique crystal structures can produce great influence for the physiochemical performances of semiconducting metal oxides. In general, semiconducting metal oxides consist of various type of defects, such as point defect, line defect, plane defect and volume defect, and some defects are beneficial for the adjusting of physiochemical performances, while some defects can be regarded as impurities that are unfavorable for their applications [75, 92]. The typical defect related to photoelectric properties is point defect, which is the simplest defect and it can be defined as a defect that deviates from the normal arrangement of a crystal structure at or near a normal lattice point on a microscopic scale. These point defects are also named as zero-dimensional defects and dependent on the ambient temperatures [93, 94]. Typical semiconducting metal oxides can be abbreviated as MO, where M represents the metal atom and O is the oxygen atom. In the absence of heteroatom, six classic point defects are involved, including a position of M replaced with O atom, a position of O replaced with M atom, no atom on a position of M, no atom on a position of O, M or O existed in interatomic space. However, with the doping of impurity atoms, these impurity atoms can occupy the orbital of M atom or O atom and even interatomic space, which consist of three various point defects. Above various point defects can adjust the electrical properties of semiconducting metal oxides, while the probability or occasion of occurrence are different. In fact, M atom and O atom are impossible to switch positions based on strong interaction between ionic compounds, while it can exist in covalent compounds [29, 95, 96]. Once the electronegativity of O atom is higher than M atom, and ionized electron can donor due to the replace of M with O, in opposite, when O atom replaced by M atom, the ionized hole will regard as donor. If there is without extra position in pristine M (or O) site, the neutral M (or O atom) will be replaced and leave two holes (or electrons), which will be motivated to valence band (or conduction band) and form free-holes based on the defect effect. The form of line defect is mainly dislocation, which produced through partial crystal slides along a sliding surface under external forces. In addition, dislocation consists of some typical features, and it is not a geometric line while a pipe with a certain width [96, 97]. A stress field can form in and near the dislocation pipe, and the average energy of atoms in a dislocation pipe is much greater than that of other regions, so dislocation is not an equilibrium defect. The dislocation can form a closed ring in the crystal or end on the surface of the crystal, or end on the intergranular boundary, but not inside the crystal. Plane defect contains three various types, including small angular grain boundary, stack layer fault, and twin crystal. Small angular grain boundary is a small area in a crystal with a certain crystallographic orientation difference, and equidistant edge dislocation array can form small

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angular grain boundary. By contrast, stack layer fault will form via the dislocation of the normal accumulation order of the atomic layer, which contains intrinsic fault and extrinsic stacking fault. Twin crystal forms with two crystals (or two parts of a crystal) forming a mirror-symmetric azimuth relation along a common crystal surface (i.e., a specific orientation relation), and it is contact with stack layer fault. Volume defect represents a region that has a different structure, density, or chemical composition on a macroscopic scale from a matrix crystal [98, 99]. However, volume defect is infrequent for the adjusting of electronic structure, which defines as the regions with different structures, densities, or chemical composition in macroscopic and matrix crystals, consisting of cavitation and microprecipitation.

1.3.3.2

Energy Band and Impurity Level of Semiconducting

The sensing performance of n-type and p-type semiconductors is also dependent on the energy band of SMO-based materials. When the dimension of the crystallite materials is on an order of the thickness of the charge depletion layer, energy band bending is no longer restricted to the surface region, but extends into the bulk of the grains, which will produce non-negligible impacts to electronic structure and electron–hole carrier [74, 100, 101]. In general, band gap energy (E g ) of semiconductor is the minimum energy required to excite an electron from the ground state valence energy band into the vacant conduction energy band. Once adsorbing a photon of energy higher than basic band gap energy, the excitation of an electron leaves an orbital hole in the valence band. The negative electron and positive hole can easily mobilize under an extra electric field, and their lowest energy state is an electrostatically bound electron–hole pair [102, 103]. Especially, the impurity doping with various compositions can induce an intraband electronic energy level that allows lower energy electron or light emission from the defect state to the ground state. In addition, the electronic band gaps of semiconductors can modulate through adjust the size, shape, and composition of semiconductors. Interestingly, in gas sensing applications, sensors with large E g as sensitive materials can work under high working temperature conditions, which also indicates that such sensors have better thermal stability. Under the condition of working temperature higher than 300 °C, the optimal band gap of gas sensor must be higher than 2.5 eV. In addition, the chemical activity of semiconducting metal oxide gas sensor is weak dependent on ambient temperature and humidity at higher working temperature [103–105]. For example, novel core–shell semiconductor can be designed where the conduction and valance band of the core and shell are staggered, inducing the segregation of the electron and the hole. It found that the shell had a minimum conduction band energy while the core was opposite. The energy band offsets segregate electron to the shell and the hole to the core, and carrier recombination can occur across the interface at a lower energy than the band gaps of either of the constituent semiconductor materials [102, 105]. The wide band gaps, availability of heterojunctions, high electron-saturation velocities and high breakdown fields endow high-speed and high-sensitive gas detection devices.

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Furthermore, band structure can determine many special properties of semiconductors, including light adsorption, charge separation and recombination, magnetic properties, and photocatalytic activity of chemical reactions, which can affect their application in photo-catalysis or photoelectric conversion [101, 106]. Especially, the type of optical transformation is dependent on the band structure of semiconductors, and optical conversion does not require a change in volatility just for direct gap semiconductor, which can absorb all the incoming light in a few microns. In addition, charge carriers in a direct gap semiconductor can reach the electrolyte only by moving a relatively short distance. While the indirect gap semiconductor requires the change of lattice fluctuation in the optical conversion process, and the incident light (photon) has a small momentum, where the indirect conversion requires the addition of photon (lattice vibration); thus, it requires a greater thickness (usually about 100 microns) to absorb incoming light. In an indirect gap semiconductor with a shorter charge diffusion length, deep charge carriers may have recombined before reaching the electrolyte [106, 107]. In brief, the influence of the energy band structure on the movement of charge carriers depends on the conduction and band gap, and the mobility of charge carriers is inversely proportional to the mass of carrier carriers. The wider band mostly curved and reduced the effective mass of carrier and improves the moving efficiency of charge [108]. Interestingly, the band structure can also affect the photoetching and thermodynamic potential energy of photoelectric response, and it determines the spectral range of light absorption in semiconductor and the theoretical maximum of the solar ammonia production efficiency. The band energy is the basic property of semiconductor, while another important parameter is impurity level of semiconducting, which can also produce enormous variation to the electron–hole carrier and microstructures [100, 109]. Generally speaking, impurity semiconductor produced by the doping engineering and introducing extra compositions. The definition of impurity semiconductor is that incorporating certain trace elements as impurities in intrinsic semiconductors, and it will make a significant change in the conductivity of the semiconductor. Once the periodicity of the potential field in the semiconductor destroyed, leading electrons or holes tied around the impurities, where generate localized quantum states and form impurity levels near the extreme band. Impurity semiconductor also consists of ntype and p-type semiconductor, and in practical applications, the impurity should be controlled and adjusted to changing the electric conductivity [106, 107, 110]. By precisely controlling the amount of doping guest and the distribution in space, it can effectively control the resistivity and minority lifetime, which require further research in gas sensors. On the other hand, according to the space position of the impurity level in the forbidden band, the impurity can be divided into two categories, such as shallow level impurity and deep level impurity. Where the energy level of former one close to the bottom of the tape or the top of the valence band, while the latter one is opposite. In most impurity semiconductors, there are two typical ways of existence for impurity parts, including gap type and alternate in the crystal space. Gap type can be defined as the position of the impurity between the lattice points of the elements or ions constituting the semiconductor, which usually combine with non-covalent bonds based on the relative atomic radius of doping guests [102, 103,

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108]. In contrast, alternate can form through impurity occupying the position of the grid, and the size with valence electron shell structure is similar, which can combine via strong covalent bonds. Thus, in most circumstances, introduce of specific impurities can effectively adjust the conductivity and mass transfer of materials, which is beneficial for the application in gas sensors.

1.3.3.3

Carrier Transportation and Electronic Structure of Semiconducting

In semiconductor physics, carrier is also named as current carrier produced based on vacancies in covalent bonds via electron loss, which consist of electrons and holes. In fact, not only the free electron of n-type semiconductor but also hole of p-type semiconductor, all of them can play an important role in conductive. According to the definition, charged particles carrying current in a semiconductor, such as electron and hole, can regard as free carrier [88, 111, 112]. In opposite, when semiconductor is in thermal equilibrium under a certain temperature, the concentration of conductive electron and hole in the semiconductor maintains a stable value, which can be considered as thermal balance carrier [80, 99, 100]. The carrier of semiconductor is a classical physical phenomenon, and it consists of three typical transports, such as drift, diffusion, and recombination, which will be affected by various factors, such as electronic structure, temperature and applied electric field, even impurities, defects and disorder of materials. Carrier drift can be defined as the motion of charged particles under an external electric field, and this motion will produce drift current based on the opposite motion direction of electron and hole [99–101]. It is found that carrier mobility (a distance that carriers migrate under unit time and unit electric field strength) influences the carrier generation and transport simultaneously, which will depend on the lattice scattering, element doping and ambient temperature. By contrast, carrier diffusion is belonging to a simple and ubiquitous motion, and it is the irregular thermal motion of carriers. It defined as the carrier moved from high concentration to low concentration, inducing the internal rearrangement of carrier, which could produce diffusion current. However, carrier recombination, the most concerned phenomenon, is a complex process and electrons/hole annihilation or disappearance [66, 102]. In addition, it can be divided into three categories, such as direct recombination, indirect recombination, and auger recombination. In a crystal at equilibrium state, a dynamic balance exists between carrier generation and recombination. One the other hand, the rate of carrier recombination can produce obvious influences to carrier lifetime; thus in effective control, the carrier recombination is important for the enhancement of photo-electricity property and gas sensor. The electronic structure is an important character of semiconductors, and semiconducting metal oxides exhibit very good electrical properties and mainly composed of transition metal elements [64, 87]. The electron structure of semiconducting metal oxides is very complex, and except for the s and p valence bond orbitals, there are d valence bond orbitals. As is known to all that d valence bond orbitals have rich

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physical and chemical properties, which can provide abundant chemical activity for various applications, especially for gas sensors.

1.3.3.4

P-N Injunction

Recently, P-N injunction semiconductors have aroused great attentions in various discipline or fields. P-N injunction usually formed through two or more semiconductors with various electronic structure assembling or combining, and one type is n-type and another is p-type semiconductor [113–115]. In general, p-type semiconductor possesses much higher concentration of hole than free electrons, which mainly rely on hole conduction. While n-type semiconductor has much higher concentration of free electrons than hole, inducing free electron conduction. When n-type and p-type semiconductors are in mutual contact, these two different carriers can motion from the higher concentration to the lower one, including free electron and hole, because of their great variation of carrier concentration and Fermi level [116–118]. This diffusion can cause p-type semiconductor lacking of initial hole and leaving negatively charged impurity ions, and n-type semiconductor lose pristine free electron and leaving positively charged impurity ions. However, these impurity ions are unable to move or diffusion; thus, these impurity ions with opposite charge will produce space-charge zone in the interface, which define as P-N injunction, and it possesses special unidirectional conductivity. With the development of nanotechnology and nanoscience, the preparation strategy of P-N injunction has obtained advanced progress, and a series of techniques applied in the synthesis process, including machine-alloying, diffusion, ion implantation, sol–gel, magnetron sputtering, electrochemical deposition, epitaxial growth, etc., and the following is a brief introduction to the principles of several methods [103, 119]. Machine-alloying adopt special impurity on the surface of n-type semiconductor and using relative high-temperature melt or partial melting impurity, and then cooling and crystallization to form P-N injunctions. Similarly, diffusion is using gas, solid, or liquid as impurity diffusion source and then form P-N injunctions under ordinary heat treatment. While ion implantation is applying impurity ions with higher energy and injection into the semiconductor substrate, which possess many advantages than traditional diffusion. However, above strategies will limit their practical applications owing to the low synthesis efficiency and high energy consumption. Among these techniques, sol–gel and electrochemical deposition exhibit excellent prospect in the preparation of P-N injunctions. Electrochemical deposition, also named as CVD technique, is adopting typical gaseous compound or mixture carrying on chemical reaction under heating surface of substrate, and then growing a non-volatile coating layer. This technique can be realized as the effective bonding between substance and impurities; in addition, it usually can be duplicated and applied for the synthesis of many planar materials. Thus, in practical application, using above techniques, many novel P-N injunction or heterostructures can be easily designed and fabricated, which have great potential in the various applications, such as gas sensor, catalysis, and energy storage.

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In conclusion, semiconducting metal oxides possess abundant chemical active and reaction sites; in addition, there are excellent physical and chemistry performances, including electronic, optical, and magnetic effect, etc. [29, 93]. These features will be affected by various factors of influences, such as crystal structure, defects, banding energy, and P-N injunction. Thus, we can adjust these parameters to control relative characterizations and design available in SMO-based materials for a series of applications.

1.3.3.5

Other Potential Properties

With a rich surface state density, a relatively rich surface Fermi energy level can be obtained, which can change the surface potential of semiconductor oxide-sensitive materials and form different semiconductor potential barriers. This potential barrier plays an important role in enhancing the sensitive characteristics, and the most representative semiconductor barrier is Schottky barrier. The electron Schottky barrier height can be expressed as U S = K(W Me − W s ), where W Me and W s are electronic work functions of metals and semiconductors, respectively. In addition, highly chemically active semiconductor oxides are more beneficial to surface redox reaction and increase the sensitivity of sensors [108, 120]. Sensitive materials should also have stable chemical activity to improve the anti-interference properties of sensors, especially in the gas environment of corrosion and easy to poison sensors. In addition, the poor moisture resistance of the semiconducting gas sensor is mainly due to the hydroxylation formed by the chemical adsorption of water molecules on the surface of the semiconductor oxide, which affects the sensitive characteristics of the semiconductor oxide. Therefore, the synthesis of semiconductor oxides with low chemical activity to water molecules may enhance the moisture resistance of sensors. The sensitive material of gas sensor must have high thermal stability under high working temperature, and the higher the thermal stability of the sensitive material, the smaller the drift of the sensor’s base resistance. Generally, good thermal stability and high sensitivity of semiconductor oxide materials do not obtain simultaneously. The sensor of this material also has better long-term stability and device reliability in the long time of high-temperature operation. In semiconductor oxides, the formation of oxygen holes leads to defects in semiconductor oxides. At the same time, the existence of oxygen holes makes semiconductor oxides have different conductivity at different oxygen partial pressure. In the process of surface REDOX reaction, the oxygen holes can move from the inside of the semiconductor oxide to the edge of the oxide’s grain boundary and interact with the adsorptive oxygen on the grain boundary surface to reach a balanced state. The relationship between the diffusion of oxygen holes and the gas-sensitive properties of semiconductor oxides and the mechanism of oxygen hole diffusion in the reaction process are still worthy of further study. When semiconductor oxides are exposed to the atmosphere under test, two conditions occur on the surface of sensitive materials: gas adsorption and desorption [121–124]. There are two main types of adsorption reactions between semiconductor oxides and gas target molecules:

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physical adsorption and chemical adsorption. Physical adsorption is neutral and no new species forming. Chemical adsorption accompanies with the exchange of charge between the adsorbent and the semiconductor oxides. It is beneficial to develop a high-performance semiconducting metal oxide gas sensor by establishing a reasonable adsorption–desorption kinetic model and clarifying the relationship between the absorption–desorption characteristics and the sensor’s sensitive characteristics. In addition, some intrinsic characteristics, including grain size, morphology, exposed crystal face and porosity, can also affect the macroapplications in many various fields, especially for porosity. For instance, within the semiconductor oxide, the porosity of nanoparticles needs to be small, which can endow smaller grain boundary potential barrier and better electronic transmission characteristics to the sensor’s sensitive receptor, which have great influence on the gas sensitivity of the sensor [32, 125]. On the surface of semiconductor oxide, the porosity of nanoparticles needs to be large, and it is beneficial for gas molecules to diffuse into the inside of the sensor’s sensitive receptor, increasing the utilization rate of the sensor and sensitive characteristics. When the pore size of the outer nanoparticles reduced to a few nanometers or even sub-nanometers, the diffusion of gas molecules on the surface to the inner particles would hinder [126–129]. This may also be the reason why semiconductor oxides with three-dimensional hierarchical structure have good sensitive characteristics because of their large external surface porosity, small internal porosity and better grain boundary contact.

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Chapter 2

Sensing Mechanism and Evaluation Criteria of Semiconducting Metal Oxides Gas Sensors

The sensing mechanism of gas sensor based on metal oxides semiconductors is mainly owing to the variation of resistance when host-materials are exposed to atmosphere containing target gases. For example, for n-type semiconductors, the gas response (S) of the sensor is usually defined as S = Ra /Rg (for reducing gases) or S = Rg /Ra (for oxidizing gases), where Ra and Rg represent the resistance in air and target gas, respectively. In general, the response or recovery time can be defined as the time taken for the sensor output reach to 90% of its saturation after applying or switching off the gas in a step function under appropriate temperature [1, 2]. For all semiconducting metal oxides, the sensitive mechanism of semiconductor gas sensor mainly divided into the following two classes: (1) Surface control type. Chemical surface adsorption of O2 can change the resistance of metal oxide, where the adsorption and catalytic reaction process of the gas on the surface of metal oxide will change the resistance accordingly. O2 (g) + xe− = Ox− 2 (ad),

(2.1)

− 2H2 (g) + Ox− 2 (ad) = 2H2 O(g) + xe

(2.2)

(2) Body resistance control type. This mechanism is suitable for γ-Fe2 O3 and ABO3 -type sensitive materials. A case study of γ-Fe2 O3 is given below, and γ-Fe2 O3 is a kind of metastable structure. It will convert into stable α-Fe2 O3 at high temperature and convert into a similar structure of Fe3 O4 under reducing gas. The conversion behavior relations between several types of iron oxide are as follows: Reduction

Oxidation

Fe3 O4 ←−−−−− γ -Fe2 O3 −−−−−→ α-Fe2 O3

(2.3)

For most metal oxides semiconductors (e.g., n-type and p-type semiconductors) sensing materials, the sensing mechanism are well suitable for the surface control © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Deng, Semiconducting Metal Oxides for Gas Sensing, https://doi.org/10.1007/978-981-99-2621-3_2

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type, which indicate that the detection behavior is dependent on the variation of carrier concentration on the electron depletion layer (EDL) of these metal oxides.

2.1 Pure Metal Oxides Semiconductors The gas sensing properties of metal oxides semiconductors are owing to the formation of electronic core–shell configuration by oxygen adsorption (Fig. 2.1). In detail, at high temperatures (>100 °C), oxygen molecules were adsorbed onto the surfaces of n-type metal oxide semiconductors (e.g. SnO2 and ZnO) and ionized into oxygen species such as O2− , O− , and O2 − by taking electrons from the surfaces of the semiconductors. In general, these oxygen species (O2− , O− , and O2 − ) are known to be dominant at 400 °C, respectively, which will induce to form an electronic core–shell structures (Fig. 2.1a), where an n-type semiconducting region exist in the cores and resistive electron depletion layer (EDL) at the shells of the particles. Similarly, the adsorption of oxygen species in p-type metal oxide semiconductors can form the hole accumulation layer (HAL) at the surface of the material because of the electrostatic interaction between the oppositely charges species (Fig. 2.1b), which again establishes the electronic core–shell configuration, forming the insulating region at the cores and semiconducting HALs at the surface of these particles.

Fig. 2.1 Formation of electronic core–shell structures in a n-type and b p-type oxide semiconductors. Reprinted with permission [2]. Copyright 2014, Elsevier

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The resistance of the semiconductors played an important role to evaluate the physicochemical property of metal oxides, and it would increase or decrease depending on both their physical nature and the gaseous analytes. For example, the sensing mechanism for n-type semiconductors is illustrated in Fig. 2.2a. It can be clearly seen that the current carrier in n-type semiconductors is electrons (e− ), and oxygen molecules adsorb onto the surface of the oxides and “grab” electrons from the surface to form oxygen anions like O2 − and O− under ambient atmosphere can decrease the electron density and increase the resistance. The electron distribution of semiconductors can only be influenced within a limited depth near the surface by the adsorbed oxygen species. In addition, the affected region with low electron density called as the electron depletion layer, whose depth from the surface defined as Debye length (L D ), which is usually typically several nanometers. The calculation of Debye length of semiconductor is given in Eq. (2.4), where ε is the dielectric constant, k B is the Boltzmann’s constant, T is the absolute temperature in Kelvins, q is the elementary charge, and N d is the density of dopants (either donors or acceptors). When ntype semiconductor exposed to reductive gases, such as CO, H2 , CH4 , C2 H5 OH and acetone, electrons would flow back to depleted oxides through the surface reaction between negative oxygen species and reductive gases. It can decrease the resistance of pristine metal oxides, while for oxidizing gases such as Cl2 , NOx , and SO2 , it can intensify the electron depletion and cause an increase of resistance. On the contrary, for p-type metal oxides, the current carrier is holes (h+ ), the variation of resistance toward reductive gases and oxidizing gases is absolutely opposite to n-type semiconductors. In a resistive-type sensor, the sensing materials are deposited across two or more electrodes, which measures the variation in the electrical resistance of these oxides when exposed in target gases. A typical simplified electric circuit of sensing measurements is illustrated in Fig. 2.2b, which indicate that the sensing material is electrically connected to the voltage dividing circuit including a certain load resistor connected in series. The resistance of sensing materials and its resistance variation upon exposure to target gases can be calculated from the output voltage of load resistor. An appropriate load resistor (RL ), whose resistance is close to the sensing material, is needed to ensure the accuracy of calculation. Usually, the selection of RL is done manually in early types of measurement system. The recently developed measurement systems introduced an automatic switch of RL , which can greatly reduce the measurement errors and increase the potentials in practical applications [3–5]. LD =

√ εkB T q 2 Nd

(2.4)

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Fig. 2.2 a Sensing mechanism of n-type metal oxide semiconductors. The conduction area expands when exposed to reducing gases. b A typical electric circuit for gas sensing measurement. RL : load resistor; V C : the circuit voltage; V out : output voltage; V H : heating voltage. Reprinted with permission [4]. Copyright 2017, Elsevier

2.1.1 N-type Metal Oxides Recently, Deng’s group focused on novel n-type semiconducting metal oxides and studied their unique sensing performance and interaction mechanism. For example, Zhu et al. [6] synthesized mesoporous tungsten oxides (WO3 ) with crystalline framework for highly sensitive and selective detection of foodborne pathogens, and the mesoporous WO3 -based chemiresistive sensors exhibit a rapid response, superior sensitivity, and highly selective detection of 3-hydroxy-2-butanone. The reaction mechanism between mesoporous WO3 -based sensor and target gases can be explained by a typical surface depletion model. As shown in Fig. 2.3, when the sensors exposed to air, oxygen molecules could chemically adsorption on the surface of WO3 to capture electrons from the conduction band and form adsorbed oxygen anions (O− , and O2− ). Meanwhile, a thick space-charge layer is formed near the surface of WO3 , increasing the potential barriers (marked in green) with a higher resistance. In contrast, when the WO3 -based sensors are exposed to the reductive 3-hydroxy-2-butanone gas, the target molecules can react with the negative oxygen species and release free electrons, leading to the decrease of the thickness of the potential barriers (marked in green) and the electrical resistance. Similarly, Xiao et al. [7] reported that the mesoporous SnO2 was employed for fabricating gas sensor nanodevices which exhibited an excellent sensing performance toward H2 S with high sensitivity (170, 50 ppm) and superior stability. The chemical mechanism study reveals that both SO2 and SnS2 are generated during the gas sensing process on the SnO2 -based sensors. As shown in Fig. 2.4, plentiful homojunctions were formed between two abutting nanograins in the crystalline pore walls

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Fig. 2.3 Schematic illustration of the 3-hydroxy-2-butanone sensing mechanism of the sensors based on mesoporous WO3 exposure at air and target gas–air mixture (E V , valence band edge; E C , conduction band edge; E F , Fermi energy). Reprinted with permission [6]. Copyright 2017, American Chemical Society

of the mesoporous SnO2 materials. In air, oxygen molecules can diffuse through the mesopores and the interspace of the nanograins, resulting in a complete cover of the surface of nanograins. The adsorbed oxygen species can extract electrons from the nanograins; therefore, the electron depletion layers are formed on the surface of SnO2 nanograins, leading to the formation of potential barriers on the boundaries. The existence of the potential barriers contributes to the restriction of the flow of electrons through the boundaries. Upon exposed to H2 S, the voltage of the loading resistor increases rapidly because of the return of electrons from H2 S to SnO2 via surface reaction, resulting the decrease of the resistance of the SnO2 sensors.

2.1.2 P-type Metal Oxides Except for n-type semiconductors, our group also designed various p-type metal oxides for gas sensing. In 2016, Wang et al. [8] synthesized ordered mesoporous carbon/cobalt oxide nanocomposites with large mesopores and graphitic walls. Due to the strong synergistic effect between the graphitic OMC with large pores and uniform active p-type CoOx nanoparticles, the obtained mesoporous nanocomposite exhibits superior performance in hydrogen sensing. The CoOx nanoparticles act as the active substance and follow the oxygen adsorption mechanism in the gas sensing process (Fig. 2.5). Firstly, oxygen molecules tend to trap and react with electrons from CoOx /C composites conduction band and cause the depletion layer containing

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Fig. 2.4 Sensing mechanism of the mesoporous SnO2 -based sensors exposure to air and H2 S-air mixture (E c , conduct band edge; E f , Fermi energy). Reprinted with permission [7]. Copyright 2017, American Chemical Society

negative oxygen species (O2− , O− , O2 − ) around the surface of CoOx nanoparticles. Then, when the sensing materials exposed to H2 -air mixture, the reductive H2 molecules would react with oxygen anions to form H2 O molecules, and the electrons flow back to the CoOx and recombine with a certain number of holes, which cause rapid increasing of the resistance.

Fig. 2.5 Schematic illustration of the H2 sensing mechanism of CoOx /C when exposed in air (left) and H2 -air mixture (right). Typically, CoOx is the p-type semiconductor material. When exposed to air at a certain temperature, the adsorbed oxygen capture electron from the CoOx and the oxygen anions (O2− , O− , O2 − ) formed near the surface of CoOx nanoparticles. When exposed at H2 -air mixture, the H2 molecules react with the negative oxygen species, and electrons return to CoOx nanoparticles which combine with the hole and cause the increase of resistance. Reprinted with permission [8]. Copyright 2016, American Chemical Society

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2.2 Metal Oxide Heterojunctions 2.2.1 n–n Heterojunctions Apart from single component of metal oxides, heterojunction structure can play an irreplaceable role in adjusting electron structure and support abundant reaction boundary. For instance, Han et al. [9] successfully fabricated a kind of novel n–n combined ordered mesoporous WO3 /ZnO (OM-WO3 /ZnO) sensor, and the prepared OM-WO3 /ZnO sensor showed the much better response, much shorter response time, much lower detection limit, and the more excellent selectivity toward NOx gas. The internal surface area of pores is much larger than that of the external surface in the prepared WO3 -ZnO, and the reaction between sensor and target gas mainly occurs on the surface of the pores. The process can be separated into three parts, which is illustrated in Fig. 2.6. From Fig. 2.6a, the Fermi level of WO3 is higher than that of ZnO, and the electrons in WO3 trend to flow into ZnO layer to maintain the equilibration of the Fermi level. Under this condition, the superfluous electrons distribute in the ZnO sides, and the superfluous holes distribute in the WO3 sides near the n–n heterojunction layers. When the electrons injected into the n–n heterojunction layer, the electrons would flow to the WO3 sides. The n–n heterojunction layers reduce the potential barrier of the electrons migrating from ZnO layer to WO3 layer under the function of the impressed current. The oxygen component will adsorb on the surface of WO3 layer once the WO3 -ZnO sensor exposed into air, and the adsorption oxygen reactions happen, where oxygen molecules can capture the electrons of WO3 layer to form a series of oxygen species (O2− , O2 − , and O− ). It is a process of electron depletion, which causes the decrease of the concentration of electrons in WO3 layer, and forms a distensible electron concentration difference between two sides of the n– n heterojunction (Fig. 2.6b). It provides the increased driving force for the electrons migrating from ZnO layer to WO3 layer under the function of the impressed current. From Fig. 2.6c, when the oxidizing NOx injected into the testing chamber, the NO2 and NO molecules were adsorbed on the surface of WO3 layer as well. When the NOx molecules captured the electrons of WO3 , the electron concentration sequentially decreased in WO3 layer. The difference of electron concentration between two sides of the n–n heterojunction was further enlarged, which would induce the electrons of ZnO layer pass through the n–n heterojunction, moving toward WO3 layer under the function of the impressed current. As a result, the resistance of the sensor will decrease sharply. In 2014, Gao et al. [10] reported a novel Al2 O3 –In2 O3 nanofibers sensor that exhibited a very high response to the NOx with concentrations ranging from 0.3 to 100 ppm at room temperature. The excellent gas sensing performance can be attributed to the synergistic effect between unique one-dimensional mesoporous tubular structure and the Al2 O3 modification role. It found that the model was introduced to explain the sensing mechanism in Fig. 2.7, and the electron transport and the NOx gas response in the meso-Al2 O3 /In2 O3 were as follows.

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Fig. 2.6 Schematic diagram of sensing mechanism of WO3 -ZnO gas sensor. Reprinted with permission [9]. Copyright 2018, John Wiley and Sons

Firstly, when the sensor (Fig. 2.7a) exposed in air, oxygen species might be adsorbed on the outer/inner layer of the tubular surfaces and throughout the mesoporous wall (Fig. 2.7b (1)). Secondly, oxygen molecules can easily trap the free electrons from their conduction band or donor level to form oxygen ions or chemisorbed oxygen (O2− or O− ). As shown in Fig. 2.7b (2), the negative charge trapped in these oxygen species will produce a depletion layer. Such chemisorbed oxygen is suggested to act as an electron donor, which depends strongly on the electrical conductivity of the material, and the formation of numerous oxygen species (O2− or O− ) is known for their good catalytic activity in gas sensors. Thirdly, when the sensor exposed to NOx gas, the gas molecules tend to attract the electrons from the conduction band or the donor of the meso-Al2 O3 /In2 O3 because of the high electron affinity of the NOx molecules. It makes electrons transfer from the meso-Al2 O3 /In2 O3 sensor to surface adsorbates NOx (Fig. 2.7b (3)). At the same time, the chemical interaction between indium oxide and NOx as well as stipulated variation of defect concentration might happen. The process traps electrons from the conduction band or donor level of the meso-Al2 O3 /In2 O3 , which finally leads to a decrease of electron density. The adsorption of NO2 on the meso-Al2 O3 /In2 O3 leads to produce extra NO, and the negative charge trapped in these NO/NO2 results in an increase of the thicknesses of depletion regions and the resistance. Besides, the target gas molecules NO2 and NO are directly adsorbed onto the meso-Al2 O3 /In2 O3 , then react with O2− /O− and later on generate bidentate NO3 − (s) and generate NO2 − . Fourthly, the chamber was purged with air to recover the meso-Al2 O3 /In2 O3 sensor resistance (Fig. 2.7b (4)). In Fig. 2.7c, the donor energy level being located below the conduction band may be formed by the oxygen vacancies/defects, which is contributed to the Al2 O3

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Fig. 2.7 Gas conductive mechanism of the meso-Al2 O3 /In2 O3 sensor and the HRTEM image. a The meso-meso-Al2 O3 /In2 O3 sensor; b gases conductive processes of the sensor: (1) air atmosphere, (2) oxygen molecules trap electrons and form chemisorbed oxygen, (3) NOx gas sensing response, and (4) NOx concentration decrease; c energy band diagram of a nanotube near the surface region, EC : conduction band, ED : donor level, and EV : valence band; d HRTEM images showing defects and heterostructure at the branching locations, the arrows point out linear and planar defects. Reprinted with permission [10]. Copyright 2014, Royal Society of Chemistry

additive. Figure 2.7d shows the presence of many defects between Al2 O3 and In2 O3 grains. In 2016, Sun et al. [11] reported 4 mol% MoO3 /WO3 composite nanostructures and exhibited enhanced gas sensing performance, giving a low limit of detection (500 ppb). It shows high responses of 28 and 18 toward 100 ppm ethanol and acetone at the operating temperature of 320 °C, which were about 2.3 and 1.7 times higher than those of the pure WO3 , respectively. For pure WO3 , when the gas sensor exposed to air, the thickness of electron depletion layer would increase due to the ionization of oxygen molecules. Therefore, the resistance of WO3 will increase, which result from the lower concentration of free electrons in the conduction band (Fig. 2.8a). When the gas sensor was exposed to reducing gases, the oxygen species would react with the target gas and release electrons into the conduction band. Thus, the thickness of

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Fig. 2.8 Schematic band diagrams of pure WO3 and MoO3 /WO3 exposed to air and target gas. Reprinted with permission [11]. Copyright 2016, Springer

the depletion layer at the interfaces will decrease, which will lead to the decrease of the measured resistance of the WO3 gas sensor (Fig. 2.8b). Compared with pure WO3 , the enhanced gas sensing properties of MoO3 /WO3 gas sensor can be attributed to the synergetic effect and the heterojunction of WO3 and MoO3 . Firstly, both MoO3 and WO3 are important sensing materials. There is a synergetic effect of different gas sensing materials, which has been observed in the other hierarchical composites. Secondly, it can be ascribed to the heterojunction, which is formed at the interface between MoO3 and WO3 . The different work functions will lead to the negatively charged carries moving from WO3 to MoO3 until their Fermi levels align, creating a thicker electron depletion layer at the interface (Fig. 2.8c, d). As a result, it exhibits enhanced sensing property to ethanol and acetone than pure WO3 . The sensing mechanism controlled by the thickness change of the electron depletion layer, which has been also found in other composites. However, the excessive content of Mo element will suppress the sensing properties of the samples, because excess dopant will reduce the available adsorption sites between WO3 and the target gas, which is in agreement with the previous report.

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2.2.2 p-p Heterojunctions Subsequently, Alali et al. [12] constructed p-p heterojunction CuO/CuCo2 O4 nanotubes via electrospinning technology for detecting n-propanol gas at room temperature. The CuO/CuCo2 O4 nanotubes composite is typical p-p semiconductor metal oxide where the holes are the charge carriers for conductivity. To further understanding the reaction mechanism between test gas molecules and sensing materials, a schematic of the reaction process in air ambient and n-propanol ambient with the changes in the band gap energy is depicted in Fig. 2.9. In the first case when the sensor is in air ambient, the oxygen molecules (O2 , named free state oxygen), will be adsorbed on the surface of the sensing materials and diffuse inside the crystal structure of the sensing materials (Fig. 2.9a). The adsorbed oxygen captures an electron from the conduction band (CB) of the sensing material and alternates to ionic oxygen in the state (O2 − ) in addition to produce many holes (h+ ) in the CB. The oxygen ions can be existed in different ion states inside the metal oxide structure according to the working temperature: at low temperature in the O2 − state and at high working temperature can be in O and O2 − states. In this case, a thick depletion layer will be formed on the surface of CuO and CuCo2 O4 crystals and the concentration of the holes in the surface layer will increase, which will lead to an increase in the conductivity of the sensing materials. A schematic diagram of the band gap energy structure in air ambient is presented in Fig. 2.9c, when the sensors are transferred to n-propanol ambient, the n-propanol molecules can react with adsorbed oxygen species on the surface of the sensing materials and the results of this reaction, CO2 and H2 O molecules, will be released into the atmosphere (Fig. 2.9b). The reaction between n-propanol molecules and adsorbed oxygen species could be described as follows: + 2C3 H7 OH + 9O− 2 + 18h → 6CO2 + 8H2 O

(2.5)

As a result of this reaction, the thickness of the depletion layer is decreased by releasing the captured electrons back to neutralize the holes in the CB of CuO and CuCo2 O4 crystals until reaching the saturation state because the p-type semiconductor has a limitation of adsorbing oxygen ions on its surface. The changes in the band gap structure after exposure to n-propanol are shown in Fig. 2.9d, where Φeff is the effective junction energy barrier height, E c is the lower level of the effective junction of the conduction energy band gap. E v is the upper level of the effective junction of the conduction energy band gap, and E f is the Fermi level. In this state, the concentration of holes in the surface layer of the sensing material is low, and a decrease in the height of the energy barrier between the crystals will be noted. Consequently, the resistance in the sensor’s circuit is increased. When the sensors were moved out of the n-propanol ambient, the conductivity increased to its initial value as a result of the reaction of air molecules with the sensing materials. These processes will be repeated for every change in the sensor’s atmosphere. Gas sensing

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Fig. 2.9 Schematic diagram of sensing mechanism of composite CuO/CuCo2 O4 nanotubes in air ambient (a) and n-propanol ambient (b). c, d Schematic of the energy band gap in air and n-propanol ambient, respectively. Reprinted with permission [12]. Copyright 2017, the Partner Organisations

materials based on well-ordered structured composites have superiorities in response and response–recovery times.

2.2.3 p-n Heterojunctions In a p–n junction, the electrons at high energy can transfer across the oxide interface to unoccupied low energy state to equalize the Fermi level, resulting in a “band bending”. This energy transition can change the energy structure of both p-side and n-side and leading to a better sensitivity. Taking the p-type NiO/n-type SnO2 heterojunctions for example, the response of p-NiO/n-SnO2 is higher than pristine SnO2 or NiO [13]. The possible explanation of band structure is shown in Fig. 2.10, which indicates that the electrons will transfer from SnO2 layer to NiO layer while holes from NiO to SnO2 , until the Fermi level of the system is equalized, leading to an even wider depletion region at the oxides interface and the increased resistance. When the sensor is exposed to reducing C2 H5 OH gas, C2 H5 OH molecules will react with the adsorbed oxygen species and release the electrons back to the bulk, increasing the conductivity of the materials. Moreover, C2 H5 OH also releases electrons into p-type NiO and cause the electron-hole recombination which decreases the concentration of holes. The decreasing holes in NiO layer can result in the increase of electrons.

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Fig. 2.10 Proposed band structure model for a p-NiO/n-SnO2 heterojunction when surrounded by a air, b ethanol gas. E CB , lower level of conduction band; E F , Fermi level; E VB , upper level of valence band. Reprinted with permission [13]. Copyright 2016, Elsevier

Consequently, the concentration gradient of the same carriers on both sides of p-n heterojunction is decreased; therefore, the diffusion of carriers is greatly reduced, and resulting in a thinner depletion layer at the interface; thus, the resistance of the SnO2 /NiO composites in ethanol can be further decreased. In general, compared with the pure SnO2 sensor, the formation of p-n heterojunction between SnO2 and NiO sensor greatly increases the resistance in air and decreases the resistance in ethanol gas. This theoretical model can be also used to explain other materials systems of heterojunction, such as CuO/ZnO and CuO/SnO2 . Similarly, Kim et al. [14] uniformly coated a Sn-precursor onto Ni spheres, heated the Ni spheres to partially oxidize them and convert the Sn-precursor into SnO2 , dissolved the Ni metal cores of the spheres, and subsequently heated the hollow spheres to prepare SnO2 hollow spheres whose inner walls were decorated with NiO nanoparticles. In addition, the NiO-decorated SnO2 hollow spheres showed ultrarapid recovery rate ( 2L), the highly conductive core region hardly participates in the discharge-recharge process when exposed to oxidizing gases and reducing gases, thus exhibiting poor response. For grains whose D value approaches but is still larger than 2L (D > 2L), a constricted channel forms by the depletion region that surrounds each neck, and consequently, the conductivity depends on both grain boundary barriers and the cross section area, therefore the sensitivity is enhanced and becomes grain size dependent. When D < 2L, the crystallites are almost fully depleted, implying that the whole grain participates in the charge transfer interactions with gas molecules. The energy bands of these interconnected grains are nearly flat, because there is no significant barriers for charge transfer in grain boundaries. Xu et al. [43] have studied the grain size effects of porous SnO2 sensor and found that, when the SnO2 crystallite size (D) was controlled in the range of 5–32 nm, the responses for H2 , CO and i-C4 H10 increased greatly as D decreases to be comparable with or less than 2L (~6 nm) (Fig. 2.26). It mentioned that even though small crystallites could also yield the highest sensitivity theoretically, and the practical application of the ultra-small metal oxide particles was limited due to the steeply decreased conductivity and unstable nature of the nanoparticles at high operating temperature. For example, Pratsinis’s group [46] proposed a SiO2 doped WO3 sensing film, which is composed of ε-WO3 , a metastable phase with high selectivity to acetone. The effect of non-toxic silicon doping on ε-WO3 content, crystal and grain size was deeply studied (Fig. 2.27), which were particularly crucial for the acetone sensing properties.

2.6 Gas Sensor Evaluation Criteria As an essential part of Internet of Things (IoT), gas sensors are widely applied in industry processes, pharmaceutics, environmental monitoring, safety, and so on. Generally, for whatever application field the new sensing materials are developed, basic requirement of “5S” (Sensitivity, Selectivity, Stability, Simplicity, and System) of a gas sensor is always the research motor that should be followed in order to achieve rapid, reliable, and comprehensive detection. In this chapter, we will introduce the definition and understanding of several most important evaluation criteria for gas sensors.

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Fig. 2.25 Schematic model of the effect of the crystallite size on the sensitivity of metal oxide gas sensors: a D >> 2L; b D > 2L; c D < 2L. Reprinted with permission [42]. Copyright 2004, American Institute of Physics

2.6.1 Sensitivity Sensitivity is one of the most essential indicators which describes the activity of a sensing material to target analyte. Sensitivity (S) is defined as the ratio of output change dy to input change dx. S=

dy dx

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Fig. 2.26 Grain size effect on the responses of porous SnO2 under 800 ppm H2 or CO. Reprinted with permission [43]. Copyright 1991, Elsevier

Fig. 2.27 Effect of different SiO2 doping amounts on ε-WO3 content and grain size. Reprinted with permission [46]. Copyright 2010, American Chemical Society

In the case of chemiresistive gas sensors, the output is the response (R) and the input is the concentration of target gas. Response is generally calculated from the ratio of the resistance (Ra ) of the sensor in clean carrier (usually air) to the resistance (Rg ) upon exposure to certain concentration of target analyte. R=

Ra Rg

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Sometimes the concepts of sensitivity and response are ambiguous since both of them describe the variation degree of the resistance change. Therefore, other expression forms of sensitivity are also in use: | | | Ra − Rg | ΔR = S= Rg Rg Ideally, S is a function of the partial pressure p and temperature T only. A phenomenological relationship describing the dependence of the conductance G or resistance R of SnO2 -based sensors on concentrations of target reducing gases can be expressed by: G=

1 = Acβ R

where c is the concentration of target gas, A and β are individual constants depending on empirical calibration.

2.6.2 Operating Temperature As well known, chemical and physical properties of semiconductor metal oxides are largely dependent on its operating temperature. As for chemiresistive gas sensors, working temperature controls the reaction kinetics, conductivity and electron mobility. Generally, traditional MOS gas sensors are usually operated at the temperature of 200–500 °C, due to the thermal energy required by surface redox reaction to overcome the activation energy barrier [6]. In most cases, the response of gas sensor to certain gases undergo an increasingand-decreasing change with the rising of working temperature, creating a volcano curve as illustrated in Fig. 2.28. The sensitivity change toward temperature is mainly credit to the balance between the activity of adsorbed oxygen species and the desorption of reactants on the sensing material surface. When temperature is relatively low (area I in Fig. 2.20), the response increases with temperature rising due to the increasing activity of adsorbed oxygen species: 1/2O2 (gas) → 1/2O2 (phys) → 1/2O− 2 (chem) → O− (chem) → O2− (chem) Simulations of oxygen exchange of SnO2 surface were carried out by Pulkkinen et al. using the kinetic Monte Carlo method (Fig. 2.29) [47]. O2− was found as the dominate ionic adsorbents at relatively low temperatures, while at higher temperature oxygen species were in the form of O− . In addition, the surface-adsorbed reactants

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Fig. 2.28 Illustration of a typical operating temperature-sensitivity curve

will be easier desorbed under high temperature, which causes less electron transfer between target gas and material surface and thus lowering the sensitivity. Therefore, further increasing of working temperature leads to in a decline of sensor response because of the reduction of the surface coverage by progressive gas desorption (area II in Fig. 2.28). Other cases when there is no peak or double peaks shows in operating temperaturesensitivity curve also exist, since the adsorption–desorption behavior and the surface reaction model can be different on different materials. The high sensitivity of MOS gas sensors usually requires high operating temperature, because of which the applications of MOS sensors are limited, since high

Fig. 2.29 Simulated equilibrium coverages of the oxygen species on SnO2 surface. Reprinted from Ref. [47], copyright 2001, with permission from Elsevier

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temperature usually causes energy waste and safety issues when the active materials are exposed to flammable and explosive gases. Besides, high-temperature operation also causes signal drift of sensors which may lead to imprecise results or false alarm. Therefore, lowering the operating temperature while maintaining acceptable sensitivity has become the develop tendency of MOS gas sensor. Room temperature (RT) sensing has evoked intensive research interest due to its advantages of significantly reducing energy consumption. Cui et al. [48] proposed an excellent RT NO2 sensor based on a novel 2D tellurene nanoflake (Fig. 2.30), which was fabricated via a facile liquid-phase exfoliation. This RT sensor behaves with an outstanding overall performance with sensitive response (201.8% to 25 ppb), ultra-low theory detection limit (DL) of 0.214 ppb, repaid response and recovery speed (26/290 s toward 100 ppb), which is superior to the reported 2D nanomaterials. The DFT calculations were further conducted to disclose the mechanism of enhanced sensing performance. The results indicated that the electrical conductivity was improved after NO2 adsorption due to the interfacial electron transfer from tellurene to NO2 .

Fig. 2.30 Room temperature NO2 sensor based on tellurene nanoflake. Reprinted from Ref. [48], copyright 2020, with permission from American Chemical Society

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2.6.3 Selectivity In general, the selectivity of sensor represents the anti-jamming capability toward target gas under various interfering gases. In other words, the selectivity Q reflects the ability of sensor to differentiate between the specific gas to be detected and the other components of the gaseous environment x, and expresses the cross-sensitivity. Q(% ) = 100 ·

dy/dx ' dy/dx

However, a major shortcoming of some MOS-based sensing materials is its low selectivity, due to the presence of abundant adsorption sites on its surface that cannot distinguish the contribution of each type of gaseous molecules to the total electrical signal [5]. One of the ways to improve its selectivity is the surface modification of a highly dispersed oxide matrix with clusters of transition metals or their oxides, which may affect the electrophysical and chemical properties of the materials [49– 51]. To maximize the MOS sensor response toward any particular gas, it is necessary to analyze the chemical nature of the interaction among semiconductor matrix, modification agent, and the target gas molecules. The selectivity of sensor is typically displayed with bar chart (Fig. 2.31), and the doping of 0.5% of Pt can greatly improve the sensitivity and selectivity to CO [51]. Some materials can show different selectivity at different operating temperatures due to the disproportionate sensitivity change toward various gases during the changing of temperature. Jing et al. reported a porous ZnO nanoflakes as a multifunctional selective gas sensing material [52]. The maximum sensitivity to chlorobenzene obtained was at 200 °C (Fig. 2.32), and the sensitivity to ethanol was relatively low. While the sensor conducted at 380 °C, the material showed two times higher response to ethanol than chlorobenzene. In other words, the sensor can be used as an excellent Fig. 2.31 Responses of WO3 and Pt-doped WO3 to various target gases. Reprinted from Ref. [51], copyright 2017, with permission from WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

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Fig. 2.32 Gas response versus operating temperature of porous ZnO nanoplate sensor to 100 ppm chlorobenzene and ethanol. Reprinted from Ref. [52], copyright 2008, with permission from WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

chlorobenzene sensor at low operating temperatures (150 °C < T < 250 °C), and good ethanol sensor at higher temperatures (250 °C < T < 450 °C). Molecular sieving or gas separation has been verified to be a feasible solution to improve selectivity, when the detected gases are similar in chemical properties or reaction performance, but different in the molecular size or diffusivity. Lee and coworkers [53] designed a TiO2 -based sensor capable of highly selective discrimination of ppb level formaldehyde at room temperature, which showed a high selective response to formaldehyde and ethanol under ultraviolet irradiation (Fig. 2.33). The sensor was composed of a highly photoactive TiO2 sensing film on a polyethylene terephthalate (PET) substrate coating with a molecular-sieving zeolitic imidazole framework (ZIF-7)/polyether block amide (PEBA) composite overlayer, which could completely eliminate the interference of ethanol via molecular sieve separation, enabling an ultra-outstanding selectivity (S F /S E > 50) and sensitivity (Ra /Rg > 1100) toward 5 ppm formaldehyde at room temperature. Methanol poisoning can lead to blindness, organ failure, and even death. However, it remains extremely challenging to selectively detect methanol in the presence of ethanol with high concentration, which often occur after consumption of contaminated alcoholic beverages and during therapy with ethanol as an antidote. Güntner group [54] described a cheap and convenient handheld sensor for highly selective methanol detection, which includes a separation column (Tenax) to separate methanol from interference such as ethanol, acetone, or hydrogen, as well as a gas sensor (Pd-doped SnO2 nanoparticles) for quantitative analysis (Fig. 2.34). The device can respond to relevant concentration range of methanol (from 1 to 1000 ppm) within

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Fig. 2.33 Exclusive and ultra-sensitive detection of formaldehyde at room temperature with ZIF-7/ PEBA-coated TiO2 sensors. Reprinted from Ref. [53], copyright 2021, with permission from Nature

2 min under the interference of ultra-high concentration ethanol (over 62,000 ppm). Methanol concentrations in spiked breath samples and liquor were reliably examined, confirming its potential application in future breath analysis or air quality detection.

2.6.4 Stability MOS solid-state gas sensor is the most extensively applied type because of the thermal and chemical stability of metal oxides. The stability describes the endurance of a sensor material to maintain its output signal over a long period of time and to target systematically varying concentrations of analyte gas. This reproducibility can be affected by thermal aging of the sensor layer as well as by poisoning of the sensor’s surface comparable to the properties of catalysts. One of the most common reasons causing changes in sensing performances of MOS sensors is the crystal grain growth under high operating temperature [55]. The crystallization and grain growth of metal oxides and lead to base line resistance shift as well as decreasing sensitivity of a sensor. It shows that each size of crystallite has its own critical temperature, above which a tendency of grain size growing occurs. As for the boundary for SnO2 the relationship between threshold temperature T th and grain size t (in nm) can be

2.6 Gas Sensor Evaluation Criteria

69

Fig. 2.34 Highly selective detection of methanol over ethanol by a handheld gas sensor. Reprinted from Ref. [54], copyright 2019, with permission from Nature

expressed as: [56] Tth = 420 · (lg t) 4 (◦ C) 3

Therefore, sensing materials with higher crystallinity often show better long-term stability than amorphous or polycrystalline metal oxides [57, 58]. Zhang et al. [59] reported a strategy that could significantly enhance the long-term stability of WO3 hydrate nanowires on the electronic molecular sensing of aldehydes (nonanal, a biomarker), and they performed a simple surface treatment of WO3 using a strong acid, remarkably promoting both the oxidation of nonanal and the desorption of products at 50 °C, which is lower than the operating temperature (300 °C) required for untreated WO3 . The thermal persistence of acid-treated WO3 hydrate nanowires at 300 °C was demonstrated to be over 10 years, so this sensor could perform longterm stable operation and maintain sensitivity (four orders of magnitude) for many years.

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2.6.5 Response–Recovery Time A quick response and recovery behavior is always preferred in the gas sensing applications. The response or recovery time is expressed as the time Δt 90% of the output signal to reach 90% of its saturation value after turning on or off an analyte gas, respectively. A limit of 63% (1–1/e) of the steady-state resistance is employed to calculate response–recovery time. The response kinetics is mostly determined by (1) gas diffusion processes and (2) chemical reaction between solid surface and gaseous molecules. Generally, sensing materials with higher porosity provide more channels for gas diffusion and thus ensure faster response and recovery than solid materials [60–68]. Increasing of operating temperature can also speed up the response and recovery behavior by improving the reaction rate.

2.6.6 Limit of Detection Limit of detection of a gas sensor is generally defined as the lowest gas concentration that can be detected, which is an indispensable indicator for evaluating the overall performance of a gas sensing device. High-performance gas sensors are usually required to accurately monitor the concentration of low-content harmful gases, such as CO, NO2 , SO2 , to effectively avoid safety accidents, reduce the risk of these toxic gases to human health, and realize the maximum protection of personal safety. For instance, it has been studied that when the body is exposed to NO2 with the concentration exceeding 200 μg/m3 (about 100 ppb) for a short time, it can cause serious respiratory diseases, especially people at risk from the coronavirus disease (COVID-19) [69]. In addition, during the early screening of major diseases, most of the marker gases (such as acetone) exhaled by the human body are often in the ppm or ppb level [70]. In terms of environmental protection, trace level of NO can lead to the formation of acid rain that may damage animals, plants, and ecosystem. And it may cause photochemical smog, which is hazardous to human health [71]. For food safety detection, the concentration of characteristic gases (such as H2 S) secreted during food spoilage is also as low as ppb level [72]. Therefore, the ability of gas sensors with a low detection limit is of great practical significance.

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Chapter 3

Semiconducting Metal Oxides: Morphology and Sensing Performance

Semiconducting metal oxide materials have been widely used in resistive-type gas sensors design including both single and multicomponent metal oxides [1]. Different structural states of these metal oxide materials including amorphous-like state, glassstate, nanocrystalline state, polycrystalline state, and single crystalline state used in those resistive type gas sensors were reported [2]. The specific physicochemical properties and characteristics of each state can greatly influence sensing performance [3]. Among them, nanocrystalline and polycrystalline materials due to small crystallite size, cheap design technology, and stability of both structural and electrophysical properties have been found to be suitable in solid-state gas sensors. It is difficult to study the effect of nano- and polycrystalline state on gas sensing properties because great number of physical–chemical parameters should be taken into account [4]. Therefore, in order to clearly make out the effect of nano- and polycrystalline oxides on gas sensing performance, it is necessary to study how the morphology and crystallographic structure affect the gas sensing performance. It has been reported that the gas sensing performance of metal oxide can be affected by the following parameters: grain size, agglomeration, area of intergrain, interagglomerate contacts, porosity, dominant orientation, faceting of crystallites, and forming gas sensing surface [5].

3.1 The Effect of Morphology and Structure on Gas Sensing 3.1.1 Grain Size One of the influences of grain size is the so-called dimension effect, which is a comparison of the grains size (d) or necks width (X) with the Debye length (L D )

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Deng, Semiconducting Metal Oxides for Gas Sensing, https://doi.org/10.1007/978-981-99-2621-3_3

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√ LD =

εkT . 2π N e2

(3.1)

Here the k represents the Boltzmann constant, T represents the absolute temperature, e represents the dielectric constant of the material, and N represents the concentration of charge carries. The role of necks in the conductivity of polycrystalline metal oxide matrix and the potential distribution across the neck are presented in Fig. 3.1. It is obvious that the width of the necks confirms the height of the potential barrier for current carriers, and the length of the necks confirms the depletion layer width of the potential barrier. It is also necessary to mention that the increase of the necks’ length decreases metal oxide conductivity, and correspondingly in gas sensing performance [6]. The grain size of obtained metal oxides determines the distribution of valley among the polycrystalline metal oxide grains. Corresponding potential diagrams for one-dimensional structures are shown in Fig. 3.2. It is necessary to provide the

Fig. 3.1 Diagram illustrating the role of necks in the conductivity of polycrystalline metal oxide matrix and the potential distribution across the neck. Reprinted with permission from Ref. [6]. Copyright 2008, Elsevier

3.1 The Effect of Morphology and Structure on Gas Sensing

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following arguments before explanation of the “dimension effect” on the gas sensing effect [7]. For large crystallites with grain size diameter d >> 2Ls, where Ls represents the width of surface space charge for a small width of necks (d < Ls), the conductance of both the film and ceramics is usually limited by Schottky barriers (Vs) at the grain boundary. In this case, the gas sensitivity is practically independent of d. In the case of d ~ 2Ls, every conductive channel in necks between grains is overlapped. If the number of long necks is larger than the intergrain contacts, they control the conductivity of the gas sensing material and determine the size dependence of the gas sensitivity.

Fig. 3.2 Diagram illustrating the influence of grain size on potential. Reprinted with permission from Ref. [6]. Copyright 2008, Elsevier

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If d < 2Ls, every grain is fully involved in the space-charge layer, and the transformation of electron can be affected by the charge at the adsorbed species. It has been reported that, as the grain size is comparable to twice of the Debye length, a space-charge region can exist in the whole crystallite metal oxides. The latter case is the most favorable to gas sensing properties, since this model promise the maximum of sensor response [8]. The influence of grain size on the response sensitivity of nanocrystalline metal oxide gas sensors was also reported by analyzing a typical sensing material of SnO2 with different grain sizes between 5 and 80 nm. The effective charge carrier concentration was regarded as an important surface state to design the model [9]. And the research found that as the surface state density reached a critical value, the charge carrier concentration decreased sharply corresponding to the condition that all the electrons were trapped at the surface.

3.1.2 Grain Phase During the gas sensing process, the gas–solid interaction occurred on the external planes of metal oxide nanocrystals, which were reported to determine the gas sensing properties of nanostructured materials. In addition, different parameters such as crystallographic planes, intergrain contacts, area of intergrain contacts, gas permeability and so on, may play a vital role in gas sensing performance [5]. Every crystallographic crystal of different metal oxides possesses unique crystalline framework, and every crystallographic plane owns unique surface electron parameters, including surface state density, energetic position of the levels, adsorption/desorption energies of interacted gas molecules, concentration of adsorption surface states, the energetic position of surface Fermi level, activation energy of native point defects, and so on. This character led to the different behavior of chemisorption characteristics due to the different crystal surface orientation. Thus, there is a large surface dependence at the atomic level for chemical bonding of the adsorbed species. The orientation and grain size are the most important parameters to affect the adsorption/desorption process for their specific surface energy. When the crystal size decreases into the nanometer scale, it shows high surface area and more crystal planes which can notably influence the adsorption properties. As mentioned before, both the shape and the size of metal oxide crystals have a great influence on the behavior of adsorbed species and on the type of bonding to the surface. It has been reported that some chemical species may usually preferred adsorbed on the edge, corner, and plane facet of nanocrystals by different types of bonding models. Thus, due to the unique combination of structural, electronic, and adsorption/desorption process parameters of nanocrystallites, it shows different gas sensing performance toward different crystal forms. The change of surface energy parameters such as atomic and electronic properties can cause the corresponding changes of gas sensing properties. For example, as to SnO2 crystal, [10] the (110) and (101) planes of the SnO2 crystal are F (plane) faces,

3.1 The Effect of Morphology and Structure on Gas Sensing

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and the (111) plane is K (kinked), which means that compared with both (110) and (101) planes, the (111) plane has a much rougher surface. The surface concentration of non-saturated cations and weakly bounded bridging oxygen also play the important role in catalytic activity; therefore different plane exhibits different catalytic activity (CA), and they proposed: CA(110) < CA(001) , CA(100) < CA(101) . Tin atoms are centers of oxygen chemisorption, and therefore it is easy to understand that the change of indicated distance influence the rate of dissociative oxygen chemisorption, which in many cases is a determinant factor of gas sensing properties [11].

3.1.3 Surface Geometry The concept of an active site is fundamental parameter in heterogeneous catalysis process. This concept implies that not all the catalytic surfaces are active, and the active sites only exist in a special arrangement of surface atoms (including defects), or a particular chemical composition are actually reactive. Such as, extensive results have proved that monoatomic steps are highly active sites for many surface reactions. It was reported that the surface steps in diffusion also take place in perovskites, and it was found that the in-diffusion of surface oxygen vacancies occurred mostly at the step edges [12]. A theory model of the SrTiO3 surface structure was established by a Monte Carlo simulation, and during the deposition process by laser molecular beam epitaxy, the concentration of oxygen vacancies near the edges was larger than that on terraces [13]. It can be deduced that oxygen vacancies near step edges tend to accumulate due to their slow diffusion rate around the step corner. The results [13, 14] also proved that the in-diffusion rate of oxygen vacancies is the rate-determining step which is limited by the step corner. The authors also confirmed that the oxidation process was dominated by the in diffusion of oxygen vacancies near step edges no matter how large the surface diffusion rate. Their findings show that the adsorption properties of many gases related to different metal oxides specified surface geometry. Due to the different gas selectivity of different surface geometry, a variety of solidstate gas sensors toward different gases can be exploited. Noble metals such as Pt, Au, Pd, and so on due to their excellent catalytic performance have been widely used as the gas sensing materials, and the effect of surface geometry on noble metal also shows profound influence on gas sensing performance. It has been declared that the dispersion state of surface catalyst particles and particle size affect the gas sensing performance, [15] and it has been shown that the small particle size and high dispersion on the matrix can enhance the catalytic process, thus improving the sensitivity [16]. The size of clusters and nanoparticles also depends on the property of the metal itself. Such as, they have studied the dispersion behavior of Pt and Pd atoms on ZrO2 surface, and the results suggests that the Pd-atom exhibits the much higher surface mobility than Pt, and thus Pd is easily to form larger metal clusters, where the results are in agreement with the other research [17]. It shows that the cluster size distribution of Pd on the TiO2 (111) surface readily aggregate to form large particle sizes after annealing at 700 K. However, the size distribution

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3 Semiconducting Metal Oxides: Morphology and Sensing Performance

of Pt is unchanged after annealing under same conditions, suggesting that smaller Pt clusters can be stable at higher temperature. In addition, it was also reported that Pd could adsorb on the both terrace and step sites during deposition, while Pt is observed only on the steps.

3.1.4 Grain Networks, Porosity, and the Area of Intergrain Contacts By analyzing the numerous parameters which can affect sensors performance, it can be concluded that it is necessary either to increase the role of surface conductivity or to increase the contribution of intercrystalline barriers by decreasing the contact area or the width of the neck to obtain the maximum gas sensing response [7]. The present dates show that there is no doubt that with the increasing porosity of materials, the gas sensitivity is also improved [18]. It is well known that the active surface and sensor response of porous material is much higher than that of bulk materials [11]. A material with abundant porosity promises a small number of contacts of nanocrystals and under interaction with the surrounding gas they are not being overlapped. The research results of SnO2 films [19] are in good agreement with this conclusion. The SnO2 films showed high sensitivity, although SnO2 was highly agglomeration. Furthermore, it was found that material with minimal contact area between agglomerates showed the maximum sensitivity. And that can be clearly confirmed by SEM images [19]. In addition, with the increased porosity, the probability of forming so-called capsulated zones also decreased in the volume of the gas sensing layer which facility the gas sensing materials to achieve the maximum sensitivity. Capsulated zones are the particles that isolated from contact with atmosphere, and their resistance is almost immune to external influences [20–22]. It is necessary to mention that the increase of specific surface area of a gas sensing material can reduce the working temperature to achieve the maximum sensitivity because the high specific surface area of materials facilitates diffusion of gas molecular inside the gas sensing matrix, therefore lowering the operating temperatures [21]. The grain size can be calculated by using X-ray diffraction (XRD) method and the porosity can be estimated by classical multipoint Brunauer–Emmett–Teller (BET) adsorption techniques. These characterization methods provide a more complete and reliable description of the gas sensing material [23]. Thus, to draw a conclusion, as the porosity and specific surface area of the gas sensing material increases, the sensor response increases as well. The porosity and specific surface area are the important factors that affected the solid/gas interactions and finally influence the gas sensing performance of material [16]. However, it is necessary to mention that in some cases, non-porous or low specific surface area metal oxides may compensate their lower sensitivity and other shortcomings by having higher thermal stability and can perform under harsh conditions [24]. The

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results also show that gas sensors with higher porosity and higher surface area can achieve the faster response. It is necessary to note that with the increase of porosity, the effect of film thickness on sensor response and the response speed can be obviously weakened. In addition, they have reported that they studied the influence of film thickness on dense material and porous material [25]. And it indicated that for dense films, with the increase of film thickness in the range 30–200 nm, the decrease of sensitivity occurred. However, for porous films, the same change of film thickness could not affect gas sensitivity.

3.1.5 Agglomeration It is well known that the agglomeration of material is universal phenomenon almost occurs everywhere in nature. The phenomenon occurs from metallic polycrystals to colloidal aggregates and particles, as well as lipid-protein viscoelastic matrices. In addition, the metal oxide films are also easy to agglomerate [11]. Here, the agglomerate resistance is an integral resistance, that is to say, the integral material includes the three-dimension grain network. Thus, it can be deduced that if there are changes occur on the porosity of either agglomerates or the gas sensing matrix, the effect of above-mentioned gas sensing parameters (e.g., grain size, agglomeration, area of intergrain and interagglomerate contacts, porosity and dominant orientation, and faceting of crystallites) on sensor response changes. Such as, the decrease of grain size can lead to the increase of grains in gas sensing effects. The solid agglomerate with low gas permeability promotes an increase in the influence of interagglomerate contact on sensor response. These results are in accordance with the observations, [26] and thereby confirming that agglomeration of small crystallites into large crystal is a key phenomenon and can lead to the great variations of the apparent response characteristics. Research has shown that both the sensor response performance and kinetics process are up to the nature properties of agglomerates itself. That is, larger and denser agglomerates always exhibit the weak response and long recovery times. It is important to mention that this effect is related to the nature of the detected gas, especially on the reactivity and diffusion coefficient of this gas in the metal oxide matrix. It should be also mentioned that a high degree of agglomeration also accelerates the formation of capsulated zones, i.e., zones with closed porosity. Normally, dense and high-level aggregated ceramics exhibit the much lower active surface area [27], and as mentioned previously, this effect may have a strong negative impact on the gas sensing performance.

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3.2 Synthesizing Approaches to Metal Oxides Sensing Materials 3.2.1 Sol–Gel The sol–gel method can roughly be regarded as the conversion of a precursor solution into the inorganic solid by using solution phase hydrolysis and condensation chemical way as described in Fig. 3.3. Generally, the inorganic precursor is either an inorganic metal salt or a metal organic species such as the metal alkoxide or acetylacetonate. Metal alkoxides are the most widely used precursors in aqueous systems, and their chemical transformation into the oxidic network involves hydrolysis and condensation reactions [28]. For aqueous sol–gel processes, the oxygen is supplied by the water molecules for the formation of the oxidic compound. However, for nonaqueous sol–gel systems, there is no water to supply the oxygen for the formation of metal oxide. It has been reported that similar to the non-hydrolytic preparation method to form bulk metal oxide [29], and the oxygen of metal oxide nanoparticle formation is provided by the solvent (ethers, alcohols, ketones, or aldehydes) or by the organic constituent of the precursor (alkoxides or acetylacetonates). For example, researchers have used the titanium isopropoxide and titanium chloride as the precursors to synthesize the anatase nanocrystals [30]. Ether elimination was an important reaction to form the M–O–M bond. The mechanism is that the reaction occurs by condensation of two metal alkoxides under elimination, which is also reported for the formation of hafnium oxide nanoparticles. Ester elimination process involves the reaction between metal carboxylates and metal alkoxides, by which zinc oxide, titania, and indium oxide have been synthesized. Reaction between metal oleates with amines is analogous to ester eliminations, such as the controlled synthesis of titania nanorods [31]. However, by using ketones as solvents, the release of oxygen usually involves aldol condensation, which the two carbonyl compounds react with each other by (formal) elimination of water, and the released water molecules act as the oxygen-supplying agent for metal oxide formation. It has reported the synthesis of ZnO and TiO2 by using acetone as solvent.

3.2.1.1

Surfactant-Controlled Synthesis of Metal Oxide Nanoparticles

In 1993, Murray et al. synthesized monodisperse CdX (X = S, Se, or Te) nanocrystallites in molten trioctylphosphine oxide (TOPO) [33]. This work provided the basis for the so-called hot-injection method, which involved the injection of a room temperature solution of precursor molecules into a hot solvent in the presence of surfactants. The use of surfactants consists of a coordinating head group and a long alkyl chain provides a few advantages. The long alkyl chain coating on the nanoparticles can prevent the agglomeration of nanoparticles in the synthesis process and result in good colloidal stability of the final product in organic solvents. The surfactants usually selectivity adsorb and desorb onto the specific crystal faces during the

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Fig. 3.3 Schematic representation of sol–gel process. Reprinted with permission from Ref. [32]. Copyright 2019, Multidisciplinary Digital Publishing Institute

particle growth therefore sometime enabling the control over particle size, size distribution, and morphology[34]. Moreover, the surfactants can be exchanged with other ones in postmodification process, ensuring the controllable chemical modification of the surface properties of the nanoparticles.

3.2.1.2

Solvent-Controlled Synthesis of Metal Oxide Nanoparticles

Compared with the synthesis of metal oxides based on surfactants, the solventcontrolled synthesis method is rather simpler. The initial reaction system only includes two parts, the metal oxide precursor and a common organic solvent. The synthesis temperature is usually in the range of 50–200 °C, which is remarkable lower than that by using hot-injection method. The simple synthesis system facilitates the estimation of chemical reaction mechanisms and simplifies the characterization of the final reaction solution. Moreover, due to the simple reaction system of surfactant free synthesis method, the product is easy to be purified. In addition, by using surfactants, the surface-adsorbed surfactants may passivate nanoparticles [35] and lower the accessibility of the nanoparticle surface which can affect the catalytic and

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sensing performance, whereas these drawbacks are not present by using surfactantfree approach. Large variety of metal oxide precursors such as metal halides, acetates, acetylacetonates, alkoxides and the mixture of different metal precursors have been studied in the surfactant-free synthesis method in the last few years. Common solvents are oxygen-containing organic solvents such as alcohols, ketones, or aldehydes as well as oxygen-free solvents like amines or nitriles with short alkyl chains. Even “inert” solvents such as toluene or mesitylene can be used. The use of the appropriate solvent mainly depends on its function in the process of nanoparticle growth, the targeted morphology and compositional characteristics of the final product. As mentioned above, the oxygen containing solvents usually provide the oxygen for the formation of the oxidic compounds. However, metal oxide prepared by using non-oxygenated solvents is generally depending on the use of oxygen-containing precursors. The organic species formed during the reaction process act as capping agents in the beginning, which bind to the particle surface and thus limit the crystal growth and influence particle morphology as well as assembly behavior. Highly stabilized organic species adsorbed on the specific crystal plane not only inhibit crystal growth, but also can lead to anisotropic crystal growth due to that the organic species can selectivity bind to crystal facets.

3.2.2 Hydro- and Solvothermal Methods Hydrothermal and solvothermal synthesis techniques are the most important and well-established approaches for the laboratory and industrial synthesis of nanomaterials [36]. The production of nanomaterials by using hydrothermal and solvothermal approach owns many advantages, such as easy operation, a large-scale synthesis and tunable reaction parameters. They provide the access from metastable phases and to nanoscale morphologies that are difficult to obtain through other methods. However, this experimental versatility also renders a considerable challenge for the planning and understanding of hydrothermal processes, because their mechanistic principles still remain to be fully understood and to be embedded into a general theoretical concept. Although hydrothermal approaches are very useful, especially for the synthesis of metal oxides and other related materials, it is also difficult to control the tailored phases and exact morphologies. While numerous progress has been studied in the engineering of autoclave types over the past years so that sophisticated inlet and sensing techniques as well as continuous flow reactors are now available [37]. Expect to the continual development of traditional hydrothermal methods, nonaqueous approaches to metal oxide nanomaterials have attract extremely research interest [38]. Over the past years, extensive hydrothermal researches have been focused on the synthesis of oxide-based nanomaterials, such as the most common binary oxides (e.g., SnO2 , ZnO, or TiO2 ) and a multicomponent metal oxide (e.g., SrWO4 , Ce0.6 Zr0.3 Y0.1 O2 , or LiVMoO6 ) as well as metal organic framework (MOF) materials [39]. Herein, we have listed the recent progress in controllable synthesis metal

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oxides by hydrothermal approach. For example, Liu et al. [40] synthesized highly water-dispersible magnetite Fe3 O4 nanoparticles with uniform size via improved solvothermal method using trisodium citrate (Na3 Cit) as stabilizer. The magnetite particles possess a nearly uniform size of ~250 nm and spherical shape (Fig. 3.4). The size of Fe3 O4 particles can be delicately regulated over the wide range of 80– 410 nm by changing the concentration of FeCl3 precursor or Na3 Cit. It was found that the reaction temperature was the key to the preparation of uniform magnetic spheres and the particle size increased with the increase of reaction temperature. Each Fe3 O4 particle has a large number of primary magnetite nanocrystals with the size of 5–10 nm, which gives it a superparamagnetism and high magnetization of 56–82 emu·g−1 , and this feature can enhance their response to external magnetic fields, facilitating their practical application. Remarkably, due to the presence of anchored citrate groups in the particles surface, the as-made magnetite particles not only possess excellent dispersion stability, but also have low cytotoxicity, outstanding biocompatibility, as well as the satisfactory capacity for efficient and convenient enrichment of trace peptide. Therefore, they have a broad application prospect in the bio-related fields such as cell imaging and cell sorting. Recent literature survey on the topic is summarized in Table 3.1. Controlling the metal oxide shape in the nanoscale through hydrothermal process is still a great challenge, due to the problem of how to pick out the desired phase among all the potential products. However, epitaxial growth theory based on anisotropic structural is an extremely simple method to obtain the anisotropic morphologies. Therefore, hydrothermal approaches are usually to be used to obtain a specific morphology of the products. Because the natural products and polymers have the advantage of abundant reserves, and they are generally applicable for shaping nanostructured oxides. These biomacromolecules have been used in hydrothermal

Fig. 3.4 Fe3 O4 microspheres synthesized by solvothermal method. Reprinted with permission from Ref. [40]. Copyright 2009, Wiley

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Table 3.1 Morphology control of binary and ternary oxides Oxide

Solvent/additives

Morphology

Refs.

CeO2 , CuO, Co3 O4 , Fe2 O3 , MgO, NiO

Water, glucose

Nanoparticles, arranged in hollow microspheres

[41]

CuO

Water, sodium citrate

Nanosheets, rods, stars

[42]

Fe2 O3 , Fe3 O4

Water, Na2 SO4 / Na2 HPO4

Short nanotubes

[43]

Fe3 O4

Water/ethanol, oleic acid Nanocubes

[44]

MoO3

Water, acidic and ionic additives

Nanorods

[45]

MoOx

Ethanol/water, hexadecylamine

Nanoribbons

[46]

SnO, SnO2

Water (HCl/NaOH)

Nanoparticles, nanoplatelets (influence of pH)

[47]

SnO2

Water/ethanol

Nanorods

[48]

TiO2

Water (NaOH)

Nanorods, nanotubes (influence of precursor size/structure)

[49]

TiO2

Water, HCl, or acetic acid

Nanoparticles (phase selective)

[50]

VOx

Acetone, hexadecylamine

Nanotubes

[51]

ZnO:Co/Mn

Benzyl alcohol/anisole

Nanoparticles, rods, fibers (depending on solvent mixture)

[52]

Bi2 WO6

Water, PEO-PPO-PEO (P123)

Annular arrangement of nanoparticles

[53]

Bi2 WO6

Water

Nanoplatelets in flower-like arrangement

[54]

SrWO4

Microemulsion: water/ cyclohexane/n-pentanol/ CTAB

Nanoparticles, nanorods

[55]

W/Mo oxides

Water, inorganic additives (alkali chlorides)

Spherical arrangements

[56]

(Earth) alkali vanadates

Water, inorganic additives

Microrods, fibers

[57]

Kx Mn1−y Coy O2

Water

nanorods, nanoplatelets in flower-like arrangements

[58]

KTa1−x Nbx O3

Water (KOH), PEG

Nanocrystals

[59]

Ba1−x Srx TiO3

Ethylenediamine/ ethanolamine, KOH

Nanocrystals (20–40 nm, depending on Sr content)

[60]

Ba2 MTi2 Nb3 O15 (M=Nd, Sm)

Water, acetic acid

Particles with irregular shape (>200 nm)

[61]

Multimetal oxides

(continued)

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87

Table 3.1 (continued) Oxide

Solvent/additives

Ca1-2x (Eu,Na)2x WO4

Water, HNO3 , citric acid, Nanoparticles with irregular NaOH shape (ca. 20–50 nm)

Morphology

Refs. [62]

Ca0.8 Sr0.2 Ti1-x Fex O3

Water (KOH); flow reactor

Cuboidal nanocrystals (20–30 nm)

[43]

Ca2 Nb2-x Tax O7

Water

nanoparticles (5–15 nm)

[63]

Ce0.6 Zr0.3 Y0.1 O2

Water, urea

Nanoparticles (250 °C). Luo’s group [100] compounded graphene with ordered mesoporous WO3 to fabricate the mesoporous WO3 @graphene aerogel nanocomposites (designed as mWO3 @GA), in which graphene aerogel (GA) was applied as a macroporous substrate, mesoporous WO3 was uniformly grown on both sides of graphene sheets by solvent evaporation-induced self-assembly (EISA) strategy with diblock copolymer poly(ethylene oxide)-b-polystyrene (PEO-b-PS) as a template. This synthesis route can effectively inhibit the uneven loading of metal oxides caused by the agglomeration of graphene sheets, and the strategy possesses good universality, which is also suitable for the synthesis of mesoporous TiO2 / graphene and SnO2 nanocrystalline/graphene composites. The obtained mWO3 @GA nanocomposite had a well-connected macroporous graphene network covered by mesoporous WO3 layer, which presented uniform pore size (19 nm), large specific surface area (167 m2 g−1 ) and high pore volume (0.26 cm3 g−1 ). Benefitting from its unique composite structure and multicomponent synergies, the resultant mWO3 @GA nanocomposites presented outstanding sensing properties for acetone gas at low temperature (150 °C), with fast response and recovery dynamics (13/65 s), high sensitivity, and excellent stability and selectivity toward 50 ppm acetone, which was far superior to single-component materials (Fig. 4.30).

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141

Fig. 4.30 Responses (S = Ra /Rg ) of the mWO3 @GA nanocomposites-based sensor to 20 ppm acetone at different temperatures a, response–recovery curve of the sensor to acetone at different concentrations (2–50 ppm) at 150 °C b, response of the sensors vs. acetone concentrations c, response–recovery curve of the mWO3 @GA sensor to 50 ppm of acetone at 150 °C d, responses of the sensor to different gases of 50 ppm at 150 °C e, response–recovery curve of mesoporous WO3 and GA sensors to acetone at different concentrations (2–50 ppm) at 150 °C f. Reprinted with permission from Ref. [100]. Copyright 2019, Elsevier

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26. Kaneti YV, Moriceau J, Liu M, Yuan Y, Zakaria Q, Jiang X, Yu A (2015) Hydrothermal synthesis of ternary α-Fe2 O3 -ZnO-Au nanocomposites with high gas-sensing performance. Sens Actuators B 209:889–897. https://doi.org/10.1016/j.snb.2014.12.065 27. Chung F, Wu R, Cheng F (2014) Fabrication of a Au@SnO2 core-shell structure for gaseous formaldehyde sensing at room temperature. Sens Actuators B 190:1–7. https://doi.org/10. 1016/j.snb.2013.08.037 28. Chung F, Zhu Z, Luo P, Wu R, Li W (2014) Au@ZnO core-shell structure for gaseous formaldehyde sensing at room temperature. Sens Actuators B 199:314–319. https://doi.org/ 10.1016/j.snb.2014.04.004 29. Ramgir NS, Sharma PK, Datta N, Kaur M, Debnath AK, Aswal DK, Gupta SK (2013) Room temperature H2 S sensor based on Au modified ZnO nanowires. Sens Actuators B 186:718–726. https://doi.org/10.1016/j.snb.2013.06.070 30. D’Arienzo M, Armelao L, Cacciamani A, Mari CM, Polizzi S, Ruffo R, Scotti R, Testino A, Wahba L, Morazzoni F (2010) One-step preparation of SnO2 and Pt-doped SnO2 as inverse opal thin films for gas sensing. Chem Mater 22:4083–4089. https://doi.org/10.1021/ cm100866g 31. Shin J, Choi S, Lee I, Youn D, Park CO, Lee J, Tuller HL, Kim I (2013) Thin-wall assembled SnoO2 fibers functionalized by catalytic Pt nanoparticles and their superior exhaled-breathsensing properties for the diagnosis of diabetes. Adv Funct Mater 23:2357–2367. https://doi. org/10.1002/adfm.201202729 32. Karmaoui M, Leonardi SG, Latino M, Tobaldi DM, Donato N, Pullar RC, Seabra MP, Labrincha JA, Neri G (2016) Pt-decorated In2 O3 nanoparticles and their ability as a highly sensitive (> 2L), the majority of the grain volume was not dependent on external gases, and the influence of external gases on the electrical conductivity was mainly reflected in the barrier height of the grain boundary region [4, 7, 64, 65]. In addition, the higher conductive core region virtually involves in the discharge-recharge reaction under reducing gases or oxidizing gases, thus inducing relative weak response and sensitivity. Especially, the sensing mechanism of these larger grains is belonging to grain boundary control, and grain boundary barrier is independent of grain size, which cause that there is no correlation between sensing performance and grain size [66–68]. On the other hand, some grains whose D value is close but still larger than 2L (D ≥ 2L), constricted channel forms by the depletion region that surrounds each neck, and a shell region also produces via the depletion layer deep into the grain [69]. Thus, the conductivity of materials depends on both grain boundary barriers and the cross-section area of adjacent grain, and the effective cross section of the neck for inter-crystalline current transmission is sensitive to the ambient gases. The sensing mechanism of these medium grains is belonging to neck control, and the sensitivity is improved and translate to grain size dependent compared the former case via the synergistic effect of current concentration and grain boundary barrier. Interestingly, in this case, the sensitivity of materials can gradually increase with the decrease of grain size. In the end, when the grain size D is less than 2L, the crystallites are almost fully occupy with depletion layer area, implying that the whole grain participates in the charge transfer interactions with gas molecules, and inducing the disappearance of conductive channels around inter-crystalline and a sharp drop of conductivity [7]. The energy band structure among interconnected grains becomes gentle, which means that the grain boundary barrier has almost no effect on carrier transport between grains, and it only replies on the conductivity of inter-crystalline. Hence, the sensing mechanism of these small grains is owing to grain control, and these materials will have optimum sensing performance. Typically, Rothschild et al. [69] evaluated the effect of grain size on the sensitivity of semiconductor gas sensors via the numerical simulation between effective carrier concentration and surface state density. It found that SnO2 with various grain sizes range from 5.0 to 80 nm had certain critical value for the surface state density once forming the fully depleted layer, and the critical value is proportional to the grain size. However, in the practical applications, even though small grain size can yield the highest sensitivity theoretically, the nanoparticles are limited based on the steeply decreased conductivity and unstable nature

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of the nanoparticles at high operating temperature. Thus, available grain size is the important factor for us to design and fabricate efficient gas sensors. In addition to these theoretical studies, many researchers also designed and fabricated successfully efficient semiconducting metal oxides with various grain size and systematically studied the effect of grain size on the sensing process. For example, early in 1997, Gurlo et al. [70] prepared polycrystalline In2 O3 thin films with two kinds of grain size under various calcination temperature. It found that the In2 O3 with particles sizes of about 5–6 nm showed higher agglomeration, higher sensor resistance, and response to NO2 than larger grains with 20 nm. The same year, Ansari et al. [71] studied the sensing performance of SnO2 nanoparticles sensor layer to hydrogen, and it showed that the gas sensitivity using 20 nmnanoparticles was higher ~10 times than that in 25–40 nmnanoparticles. In addition, in terms of small grains and narrow necks, once the grain size is less than twice the thickness of surface charge layers, the

Fig. 5.12 Schematic model of the effect of the crystallite size on the sensitivity of metal oxide gas sensors: a D >> 2L; b D > 2L; c D < 2L. Reprinted with permission [69]. Copyright 2004, AIP Publishing LLC

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grain will fully connect with the space-charge layer, and then, we should consider the surface influence on free charge carrier’s mobility. Similarly, in 2005, Vuong et al. [66] designed crystalline SnO2 sols with various grain sizes (6–16 nm) and showed excellent sensing properties to H2 S gas (Fig. 5.13). Moreover, the sensor response can obviously improve with the increasing of grain size up to 16 nm, which is correspond to the gas diffusion-reaction theory. Interestingly, using the as-prepared thin film gas devices, the sensor response to H2 increases sharply with the increasing of grain size D. Xu et al. [72] reported ZnO nanomaterial with various particle size and showed superior sensitivity to a series of gases, such as H2 , SF6 , C4 H10 , gasoline, and C2 H5 OH. It also finds that the gas sensitivity of ZnO sensor depends on its grain size. Thus, grain size can produce great influence to gas sensing performance of semiconducting metal oxides, and available grain size is beneficial for the natural applications. Since gas sensor is a typical gas–solid interaction reaction process, the sensing properties are dependent on the porosity of solid-phase materials, which based on the more accessible porous ratios can provide abundant adsorption or reaction sites and larger resistance change. In addition, the porous structure plays an important role in adjusting the surface area and diffusion rate of gases. The average pore size as well as the degree of inter-connectivity of pores can affect the gas diffusion and mass transfer behavior. Porous sensing materials can facilitate gas diffusion deep insides of the films and obtain high gas sensitivity. According to the classification of IUPAC, materials have abundant pores with diameters ranging from 2.0 to 50 nm, which can donate as mesoporous. When the pores larger than 50 nm named as macroporous, while less than 2.0 nm defined as microporous. These abundant porous structures are beneficial for many applications, such as separation, energy storage, catalysis, and gas sensors [73–75]. For gas sensors, large pores can avail to the gas diffusion but decrease surface area, while small pores provide higher surface area but without unqualified pathway for gas diffusion within the oxide sensor layer. For example, Deng’s group designed ordered mesoporous n-type semiconductor ZnO with average pore size 29 nm and compared with none-porous ZnO materials [76]. The results show

Fig. 5.13 a Sensor response of thin film devices to 5 ppm H2 S tested at various temperatures. b Sensor response to 5 ppm H2 S for thin film devices different in crystallite size (thickness 200 nm) as correlated with operating temperature. Reprinted with permission [66]. Copyright 2005, Elsevier

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that the obtained mesoporous ZnO exhibits excellent ethanol sensing performance with a faster response (6 s) and recovery (7 s) and higher sensitivity and selectivity due to its well-connected bimodal mesoporous, high surface area, and crystalline framework. In 2018, Xiao et al. synthesized ordered mesoporous n-type crystalline SnO2 materials with a hexagonal mesostructure (space group P63 /mmc) and crystalline pore walls, which exhibit an excellent sensing performance toward H2 S with high sensitivity (170, 50 ppm) and superior stability (Fig. 5.14) [13]. Thus, the abundant mesoporous structure is favor to the gas diffusion and exposing more active sites. Similarly, Tian et al. [77] also reported hierarchical SnO2 mesoporous microfibers with various pore sizes (9.4–13.7 nm) as formaldehyde sensors, which displayed remarkable gas sensitivity based on the large accessible surface area derived from abundant porous structure. Interestingly, it found that the pore size and its resultant effective surface area rather than specific surface area exhibited significant function in enhanced sensing properties. It also find that smaller pores possess adverse gas transport to more active sites, and larger pores allow most detected gas molecules diffuse easily inside the deeper region of materials and react with abundant oxygen species adsorbed on these effective surface. Nonetheless, in a regime of larger pore size, the further increase of pore size makes against the gas sensors. For instance, Xu et al. [78] displayed reduced graphene oxide-based ordered mesoporous metal oxides (e.g., rGO-SnO2 , rGO-Fe2 O3, and rGO-NiO) with various large pore size using PS microspheres as hard templates (200, 750, and 1000 nm). It shows that the metal oxides with a template of diameter 200 nm exhibits the superior response to C2 H5 OH based on heterojunctions and porous structures. Except for the effect of pore size and porosity, the type of porous can also affect the sensing performances. Typically, Rossinyol et al. [48] designed and synthesized two crystalline Cr-doped mesoporous WO3 replica with various porous structure via using SBA-15 (2D hexagonal structure) and KIT-6 (3D cubic structure) as hard templates, respectively. The results show that the gas sensitivity of WO3 in 2D hexagonal p6mm is lower than that in 3D cubic Ia3d due to the different porous structures. The KIT-6 replica exhibits a sponge-like mesoporous structure, while the other one tends to form aggregates with weaker ordering. In order to explain the gas diffusion process in porous system, a series of diffusion models have been proposed for various porous systems. In most mesoporous systems, gas diffusions occur in turn in the following order as the increasing of pore size: surface diffusion, Knudsen diffusion, and molecular diffusion. When the pores range increased from 1.0 or 2.0 to 100 nm, gas diffusion is leading by Knudsen diffusion. According to the models, larger pore size and smaller target gas molecule can accelerate gas diffusion and improve reaction kinetics. It assumed that inflammable gas molecules were interacting with oxide grains and at the same time were diffusing inward through the pores to access grains located inside. Under the steady-state condition, the gas concentration inside the sensing layer will decrease with increasing diffusion depth, resulting in a gas concentration profile dependent on the rates of diffusion and surface reaction [65, 67, 77]. Thus, for larger pores (usually diameters more than 50 nm), the average free path of gas molecule is less than the pore size, which allows the target gas molecule to easily diffuse inside the framework. In opposite, smaller pores can

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Fig. 5.14 a Response and recovery curve of the mesoporous crystalline SnO2 -5–450-air-based sensor to H2 S at different concentrations (5–100 ppm) at the operating temperature of 350 °C. b Relationship between the response and the concentration of H2 S at 350 °C. c Cycle curve of the crystalline mesoporous SnO2 -based sensor to H2 S at the concentration of 50 ppm for four times. d Responses of the crystalline mesoporous SnO2 -based sensor to different gases at 50 ppm. e H2 S sensing mechanism of the sensors based on mesoporous SnO2 material exposure to air and H2 S-air mixture (E c , conduct band edge; E f , Fermi energy). Reprinted with permission [13]. Copyright 2018, American Chemical Society

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produce obvious confinement effect of gas diffusion by the pore boundaries [48, 78, 79]. It is noteworthy that the average free path of gas molecule is also dependent on ambient temperature and gas–solid ratios. Thus, accessible pore size and structure can efficiently adjust physiochemical performance and sensing process. For example, Zhu et al. [80] developed a ligand-assisted solvent evaporation induced co-assembly strategy to synthesize ordered mesoporous WO3 materials with high specific surface area and highly connected pore, which were firstly applied to construct high-performance gas sensors to quickly and selectively detect foodborne pathogens. Remarkably, the acetylacetone (AcAc) was cleverly employed as a coordination agent to slow down the hydrolytic cross-linking of tungsten precursors, resulting in the assembly process controllable. Benefitting from its unique pore structure and sensitive properties, the ordered mesoporous WO3 exhibited ultrafast response speed ( 50), and outstanding selectivity for 3-hydroxy-2-butanone, which made it to be a promising material for rapid and effective detection of microbial contamination in food and water. Furthermore, in-situ GC–MS analysis was performed to identified the products of 3-hydroxy-2butanone during the sensing process, which disclosed that the final product was acetic acid, rather than water and carbon dioxide recognized widely, thus providing direct evidence to reveal the sensitive mechanism and new ideas for the design of novel gas sensors (Fig. 5.15). Most ordered mesoporous metal oxides have been reported to possess monomesoporous structure, which is not conducive to the diffusion of gas molecules and the interaction between host and guest in the process of gas sensing. It is greatly challenging to construct ordered mesoporous metal oxides with bimodal or hierarchical pores and crystalline frameworks. Li et al. [81] developed a pore engineering strategy to precisely tailor pore structure of WO3 (Fig. 5.16), and the ordered mesoporous WO3 with well-interconnected bimodal pores and crystalline pore walls was fabricated by using amphiphilic poly(ethylene oxide)-block-polystyrene block copolymers (PEO-b-PS) as the pore-forming agent of primary mesoporous and hydrophilic

Fig. 5.15 Schematic diagram of 3-hydroxy-2-butanone gas sensor based on ordered mesoporous WO3 . Reprinted with permission [80]. Copyright 2017, American Chemical Society

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Fig. 5.16 a Synthesis process of the ordered dual-mesoporous tungsten oxide via the resol-assisted pore engineering strategy through solvent evaporation induced co-assembly of PEO-b-PS, resols, tungsten precursor, and chelating agent in THF/ethanol mixed solution. b Schematic illustration for the detailed pore evolution during thermal treatment of the as-made organic–inorganic nanocomposite in N2 and air. Reprinted with permission [81]. Copyright 2019, Wiley

resols as the sacrificial carbon source of secondary mesoporous. The synthesized ordered mesoporous tungsten oxide materials are characterized by double mesoporous size (5.8 and 15.8 nm), high specific surface area (128 m2 g−1 ), enlarged window size (7.7 nm), and highly crystalline frameworks. The dual-mesoporous WO3 was proved to be an excellent H2 S gas sensor, showing outstanding gas sensing properties even at low concentrations (0.2 ppm) with fast response/recovery kinetics, (3/14 s) and high response, which was far superior to previously reported WO3 -based sensors. In addition, Zhao et al. [82] proposed confined interfacial micelle aggregating assembly approach for the synthesis of ordered macro–mesoporous WO3 (OMMW) nanostructures, where the three-dimensional SiO2 photonic crystals (PCs) were employed as nanoreactors for the constrained assembly of tungsten precursors and PEO-b-PS templates (Fig. 5.17). Owing to induction of dual pore-forming agents, the OMMW obtained after calcination and etching process possessed a hierarchical porous structure of close-packed spherical mesopores (~34.1 nm) and well-connected

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Fig. 5.17 a Schematic of the confined interfacial micelle aggregating assembly approach for the preparation of OMMW nanostructures. SEM images of the SiO2 PCs b, Tungsten precursor/ SiO2 /PEO-b-PS composites c, ordered macro–mesoporous C-WO3 nanostructures d, and OMMW nanostructures e. Reprinted with permission [82]. Copyright 2019, The Royal Society of Chemistry

macropores (~420 nm), thus presenting a large specific surface area of 78 m2 g−1 . Thanks to the special porous structures with enhanced pore interconnection and a highly crystallized framework, the resultant OMMW nanostructures could be integrated as H2 S gas sensors, demonstrating superior comprehensive sensing behaviors, including fast response–recovery dynamics, as well as high selectivity and long-term stability.

5.4 Surface Area and Heterogeneous Interface Specific surface area of materials is also important for the gas sensors, which owing to the larger surface area can provide sufficient contact area and reaction interface based on the main reaction existed between surface oxygen species and target gases [83, 84]. For porous materials, the effect of surface area is in accordance with porosity, while for non-porous materials, the surface area will show unique influences for gas sensors. For example, Kawi’s group [85, 86] designed a series of high surface area SnO2 semiconductors, and the results showed that higher surface area possessed higher sensitivities for hydrogen. According to the mentioned above, the smaller grain size can provide larger surface area based on the particle stacking principle;

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thus, available grain size and surface area can realize through similar strategy. In addition, the approximate linear relationship implies clearly the linear dependence of the sensitivity on the surface area of the sensor. Similarly, Li et al. [85] found the linear relationship between the surface area of SnO2 and gas sensitivities to hydrogen and CO. The abundant surface active sites, such as surface-adsorbed oxygen species, provided via larger surface area, inducing larger changes of the electrical conductivity of the sensor. However, when the pore size increased over the critical values, the surface area would decrease with increasing pore size. Therefore, in order to obtain an optimum comprehensive sensing performance, a balance of pore size and surface area have to be required [83]. As far as microstructure is concerned, heterogeneous interface is the most significant factor to decide the properties of semiconductors based on the efficient electronic structure and active interface. Once existing intimate electrical contact or collision at the interface between two dissimilar semiconducting metal oxides or other materials, the Fermi levels in the interface will equilibrate to the same energy, often inducing charge-transfer and producing a charge depletion layer [8, 87, 88]. In addition, extra anomalies usually happen in two dissimilar semiconducting materials in close proximity with both interfaces exposed to the atmosphere, and these heterostructures show great potential in gas sensors because of high surface-to-volume and synergistic effect [16, 87]. According to the types of participating semiconductors, the heterostructure consists of four classic models, such as p–n injunction, n–n injunction, p–p injunction, and n–p–n injunction. p–n injunction is the most widely used in multidisciplinary field, and there is equivalent to electron–hole recombination near by a p–n injunction, which defines as “Fermi level-mediated charge transfer” [89, 90]. For example, an n-type ZnO nanowire decorated with p-type Co3 O4 nanoparticles [91], the normal ambient resistance of the nanowires in air is even higher than without the heterojunction via the depletion region at the heterojunction interface extending into the ZnO nanowires, which will decrease the width of the charge conduction channel. In addition, less cross-sectional area is available for charge conduction in the nanowire and induced a higher resistance, and charge conduction across the p–n interface can further facilitate the increase of the resistance. Many scientists have aroused great attentions in the design and fabricate of SMO sensors with heterojunction interface. Early in 1979, Waldrop et al. [92] studied firstly the energy band discontinuities of semiconductor heterojunction interfaces, and it found that it is non-transitivity and provided theory reference for the application of heterojunction. Afterward, Zhang et al. [89] designed flower-like p-CuO/n-ZnO heterojunction nanorods and exhibited excellent gas sensing properties for C2 H5 OH (Fig. 5.18), where the response of p-CuO/n-ZnO (1:4) sensor to 100 ppmC2 H5 OH reached to 98.8, which is 2.5 times than that in pure n-ZnO. The astonishing properties can attribute to a wider depletion layer and higher resistance on the p-CuO/n-ZnO surface, inducing the stable p–n injunction. Similarly, Ju et al. [93] designed a p–n heterojunction combined with ntype SnO2 hollow spheres and p-type NiO nanoparticles through chemical deposition technique (Fig. 5.18), and it showed high response and selectivity to trimethylamine (TEA) gas, even low detection limit (2 ppm). It also found that the superior gas

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Fig. 5.18 Low and high magnification FE-SEM images of samples: (a and b) pure ZnO, (c and d) 0.125:1 CuO/ZnO, (e and f) 0.25:1 CuO/ZnO, and (g and h) 0.5:1 CuO/ZnO. (i) The energy band structure diagram of p-type NiO/n-type SnO2 heterocontact, (j) schematic model for the p-type NiO/ n-type SnO2 heterojunctions-based sensor when exposed to TEA gas. (a)–(h) are Reprinted with permission [89]. Copyright 2014, Elsevier. (i), (j) are Reprinted with permission [93]. Copyright 2015, Elsevier

sensing properties could due to the formation of depletion layer at the p–n injunction interface. In comparison, n–n injunction and p–p injunction can also adjust the sensing properties and change the electronic structure, and these special interfaces are formed by the combination of two homogeneous semiconductors. Xu et al. [87] reported TiO2 / SnO2 n–n heterojunction nanosheets and exhibited high sensing properties to TEA gas. It can efficiently decrease the resistance in TEA gas and increase the resistance in air. Ma et al. [94] synthesized n-ZnO/n-In2 O3 heterojunction via a simply surface-modification route. This interface can enhance the gas response to HCHO and lower the detection limit based on the modulation of electron transfer (Fig. 5.19). The coalesced layer of the ZnO on the surface of In2 O3 nanostructure facilitated the formation of an n–n heterojunction on the ZnO/In2 O3 interface, which considered as a bridge of electron transfer to enhance the changes of resistance. In addition, the interface of an n–n injunction is easily transferring electrons into the lower energy conduction band, producing an “accumulation layer” rather than a depletion layer [26, 93, 95]. The formed accumulation layer will deplete via subsequent oxygen adsorption on the surface of host substrate, which can further improve the potential energy barrier at the interface and enhance the response sensitivity. Except for n–n

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Fig. 5.19 Sensing properties of In2 O3 nanostructure and ZnO/In2 O3 -2 heterojunction at 300 °C. a The responses of the sensor to HCHO as the concentration are varied from 5 to 500 ppm. b Sensitivity response of the gas sensor versus the concentration of HCHO. c The response/recovery time for ZnO/In2 O3 -2 exposed to 100 ppmHCHO. d Stability of ZnO/In2 O3 -2 sensor exposed to 100 pmHCHO. Reprinted with permission [94]. Copyright 2016, Elsevier

injunction, p–p injunction has been also designed and applied in many fields, and the interaction process is similar to n–n injunction. The last type of injunction is n–p–n injunction, which combined with more than two semiconductors. Huang et al. [9] designed a typical n–p–n response inversion in SnO2 @ZnO core–shell nanorods with unique physiochemical performances, and it displayed higher selectivity and sensitivity to hydrogen. It confirmed that mixed Zn–O–Sn phase produced at the interface with intrinsic p-type behavior based on the acceptor-type doping of Zn2+ on Sn4+ sites. Thus, available design and fabricate SMO-based materials with heterojunction are beneficial for the gas sensors, and we should select efficient substrate and extra heteroparts. The crystalline metal oxide nanosheet presents attractive catalytic potential due to its large surface-to-volume ratio and quantum confinement effect. However, developing a simple and universal approach to synthesize metal oxide nanosheets remains a major challenge. A co-crystallization induced spatial self-confinement strategy was reported by Yang et al. [96] for the synthesis of various metal oxide nanosheets. Taking the preparation of SnO2 nanosheets as a typical example, the

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solvent volatilization of KCl and SnCl2 solution led to co-crystallization of KCl and K2 SnCl6 , and the obtained K2 SnCl6 -coated composite could be in-situ converted into SnO2 nanosheets confined in KCl matrix. And after the removal of KCl by washing, porous SnO2 nanosheets can be obtained. It was noteworthy that this effective and environmental route was confirmed to be universal and could be applied to synthesize a series of metal oxide nanosheets (such as Fe2 O3 , Co3 O4 , NiO, CuO, ZnO, In2 O3 , and CeO2 ). Especially, the porous CeO2 /SnO2 nanosheets with abundant surface-adsorbed oxygen species and oxygen vacancies possessed excellent overall gas-sensitive properties of 3-hydroxy-2-butanone (3H-2B). The significant surface/interface effects of metal oxides play an important role in surface catalytic reactions, which also contribute to enhanced sensing performance and improved adsorption and desorption processes. Further investigation of the gas sensing mechanism found that the significant surface/interface effects of metal oxides were particularly crucial for surface catalytic reactions, which favorably affected the boosted sensing characteristics and enhanced adsorption/desorption processes. The significant improved catalytic performance of 2D porous CeO2 /SnO2 nanosheets over pure SnO2 was declared by GC–MS detection of the intermediates in the gas sensing process. 3-methyl-2,4-pentanedione and acetic acid were determined as the main products of 3H-2B catalyzed by CeO2 /SnO2 nanosheets, while only acetic acid was found for SnO2 . Furthermore, the lattice oxygen concentration and Ce4+ concentration of sensitive materials after exposing in 3H-2B decreased which was demonstrated by XPS tests, implying that the target gas molecules could react with lattice oxygen at the CeO2 /SnO2 interface via the Mars-van-Krevelen mechanism. Moreover, the effect of porous nanosheet structure on the 3H-2B sensing performance

Fig. 5.20 a Side view of charge density differences in CeO2 /SnO2 nanosheets. The red and blue regions represent electron accumulation and depletion, respectively. The Sn, Ce, and O atoms are marked in gray, white, and red, respectively. b Schematic of the surface sensing reaction of porous CeO2 /SnO2 nanosheets toward 3H-2B and the corresponding band diagram of sensing mechanism. Reprinted with permission [96]. Copyright 2016, Elsevier

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was also detailly investigated through comparing the sensitivity of the synthesized irregular-morphology CeO2 /SnO2 composites with that of porous CeO2 /SnO2 nanosheet under the same testing conditions. It was clarified that porous CeO2 /SnO2 nanosheets displayed higher response values and faster response/recovery kinetics toward 3H-2B, indicating that porous nanosheet structure was significantly favorable in the improvement of sensing performance. On the one hand, the two-dimensional nanosheet structure was conducive to the uniform dispersion of CeO2 nanocrystals, forming more surface n–n heterojunctions and providing a large number of available reactive oxygen species, thus leading to better sensing properties. On the other hand, the porous nanosheet endowed the materials with large surface area and short diffusion path, which could also substantially facilitate the gases adsorption and desorption (Fig. 5.20).

5.5 Crystal Structure and Internal Defects To the best of our knowledge, the chemical active of SMO sensors is strongly dependent on the crystallinity (i.e., single crystal, polycrystalline, mixed crystal, etc.) [97– 99], crystal form (i.e., α, β, γ, etc.) [100], and exposed crystal face (i.e., {110}, {101}, and {100} for SnO, {0001}, {10-10}, and {10-11} for ZnO, etc.) [101, 102]. In this part, we will introduce the relevant influence for practical sensing applications. In most cases, the as-prepared or natural metal oxides semiconductors are existed in the form of polycrystal or mixed crystal, which owing to the high surface energy under the ordinary medium. In 2016, Wang et al. designed ordered mesoporous carbon-cobalt oxide nanocomposites and it showed high crystalline, while it belonged to mixed crystal and the crystallinity could easily adjust through changing annealing temperature [54]. This nanocomposite showed excellent sensing performance for hydrogen. Subsequently, our group reported the mesoporous WO3 with high crystalline framework (mixed crystal) and exhibited superior detection performance to 3-hydroxy-2-butanone than that in low crystalline WO3 [80]. Except for common mixed crystal, polycrystalline semiconductors are also important in the practical production. For example, Gurlo et al. [103] reported polycrystalline cubic and hexagonal In2 O3 and exhibited high sensitivity to ozone under low level. Wang et al. [104] prepared polycrystalline rutile-phase SnO2 nanowires via a solution phase reaction, and this material produced similar sensing behavior to CO and hydrogen. In addition, it thought that the variation of resistance effected through the adsorption and desorption of gas molecules, which due to the small grain size, crystalline, and superior surface-to-volume ratios [7, 67, 69]. Similarly, Wang et al. [105] described polycrystalline WO3 nanofibers with controllable diameters (Fig. 5.21), and it showed rapid response to ammonia under a series of concentrations. Furthermore, decreasing the crystallite size beyond the “nano” size can result in a greater proportion of crystallite surface atoms, which are more prone to react with the surrounding ambient or to

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Fig. 5.21 a, c, e, g: SEM images of the WO3 /PVAc composite nanofibers with various W(iPr)6 volume percentages of 20%, 25%, 30%, and 35%. b, d, f, h: their corresponding products after calcination. Reprinted with permission [105]. Copyright 2006, American Chemical Society

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exhibit unusual structural features, and it indicated that polycrystalline structure is beneficial for the adjusting of microstructure. Interestingly, Fan et al. [98] studied the gas sensing behavior to hydrogen of one-dimensional and two-dimensional polycrystalline ZnO under UV light, and it found that the polycrystalline structure-maintained stability and chemical actives. Although single crystal materials are difficult to form and grow, many scientists design and fabricate successfully a series of single crystal metal oxides, which shows excellent sensing performance and elegant crystal structure. The more common single crystal SMO materials (i.e., ZnO, SnO, Fe2 O3 , In2 O3, and WO3 ) have been applied in multiple sensing devices [99, 106–108]. For example, Rai et al. [106] synthesized single-crystalline ZnO nanoparticles growing along c-axis with hexagonal wurtzite structure, and it exhibited high selectivity for NO2 under low while for acetaldehyde under high working temperatures. As shown in Fig. 5.22, the gas sensing mechanism can be attributed to the resistance change via the chemical and electronic interaction between target gas and the ZnO nanoparticles. The chemical interaction involves the adsorption of the target gas on the ZnO surface, and subsequent react with oxygen species and result in the detection for the target gas. In addition, this group designed single-crystalline ZnO nanorods through traditional microwave-assisted hydrothermal method, and it exhibited preferable detection performance to ethanol (250–50 ppm) and CO (1000–200 ppm) [107]. Liu et al. [97] reported novel singlecrystalline ZnO nanosheets with abundant porous structures, which displayed high gas sensing responses, short response and recovery time, and significant long-term stability for the detection of formaldehyde and ammonia. Thus, the single-crystalline structure can improve semiconductive properties and long-term stability of materials. SnO2 as a fascinating semiconductor has also been focused and studied, and Cheng et al. [109] synthesized for the first time single-crystalline SnO2 nanorods via solution reaction, which showed tunable electrical, optical, magnetic, and chemical properties. Moreover, Chen et al. [99] also fabricated single-crystalline SnO2 nanorods with narrow diameters (4–15 nm) and showed high sensitivity for 300 ppmC2 H5 OH (~31.4). Except for pure single-crystalline SnO2 semiconductor, Wan et al. [33] studied single-crystalline Sb-doped SnO2 nanowires and applied for the detection of C2 H5 OH, and single-crystalline structure with Sb-doping could enhance the sensing performance based on the orientation texture and lattice distortion. Li et al. [110] reported high-aspect-ratio single-crystalline porous In2 O3 nanobelts and showed promising potential in the detection of various gases, such as methanol, ethanol, and acetone, under ppb level. To some extent, single-crystalline SMO materials exhibit superior sensing and physiochemical performance than other crystals. However, the carrier mobility of these crystals is nearly the same (magnitude and temperature dependence) and not sensitive to the gain boundaries; in addition, the potential carrier mobility is strongly dependent on the crystal lattice. Once the gram boundaries were to constitute a significant limitation on the mobility of the carriers, significantly, smaller Hall mobilities would arise in the polycrystalline material than in the single crystals [98–100].

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Fig. 5.22 Schematic diagram of gas sensing mechanism of ZnO NPs. Reprinted with permission [106]. Copyright 2012, Elsevier

Apart from crystallinity can affect the sensing performance of SMO-based sensors, crystal form can also change the basic feature of sensing reaction. The most common expression is Fe2 O3 -based materials with various crystal form, such as α-Fe2 O3 , β-Fe2 O3 , γ -Fe2 O3 , δ-Fe2 O3 , η-Fe2 O3 , and ε-Fe2 O3 , and these different forms will show unique physical and chemical performances [111–113]. Thus, taking Fe2 O3 -based materials as an example to discuss the effect of crystal form in sensing process. α-Fe2 O3 also defined as hematite, which is existed with the most stable iron oxide, is belonging to n-type semiconductor. It exhibited promising applications in gas sensing based on its low-cost, high stability, and especially for reductive vapors or gases (i.e., acetone, C2 H5 OH, and H2 S) [111, 112, 114]. By contrast, β-Fe2 O3 , a metastable bixbyite phase of Fe2 O3 , is a rare metal oxide, and the application of β-Fe2 O3 in gas sensors has rarely reported, which may attribute to the weak chemical stability. However, Giorgio Carraro et al. [115] designed noble metal (Au and Ag) nanoparticles functional β-Fe2 O3 for the first time and applied in gas sensor fields, which could detect various gases, such as hydrogen, C2 H5 OH, and acetone. γ -Fe2 O3 , also named as maghemite, is a typical defective oxide with unit cell (3/4)[Fe3+ [Fe3+ 5/3 ∎1/3 ]O4 ] (“∎” represents vacant sites). Many studies found that γ -Fe2 O3 could detect common combustible gases, including LPG, hydrogen, and toxic gases. Biswal et al. [116] synthesized nano γ -Fe2 O3 through sonochemical process and showed high sensitivity to acetone. To the best of our knowledge, there has been no report on δ-Fe2 O3 and its derivatives with good gas sensing properties. For η-Fe2 O3 , it was found that η-Fe2 O3 microspheres with a special hollow core–shell structure exhibit excellent sensitivity to acetone (~15.69) and C2 H5 OH (~8.15) under 0.5 ppm, while pure η-Fe2 O3 without unique or uniform morphology has not obvious potential in sensing process. In term of ε-Fe2 O3 , it is a rare and

5.5 Crystal Structure and Internal Defects

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metastable phase, and Peeters et al. [117] synthesized Au-doped ε-Fe2 O3 materials, which showed promising performances for the selective detection of gaseous NO2 at moderate working temperatures. But above all, it finds that various crystal forms will produce obvious diversity in sensing process. Among these Fe2 O3 -based materials, α-Fe2 O3 and γ -Fe2 O3 can demonstrate superior sensing potential even though using pure guests, which is dependent on its peculiar surface defectivity and electronic structure. In practical application, exposed crystal face of SMO materials can play another significant role in the gas sensors. The most common ones are TiO2 , SnO2, and ZnO-based materials, and TiO2 -based materials can produce strong variation in various applications not only in gas sensor. Typically, dissimilar facets/surfaces usually possess different geometric and electronic structure that results in dissimilar functional properties. Many studies unambiguously indicated that the gas sensing activity of SMO semiconductors is dependent on the nature of surfaces exposed to ambient gas [19, 101, 118]. Thus, controlling and adjusting the exposed crystal face are necessary for the enhancement of selectivity and sensitivity in gases sensing. The macroscopic SnO2 is enclosed by {110}, {101}, and {100} faces, which have rutile-type microstructure (space group P42 mnm, Nr. 136, a = 4.734 Å, c = 3.185 Å, Z = 2). These crystal faces have usually unequal potential energy surface, which will change chemical activity and surface stability [119]. In general, the {110} crystal face possess the most stable interface energy due to oxygen potential, temperature, and size of the crystals [120]. Han et al. [121] provided first many evidences and prepared successfully shape-controlled SnO2 crystals with various ratios of {221} and {110} crystal faces, and the unique {221} crystal face was a uniform tetragonal dipyramid (Fig. 5.23). It found that these various ratios of {221} and {110} facets exposed to VOCs, producing obvious influences to the sensor response of SnO2 crystals to C2 H5 OH [122]. The higher sensing activity of {221} crystal face dues to the enhanced energy of these surfaces accompanied via their high reactivity; moreover, {221} crystal face contains unsaturated cations and regard as surface active sites. Another typical one is hexagonal ZnO, which have a wurtzite-type structure (space group P63 mc, Nr. 186, a = 3.249 Å, c = 5.205 Å, Z = 2). The common crystal faces consist of {0001}, {10-10}, and {10-11}, and it finds that the {0001} surfaces have high sensing activity and sensitivity to C2 H5 OH than that in {10-10} or {1011} crystal faces [110]. Interestingly, the non-polar {00-10} crystal face contains abundant dangling bonds and Zn–O surface dimmers which endow them highly reactive for NO2 and oxygen adsorption, while the reactivity of polar {0001} crystal face, either Zn-terminated or O-terminated, depends on their termination. In reality, except for SnO2 and ZnO, In2 O3 and WO3 have also similar crystal face characteristics. Many studies have focused on the availability of well-defined shapes enclosed with set of symmetrical {hkl} planes as well as on the relationship between exposed crystal face and gas sensing properties.

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Fig. 5.23 a Typical low-magnification TEM image of an octahedral SnO2 particle viewed along the [109] direction; inset: the corresponding SEAD pattern. b Schematic model of an ideal SnO2 octahedron enclosed within {221} facets, projected along the [109] direction. c HRTEM image taken from the top apex of the octahedron. d HRTEM image taken from the bottom apex of the octahedron. e Low-magnification TEM image of the same SnO2 octahedral particle viewed along the [110] equation image direction; inset: the corresponding SAED pattern. f Schematic model of an ideal SnO2 octahedron enclosed within {221} facets, projected in the [110] direction. Reprinted with permission [121]. Copyright 2009, John Wiley and Sons

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On the other hand, material defects, such as point defect, line defect, and plane defect, formed by crystal vibrates at equilibrium of the atom at the lattice position, have a significant impact on the adsorption process of gas targets on SMO materials [123–125]. It dues to these unique defects possess classic substantial effects on the electronic, structural, and optical properties via inducing charge transfer and adjusting their surface reactivity. In gas sensors, the response of SMO-based materials is generated through the equilibrium of oxygen with ionic and electronic point defects, and in most metal oxides, the intrinsic ionic point defects are typical oxygen vacancies (such as Shottky defects). Under relative low oxygen partial pressures, the mainly point defects are oxygen vacancies and electrons, while in relative high oxygen partial pressure, the predominant point defects are metal vacancies and holes [14, 126]. Moreover, the oxygen partial pressure is dependent on the electronic conductivity, and the relevance of this interdependency is relevant to donor doping and elemental ratios. Point defects within the bulk grains demand longer distance for diffusion to the surface, and it can increase the speed of sensor response and interfacial activity. Solid surfaces or crystal faces consist of segregated impurities, adsorbed gases that act as sources and sinks of electrons, and associated space-charge regions [1, 124, 125]. Thus, the control of defects and associated charge carriers is of paramount importance in applications that exploit the wide range of properties of SMO-based materials. Taking ZnO as an example, where have abundant defect states within the bandgap of ZnO, including donor defects and acceptor defects, and the defect ionization energies differ from ~ .05 to 2.8 eV. This variation will induce that the relative concentrations of the various defects depend strongly on ambient temperature, and the defect controlled ZnO nanowire exhibit fast response and recovery behavior to NO2 [46, 72, 127]. It found that gas sensitivity was linearly proportional to the photoluminescence intensity of oxygen-vacancy-related defects in both as-fabricated and defect-controlled gas sensors. Meanwhile, line defects, also defined as one-dimensional defects or simply dislocations, are the simplest extended defects in comparison with two-dimensional and three-dimensional defects in the sense of having less broken bonds involved and hence having a lower formation energy [128, 129]. It can also produce great influences to the sensing performance of SMO-based materials; for instance, Adepalli et al. [129] synthesized single crystal TiO2 and discussed the effect of line defects. It found that both ionic and electronic carrier concentration could increase locally once changing the material from predominant electronic to ionic conduction, and the line defects was important for adjusting the activity and spatial distribution. Despite all this, the line defects of SMO-based materials rarely used or produced in gas sensors than point defects, owing to the more complicated forming process of line defects.

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Chapter 6

Interfacial Interaction Model Between Gas Molecules and Semiconducting Metal Oxides

Actually, in a typical sensing process of MOS-based gas sensor, the injected gas molecules quickly diffuse and fill the entire chamber, and part of the gas is absorbed to the surface of the gas-sensitive material, resulting in the changes of the resistance or conductivity of the MOSs and finally generating a response [1]. In other words, the adsorption of the target gases marks the beginning of the response process, which is a key step in gas sensing. The desorption of gas molecules, as an inverse process of adsorption, occurs simultaneously, thereby releasing part of the adsorbed gas back into the air. The remaining adsorbed molecules would undergo a coupled diffusion– reaction (DR) process [2], in which the gas molecules diffused into the sensitive material and reacted on the exposed active sites, thus changing the resistance. The consumption and/or conversion of gas molecules can cause a rapid change in resistance, increasing the response accordingly. After a period of equilibrium, when the test gas is discharged, the test gas and its derivative molecules are desorbed from the surface of the MOS materials and released into the air, finally leading to the conductivity of the sensing materials return to the initial state. Therefore, in order to deeply understand the sensing mechanism, it is essential to reveal gas adsorption, desorption, and diffusion behaviors in the whole process of gas sensing (Fig. 6.1). And it requires the assistance of a large number of mathematical and physical models [3–9], which are generally ignored in most previous reports in designing sensing materials. Accordingly, this chapter focuses on the close relationship between the dynamic processes of gas molecules and the sensing mechanism and discusses the effects of adsorption and desorption in the gas sensing process in detail. According to different models, the adsorption mechanisms on MOS are mainly divided into three types, namely oxygen adsorption, chemical adsorption, and physical adsorption. The power law rules and the response/recovery behaviors are further elaborated, which have strong relationships with gas absorption and desorption. Additionally, this chapter also introduces some representative diffusion models, relevant mathematical and physical theories, as well as detailed derivations and inferences, which lays a solid foundation for a clear elucidation of gas-sensitive mechanism.

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Deng, Semiconducting Metal Oxides for Gas Sensing, https://doi.org/10.1007/978-981-99-2621-3_6

189

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Fig. 6.1 Schematic diagram of gas adsorption/desorption and diffusion at gas–solid interface and its role in improving the sensing performance of MOS gas sensors

6.1 Adsorption and Desorption 6.1.1 Classical Adsorption and Desorption Theory In general, adsorption refers to the adhesion of particles in a fluid to a surface. According to the International Union of Pure and Applied Chemistry (IUPAC), adsorption is defined as “an increase in the concentration of a substance at the interface of a dense layer and a liquid or gaseous layer due to the action of surface forces”, and the desorption is the reverse process of adsorption. Adsorption is generally divided into physical adsorption and chemical adsorption, which are mainly distinguished by the activation energy. They are respectively considered as physical adsorption when the activation energy is lower than 25.116 kJ mol−1 and chemical adsorption when the activation energy is higher than 62.79 kJ mol−1 . For physical adsorption, the electronic structure of the particle is almost unaffected before and after adsorption through the interaction of van der Waals force and polarization. When chemical bonds are formed, chemical adsorption occurs, which involves carrier exchange [10]. The adsorption capacity of particles can be accurately described by isothermal formulas based on various models at fixed temperatures. Common isotherms mainly include Henry adsorption isotherm, Freundlich equation, Langmuir equation, and Brunauer–Emmett–Teller (BET) theory. The Henry adsorption isotherm is one of the simplest models, named by the English chemist William Henry, based primarily on Henry’s rule, which states that the amount of gas dissolved in a solution is proportional to its partial pressure in the gas phase. Therefore, the quantity of gas molecules adsorbed on the surface has a positive linear correlation with the partial pressure of the gas, according to Henry adsorption isotherm, as shown in Eq. 6.1.

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191

X = KH P

(6.1)

where X, K H , and P represent surface coverage, Henry adsorption constant, and gas partial pressure, respectively. In fact, all the isotherms mentioned above follow a linear relationship at low pressure, while the Henry adsorption isotherm is only suitable for adsorption with low surface coverage. The Freundlich equation, named by German chemist Herbert Freundlich, is an extension of Henry’s adsorption isotherm, which can be described as the mass of adsorbate on unit mass adsorbent is proportional to the power of equilibrium pressure, in terms of an empirical estimate. The Freundlich adsorption isotherm can be expressed as Eq. 6.2. x 1 = Kpn M

(6.2)

where x, M, and p refer to the mass of adsorbate, the mass of adsorbent, and equilibrium pressure, respectively, and K and n are constants for a specific adsorbent at a certain temperature. n is close to 1 at high temperature, and the Freundlich adsorption isotherm can be converted into Eq. 6.1. Since the isothermal equations are usually fitted by empirical estimation, there are some limits in the derived equation. The biggest limitation of Freundlich equation is that it is not applicable to high pressure. Adsorption saturation will occur according to this model under high pressure, which is completely different from the actual experimental results. For example, when H2 S is adsorbed on the activated carbon, different results can be deduced from the Freundlich isothermal equation under high pressure and low pressure [11]. In 1918, American physical and chemical scientist Irving Langmuir proposed a semi-empirical adsorption isotherm equation, namely Langmuir adsorption isotherm equation, based on the kinetic hypothesis and statistical thermodynamics theory. Langmuir adsorption isotherm equation has become one of the most common isotherm equations due to its simplicity and effectiveness under various adsorption conditions, which is mainly based on the following four assumptions [12]: (a) All adsorption sites are equivalent, and each site can only be adsorbed by one gas molecule; (b) The adsorbent surface is homogeneous, and there is no interaction between adsorbate molecules; (c) There is no phase transition in the adsorption process; (d) During the adsorption process, gas molecules only form a single adsorption layer. Based on dynamics, thermodynamics, or statistical mechanics, Langmuir adsorption isotherm can usually be described as Eq. 6.3: θ=

KP 1+KP

(6.3)

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where θ , K, and P represent surface coverage, equilibrium constant of adsorption– desorption reaction, and partial pressure of gas, respectively. At low pressure, θ ≈ KP; under high pressure, θ ≈ 1. Burke et al. [13] proposed that the adsorption enthalpy of gas molecules should be considered when using the above isotherm models, and it is a common mistake to select an adsorption isotherm only according to the best-fitting data. Langmuir equation has a wide range of applications; however, it cannot be applied to multilayer adsorption, which refers to the re-adsorption of gas molecules on the adsorbed monolayer gas. In 1938, American chemists Stephen Brunauer, Paul Emmett, and physicist Edward Teller proposed a new isothermal formula (BET theory), which involved multilayer adsorption and was based on the following four assumptions [14]: (a) Physical adsorption of gas molecules occurs on a solid with infinite layers; (b) Only gas molecules in adjacent layers interact with each other, and Langmuir theory can be applied to each monolayer; (c) The adsorption enthalpy of the gas molecules in the first layer is constant and much higher than that of the higher layers; (d) The adsorption enthalpy of gas molecules in higher layers is the same as their liquefaction enthalpy. The BET theory can be expressed as Eq. 6.4: x 1 + x(c − 1) = v(1 − x) vmon c

(6.4)

where x represents the partial pressure of adsorbate molecule, v refers to the volume of adsorbate under standard conditions for temperature and pressure (STP), vmon stands for the volume of adsorbate required to form single-layer adsorption under STP, and c represents the equilibrium constant of adsorption–desorption reaction. Although multilayer adsorption is considered in BET theory, adsorption experiments are usually carried out at the boiling point of N2 (77 K), which cannot be applied to gas sensing studies due to their operating temperature of more than several hundred Kelvin. Given that the adsorption of gas molecules at higher layers contributes little to the gas response, the Langmuir adsorption isotherm equation is the most commonly used adsorption model in the actual gas sensing research [15–17]. In addition, the adsorption–desorption mechanism is also fitted to the surface reactions of gas molecules. Irving Langmuir (1921) and Cyril Hinshelwood (1926) proposed a reaction mechanism (Langmuir–Hinshelwood model, or L–H model for short), which states that two molecules are first adsorbed to adjacent sites, then a bimolecular reaction occurs, and the final equilibrium constant is related to the surface coverage of reactants on materials and the rate constant of the adsorption–desorption process [18– 20]. In 1938, British chemists Dan Eley and Eric Rideal present another mechanism (Eley–Rideal model, abbreviated as E-R model), which indicates that a molecule that has been adsorbed on the surface can react with another unadsorbed molecule in gas phase, and the final equilibrium constant is linked to the surface coverage of a

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193

molecule adsorbed on the material [21–23]. In 1954, Dutch chemists Mars and Van Krevelen further propounded a new adsorption-based catalytic mechanism (MVK mechanism [24], also known as redox/regeneration mechanism [25]), which implies that the lattice components of the catalyst react with the reactants and consume, resulting in the formation of a surface vacancy that may be filled with subsequent reconfiguration. Generally, in the MVK mechanism, functional groups are generated on the surface of the catalyst before its lattice components are released into the gas phase and consumed (such as free radical coupling reactions of CH4 ) [26]. Further, Wolkenstein argued that the Langmuir adsorption isotherm equation could be used in electron adsorption theory, thus upgraded this formula through pointing out that K in Eq. 6.3 is not only related to temperature, but also to the Fermi level of materials [27]. This means that the adsorption behavior is affected by the electronic properties of the solid material and its surface, mainly reflected in the band gap. Therefore, the Wolkenstein isotherm may be the most complete model to explain the adsorbent–adsorbent interaction. Geistlinger proposed that various forms such as Henry, Freundlich, and logarithmic isotherms can be observed in the electron adsorption model under different conditions (Fig. 6.2). For example, the S − 1/D relationship curve between the gas response value (S) and the grain size (D) observed in the experiment can be correctly interpreted through numerical research using this theory [28]. In addition, the change in the resistance of the metal oxide after exposure to a reducing or oxidizing gas can be calculated via treating the adsorbed species as defects of the surface donor or acceptor, at least when the Fermi level is fixed in the corresponding state [29]. In 1920, French chemist Paul Sabatier put forward a principle, which suggested that the interaction between catalyst and reactant in catalytic reaction should be moderate (Sabatier equilibrium). Otherwise, if the interaction is weak, the reactant is difficult to be activated; when the interaction is strong, the product is difficult to desorb [31]. In 1969, Balandin further proposed that the relationship between reaction rate and catalyst bonding ability can be described by a volcanic curve with a maximum peak (Fig. 6.3) [32]. The Sabatier-Balandin principle has been widely used in qualitative understanding of designing excellent catalyst.

6.1.2 Effects of Adsorption and Desorption on Gas Sensing of MOS Materials It is well-known that the MOS-based sensing materials produce responses to different gases through the change of resistance. In actual gas sensing experiments, physical adsorption is usually weak and easily affected by experimental conditions (such as humidity and temperature), while chemical desorption plays a dominant role in the sensing process and is more stable due to the strong chemical bond interaction. In other words, the resistance of MOSs changes rapidly due to the formation of chemical bonds between the adsorbed gas molecules and the MOS materials. However, because

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Fig. 6.2 Wolkenstein isotherm. The species coverage (θ ) of chemisorption on n-type ZnO films can be shown as highly compensated (denoted by i) and uncompensated (denoted by n) according to the difference of O2 partial pressure (p). a Curve of θ-lg p; b Curve of lg θ-lg p; c and d Logarithmic relationship between the coverage of charged enhanced chemical adsorption c and neutral weak chemical adsorption species d and the partial pressure of O2 . Reprinted with permission [30]. Copyright 1993, Elsevier

Fig. 6.3 Qualitative diagram of the Sabatier-Balandin principle. Reprinted with permission [31]. Copyright 2015, Elsevier

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of the widespread presence of oxygen in the test environment, the adsorption of O2 molecules is very significant and cannot be ignored and is often listed separately from chemisorption [33]. In addition, if the depletion layer theory derived from the oxygen adsorption model is combined with the adsorption and reaction process of gas molecules, the power law rule of MOS materials in gas sensing, which refers to the power function relationship between the material resistance and the partial pressure of gas molecules, can be further obtained [9]. In addition, the chemisorption of oxygen and test gas and the desorption of products are particularly crucial for the response and recovery of gas sensors, which can be used to control and enhance the sensitivity and selectivity of sensing materials.

6.1.2.1

Oxygen Adsorption Model

Although N2 has a high volume fraction in air (78%), it has a trivial effect on the resistance of MOS materials due to its stable N≡N structure and high bonding energy (946 kJ mol−1 ). Hoa et al. [34] reported that nitrogen is chemically inert in p-type semiconductor CuO films. Thus, N2 is usually applied as a carrier gas in the study of chemical kinetics between gas and MOS materials [35]. Besides, the volume fraction of O2 in the air reaches 21%, the second highest concentration gas in the air, next to N2 , which can be adsorbed on the MOS materials and have a great impact on the gas sensor. When the n-type semiconductors, taking electrons (e− ) as carriers, are exposed to the air, O2 molecules can adsorb on their surface and grab electrons from materials to form reactive oxygen species, leading to the generation of electron depletion layer (EDL) with electron core–shell structure on the surface of MOSs and increasing the resistance of the MOSs material (Fig. 6.4a). The electrons in EDL are distributed at a limited depth of Debye length (λD , a few nanometers) above the surface of the material, and are almost unaffected by the adsorption of O2 molecules [36, 37]. Debye length, also called Debye radius, is a parameter about the electrostatic effect of charge carriers in solution and reflects the electrostatic shielding effect of plasma. When the scale discussed is larger than the Debye length, the plasma can be regarded as electrically neutral; otherwise, it can be considered as charged. For n-type semiconductors, Debye length is defined by Eq. 6.5 [38]: √ λD =

εk B T e2 Nd

(6.5)

where ε, k B , T, e, and N d represent dielectric constant, Boltzmann constant, temperature, charge, and number density of dopant, respectively. Li et al. [40] reported a simple but effective beer detection sensor using n-type SnO2 , which shows that SnO2 with stoichiometric ratios exhibited highly doped semiconductor properties when fed into inert or reducing gases at high temperatures, mainly due to the oxygen vacancy formed by the separation of the O atom at the surface (Eq. 6.6, Fig. 6.5a). When O2 molecules are adsorbed on the surface of MOS

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Fig. 6.4 Electron depletion layer with electron core–shell structure in a n-type and b p-type semiconductors. Reprinted with permission [39]. Copyright 2014, Elsevier

materials, they first occupy the previously formed oxygen vacancies and capture electrons from the conduction band of the sensing materials, leading to the generation of various reactive oxygen species (O2 − , O− , and O2− ), thus generating EDL above the surface of the materials. Overall, O2 − , O− (dissociated oxygen), and O2− (lattice oxygen) are dominant in the temperature range of 400 °C, respectively [36, 37]. The adsorption and desorption capacity of O2 molecules can be further improved by increasing the working temperature, doping, and reducing the grain size [41]. Additionally, if the thickness of the MOS materials is less than twice that of λD , the EDL can be extended to the entire material, making the maximum change in the resistance. The studies demonstrated that the EDL in single crystal SnO2 nanoribbon could be modified within a few nm by metal catalyst nanodots and the species adsorbed on the surfaces [42]. As depicted in Fig. 6.5b, adsorption of O2 molecules reduces the Fermi level and causes the conduction band to bend toward higher energy levels, further increasing the resistance of the material. For n-type semiconductors, when exposed to a reducing gas, a redox reaction occurs on the surface of the MOS material (Fig. 6.5c), which reduces the concentration of adsorbed oxygen on the surface and the depth of EDL, thus restoring the resistance

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as the Fermi level and conduction band return to normal levels. 1 O0 ↔ VO¨ + 2e, + O2 (g) 2

(6.6)

Fig. 6.5 Schematic illustration and the corresponding energy band diagram of a non-stoichiometric SnO2 surface with oxygen vacancies, b partially repopulated SnO2 with adsorbed oxygen, and c reaction between C2 H5 OH and pre-adsorbed oxygen atoms. The gray, red, blue, black, and white balls represent Sn, O, adsorbed O, C, and H, respectively. Reprinted with permission [40]. Copyright 2016, Elsevier

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In the sensing process of reducing gas, the adsorbed gas molecules are oxidized by various oxygen species and subsequently consumed. At lower temperatures, the more reactive adsorbed oxygen reacts preferentially with the reducing gas, while the surface activity of the materials improves at higher temperatures, and the free adsorbed oxygen species are further consumed. The transfer of negative oxygen ions occurs on the lattice surface of MOS materials, and the lattice oxygen will directly react with reducing gas and generate surface oxygen vacancy. At this time, the catalytic activity of the material surface is further enhanced, following the typical MVK mechanism, which can be verified by isotope exchange method [43]. The oxygen vacancies on the surface of the materials can be refilled by the curing of free oxygen in the air. On the other hand, for p-type semiconductors with holes as charge carriers, the adsorption of surface oxygen molecules also reduces electron density, but a hole accumulation layer (HAL) is formed (Fig. 6.4b) [44]. It has been proved that the electron core–shell structure is suitable for both n-type and p-type semiconductors, but in p-type semiconductors, the core is insulator and the shell is conductor, which is completely different from n-type semiconductors. Therefore, when the p-type semiconductors are exposed to the air, oxygen molecules will be first adsorbed into the oxygen vacancy, and at this time, oxygen negative ions can also be combined with holes (Eq. 6.7) [45]. Iwamoto et al. [46] studied the surface oxygen desorption amount of different MOS materials at 560 °C (V560 ) through the temperature-programmed desorption (TPD) method (Fig. 6.6), which found that the p-type semiconductors (such as CuO, Co3 O4 , MnO2 , NiO, and Cr2 O3 ) have higher V560 values than n-type semiconductors. And the V560 value decreases roughly with the increase of formation enthalpy. Generally, the p-type semiconductors are more thermodynamically unstable than n-type semiconductors and have a variety of oxidation states, which can improve the redox reaction, so the oxygen adsorption capacity is higher. O2 + VO¨ + e' ↔ (O− ¨ )ad 2 − VO

6.1.2.2

(6.7)

Chemical Absorption and Desorption

In general, the adsorption of gas molecules to the surface of MOS materials can influence the electron distribution of this material, resulting in specific resistance changes. Strictly speaking, the oxygen adsorption on MOS materials discussed above also belongs to chemical desorption. However, due to the widespread presence of O2 in the air, oxygen adsorption models should be listed and discussed separately. The oxygen adsorption model seems to be versatile in explaining the mechanism of traditional MOS materials, but the above theory is inevitably inadequate under many more complex conditions, where the chemical adsorption and desorption of the tested gases have a great impact on the gas sensing. An exception to the traditional oxygen adsorption model is the anaerobic system. Moradi et al. [11] reported the oxygen-free sensing of CO with Pd/Pt decorated

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Fig. 6.6 Correlation of the amounts of desorbed oxygen (V560 ) with the heat of formation of oxides per g atom of oxygen (−ΔH 0f ). Reprinted with permission [46]. Copyright 1978, American Chemical Society

SnO2 as the sensing material, which exhibited an attractive response to CO at low O2 concentration (20 ppm), and the response value decreased with the increase of O2 concentration [42]. The anaerobic sensing test of CO is usually carried out under the ultra-high vacuum condition, in which the adsorbed CO molecules directly react with the lattice oxygen (Olat ) on the material surface, following the MVK mechanism. At low O2 concentration, due to the small number of oxygen vacancies occupied by adsorbed oxygen, the dominant mechanism is similar to that of anaerobic conditions. With the increase of O2 concentration (25–50 ppm), the number of active sites covered by CO molecules decreased, and the sensing mechanism is gradually transformed into an oxygen adsorption model. Further studies were conducted to explore the influence of different O2 background concentrations on H2 and CO sensing performance for SnO2 -sensitive materials [47, 48]. Henrich et al. [49] proposed a possible mechanism to explain H2 sensing in the absence of oxygen, including the dissociation of H2 molecules, the adsorption of H atoms to surface lattice oxygen ions, and the formation of rooted hydroxyl groups (OHO + ), which have lower electron affinity than lattice oxygen ions and can thus be used as an electron donor. This results in an increase in the electrons in the conduction band and a decrease in the resistance of the materials. And an electron accumulation layer (EAL) is formed on the surface of the material, which makes the energy bands bend upward, similar to the HAL produced by p-type semiconductors exposed to air. However, the conductivity of the material could be enhanced with the increase of H2 concentration, but it will reach saturation when the energy level of the electron donor crosses the Fermi energy level, because the electron cannot exceed the Fermi energy level. In short, as long as there are enough oxygen ions on the surface of the material, the sensing mechanism will be governed by the concentration of surface electron acceptors. While for the materials with a small number of electron acceptors, the sensing mechanism is dominated by the surface electron donor, resulting in the production of EAL in n-type semiconductors.

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Under aerobic conditions, both mechanisms mentioned above are involved (Fig. 6.7a and b), but the response value is lower than that in the absence of oxygen when only H2 adsorption occurs. This is due to the fact that the ionization of the electron donor does not cause an increase in the surface carrier concentration of SnO2 under aerobic conditions. On the contrary, the electrons released by the electron donor will eventually be captured by the acceptor, and further ionization adsorption of oxygen will occur. Since the surface charge does not change, the energy band does not bend. In fact, only the oxygen adsorption model makes a practical contribution to the response in the presence of oxygen, because it reduces the concentration of adsorbed oxygen ions and thus increases the concentration of free carriers inside the semiconductor. Therefore, these two mechanisms cannot be simply superimposed, and similar results have also been verified in CO sensing [48]. Zhu et al. [50] deeply analyzed the relative adsorption energy of H2 molecules and the optimal active site on the surface of SnO2 under oxygenated and oxygen-free conditions, using the Vienna Ab initio Simulation Package (VASP) for atomic-scale material simulation. The first calculation showed that the adsorption energy of O2 on the most thermodynamically stable (110) crystal plane of SnO2 was relatively low (−0.38 eV), indicating that the O2 adsorption was unstable. And the adsorption energy of H2 molecule on adsorbed oxygen is lower (−0.018 eV). On the other hand, among the different adsorption sites, the adsorption energy of H2 molecule at the end oxygen was the highest (−0.029 eV), implying that H2 molecule preferred to directly adsorb on the surface of SnO2 in the absence of oxygen, rather than to further adsorb on the pre-adsorbed oxygen ion (Fig. 6.7c–f), which was consistent with the experimental results. Similarly, the density functional theory (DFT) was performed to reveal the mechanism of CO adsorption and oxidation on different planes of SnO2 grains. For example, Lu et al. [51] pointed out that the oxidation of CO on the SnO2 (110) crystal plane follows the MVK mechanism rather than the L–H model, in which the adsorbed oxygen is converted into various oxygen anions by transferring electrons to chemisorbed oxygen. Zakaryan et al. [52] further proved that on other crystal planes such as (101) and (001), CO molecules were adsorbed by bonding between C atoms and lattice oxygen, which made the MVK mechanism ineffective. Another typical sensing mechanism dominated by chemical adsorption and desorption is the testing of gas molecules that are able to directly contact with the grain and undergo chemical reactions, eventually resulting in changes in material resistance [33]. This process is usually performed in conjunction with the oxygen adsorption model, but can contribute more to enhancing gas response than the latter. Xu et al. [53] prepared a CuO thin film composed of monolayer colloidal particles via a self-assembly process, which shows an excellent gas sensitivity to H2 S, and its recoverability depends on the concentration of H2 S. Specifically, the sensor recovers easily at low H2 S concentrations, but difficultly at high H2 S concentrations (Fig. 6.8a, b). The two different mechanisms were proposed to explain this gas sensing behavior (Fig. 6.8c–e). As a p-type semiconductor with holes as carriers, when CuO was exposed to air, O2 adsorbed on the surface to form HAL. When exposed to low concentration of H2 S (1 ppm), H2 S molecules tended to be adsorbed on the surface of CuO and reacted with it, because there were not enough negative oxygen ions to occupy the active sites of CuO (Fig. 6.8d) [55]. Since the bandwidth of the product CuS was narrower than that of CuO, the resistance of the material decreased rapidly, which was the opposite of the sensing mechanism of traditional p-type semiconductors. The sensor could not be fully recovered from the room temperature range to 300 °C, which was mainly attributed to the low rereduction rate of CuS [55]. For ultra-high concentrations of H2 S (>100 ppm), both mechanisms contributed to the sensing response (Fig. 6.8e). As shown in Fig. 6.8b, oxygen adsorption model was dominant at the initial stage after H2 S injection, causing the resistance to rise. However, soon after, due to the chemical adsorption and direct reaction of H2 S on the surface of CuO, the resistance of the material decreased. With the formation of more CuS, the resistance dropped rapidly, indicating that CuS played a decisive role in the gas-sensitive mechanism. On the other hand, for n-type MOS sensors, H2 S chemisorption can directly enhance the response. For example, our group has conducted the research on H2 S sensors based on ordered mesoporous MOS materials, including Fe2 O3 , SnO2 , and WO3 [56–61]. The chemical adsorption of H2 S on the MOS sensing layer leads to the generation of metal sulfides (SnS2 , WS2 , etc.), forming a heterojunction with the original oxide materials [59–61], which causes the further decrease in the resistance of n-type semiconductors owing to the metal sulfides with narrower bandwidths, unlike the response reversal in CuO. However, recovery times are inevitably prolonged because the resulting sulfide must be re-oxidized to return to its original state. Another common chemisorption in gas sensing is the adsorption of H2 O molecules. Despite the adsorption of H2 O molecules is generally considered to be physical adsorption, in fact chemisorption also exists, and the adsorption mechanism of H2 O molecules is determined by the proportion of active sites occupied by H2 O molecules. At low humidity, the adsorption of H2 O is dominated by monolayer chemical adsorption. At high humidity, multilayer physical adsorption may happen because the active sites on the surface of the material are occupied. Humidity also has a negative impact on response in gas sensing process at lower temperatures. In dry air (relative humidity lower than 20%), the traditional chemical absorption and desorption model mentioned above (especially oxygen adsorption) plays a dominant role in the gas sensitivity [35]. While under wet conditions, the dense H2 O molecules undergo dissociation and adsorption on the surface of the material and are ionized to produce H+ and OH− . Heiland et al. [62] proposed two different H2 O molecular adsorption mechanisms on the surface of SnO2 . In the case of an H2 O molecule reacting with two metal sites (Fig. 6.9a, Eq. 6.8), the dissociated OH− bonds directly with the Sn atom, while the remaining H+ bonds with a lattice oxygen, forming two Sn-OH dipoles and releasing two free electrons. In addition, for an H2 O molecule reacts with a metal site (Fig. 6.9b,

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Fig. 6.8 a Dependence of the CuO sensor’s response on the operating temperature on exposure of 10 ppm H2 S. b Gas response of the CuO sensor for 400 ppm H2 S. c–e Different sensing mechanisms of the CuO film exposed to different concentrations of H2 S. (C) In air, the O2 molecules are adsorbed to form the negative charge oxygen on the CuO surface, which generates the holeaccumulation layer. d When low-concentration hydrogen sulfide is injected, the electrons released from the reaction of H2 S with O2− adsorbed on the CuO surface decrease the accumulation of holes, thus improving the gas response of the CuO film to H2 S. e When high-concentration H2 S is injected, besides the H2 S oxidation reaction, a CuS layer appears on the CuO surface due to the reaction of H2 S with CuO, which decreases the gas response of the CuO film to H2 S. Reprinted with permission [53]. Copyright 2019, American Chemical Society

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Eq. 6.9), H+ diffuses into the sensing material and binds to lattice oxygen, while the remaining OH− also binds to the Sn site. The anchored OH− groups mainly act as electron donors because of their weak electron affinity and ionization. In short, the adsorption of H2 O molecules has a great influence on the gas response, either by changing the conductivity of the sensing material or by occupying the active site. The above theories are completely insufficient to expound the adsorption mechanism of H2 O molecules under all conditions, because most of them ignore the physical adsorption of H2 O molecules mentioned below. ) ( H2 O(g) + 2(SnSn + OO ) ↔ 2 SnσSn+ − OHσ − + VO2+ + 2e−

(6.8)

) ( − H2 O(g) + (SnSn + OO ) ↔ SnσSn+ − OHσ − + (OH)+ O +e

(6.9)

In conclusion, the chemical adsorption/desorption model is closely related to the gas sensing process, but has been neglected in many studies, which is an important supplement to the traditional oxygen adsorption model.

Fig. 6.9 Mechanisms of humidity adsorption on the surface of tin oxide; a one water molecule for two metal sites and b one water molecule per metal site. Reprinted with permission [35]. Copyright 2014, Cognizure

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6.1.2.3

205

Physical Adsorption/Desorption and Humidity Influence

Physical adsorption is another common adsorption process in gas sensing, but different from the oxygen adsorption and chemical adsorption mentioned above. The physical adsorption theory is rarely used to reveal the gas-sensitive mechanism, which is due to that physical adsorption only has negligible influence on the conductivity of MOS materials under most conditions [33]. As discussed above, H2 O molecules are mainly adsorbed on sensitive materials by physical adsorption; therefore, humidity sensor has been widely reported as a kind of MOS sensor based on physical adsorption mechanism. Even so, the response of humidity sensor is still affected by chemical adsorption, including oxygen adsorption [63]. In addition to the chemical adsorption at low humidity analyzed above, the adsorption of H2 O molecules mainly exists in the form of physical adsorption at high humidity. At higher humidity, Morrison further proposed the concept of “co-adsorption” [64], which refers to that H2 O molecules could drive away other molecules that have occupied the active site, and thus single-layer chemical adsorption and multilayer physical adsorption will occur simultaneously on the surface of the material, resulting in co-adsorption. In this case, according to the Grotthuss proton jumping mechanism, H+ in the physical adsorption layer can flow freely between H2 O molecules through hydrogen bonding (Fig. 6.10) [65]. In particular, due to the strong correlation between the jumping mechanism and adsorption/diffusion, the mechanism will be described in detail in Sect. 6.3 of this chapter. The decrease rate of material resistance depends on the adsorption model, which is usually the fastest in chemical adsorption, moderate in physical adsorption, and almost zero in co-adsorption. Besides, humidity also affects the intrinsic properties of the sensing materials. For example, Deng et al. [66] prepared a p-type semiconductor CuScO2 , which generated the “pseudo-n-type” behaviors to NH3 . Due to the high resistance of CuScO2 itself, the conductivity of the sensitive material is mainly reflected in the H2 O layer adsorbed on the surface in a humid environment. At this time, the NH3 molecules dissolve and ionize, providing NH4+ and OH− for the H2 O layer. In addition, according to the Grotthuss proton jumping mechanism, the flow of H+ improves the conductivity of the material, thus establishing the pseudo-n-type mechanism. Later, since the H+ affinity of NH3 is stronger than that of H2 O [67], the flowing H+ can be captured by NH3 molecules, resulting in a pseudo-p-type mechanism (Fig. 6.11a). As H2 O evaporates, the desorption of H2 O molecules weakens the pseudo-P-type response (Fig. 6.11b). However, at high temperature (>100 °C), all H2 O molecules desorbed, thus exhibiting the P-type property of CuScO2 itself (Fig. 6.11c). Apart from H2 O molecules, O2 molecules can also interact with sensitive materials through physical adsorption at room temperature. Hong et al. [69] applied an O2 sensor prepared by a field-effect transistor (FET) to explore the physical adsorption of O2 molecules at room temperature and found that the physical adsorption of O2 had little effect on the gas response. For oxygen adsorption model at low temperature, O2 molecules are also mainly adsorbed on the surface of sensitive materials by physical adsorption. Even though the physical adsorption of O2 molecules does not affect the

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Fig. 6.10 Schematic representation of humidity sensing at ZnO. Reprinted with permission [68]. Copyright 2010, Elsevier

Fig. 6.11 Schematic diagram of the sensing mechanism of the CuScO2 at different operating conditions: a at RT in moist air, b at RT in dry air, and c at high temperature. Reprinted with permission [66]. Copyright 2019, American Chemical Society

electronic structure of sensing materials, it was estimated that it would interfere with the carrier transport of sensitive materials, reducing its mobility and resulting in a slight increase in resistance. In general, this oxygen physical adsorption effect seems to dominate in two-dimensional (2D) materials such as graphene or Mxene layers, while the chemical resistance in the bulk phase or even mesoporous metal oxides is principally dependent on the change of free carrier concentration caused by chemical adsorption [70].

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6.1.2.4

207

Power Law Rule of MOS-Based Gas Sensor

The relationship between the resistance of MOS gas sensor and gas concentration has been studied for a long time. In 1987, Morrison proposed a mass action rule of reducing gas on SnO2 material; that is, the resistance changes with PR −0.5 at low PR , where PR is the partial pressure of reducing gas [71, 72]. In subsequent researches, some empirical conclusions have been acquired [2, 73], but the sources of power law rules have not been systematically clarified. Then, based on the gas adsorption theory mentioned above, Yamazoe’s group [8] further proposed that when MOS material is exposed to a gas with partial pressure P, its resistance is proportional to Pn , where n is a specific constant for the gas, providing a theoretical basis for the power law rule of MOS material gas sensor. It is generally believed that the material resistance has a roughly linear relationship with gas concentration, especially at low concentration, which has been applied in diffusion theory [5] and calculation of limit of detection (LoD) [32]. The power law rule can expand the research scope of gas diffusion [72, 74], which can combine the function of electron donor (adsorption and reaction of gas molecules) with the function of sensor (surface potential change); that is, the chemical process of oxide surface is linked to the physical process of MOS material surface through the power law rule. Traditional basic theories of semiconductor and grain effects, such as the double Schottky layer and tunnel model, are adopted, and the chemical changes resulting from adsorption of different gas molecules are discussed in depth [37, 75]. In order to simplify the model, it is assumed that the negative oxygen ion formed after oxygen adsorption is O− , and its accumulation can be expressed as Eq. 6.10, where k 1 and k −1 represent the rate constants of the positive and reverse reactions, respectively. On the other hand, for the physical changes on the surface of n-type semiconductor, the conducting electron density can be described as Eq. 6.11, where N d and m refer to the number density of electron donor and the reduced depletion layer depth, respectively. By combining the chemical and physical processes mentioned above, the power exponent n can finally be depicted as Eq. 6.12. When m is large enough, n is equal to 0.5, which is the limit of the power law rule. Later, Yamazoe et al. [76] extended the derivation of the power law rule to other negative oxygen ions and pointed out that if the absorbed negative oxygen ions were completely O2− , then the power index n would be 0.25. d[O− ] = k1 PO2 [e− ]2 − k−1 [O− ]2 dt

(6.10)

[ −] m2 e = N d e− 2

(6.11)

dlgR m2 = dlgPO2 2(m 2 + 1)

(6.12)

n=

As mentioned above, the formation of oxygen anion species on the surface of MOS materials is related to the operating temperature of the gas sensing process, thus

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affecting the power index of the power law rule. The response value of a gas sensor is usually defined as S = Ra /Rg or S = (Ra − Rg )/Ra × 100%. Windischman et al. [77] took a typical case of CO adsorption and proposed that the redox interaction on the surface of SnO2 could clarify the chemical resistance response value S, as described in Eq. 6.13. ( S = eμd

α PR γ

)β (6.13)

where e represents elementary charge; μ refers to electron mobility; d means sensing layer thickness or characteristic grain size; α is the coefficient representing the adhesion of reducing gas R to the surface; γ represents surface recombination coefficient; and PR refers to the partial pressure of gas R. According to the results of many experiments, if a simple gas molecule interacts with the metal oxide surface and the resulting adsorbed species provide a free electron to the conduction band of sensing material, then the power index β value in Eq. 6.13 is 0.5. Extending to larger gas molecules, such as volatile organic compounds (VOCs), which are involved in a series of chemical reactions on the surface of the sensitive material that transfer more than one electron, the value of power index β deviates by 0.5. In addition to considering the influence of gas molecules, Scott et al. [78] investigated the effect of surface modification of MOS on β values, in which SnO2 with anti-opal structure acted as a spherical “aggregate”. The irregular microstructure and the presence of surface aggregation may increase the β values by a deviation of 0.5, but not more than 1 [79], which is determined by the surface charged species, especially the negative oxygen ions [78, 80]. Specifically, the high surface coverage of O2− reduces the β values by less than 0.5, while abundant O− is conducive to increasing the β values to close to 1 and improves the sensing response [81]. Bai et al. [81] constructed a sequence of C2 H5 OH sensors with rich oxygen vacancies via doping different rare earth (RE) elements on In2 O3 nanotubes (NT) and further studied the power law rules between gas response values and sensors with different RE elements dopants. The β value of pure In2 O3 NT is close to 0.5 (Fig. 6.12a), which is consistent with the conclusions of previous models. With the introduction of different RE elements, β value increases, and the proportion of O− in various negative oxygen ions rises simultaneously, which is ascribed to the abundant surface oxygen vacancy, enhancing the gas response. The extremely high β value (1.07356) of Tb-In2 O3 is obtained, exceeding the traditional limit of 1, which is mainly assigned to the high proportion of O− on the surface of sensing material (Fig. 6.12f). It is worth noting that extending from linear correlation to power law rules is critical at high gas concentrations. The gas sensing test at high concentration is usually of significant practical application value (e.g., H2 ) [82]. Besides, if the molecular structure of the target gas is too stable to generate appropriate response at low concentration (e.g., CH4 ), high-concentration testing is generally required [83]. For example, the concepts of LoD and limit of quantitation (LoQ) correspond to an extremely

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Fig. 6.12 Calibration curves of response versus ethanol concentration of pristine and RE-doped In2 O3 NTs in the range of 5–500 ppm. Reprinted with permission [81]. Copyright 2020, Elsevier

low concentration at which response values and background noise levels are comparable [84, 85]. In this case, the power law rules can certainly be converted to linear correlation in the actual calculation [86, 87].

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Effects of Gas Absorption/Desorption on Response/Recovery Processes

When switched to test gases, the response of the MOS material sensor can be expressed as a change in resistance or conductivity. When fresh air is injected, the resistance of the material returns to initial state. Generally, if the conductivity of the sensing material increases when exposed to target gas, such as an n-type semiconductor connect with a reducing gas, its response value can be defined as Ra /Rg or S = (Ra − Rg )/Ra × 100%, where Ra and Rg represent the resistance value of the material exposed to the air and the test gas, respectively. If the conductivity is reduced, the response value can be defined as S = Rg /Ra or S = (Rg − Ra )/Rg × 100%. As a rule of thumb, the response/recovery rate can be measured by the time (τ ) required for 90% of a complete response/recovery process [1]. In practice, the response value of the sensor is determined by the two equilibrium states before and after, the intermediate transient of response/recovery is often ignored, and a dynamic measurement system must be introduced to achieve a thorough study of the transient [88]. However, the response transient of the sensor is actually determined by the chemical reactions occurring on the surface and inside the sensing layer [3], and thus, it plays an irreplaceable role in the research of gas sensing. Lundstrom et al. [3] discussed response/recovery transients in solid sensing materials through applying Langmuir-like isotherms and assuming first-order kinetic processes, and two-time constants (τ f and τ r ) were introduced to represent the forward reaction of response process and the reverse reaction of the recovery process, respectively. The initial rate of response and recovery can be calculated by Eqs. 6.14 and 6.15. ( ) dθ 1 = (6.14) dt 0 τf ( ) dθ θs 1 =− =− (6.15) dt 0 τr τ f + τr where θ represents the response value. The value of Eq. 6.14 is always larger than that of Eq. 6.15, indicating that the recovery time is longer than the response time in the first-order dynamic process. Later, the second-order kinetic process was further derived (e.g., H2 adsorption on Pd [89], forming PdHx ), and it was found that the response was close to the recovery rate. Korotcenkov et al. [8] discussed the response/recovery dynamics in SnO2 sensing and measured the corresponding activation energy at different operating temperature (Fig. 6.13), which suggested that lattice oxygen had no effect on the formation of H2 O molecules. Therefore, the response dynamics of the sensor in the absence of oxygen was determined by the intrinsic properties of the material, while the difference in response and recovery times was weakened under oxygenated conditions. It was also demonstrated that the response time is close to or longer than the recovery time above 150 °C, and the response time is shorter than the recovery time below 150 °C. The former belonged

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Fig. 6.13 Temperature dependencies of time constants of transient characteristics of SnO2 film gas response, measured in wet atmosphere (30–50% RH): (1) d∼30–60 nm; (2) d∼200 nm. Reprinted with permission [8]. Copyright 2004, Elsevier

to the first-order dynamics, and its determination speed was only the adsorption and desorption process of gas molecules, without further dissociation, while the latter involves second-order kinetics, and its determination step was the dissociation and adsorption of surface substances (especially OH- and O atoms). The dynamics of In2 O3 sensor was further studied [90]. The transient change of SnO2 possesses a power function relationship with time [8], while for In2 O3 , it shows a more complex time dependencies, such as power function, square root, and direct proportional, which is attributed to the large number of active sites on the surface generated by different oxygen species (chemisorbed oxygen, oxygen vacancy, and lattice oxygen). Moreover, In2 O3 exhibits contrasting behaviors when exposed to different gases. For CO and H2 , they display acceptor properties at temperatures below 250 °C and donor characteristics at temperatures above 250 °C. For O3 , the recovery time (τ rec ) is much longer than the response time (τ res ). The gas sensing mechanism of In2 O3 was further proposed, which can be divided into two different processes, including adsorption–desorption process and redox process. And the former mainly involves the adsorption/desorption and dissociation of gas molecules, surface oxygen diffusion, surface reaction, and product desorption. The latter, in addition to the above processes, includes the interaction of gas molecules (O2 , H2 O, and test gases) with the lattice of In2 O3 (reduction or reoxidation), surface remodeling, and the bulk phase diffusion of oxygen species. For example, the recovery of CO and H2 is surface reoxidation of In2 O3 , while the recovery of O3 is merely involved in desorption. In the redox process, the response time is generally close to the recovery time. In the process of desorption, the recovery time is longer. In addition, although the reducing gases chiefly follow the redox mechanism, the adsorption and desorption of O2 molecules are also very important. Such as the chemical reactions between CO and different oxygen species will also be distinct, in which lattice oxygen acts as donor, while adsorbed oxygen acts as acceptor, and the response time of the latter is also shorter. In addition to pure MOS materials, more complex materials such as polymers and heterojunction materials have been investigated [91, 92]. For example,

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Hu et al. [91] analyzed the adsorption kinetics of NH3 on polyaniline films, applying Langmuir and Freundlich models simultaneously, and finally found that the response of NH3 follows the latter. On the other hand, it is a common misunderstanding to select a model based on the best-fitting data [13], so the reliability of this conclusion may be compromised. Furthermore, Mukherjee et al. [88] deliberated the H2 response and recovery kinetics of ZnFe2 O4 sensors. If the first-order kinetic process is considered, it can be determined that the decisive step of the response process is the chemical reaction between the chemisorbed oxygen and the test gas, while the desorption of the oxidation product is the rate-determining step of the recovery process [84]. Additionally, the temperature dependence of the time constant follows the Arrhenius equation, as shown in Eqs. 6.16 and 6.17. EA

τres = τ0 e 2kB T '

ED

τrec = τ0 e 2kB T

(6.16) (6.17)

where E A and E D represent activation energies of gas adsorption and reaction product desorption, respectively, and τ 0 and τ 0 ' are pre-exponential factors, which are only determined by the reaction itself [83]. Based on the above theories, Wu et al. [87] further conducted CO and H2 sensing tests on Co-doped ZnO. As displayed in Fig. 6.14a, G0 and G0' hardly change with the concentration of CO, while G/(1 − G) has a linear relationship with the CO concentration (Fig. 6.14b), indicating that it is consistent with Langmuir adsorption mechanism [91]. Li et al. [83] synthesized SnO2 nanosheets loaded with PdPt alloy, which exhibited double selectivity for CO and CH4 at different temperatures, and the dynamic changes of the response process were detailed and discussed (Fig. 6.15). For the CO molecules with relatively active properties, a break point appears in the transient response, and the activation energy at high temperature is higher than that at low temperature (Fig. 6.15a and b), demonstrating that CO undergoes two different oxidation pathways. At low temperature, only surface-adsorbed oxygen participates in the oxidation reactions. And at high temperature, surface-adsorbed oxygen and lattice oxygen are both involved, so that the average activation energy increases. However, due to the stable regular tetrahedral structure of CH4 molecules (Fig. 6.15c), which requires higher temperatures for activation, only one oxidation path can be observed, in which surface-adsorbed oxygen and lattice oxygen participate simultaneously. In addition, among different sensing materials, the activation energy of SnO2 modified by PdPt alloy (denoted as 1P-PdPt/ SnO2 -A) is the lowest (the blue line in Fig. 6.15), which can be attributed to the overflow effect of precious metals, which acts as the active sites to enhance the dissociation and adsorption of surrounding oxygen, thus improving the gas oxidation efficiency [41, 93].

6.2 Gas Diffusion

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Fig. 6.14 a Response and recovery transients of Co-doped ZnO thin film sensors (symbols) exposed to various CO concentrations (ranging 5–500 ppm) measured at 300 °C. b Linear plot of G/(1 − G) versus CO concentration. Reprinted with permission [74]. Copyright 2017, the Owner Societies

Fig. 6.15 Temperature dependence of linear fitted plots of ln(τresponse) in the presence of a, b CO, c CH4 using SnO2 , 1P-PdPt/SnO2 -A, and 1P-PdPt/SnO2 -B as sensing materials. Reprinted with permission [83]. Copyright 2019, American Chemical Society

6.2 Gas Diffusion 6.2.1 Classical Diffusion Theory and Model 6.2.1.1

Fick’s Law of Diffusion

In thermodynamics, the transport refers to the processes that a thermodynamic system undergoes when it transitions from a non-equilibrium state to an equilibrium state, such as heat conduction, viscous phenomena, and diffusion phenomena. If the density of particles in the system is not uniform, the particles will migrate from high to low concentration by thermal motion, which is called diffusion. Due to the complexity of the actual system, diffusion always occurs together with other macroscopic processes, which makes it complicated. In particular, in a single subsystem with constant temperature and pressure, pure diffusion caused only by concentration differences is called

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“self-diffusion”; for systems containing different particles with similar diffusivity (such as CO and N2 ), the diffusion process is called “mutual diffusion”. In 1855, Fick proposed two laws to explore the nature of diffusion. Fick’s first law relates the diffusion flux to the concentration gradient, stating that the flow of diffusion moves from a region of high concentration to a direction of low concentration. Under different circumstances, Fick’s first law can be expressed in different forms. For the 1D case, the most common form is shown in Eq. 6.18. J = −D

dϕ dx

(6.18)

where J stands for diffusion flux, which is used to measure the amount of substances involved in the diffusion process; D refers to diffusion coefficient or diffusivity; ϕ represents the concentration of substance; and x is the position, which is the length along the 1D diffusion path. Fick’s second law shows how diffusion affects concentration over time, and its most common form is Eq. 6.19, where t represents time. In fact, Fick’s second law can be derived from Fick’s first law and the law of conservation of energy. ∂ 2ϕ ∂ϕ =D 2 ∂t ∂x

6.2.1.2

(6.19)

Derivation of Hard Sphere Model and Diffusion Coefficient

Based on collision theory and Newton’s first law, the hard sphere model was proposed, which has been widely used in molecular dynamics (MD) simulation [94, 95]. In this case, if a molecule is reduced to a ball of diameter d (molecular dynamics diameter), it will remain in uniform motion in a straight line through space until it collides with another molecule [96]. The total motion path of the molecule is a broken line, so its collision range in space can be simplified to a bent cylinder with a base radius of d. The total distance the molecule travels along the bent cylinder before collision is defined as the free path (λ). The mean free path of a specific gas molecule can be calculated by Maxwell’s law of distribution (Eq. 6.20). 1 λ= √ 2nσ

(6.20)

where n and σ represent the mean free path, number density, and effective collision area of the molecule, respectively. For an ideal gas, Eq. 6.20 can be expressed as Eq. 6.21: kB T λ= √ 2 pσ

(6.21)

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where k B , T, and p stand for Boltzmann constant, temperature, and pressure, respectively. The relationship between the mean free path λ and the temperature T is a key problem, and according to Eq. 6.21, it seems that λ is proportional to T. However, the parameter T does not appear in Eq. 6.20, so it is generally believed that λ is independent of the value of T when the volume is constant. In fact, with the increase of temperature, although the average collision time decreases, the required average travel distance before the collision is not affected. Besides, as the temperature rises, it decreases slightly. This phenomenon is mainly due to the fact that the vibration of molecules enhances with the increase of temperature, which leads to a slight rise in σ value and ultimately a decrease in the mean free path. Controlling the mean free path by adjusting temperature is an effective approach to improve gas response [97], which will be discussed later. The diffusion coefficient (D) can be defined by Eq. 6.22: √ 8RT 1 1 D = u Aλ = λ 3 3 πM

(6.22)

where R and M respectively represent the root mean square (RMS) rate, universal gas constant, and molecular weight of gas molecules [98]. For an ideal gas, it can be further expressed as Eq. 6.23, where N A stands for Avogadro constant. It can see that D is inversely proportional to the square root of M and to d squared. Thus, smaller molecular weight and kinetic diameter gas molecules preferentially diffuse, leaving larger molecules behind [96]. 2 D= √ 3 Md 2 pNA

6.2.1.3

(

RT π

) 23 (6.23)

Constrained Diffusion and Diffusion Mechanism in Porous Materials

In addition to the normal Fick diffusion that occurs when gas molecules freely diffuse and collide with each other [99], gas molecules also undergo constrained diffusion at an external physical scale (e.g., the size of a gas container or the diameter/radius of a pore in a porous material, Fig. 6.16) [100, 101]. In fluid mechanics, the relationship between the mean free path and the physical length scale is measured by a quantity of dimension 1, called the Knudsen number (K n ), defined by Eq. 6.24. Kn =

λ L

(6.24)

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Fig. 6.16 Schematic of the mean free path of a particle and the physical length scale of diffusion

where L represents the representative physical length scale. A high K n value indicates that the mean free path of the fluid is greater than the physical length scale, allowing the fluid to move more freely through the corresponding container. Typically, four different flows can be defined as a rule of thumb for different K n values [102]. K n < 0.01 shows continuous flow or Poiseuille flow, where the mean free path of the fluid is much smaller than the physical length scale, so the fluid will pass through the channel as a continuous mass transfer (bulk phase fluid) rather than as a discrete particle. In macroscopic systems, the fluid concentration is very high, resulting in increased viscosity. K n > 10 shows free molecular flow or Knudsen flow, in which the flow of particles can be considered independent of each other, so that each particle travels in a straight line. In a macroscopic system, the fluid concentration is very low (usually less than 0.1 Pa) and is in a high or ultra-high vacuum state, so the viscosity tends to zero. Then, 0.01 < K n < 0.1 shows slip flow, in which several fluid particles also move continuously, but the scale is not as large as the continuous mass transfer process of continuous flow, and the particles are still interrelated. In macroscopic systems, the fluid pressure is relatively high. Here, 0.1 < K n < 10 shows transition flow, where the mean free path is equivalent to the physical length scale and belongs to the superposition of continuous flow and free molecular flow. Fluid has the properties of both continuous flow and free molecular flow. Although flow and diffusion are similar and occur simultaneously in some systems, they come from different sources. The former is driven by the pressure gradient and the latter by the concentration gradient. In other words, in a system of uniformly distributed particles and molecules, no flow or diffusion occurs because there is no gradient-driven mass transfer process. The gas molecules first diffuse into the interior of the sensing material and react on the active site. For porous materials, different pore sizes lead to variations in the K n value, resulting in different forms of diffusion. Li et al. [103] studied the diffusion of particles in membrane materials and plotted different gas diffusion mechanisms in membrane materials with different pore sizes, as shown in Fig. 6.17. When the pore size is larger than λ, body phase Poiseuille flow, also known as convective flow, occurs (Fig. 6.17a). At this time, different gas molecules will freely pass through the pore in the form of body phase fluid, so the selectivity is low. When the scale of the diffusion system is close to or slightly less than λ, Knudsen diffusion occurs (Fig. 6.17b), and K n is close to or slightly greater than 1. In this case, gas molecules may collide with sensing materials (membrane materials, MOS materials, etc.). For

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Fig. 6.17 Schematic diagram of gas diffusion mechanism. a Body phase Poiseuille flow, b Knudsen diffusion, c size-limited diffusion (left: surface model, right: gas translation model), and d solid diffusion mechanism. Reprinted with permission [103]. Copyright 2017, Elsevier

porous materials, the radius of the pore can be viewed as the diffusion scale (λ/2). Therefore, the Knudsen diffusion coefficient (DK ) can be expressed as Eq. 6.25, thus combining the intrinsic parameter (M) of the molecule with the external diffusion scale (r). 4r DK = 3

√

2RT πM

(6.25)

Similarly, the inverse relationship between DK and the square root of M gives Knudsen diffusion an improved selectivity than the body phase Poiseuille flow, but it is still not enough. Size-limited diffusion occurs when the pore size is between two different gas molecules (Fig. 6.17c), also known as molecular sieve diffusion. Larger molecules (green balls) will not be able to diffuse into the pore, and only smaller molecules (red balls) could pass through, resulting in greater selectivity. Size-limited diffusion can be further divided into two categories: surface models (Fig. 6.17c left) and gas translation models (Fig. 6.17c right) [55]. Solid-state diffusion (Fig. 6.17d) is a special form of diffusion in membrane materials where there are almost no pores in the membrane. In this case, gas molecules adsorb to the surface of the membrane, and some of them dissolve into the membrane material. After undergoing a series of processes within the membrane material, the gas molecules are desorbed and released from the opposite side of the membrane surface. It is worth noting that the above four diffusion mechanisms are mainly applicable to the diffusion of membrane materials. For other materials, such as porous MOS materials, the pore size range is not as wide as that of membrane materials, so the molecular diffusion in their pores is correspondingly less variety. In particular, when the pore size is so small that gas molecules cannot pass through each other and form 1D channels, the size-limited diffusion is called “single-file diffusion” or “configurational diffusion” [104, 105]. Some other concepts regarding restrictive diffusion in porous materials have been proposed (e.g., “non-flux diffusion” and “suspension effect”) [101–109], but their underlying mechanisms remain unclear [110]. Another empirical classification of different diffusions includes molecular diffusion, Knudsen diffusion, configuration diffusion, and surface diffusion [104]. According to IUPAC,

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the pore size smaller than 2 nm is micropore, the pore size larger than 50 nm is macropore, and the pore size between 2 and 50 nm is mesoporous. With the increase of pore size, the diffusivity of gas molecules into the pore increases [5], thus experiencing the process of surface diffusion, Knudsen diffusion, and bulk phase molecular diffusion in turn [54, 111]. When the pore size is 1–100 nm, the mean free path a of gas molecules is equivalent to the pore size, and Knudsen diffusion dominates. In other words, a large aperture will promote the free diffusion of gas molecules through, while a small aperture will restrict the diffusion more significantly [38]. Therefore, mesoporous materials are widely used in gas sensing, not only because of their own advantages [56, 112–115], but also because they promote Knudsen diffusion of gas molecules [38, 61, 113, 115–119], so as to further improve the sensing performance. The difference between Fick diffusion and constrained diffusion in porous materials has been studied. Raccis et al. [99] carried out a Brown dynamics (BD) study (Fig. 6.18), taking the inverse opal structure as a model, which can generate various cavity sizes and openings by adjusting parameters such as particle size (a), pore size (L), and cavity radius (R). The existence of conventional Fick diffusion is predicted under various conditions, but the non-Fick index is attributed to the polydispersity of cavity openings. Malek et al. [120] used dynamic Monte Carlo simulation to study the influence of surface roughness of nanoporous media on Knudsen self-diffusion and Fick diffusion. Because Knudsen self-diffusion coefficient is related to residence time of molecules, it is influenced by surface roughness, but Knudsen Fick diffusion is not affected.

Fig. 6.18 Sketch of the process of multiple collisions that a tracer particle undergoes against the cavity walls before it escapes to the neighboring cavity. For small ratios a/L between particle size and cavity openings (a), a small number of bounces suffices, but for large ones (b), a large number of collisions with the walls take place before the particle escapes. Reprinted with permission [99]. Copyright 2011, American Chemical Society

6.2 Gas Diffusion

6.2.1.4

219

Diffusion of Adsorption Effect

Except for the constrained diffusion with external physical scales discussed above, in many cases diffusion is associated with adsorption effects. For example, surface diffusion can be distinguished from bulk phase molecular diffusion by comparing whether gas molecules are adsorbed to the adsorbent surface (Fig. 6.19). In this case, for gas molecules, the dimensional direction perpendicular to the adsorption crystal plane will not be able to enter [98]. In addition, in porous materials with pore size less than 1 nm, surface diffusion of gas molecules dominates, resulting in low diffusion flux in pores. Compared with other diffusions, surface diffusion is considered to be driven by the chemical potential gradient on the adsorbent surface, and the diffusion barrier is related to the chemisorption energy. Jump model is one of the models widely used in surface diffusion analysis, in which the adsorbed molecules jump between adjacent adsorption sites on the adsorbent surface [122]. In particular, in H+ philic solvents, the mechanism of H+ migration can therefore be explained by the Grotthuss H+ jump model [65]. H+ is constantly attached to the solvent molecules and then jumps to adjacent sites. In this process, H+ ion clusters (H3 O+ , H9 O4 + , H5 O2 + , etc.) are constantly produced and dissociated. Agmon pointed out that H+ migration is an incoherent H+ hopping process, and that the decisive step is hydrogen bond cleavage rather than H+ movement or ion cluster generation [65]. In addition to H+ , the jumping of different particles at different interfaces has also been modeled and structured. For example, oxygen overflow in carbon-based catalysis plays a key role, so Radovic et al. [123] constructed the study of graphene surface oxygen jumps via DFT calculations, looking at prototype clusters and periodic structures. When graphene contains free edge sites and oxygen functional groups resulting in an increase in electron density, a decrease in the jumping barrier is observed. The oxygen migration under optimal conditions is the same as the diffusion of O2 molecules in the gas phase in the micropore, which is consistent with the leading (rather than bystander) role of base oxygen in various adsorption and reaction processes involving sp2 hybrid carbon materials. Beyond the gas–solid interface, Skaug et al. [121] further applied single-molecule tracking

Fig. 6.19 Schematic diagram of molecular surface diffusion that combines periods of immobilization with episodes of bulk diffusion above the surface. The black curve is the true 3D molecular trajectory, and the magenta line is the effective 2D trajectory across the surface. Reprinted with permission [121]. Copyright 2013, American Physical Society

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to the solid–liquid interface and found that a series of different molecules experienced intermittent random walks with non-Gauss shifts, which was in contrast to the usual assumption of molecular surface diffusion based on random walks and Gauss statistics. Furthermore, intermittent hopping is assumed to be the main form of molecular surface diffusion at the solid–liquid interface. In general, for most gas–solid systems, the gas adsorption energy far exceeds the gas collision energy, so the gas adsorption energy will decrease with the increase of gas pressure, thus improving the gas surface diffusion capacity. If the gas adsorption energy and gas collision energy are similar, the surface diffusivity of gas molecules and gas pressure will show an opposite trend, and 2D gas behavior will occur, that is, the form dominated by the collision between adsorbed molecules and characterized by surface λ. Since surface λ far exceeds the spatial distance of adjacent adsorption sites, the jump model is no longer available [98]. Lu et al. [51] investigated the surface diffusion mechanism of different gas molecules on graphene. The single-layer structure of graphene makes its weak interaction with gas molecules physically adsorbed on it, leading to doubts about the reliability of traditional jumping models. CH4 and CO2 molecules were modeled using MD methods, and the diffusion coefficients on graphene surfaces were simulated and calculated under different gas pressures (Fig. 6.20a). According to Einstein Equation (Eq. 6.26), surface diffusion coefficient is calculated by using the slope of mean square displacement (MSD) and the linear relationship between time [98], where x and y represent the molecular coordinates at time t, respectively. And x 0 and y0 respectively refer to the molecular coordinates at the initial adsorption time t 0 . The surface diffusion coefficients of both CH4 and CO2 molecules decrease with increasing gas pressure (Fig. 6.20b), indicating that gas diffusion on graphene surfaces belongs to 2D gas behavior and is dominated by intermolecular collisions rather than jumping models. The surface diffusivity was then compared with the corresponding bulk phase molecular diffusivity (Fig. 6.20b dotted line), which was predicted by the hard sphere model (Eq. 6.22). The results show that there is a qualitative correlation between the surface diffusion coefficient and the volume phase molecular diffusion coefficient, and it decreases with the increase of air pressure. However, due to limitations caused by interactions between other gas molecules, as well as between gas molecules and the graphene layer, the surface diffusion coefficient values are lower than the corresponding volume phase molecular diffusion coefficient, especially for CO2 molecules with higher adsorption degree. Further research was carried out on multilayer graphene [49]. Due to the strong interaction between the gas molecules and the graphene layer, a single-atom-thick region of high gas density is formed above the graphene surface (the adsorption layer, Fig. 6.20c), suggesting that monolayer gas adsorption occurs on the graphene surface. As the number of graphene layers increases, the interaction between graphene and gas molecules increases, thus enhancing the adsorptions. However, when the graphene layer number exceeds 2, the adsorption reaches a saturation, mainly because the number of available C atoms on graphene that can actually interact with the gas molecules is almost constant. As the number of graphene layers increased, the gas adsorption energy and gas collision energy remained close to each other, implying 2D gas behavior. On the other hand,

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the increase in the value of gas adsorption energy will produce more restrictions on gas molecules, so the gas diffusion coefficient will decrease (Fig. 6.20d). The diffusivities of different gas molecules over the same number of graphene layers can be explained by a hard sphere model. The molecular weight and kinetic diameter of CH4 and N2 molecules are smaller, so the diffusion coefficients are larger. While the molecular weights and kinetic diameters of H2 S and CO2 are larger, so the diffusion is constrained. Besides, H2 S and CO2 molecules are more conducive to adsorption on the graphene surface, so there are fewer molecules in the gas phase above the graphene, and the pressure is lower. D=

⟨(x − x0 )2 + (y − y0 )2 ⟩ ⟨(x − x0 )2 + (y − y0 )2 ⟩ = 4(t − t0 ) 4Δt

(6.26)

Fig. 6.20 a Model for calculating diffusion coefficients using Einstein equations. b Surface diffusion coefficients of CH4 and CO2 molecules under different pressures. The dotted lines represent the theoretical values of the volume phase diffusion coefficients predicted using the hard sphere model based on the ideal gas dynamics. Reprinted with permission [51]. Copyright 2013, Royal Society of Chemistry. c Molecular number density distribution of CH4 adsorbed on the surface of monolayer graphene along the z direction. d Diffusion coefficients of CH4 , H2 S, CO2 , and N2 vary with the number of graphene layers. The error bar for the diffusion coefficient is plotted based on 5% uncertainty. Reprinted with permission [96]. Copyright 2017, Elsevier

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6 Interfacial Interaction Model Between Gas Molecules …

Interface Mass Transfer and DR Coupling Theory

As a typical mass transfer process driven by concentration gradient, diffusion plays an important role in mass transfer and in moving the system from the current unbalanced state to a new equilibrium state. For multiphase systems, the diffusion of a particular component at the interface will transfer its mass to the other phase, which is particularly crucial for strengthening the heterogeneous connection between the two phases. One of the most famous interfacial mass transfer processes is the Kirkendall effect (named after the American chemist and metallurgist Ernest Oliver Kirkendall), which describes the movement of metal–metal interface between two metals with different diffusers due to mass transfer [124, 125]. Previously, the diffusion mechanism of metal atoms was thought to be only the position exchange between diffused atoms and other atoms or other holes, and there was no interface movement during the diffusion process. However, the Kirkendall effect indicates that the diffusivity of metals is different, and the metal–metal interface will move toward the phase with high diffusivity during the diffusion process, resulting in the formation of holes (Kirkendall holes) [126]. A more widely accepted form of interfacial mass transfer occurs in a multiphase system containing one or more fluid phases, that is, the gas–solid interface or the gas–liquid interface, which is a common phenomenon in the fields of heterogeneous catalytic systems and gas sensing. Taking the gas–solid interface as an example, Fig. 6.21 shows the mass transfer process in a porous solid material based on the hysteresis model. It is generally assumed that the gas molecules flow around the solid particles, driven by the gas pressure gradient, and then a boundary layer is generated between the gas and the outer surface of the solid phase. The gas flow in the boundary layer is much more static than that in the volume phase, resulting in a concentration gradient across the boundary layer. The gas molecules will diffuse rather than flow in the boundary layer until they reach the gas–solid interface, and then they adsorb to the solid surface, which enables the transfer and aggregation of fluid molecules in the homogeneous fluid phase to the solid surface, known as the “external diffusion” [127]. On the other hand, when the gas molecules come into contact with the surface of the solid phase, they may undergo multiple processes that occur in parallel, that is, “coupled DR”. During this process, gas molecules may further diffuse deeper into the solid phase system and come into contact with the internal solid particles, especially in porous materials where constrained diffusion occurs, which is called the “internal diffusion” [127]. Others will be consumed by reactions somewhere inside the solid system or directly on the surface. In most cases, the entire process that gas molecules undergo from the bulk phase is shown in Fig. 6.21, which is a combination of both external and internal diffusion driven by a concentration gradient. In actual DR coupling conditions, since diffusion and reaction occur simultaneously, the effect of diffusion is usually ascribed to its comparison with the final reaction. Some hypotheses, such as the Mears hypothesis (Cm) and the Weisz-Prater hypothesis (CWP), have been proposed to measure the role of external and internal diffusion in the whole DR coupling process at the gas–solid interface [127–132]. In addition, the diffusivity of gas molecules in the actual solid system is less than the

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223

Fig. 6.21 Diagram of a spherical porous solid particle with radius R. The concentration of substance A decreases continuously from the bulk phase (C A,b ) to the surface (C A,s ) to the interior of the pore (C A (r))

ideal condition with uniformly distributed pore structure. Therefore, the effective diffusivity (DA,e ) of gas A in the solid system is defined by Eq. 6.27. DA,e =

ε DA σ τ

(6.27)

where ε, DA , σ , and τ represent the porosity of the solid system, the diffusivity of gas A in the solid system, the structure–activity factor, and the curvature, respectively [130]. Under actual conditions, the pseudo-continuous pore model is adopted to simplify the particles of the solid phase system into spheres, so that gas molecules diffuse from the body phase to the surface of the sphere and further to the depth of the pore [133]. For porous materials whose boundary layer is so thin as to be negligible, fluid molecules can be considered to have only internal diffusion. If a first-order reaction is assumed, the correlation between the concentration of A and r can be expressed as Eq. 6.28. ) ( sinh ∅ Rr CA (r ) = CA,s r sinh∅ R

(6.28)

where φ of dimension 1 is the ratio of reaction rate to internal diffusion rate, which is called Thiele modulus (named after the American scholar E. W. Thiele). For the first-order reaction (n = 1), it can be calculated by Eq. 6.29 [126, 130, 134]. √ ∅=R

kρs DA,e

(6.29)

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where ρ s represents the apparent density. A high Thiele modulus means that the reaction rate is much higher than the diffusion rate, so most of the A molecules react and consume in a shallow region near the surface of the solid, leaving most of the sphere of the solid particle unused. Conversely, a low Thiele modulus means that the internal diffusion rate is much higher than the reaction rate, and molecule A can diffuse to almost all parts of the solid spherical particle and react, thus increasing the utilization of the solid phase [130]. Therefore, another criterion to measure the importance of internal diffusion resistance is introduced, namely the internal diffusion utility factor η, which is defined by Eq. 6.30 for the first-order reactions occurring in solid spherical particles [130, 135]. The internal diffusion utility factor was negatively correlated with the Thiele modulus under different reaction conditions (Fig. 6.22), so the utilization rate of solid spherical particles could be well clarified in detail. When η approaches 1, there is no resistance to internal diffusion in solid spherical particles, so the DR coupling process is controlled by surface reaction. When η approaches 0, this process is controlled by internal diffusion [130]. η=

( ) 1 3 coth∅ − ∅ ϕ

(6.30)

Another case worth discussing is mass transfer at the gas–liquid interface. Compared with the gas–solid interface, the two phases of the gas–liquid interface are both fluids, so the dynamic mass transfer is likely to occur in the new fluid system after the gas molecules are diffused and adsorbed to the liquid surface. For this situation, several models have been proposed to describe the gas–liquid interface mass transfer, including the hysteresis model and the permeation model [136]. The hysteresis model at the gas–liquid interface is similar to that at the gas–solid interface. The only difference is that in the gas–liquid interface, the boundary layer exists not only on the gas side but also on the liquid side. In this case, the mass Fig. 6.22 Relationship between η and Thiele modulus during the first-order kinetic process in spherical catalysts. Reprinted with permission [130]. Copyright 2007, Wiley

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transfer process of gas molecules after they cross the boundary to the liquid phase will follow a new diffusion path, just as it does in the boundary layer on the gas side, thus forming a two-film model. The thickness of the two-phase boundary thin film layer is represented by hydrodynamic parameters δ 1,int and δ 2,int (Fig. 6.23a) [137, 138]. Although the two-membrane model is relatively simple, the prediction of hydrodynamic parameters is affected by the geometrical structure of materials, liquid agitation, and physical properties [136]. Furthermore, the penetration model suggests that the liquid system is less fixed, so the flow of gas molecules into the liquid system is not stable. In fact, the liquid in contact with the gas phase interface is replaced after a certain period of time (exposure time), during which it is replaced by a fresh liquid in the bulk phase [139]. The exposure time (θ ) can be measured by hydrodynamic parameters such as the speed and length of the interface [136]. Since the surface of the gas–liquid interface is constantly changing, the permeability model is therefore called the “surface renewal model” [140]. Toor et al. [141] finally combined the hysteresis model and permeation model into a new “thin film permeation model” (Fig. 6.23b). In dynamic systems, the permeability model appears to be physically more reliable than the hysteresis model, and the above models are often combined with the fluid flow model to measure the overall mass transfer effect under practical conditions [136], and further modified model studies have been produced [142, 143]. Similar to Eq. 6.28, if a first-order reaction is assumed, the concentration of gas molecule A at the liquid-phase distance from the surface depth x can be expressed as Eq. 6.31. [ ( ( ) )] C A,i sinh Ha 1 − Lx + CA,b sinh Lx CA (x) = sinhHa

(6.31)

Fig. 6.23 Schematic representation of concentration profiles in gas–liquid and liquid–liquid systems assuming two-film model and combined film-penetration model. a Two-film model. b Film-penetration model. Reprinted with permission [136]. Copyright 2011, Elsevier

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where C A,i and L respectively represent the concentration of A at the gas–liquid interface (similar to C A,s at the gas–solid interface) and the total thickness of the liquid-phase film. The Hatta number (H a ), another quantity of dimension 1, is introduced to simplify the equation, which can be expressed as Eq. 6.32 [136, 138]. If H a > 3, the reaction rate is very fast, most gas molecules will react in the liquid film near the boundary, and the whole process is controlled by diffusion. If H a < 0.3, the diffusion rate of gas molecules is enough to make it reach the bulk phase and react, and the reaction is controlled in the whole process [136]. In order to further measure the diffusion effect and utilization of the liquid film layer at the gas–liquid interface, the liquid utility factor (ηL ) was defined to describe it, which is the ratio of the actual reaction rate to the reaction rate under ideal conditions without concentration gradient and can be described as Eq. 6.33, where k c stands for mass transfer coefficient. √ k (6.32) Ha = x DA,e ( ) CA,b DA,e 1− (6.33) ηL = Ha kc LtanhHa CA,i coshHa

6.2.2 Diffusion Model of MOS Gas Sensor The process of gas diffusion can be regarded as a supplement to the common theory of absorption and desorption, which focuses on the physicochemical properties of MOS materials. Although the gas diffusion mechanism mainly plays an auxiliary role, the manipulation of gas diffusion will greatly affect the response. In addition, the external diffusion of gas molecules occurs before they are adsorbed to the surface of the MOS material, so this process contributes little to the response value of the sensor and is generally ignored. When the gas molecules are adsorbed on the MOS sensor, a large number of gas molecules will undergo internal diffusion inside the MOS material and follow the DR coupling process, in which a part of the molecules is fixed by adsorption or reaction. The fixed rate of a molecule is assumed to be greater than its diffusion rate, resulting in two distinct classes of molecules: fixed molecules and free molecules. The chemical reaction immobilizes some of the molecules, slowing down the diffusion rate. Based on Fick’s second law (Eq. 6.19), Crank modified the diffusion equation (Eq. 6.34), where C A and S A represent the concentration of free and fixed molecules of A, respectively [143]. ∂CA ∂ SA = ∇(D∇C) − ∂t ∂t

(6.34)

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6.2.2.1

227

Gardner’s Linear and Nonlinear DR Models

In 1989, Gardner proposed a linear DR model [2], in which S A is assumed to be proportional to C A . Thus, Eq. 6.34 can be reduced to Eq. 6.35. ∂CA = DA,e ∇ 2 CA ∂t

(6.35)

The relationship between gas concentration and diffusion depth (Fig. 6.24) shows that at low T values, the curve decreases significantly with the increase of distance. While at high T values, the curve gradually becomes flat, indicating that the concentration gradient and diffusion decrease simultaneously. In addition, the model assumes that the diffusion rate is lower than the reaction rate, so it is only effective in thicker porous layers, which limits its application range. In 1990, Gardner proposed a nonlinear DR model [73]. Compared with the former, S A is assumed to be proportional to the r power of C A , as expressed in Eq. 6.36. SA = BCAr

(6.36)

where B and r are the constants associated with the sensing material and the test gas, respectively. In this case, the diffusion equation can be reduced to Eq. 6.37. 1

DA ∂ 2 SAr ∂ SA = 1 ∂t B r ∂x2

(6.37)

The assumption of nonlinear relationship extends the application range of this model. In 1996, Vilanova et al. [4] modeled transient conductance based on this assumption. Traditional selectivity studies focus on producing different response Fig. 6.24 Analytical (full curve) and numerical (point) solution of the diffusion–reaction equation showing several concentration profiles in a homogeneous oxidic layer at various instants in time (expressed as a fraction of diffusive time constant). Reprinted with permission [2]. Copyright 1989, IOP science

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values to different gas molecules. However, in this study, a selective evaluation method based on response time was introduced. The equilibrium conductance ΔG(∞) of 20 ppm benzene and 50 ppm o-xylene is similar, while their normalized response times τ ' are different, mainly because of their different diffusivity; thus, the two gases can be distinguished. Besides, Lu et al. [144] pointed out that DR coupling process also exists in gas sensing experiments, thus laying a solid foundation for the application of gas diffusion theory in gas sensing research.

6.2.2.2

Yamazoe Diffusion Model

In 2001, Sakai et al. [5] systematically introduced the DR coupling model and the response sensitivity mechanism of gas diffusion control into SnO2 gas sensing. The model is based on two main assumptions: (a) Knudsen diffusion occurred in SnO2 film; (b) diffusion follows a first-order kinetic process. For the first hypothesis, SnO2 grains with pore size of 10–15 nm were synthesized by spinning coating in sol suspension, which were in the Knudsen diffusion range. Based on the above two assumptions and the DR coupling model, Knudsen diffusion equation can be expressed as Eq. 6.38. ∂CA ∂ 2 CA − kCA = DK ∂t ∂x2

(6.38)

where C A , x, and k respectively represent the concentration, diffusion depth, and reaction rate constant of the target gas. For the steady state, Eq. 6.38 is equal to zero. By introducing boundary conditions and Hatta number (H a , Eq. 6.32), the particular solution of the equation can be expressed and simplified to Eq. 6.39 [37]. √ [ [ ) ( cosh (l − x) DkK cosh 1 − Lx Ha ( √ ) = CA,s CA = CA,s coshHa cosh L DkK

(6.39)

where C A,s and L represent the gas concentration on the material surface and the thickness of the SnO2 sensing layer, respectively. As mentioned above, Ha reflects the competition between diffusion and reaction during DR coupling (Fig. 6.25a). The higher H a value indicates the stronger the reactivity and the lower the diffusion depth. The smaller the H a value suggests the more significant the diffusion behavior. In addition, the additional assumption is made that the change of conductance of SnO2 sensing layer upon contact with the test gas is linear with the gas concentration (Eq. 6.40). σ (x) = σ0 (1 + aCA )

(6.40)

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Fig. 6.25 a Simulated gas concentration profiles inside a sensing film (thickness 300 nm) for √ k various values of D K at fixed temperature. b Generalized expression of the gas sensitivity of thin films at a fixed temperature in terms of non-dimensional parameter, m, defined as with permission [5]. Copyright 2001, Elsevier

Lk DK .

Reprinted

where σ (x) and σ 0 represent the conductivity of the material in contact with the test gas and in air, respectively, and a is a constant. The linear relationship assumed here is based on conditions of low gas concentration, so its actual validity may be questionable under other conditions. By integrating the conductance, the response of the film (gas sensitivity S) can be defined by Eq. 6.41. ( √ ) k Ra aCA,s aCA,s S= = 1 + √ tanh L tanhHa =1+ Rg DK Ha L DkK

(6.41)

Specifically, the Hatta number includes three major factors in gas sensing research: reaction rate, Knudsen diffusion coefficient, and sensing layer thickness. The low Hatta number (close to zero) indicates that the internal diffusion rate is much higher than the reaction rate, resulting in uniform distribution of gas molecules in the sensing layer of the MOS sensor with almost no concentration gradient, so there is almost no effect of gas diffusion in the whole system. With the increase of H a value, the internal diffusion rate will gradually match the reaction rate, thus playing a decisive role. In the DR coupling process of the actual MOS gas sensing system, the diffusion rate in the gas should be controlled to be equal to the reaction rate, so that the change trend of the response value or sensitivity of the sensor is the most significant. At this time, H a is between 1 and 10, as shown in Fig. 6.25b. For a specific gas at a certain temperature, the values of k and DK are constant. Therefore, the H a values can be obtained by precisely adjusting the thickness of the sensor layer L, which also provides a feasible idea for quantitative experimental design. Knudsen diffusion coefficient and reaction rate constant, two main parameters, are related to temperature, and the increase of temperature will improve the diffusion rate and

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reaction rate simultaneously. Although both DK and k are positively correlated with temperature, the latter increases much faster than the former. Therefore, at high temperature, the reaction process will dominate, making diffusion a decisive step. The opposite is true at low temperatures. Although the model is relatively formal, its application is limited by the limitations, which arises from two major assumptions: the occurrence of Knudsen diffusion in SnO2 and the linear relation of conductance (Eq. 6.40). Except for Knudsen diffusion, surface diffusion in mesoporous materials is ignored. The ordered mesoporous SiO2 fibers has been investigated and pointed out that surface diffusion only accounts for about 10% of the total diffusion flow at room temperature, which proves that ordered mesoporous materials can promote Knudsen diffusion [145]. Furthermore, the main shortcoming of the model is that the hypothesis is put forward without citing any experimental data. Yamazoe et al. later proposed the theoretical basis of the power law rules for MOS sensors (discussed above) [9, 76], but the diffusion model was not further modified to adapt to the power law rules [146]. Another limitation of the model is that diffusion is assumed to be a first-order kinetic process, which is not valid under doping with other materials (such as PdO) or wet conditions [147, 148]. In 2002, Yamazoe’s group [6] further extended the diffusion model to an unstable state (Eq. 6.42). Compared with Eq. 6.38, in the unstable state, gas concentration is also related to diffusion time. A typical condition in the unstable state is the overshoot phenomenon (Fig. 6.26), which illustrates the competitive effect in the DR coupling process. Near the surface of the material, because the internal diffusion of the gas molecules occurs earlier than the reaction, the gas concentration value will over-rush to an equilibrium state and then decline steadily to an equilibrium state due to the reaction consumption, which is mainly due to the uneven local concentration caused by the inconsistent diffusion and reaction rate in the gas. In Matsunaga’s study [6], the time scale of overshoot phenomenon was ~10−6 s, which could not be detected by naked eye or experimental equipment. However, in practice, especially when DA and/ or L are taken to extreme values, a small but detectable peak may be observed before the equilibrium state, resulting in a non-flat response–recovery curve [33], which may be eliminated by adjusting experimental operations or parameters. In actual sensing studies, gas molecules first diffuse outward in the air and then adsorb to the surface of the MOS material and outward further. Although the external diffusion of the gas has little contribution to the response value of the sensor and is often ignored, when a large amount of the test gas is injected, the increase in the adsorption rate will also promote the occurrence of the reverse desorption process, allowing the gas molecules to re-enter the air and another external diffusion process to occur. In this case, the influence of the external diffusion of gas molecules cannot be ignored, and the whole system will reach a trilateral equilibrium of rapid surface absorption and desorption, internal diffusion, and reaction. In this process, the local non-equilibrium gas concentration changes rapidly, leading to slight or even significant changes in material resistance, thus showing one or more overshoot peaks before reaching the final equilibrium.

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Fig. 6.26 Time courses of gas concentration at various depths (x) in the sensing film. Reprinted with permission [6]. Copyright 2002, Elsevier

∂ 2 CA (x, t) ∂CA (x, t) = DA − kCA (x, t) ∂t ∂x2

(6.42)

In 2003, Yamazoe’s group [7] studied the gas diffusion dynamics in the response and recovery process and proposed a new diffusion model. Compared with the previous model, this model does not include any assumptions in the derivation, so the authors claim that it is valid throughout the DR process. For the diffusion equation (Eq. 6.42), the gas concentration is split into the sum of a homogeneous function and another non-homogeneous function. Although the model is not based on any assumptions and therefore does not limit its application, it only includes the diffusion constant and reaction rate and does not consider the influence of temperature and sensing layer thickness, which also play a key role in transient processes [149]. This view has been supported by experiments illustrating the reduction of response time in binary mesoporousmacroporous structures [150]. It is generally believed that three basic factors, including receptor function, transducer function, and utility factor, will affect gas sensing performance [75, 151, 152]. In 2003, Yamazoe introduced the utility factor (derived from the liquid-phase utility factor in Eq. 6.33) into the MOS gas sensor (Fig. 6.27a) to measure the accessibility of the oxide grain inside the material to the target gas, so as to evaluate the DR coupling effect inside the sensing layer [37, 153]. In spite of a volcanic curve of the relationship between sensor response value and operating temperature has been observed in previous studies, quantitative results were not obtained until the study of SnO2 film [5, 6, 153]. In particular, the sensor’s utility factor (U, Eq. 6.43) is defined as the ratio of the actual response value (S) to the ideal internal response value (S i ) under H a = 0. Figure 6.27b shows the relationship between U and H a . An inverse S-shaped curve similar to Fig. 6.25b appears, and the independent variable of both figures is H a . In order to increase the utility factor, the H a value should be

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Fig. 6.27 a Utility factors and physicochemical or material properties in MOS material gas sensors; b relationship between utility factor and H a . Reprinted with permission [153]. Copyright 2005, Elsevier

kept low. Therefore, reducing the thickness of the sensor layer L and the ratio of k/ DK is conducive to increasing the value of U, which has been confirmed by previous experiments [36]. In addition, since DK is proportional to the pore radius r (Eq. 6.25), which is thought to have a roughly positive correlation with the grain size (D), the utility factor can also be changed by adjusting the grain size. U=

6.2.2.3

S tanhHa = Si Ha

(6.43)

Further Gas Diffusion Theoretical Model and Modification

The above diffusion model has been widely used in gas sensing research, but there are still some shortcomings or limitations, so there are also attempts to correct. In 2009, Liu et al. [72] revised the previous diffusion model. Based on the H2 S sensing experiment in SnO2 film, it was found that the linear relationship between the material conductance and gas concentration (Eq. 6.40) did not agree well with the experimental results, but a power law relationship with a power index of 0.5 was obtained. As shown in Fig. 6.28a and b, compared with the previous model, the curve related to H a or gas concentration in the new model becomes flat, mainly because of the decrease in the power index. As mentioned above, in the previous

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model, the gas response values changed most significantly when H a was between 1 and 10, and this may be feasible in quantitative experimental design. However, in the new model, this trend is weakened, indicating that the sensor response value is less correlated with the gas concentration. Even though the fitting curve obtained by the new model is consistent with some experimental data, deviations still occur when H2 S concentration exceeds 10 ppm (Fig. 6.28c). At this point, the conductivity of the material is high enough to affect the power exponent, so the response value reaches saturation. Previous studies have proved that the power index will change with different target gases or high enough gas concentration [71]. Therefore, the new model is further extended to a larger range, where the exponent n of the power law rule is taken into account. Thus, the conductivity and gas response can be expressed as Eqs. 6.44 and 6.45, respectively. Similarly, when n value varies from 0.25 to 1 (n = 1 is the previous model), the sensor response changes more and more significantly with parameters, such as H a or sensor layer thickness (Fig. 6.28d, e), indicating that the influence of gas diffusion in the whole system increases. { } σ (x) = σ0 1 + a[C(x)]n )( ( ) a 6 + n Ha2 C A,s S =1+ 6 coshHa

(6.44)

(6.45)

In 2017, Ghosh et al. [74] proposed a similar model to illustrate the nonlinear relationship between material conductivity and gas concentration. It has been reported that the gas response value and temperature corresponding to maximum response (T opt ) would decrease with the increase of sensor layer thickness (Fig. 6.29a) [5]. However, in the CO sensing experiment, it was found that the gas response value and T opt first increased to a maximum value (320 nm) along with the thickness of the sensing layer and then dropped to a lower value (Fig. 6.29b). In addition, the relationship between the response value and temperature under different n values is also simulated, which is similar to the results of Liu [72].

6.2.2.4

Channel Property Regulation to Apply the DR Coupling Model

In addition to the diffusion model based on mathematical and physical equations, improving the gas diffusion capability from the perspective of MOS gas sensing material design is also widely studied. As mentioned above, in gas sensors, constrained diffusion of gas molecules inside the pore of porous sensing materials is a typical process of internal diffusion [118]. Therefore, the regulation of pore properties (pore size, pore morphology, etc.) is a common idea to regulate diffusion. Pore size is usually related to specific surface area and diffusivity. Larger pore size leads to higher diffusion rate and lower specific surface area while a smaller pore size results in a larger specific surface area and inhibits gas diffusion [38]. Therefore, the appropriate pore size is critical when applying the DR coupling model.

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Fig. 6.28 a Sensor response as a function of film thickness simulated by the former expression and modified expression. b Sensor response as a function of gas concentration simulated by the former expression and modified expression. c Correlation of the modified expression with experimental sensor response of thin films in different gas concentration. d Sensor response in reducing gas as a function of m at various power law exponent (n) values. e Relationship between sensor response and film thickness at various values of power law exponent (n) in reducing gas at a fixed temperature. Reprinted with permission [72]. Copyright 2009, Elsevier

Fig. 6.29 a Dependence of gas sensitivity on temperature at various film thickness, simulated under the conditions: E a = 50 kJ mol−1 , E k = 200 kJ mol−1 , A = 1.7 × 107 nm−1 K1/4 , and a0 = 3400 ppm−1 . Reprinted with permission [5]. Copyright 2001, Elsevier. b Experimentally obtained temperature variation of CO response (500 ppm) for film thicknesses ranging from 122 to 380 nm (symbols). Reprinted with permission [74]. Copyright 2017, Royal Society of Chemistry

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Kida et al. [154] synthesized a series of SnO2 nanoparticles with controllable grain size, and the gas-sensitive studies on H2 , CO, and H2 S were conducted. Compared with CO and H2 S, H2 concentration is less affected by the surface depth of the film (Fig. 6.30a), which can be ascribed to the smallest molecular weight and thus the highest diffusion rate of H2 , increasing the utility factor of SnO2 film. In other words, for H2 molecules, pore size has little effect on diffusion. For CO and H2 S molecules with weak diffusion ability, gas-sensitive materials with larger grain size and pore size should be used to improve their diffusion rate and utility factor (Fig. 6.30b, c). Thus, materials with smaller apertures may improve selectivity when conducting H2 sensing studies. Furthermore, the CO and H2 S were compared and found that the Knudsen diffusion coefficient DK values of the two are close (according to Eq. 6.25, the DK value of CO is about 1.05 times of H2 S), but the response of the latter is far higher than that of the former, which is mainly due to the high surface activity of CO (Fig. 6.30d, e). For CO molecules with high activity, they may be directly consumed near the surface of SnO2 sensor layer, and it is difficult to diffuse to the deep of the material, resulting in the reduction of the utility factor of the material. In contrast, the H2 S molecule is less active, so it can diffuse deep into the material, increasing the sensor response value and the utility factor of the material. So, it is vital to raise the utility factor through a combination of gas diffusion and reaction. Wang et al. [97] proposed a model to evaluate the relationship between various factors in the gas sensing process, including surface chemical reaction (SCR), gas diffusion, optimal test temperature (T opt ), gas type, etc. When T opt is lower than 300 °C, gas diffusion and SCR play a key role in elaborating the gas-sensitive mechanism.

Fig. 6.30 a–c Simulated concentrations of (A) H2 , (B) CO, and (c) H2 S as a function of depth from the surface of films with different pore radii. d, e Sensor responses to (d) CO and (e) H2 S as a function of operating temperature. Closed circles, squares, triangles, and inverted triangles correspond to samples with crystallite sizes of 7, 11, 14, and 18 nm, respectively. Reprinted with permission [154]. Copyright 2013, American Chemical Society

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At low temperatures (Fig. 6.31a), the SCR rate is very low and becomes a velocity determination step, which is controlled by the surface. Therefore, materials with a larger specific surface area have a higher response value. Therefore, the materials with a larger specific surface area have a higher response value. At high temperature (Fig. 6.31c, d), the diffusion rate increases more slowly than that of SCR, and the whole process is controlled by diffusion; thus, the material with larger pore size has a higher response value. At mild temperatures (Fig. 6.31b), gas diffusion and SCR compete equally with each other, and the system is dual controlled; thus, the materials with moderate structures have the highest response values. In short, as the temperature rises, SCR control is replaced by diffusion control. A new pore model is proposed to describe the Knudsen diffusion process of gas molecules in materials with different pore sizes (Fig. 6.31e–g). At low temperatures, the average free path of gas molecules is larger than the pore size, thus preventing the diffusion of gas molecules into the channel, so most of the response process occurs outside the channel. Both the diffusion rate and the SCR rate are low, but the latter is decisive. As the temperature rises, the mean free path of the gas molecules gradually drops to the point where it is still equivalent to the pore size, so that more molecules can diffuse into the pore and react with the oxygen in it, which is a transition state. At higher temperatures, the mean free path is smaller than the pore size, allowing more molecules to diffuse. However, the SCR rate increases much higher than the diffusion rate, resulting in a diffusion control state. Afterward, a hollow sphere model was established to describe the relationship between SCR and gas diffusion (Fig. 6.31h– j). At low temperatures (Fig. 6.31h), the oxygen adsorbed by SnO2 surface is mainly inactive O2 − , limiting the SCR rate. For gas molecules, some partially oxidize on the surface, while others diffuse into the material and undergo a series of different oxidation reactions, including partial oxidation, complete oxidation, and ionization. In the transition state (Fig. 6.31i) or at higher temperatures (Fig. 6.31j), the oxygen anions mainly exist in the more reactive forms of O− and O2− . The SCR becomes more reactive, allowing the molecules inside the hollow sphere to oxidize sufficiently, so that gas diffusion becomes a decisive step. In addition, the precise control of the sensor material morphology can further improve its sensing performance. Synthesis of ordered mesoporous metal oxide sensing materials is a promising path for MOS materials. Precise control of pore structure (pore size, pore volume, crystallinity, pore wall thickness, and pore symmetry) is critical for promoting high response value and selectivity [91]. Our group has long been committed to the synthesis and development of highly ordered mesoporous metal oxide sensing materials based on the template method, in which various structure guiding agents are used, containing soft template (amphiphilic block copolymer) poly(ethylene oxide)-block-polystyrene, PEO-b-PS), polystyrene block-poly(4-vinylpyridine), PS-b-P4VP (Styrene) [56, 57, 59–61, 112, 115–117, 119, 155–164], and urea-formaldehyde (UF) [58]. In particular, in the soft template method, the engineering research on mesoporous pore formation has been established on the basis of the solvent volatilization induced self-assembly (EISA) method [165– 174]. The synthesis of mesoporous materials with various pore sizes and morphology has been realized. Since the pore size of ordered mesoporous materials is close to

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Fig. 6.31 a–d Concentration–response curve of acetone at different operating temperatures. a 180 °C, b 220 °C, c 260 °C, and d 300 °C. e–g Schematic illustration of the target gas diffusion process in different pore sizes: e large, f medium, and g small. h–j Schematic illustration of the target gas diffusion process in the SnO2 hollow microspheres at the different temperatures. h 200 °C. Reprinted with permission [97]. Copyright 2015, American Chemical Society

the average free path of most tested gases, it is conducive to the mass transfer and diffusion of gas molecules in materials with high specific surface area and interconnected mesoporous pores [145, 160]. Liu et al. [61] synthesized a Ce-doped mesoporous WO3 gas sensor (labeled Ce-2/mWO3 ) to detect H2 S, and surface catalysis and Knudsen diffusion models were used to explain the mechanism (Fig. 6.32). The H2 S molecule first undergoes Knudsen diffusion in mesoporous and then surfacecatalyzed reaction at abundant active sites. The mesoporous structure of the sensing material promotes the diffusion of H2 S molecules to deeper depths and accelerates the formation of EDL and the transport of charge carriers. Additionally, the surface catalytic process promotes the simultaneous oxidation of H2 S molecules to produce SO2 in the gas phase and WS2 in the solid phase. The resulting WS2 has a narrower band gap and lower resistance than the original WO3 , further improving sensor response.

6.2.2.5

Strengthening of Gas Diffusion in Pulse Excitation Gas Sensor

Although the gas diffusion theory still needs to be optimized through quantitative analysis and modeling [33], it plays an important role in gas sensing research and

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Fig. 6.32 Surface catalysis and Knudsen diffusion model between H2 S and Ce-2/mWO3 . Reprinted with permission [61]. Copyright 2022, American Chemical Society

is widely used to explain the influence of different morphologies of MOS materials on response values [81, 175–177]. In recent years, some new breakthroughs have been made in improving the sensing performance [118], including optical gas sensor [178–180], surface plasmon resonance (SPR) enhanced gas sensor [181–183], pulse excited gas sensor [184–186], and FET gas sensor [69, 187–189], in which the pulse excited microgas sensor can promote the gas diffusion ability. Suematsu et al. [185] have applied pulsed heating devices to the detection of volatile organic compounds (VOCs). At heating off, gas molecules diffuse deeper into the material and deposit and undergo combustion reactions at heating on [190–193]. It is worth pointing out that pulse-fired microgas sensors were initially attracted attention because of their low energy consumption [118, 185] and their ability to enhance gas diffusion through pulse mode. Under traditional conditions, external heating continues throughout the sensing process, while gas molecules often burn out directly near the surface of the sensing layer at high temperature, which reduces the response value and utility factor. However, pulsed heating devices enable gas molecules to diffuse deep into the material even at higher operating temperatures, thus improving the utility factor [185]. Moreover, the newly defined response values of several different stages (the initial state, the final state, and the ratio of the two, respectively, expressed as S i , S e , and S p , Fig. 6.33a) clearly show the different stages (diffusion, deposition, and combustion) that gas molecules undergo during the gas sensing process [194–197], displaying that the sensor has a clear reaction to VOC molecules and has good selectivity against other gases [190, 191]. The preheating process (called dual pulse drive mode, Fig. 6.33b) was introduced before the pulse heating process to further increase the adsorption of O2− through surface modification to improve the sensing performance, which was successfully applied to the ppt level VOC gas detection [190, 193].

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Fig. 6.33 a Determination of the sensor responses: S e , S i , and S p . Reprinted with permission [191]. Copyright 2020, American Chemical Society. b Schematic model of gas detection based on doublepulse-driven mode using SnO2 -based semiconductor gas sensors. Reprinted with permission [192]. Copyright 2020, American Chemical Society

6.3 Conclusions and Prospects In the field of MOS material gas sensors, most efforts are focused on the regulation of chemical reactions between gas molecules and sensitive materials. However, the dynamic processes of gas molecules, including adsorption, desorption, and diffusion, are also closely related to the sensing process. The adjustment of adsorption, desorption, and diffusion processes can help to control the sensing process and even the performance of the sensor, including sensitivity, selectivity, and response/recovery dynamics, which are critical for the development of high-performance gas sensors. Oxygen adsorption is the most common phenomenon when the sensing material is exposed to air, which will produce EDL or HAL and lead to the change of material resistance. Further adsorption will occur between the test gas and the pre-adsorbed oxygen, producing different responses. In the absence of oxygen, the chemisorption of the reducing gas and the reaction with lattice oxygen will produce a higher response. If the test gas is able to react directly with the sensing material, this chemisorption further increases the response value or reverses the material response. Chemisorption, physical adsorption, and even “co-adsorption” occur simultaneously in the presence of H2 O molecules and are often a hindrance to improving response values at low temperatures. Physical adsorption of O2 at room temperature may also dominate and can lead to a slight increase in material resistance. By combining the theory of depletion layer on the surface of MOS material with the change of material resistance caused by adsorption, the power law rule between material resistance and partial pressure of test gas is derived. In addition, the dynamics of the response/ recovery process are discussed in detail, and the desorption process is indicated as a decisive step.

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In spite of that gas diffusion only plays an auxiliary role in gas sensing, effective control of diffusion can also improve the sensor response value and selectivity. Overall, the reaction of gas molecules with MOS sensing materials follows the DR coupling process. As the pore size of the sensing material increases, the gas molecules will gradually follow the surface diffusion, Knudsen diffusion and molecular diffusion. In Knudsen diffusion, gas molecules are more likely to collide with active sites and react. Ordered mesoporous materials are favorable for Knudsen diffusion among materials with different nanostructures. By assuming Knudsen diffusion and firstorder dynamics, a series of gas diffusion models have been established and modified, and their validity has been verified in various experiments, but further quantitative analysis is still needed. With the increase of temperature, the gas–solid interface reaction rather than diffusion becomes the dominant step, and the diffusion becomes the decisive step. High temperature reduces the mean free path of gas molecules and can activate both gas molecules and sensing materials, but it will cause a decrease in response value. Larger pore sizes will improve the diffusion capacity, but their correspondingly low specific surface area is not conducive to improving the reaction rate. For gases with high diffusivity (H2 ), the selectivity can be improved by reducing the pore size of the material. As a new test mode, the pulse-fired gas sensor separates the diffusion and reaction processes, thus facilitating gas diffusion at high temperatures and improving sensor utilization. As we all know, an ideal MOS gas sensor should exhibit the following excellent characteristics: high response value and selectivity, fast response/recovery time, and low-cost. The dynamic process of gas molecules, desorption and diffusion, plays an important role in the field of material synthesis for the preparation of better sensors. Based on this chapter, we propose some prospects, which may be expected to further improve the adsorption, desorption, and diffusion of gas in high-performance MOS gas sensors. (i) Gas diffusion models could be further improved. At present, only the effect of internal diffusion is considered in the whole sensing process, which is consistent with the assumption that gas adsorption marks the beginning of the sensing process. However, the external diffusion that occurs between gas injection and adsorption also affects the sensing results. For example, during the actual test, the overshoot phenomenon is caused by the common external diffusion. Both external and internal diffusion will eventually lead to the loss of the actual gas concentration, so it has a negative effect on the gas response value. In the future model building, the concept of utility factor in internal diffusion can be further extended to external diffusion. And the linear assumption in the diffusion model should be modified to conform to the power law rule. These improvements can be used to achieve a more accurate description of the actual sensing process. (ii) For the synthesis of sensing materials, surface modification is a feasible means to enhance the adsorption and desorption of gas molecules. For example, the MOS framework can be doped with heterogeneous elements, such as rare earths, to adjust the surface alkalinity. The active sites of these materials are usually located at non-high-priced cations (e.g., Ce3+ ), which are common Lewis bases that enhance the adsorption of acidic gas molecules. Similarly, the introduction of hydrophobic components may eliminate the effect of humidity and enhance the adsorption of hydrophobic gas molecules. Besides,

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MOS materials with different morphologies and exposed crystal surfaces can also be synthesized to enhance gas adsorption. (iii) Precise control of pore structure (pore size, pore volume, crystallinity, pore wall thickness, and pore symmetry) in the sensing material is critical for improving gas diffusion in the sensing process, and it is conducive to enhancing sensor response and/or selectivity. By comparing the competitive relationship between diffusion and reaction rate of each specific test gas, a purposeful optimization of the pore structure can be expected. For gas molecules with both high diffusion rate and high activity (such as H2 ), the pore structure has less influence on the response value than other gases. Reducing the pore size or increasing the thickness of the sensing layer may help improve the selectivity, although the response value will be reduced to a certain extent. For gas molecules with low diffusion rate and high reactivity (such as H2 S), the effect of diffusion should be completely eliminated. MOS materials with large pore size and low sensing layer thickness will be a good solution to low sensor utilization. And the chemisorption and direct reactions between H2 S and MOS materials can further improve the response value, and the recovery time can be shortened by surface modification to create more oxygen vacancies in the pore wall. For gas molecules with high diffusion rate and low reactivity (such as CH4 ), the utility factor of the sensor itself can be maintained at a high level. But the reaction rate needs to be increased. In general, high-performance noble metal catalysts such as Pd, Pt, Au, Ag, Rh, and Ru are widely used in the sensing and activation of low carbon alkanes. It would be ideal to load the precious metal in situ inside the mesoporous by multicomponent co-assembly methods, such as EISA. The gas molecules can interact with the large interface in the porous sensing layer and promote the utility factor and response value. Furthermore, by regulating the pore structure, the realization of the trumpet cylindrical mesoporous with large pore and small window is believed to be conducive to enhancing the diffusion of gas molecules into the mesoporous interior and inhibiting the outflow, so as to promote the enrichment of gas molecules in the sensing layer and realize the occurrence of reactions. For VOC molecules with very low diffusion rate and ordinary reactivity, 2D materials with ultra-high utility factors will play a vital role in eliminating the effect of diffusion. Besides, the introduction of pulse excitation to the gas sensor may be an important supplementary means to improve the response value of the sensor. Finally, according to the corresponding properties of the tested gas molecules, the thickness of the sensing layer can be adjusted to correspond to the appropriate Hatta number. In this way, the research of sensing materials needs to be changed from the traditional “material centered” to the new “gas centered”. It can be seen from the results that the study of the internal properties of MOS materials in the process of gas sensing, namely adsorption, desorption, and gas diffusion, can be realized through the combination of theoretical mathematics and physics research and precise material design (such as regulating components and crystal surfaces, as well as adjusting pore size and pore structure), which also points out the key direction for the future of MOS gas sensors.

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Chapter 7

New Approaches Toward High-Performance Gas Sensing

7.1 Optical Gas Sensing Metal oxide semiconductor gas sensors operating under UV irradiation (usually light emitting diode) have been rationalized by extensive experimental studies to detect multifarious target gases in wide ranges of concentrations at ambient condition. In addition, it was demonstrated that the sensing performance of the UV-LED-based gas sensors could be enhanced by optimizing the sensor platform and UV source parameters (e.g., wavelength and power intensity). Furthermore, the gas sensing selectivity can be tuned by modifying the microstructure of semiconductor layer or using an appropriate wavelength of LED. The mechanism of the photo-activated gas sensing is illustrated in Fig. 7.1, which is governed by various photoelectrochemical reactions. These reactions involve electrons/holes generated through the interactions between photons and the surface of metal oxide semiconductors at room temperature [1]. Sequential surface reactions occur in the sensitive layer of a photo-activated chemi-resistive gas sensor. The sensing process could be explained well using the traditional surface adsorption theory and the band theory. In general, when the metal oxide semiconductor exposed in air, oxygen species are adsorbed either in molecular form at lower temperatures or in the anion form (O2− , O− and O2 − ) at high temperatures (Fig. 7.1a). However, only partial area interacts with air molecules in dark condition, causing negligible conductance change. It indicates that illumination can enhance the surface chemical activity through increasing the number of charge carriers in the conduction band, providing abundant active sites on the surface of host-materials (Fig. 7.1b). Therefore, it improves the adsorption capacity of target molecules at both the inside and outside surface based on the interface interaction between gas molecules and electrons. Adsorption plays an essential role in the sensing process since it activates the decisive chemical bonds of the reactants. The target gas molecules react with the excited electrons/holes and adsorbed oxygen ions, which can be further converted into the final products (Fig. 7.1c). From the view of electronic structure, it can produce a significant change to the layer conductivity by changing the number © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Deng, Semiconducting Metal Oxides for Gas Sensing, https://doi.org/10.1007/978-981-99-2621-3_7

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Fig. 7.1 Photo-activated sensing mechanism under UV irradiation. Reprinted with permission [1]. Copyright 2017, Taylor & Francis Group

of electrons, enabling the detection of the gas concentration via real-time monitoring the resistance change. Finally, the detection system can recover to its initial state in the absence of the target gas (Fig. 7.1d). Such a sensing process demonstrates high stability and accurate interface control. In the past few decades, UV illumination controlled gas sensors have been a worthy technique which captures much attentions from many researchers. For example, Fan et al. [2] reported the effects of UV illumination on the electronic properties and gas sensing performance of ZnO. It was found that UV illumination can improve the sensitivity and response–recovery rate of the sensor to target gases. According to the study results about photo-response behavior of ZnO, the electrons generated from UV illumination can promote the adsorption of oxygen species and produce abundant photo-induced oxygen ions O2 − (hv). These ions O2 − (hv) are beneficial for the low-temperature gas sensing. As shown in Fig. 7.2, the illumination with UV light with energy of 3.4 eV could greatly increase the sensitivity and the response/recovery rate of both onedimensional and two-dimensional ZnO nanostructures to ppm-level H2 . It was found that these electrons produced from UV light can promote the adsorption of reactive oxygen species. In addition, these photo-induced oxygen ions O2 − (hv) are highly reactive and responsible for the room temperature gas sensitivity. Oxygen adsorption behavior is only affected by the UV illumination, but also related to the ZnO grain size. The smaller grain size, the larger number of photo-induced oxygen ions generated due to the larger surface-to-volume ratio. Since the ZnO nanoline has smaller grain size than the thin film, the nanoline shows better sensor performance. When ZnO is under UV light, the photo-induced hole interacts with the adsorbed oxygen ion (O2 − ), which will prompt the desorption of oxygen species from the ZnO surface according to the following reaction: hv → h+ + e−

(7.1)

7.1 Optical Gas Sensing

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Fig. 7.2 A Color online sensitivity versus time plot of (a) ZnO thin film in dark, (b) 400 nm wide ZnO line in dark, (c) ZnO thin film under UV light, and (d) 400 nm wide ZnO line under UV light to 100 ppm H2 , respectively. B Color online (a) variation in the resistance of the ZnO thin film after switching on the UV-LED. (b) The humidity change during the gas sensing process. Reprinted with permission [2]. Copyright 2009, American Institute of Physics

h+ + O− 2(ad) → O2(g)

(7.2)

Meanwhile, additional photo-induced oxygen anions are formed as a result of the ambient oxygen molecules reacting with the photoelectrons as the following scheme: O2 + e− (hv) → O− 2 (hv)

(7.3)

It can induce a slight increase of ZnO resistance (Fig. 7.2A). The changes in electrical resistance due to the UV-promoted oxygen adsorption is about 50%. Furthermore, the electrical properties of ZnO then gradually achieve a stable state when the rate of oxygen adsorption equals to the rate of desorption. Although a part of photoelectrons combine with the ambient oxygen, the resistance of ZnO is still much lower than that in dark because of their vast photo-induced carriers. By contrast, the chemisorbed oxygen ions are different, which can strongly attach to the ZnO surface, and the photo-induced oxygen ions O2 − (hv) are weakly bound to ZnO and can be easily removed just by turning off the UV light. Upon exposure to H2 , the photo-induced oxygen ions O2 − (hv) can participate in the redox reaction at room temperature with the following reaction: − O− 2 (hv) + 2H2(g) → 2H2 O(g) + e

(7.4)

In general, the electrons released from this reaction will result in obvious change to the electrical properties of ZnO. As shown in Fig. 7.2B, the humidity changes in the chamber during the sensing process provide more evidence. The amount of H2 O molecules greatly increases when H2 was injected into the testing chamber because water is the product of the UV-activated room temperature gas sensing reaction. Furthermore, the UV light used here possess large enough energy which can desorb

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H2 molecules based on the low adsorption energy of H2 (0.9 eV), and it facilitates the desorption process and then results in higher recovery rate. Similarly, Lacy Costello et al. [3] reported the application of UV light-emitting diodes (LEDs) to enhance the gas sensitivity of zinc oxide thick-film sensors operating at room temperature. Sensors based on zinc oxide nanoparticles activated with a UV-LED of a wavelength of 400 nm and incident light intensity of 2.2 mW/cm2 were capable of detecting acetone and acetaldehyde under very low concentrations (1 ppb). The same sensors operated under identical conditions were also capable of detecting a range of other volatiles in the low ppm range, including hydrocarbons such as hexane, butane, propane, and methane. The sensors were also found to be sensitive to low ppm levels of volatiles when operated under high humidity conditions (100% relative humidity). The optimal sensitivity of the sensor was strongly dependent on the applied light intensity, and the optimal light intensity for maximum response depends on the specific target analytes. All these results indicate that it is possible to adjust the selectivity of the sensors by changing the applied light intensity. Interestingly, the GC–MS study reveals that the UV irradiated ZnO sensor was capable to catalyst the breakdown of a range of volatiles even at room temperature. The general type of catalytic decomposition is in good agreement with the catalytic mechanism study carried out on thermally heated metal oxide sensors. Miniaturized high-performance hydrogen sensors often suffer from reliability issues in terms of baseline drift. In general, the sensors are often realized on microheaters to facilitate the redox reaction (desorption of generated water molecules at high temperature) and avoid the baseline drift. Typically, Lakshmanan et al. [4] reported a UV (λ—400 nm/Po —400 µW/Pi—18 mW) excited ZnO nanospherebased H2 sensor, which is almost independent of ambient air with minimum base line drift. The ZnO nanospheres are synthesized via microwave-assisted method and evaluated sensing performances under different temperatures in the presence/absence of UV light to optimize the operating condition. It can be observed that the UV irradiated sensor is getting sufficient activation energy to enable the current modulation at different temperatures (60 and 100 °C), including room temperature (27 °C) in various concentrations of H2 (1–4%). By exploiting the UV radiation, flow independent (250–1000 sccm) with negligible baseline drift H2 sensor demonstrated with fast response–recovery time at room temperature (27 °C). Theoretical reasoning for sensing characteristic can be explained by scanning electron microscopy, Xray diffraction, ultra-violet–visible–near infrared, and photoluminescence spectrum characterizations. Flow dependence, repeatability, cross-selectivity, and humidity tests were also conducted to study the reliable issues. Wagner et al. [5] reported that In2 O3 showed an interesting photocatalytic behavior which could be used for gas sensing applications, and mesoporous In2 O3 exposed to blue light (460 nm) could give faster and stronger sensing response than that when the sensor was exposed in ozone (O3 ) or NO2 , making sensors to oxidizing gases at room temperature generally possible (Figs. 7.3 and 7.4). It was also found that humidity could influence the ozone reaction due to poisoning of active surface sites, and the positive influence of light could be applied to perform low-temperature NO2 detection. Moreover, the observed properties have also formed a new sensing

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model for nanostructured In2 O3 , explaining the effect of light by a structure–property relationship based on oxygen in-and-out diffusion rather than oxygen surface groups. Light illumination has emerged as a novel and effective approach for the further advancement of room temperature gas sensing performance. However, the development of light-assisted room temperature sensor is hindered by the large gap band and the serious carrier recombination of semiconductor. Chen and coworkers [6] reported a novel TiO2 nanoparticle film modified with polyoxometalate (POM) and organic dye molecules, which achieved accelerated NO2 gas sensing at room temperature (25 °C) with the assistance of visible-light illumination (Fig. 7.5). The POM molecules were used as electron acceptors in the dye/TiO2 films to facilitate rapid separation and transport of photogenerated carriers, and therefore the POM modified dye/TiO2 films show superior sensing properties under the sensitization by organic dyes, such as high sensitivity (Ra /Rg = 233.1 to 1 ppm NO2 ), short response and

Fig. 7.3 Change of electronic resistance of mesoporous In2 O3 in a synthetic air (20.5% O2 , 79.5% N2 ) and b pure nitrogen (N2 ) immediately after the UV source turned off. Reprinted with permission [3]. Copyright 2012, John Wiley and Sons

Fig. 7.4 Gas response of mesoporous In2 O3 to 5 ppm NO2 (100 °C) with (a) and without (b) illumination with UV light (350 nm) (R and R0 are the electronic resistances in the presence and absence of NO2 ). Reprinted with permission [3]. Copyright 2012, John Wiley and Sons

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Fig. 7.5 Polyoxometalate as electron acceptor in dye/TiO2 films to accelerate room temperature NO2 gas sensing. Reprinted with permission [6]. Copyright 2023, Elsevier

recovery times (48/66 s) relatively low detection limits, and excellent selectivity over a wide range of NO2 concentrations (50 ppb–5 ppm).

7.2 Surface Plasmon Resonance (SPR) Enhanced Gas Sensing Surface plasmon (SP) is regarded as the collective coherent oscillations of delocalized electrons, which are stimulated by incident illumination at the interface between metal (e.g., Cu, Au, and Ag) and dielectric medium. The surface plasmon resonance (SPR), derived from SP, can strengthen the localized electromagnetic field tremendously. Additionally, SPR is very sensitive to the refractive index of surrounding medium attached to the surface of the metal film. The resonance spectral response of the SPR will change quickly when the conditions of the medium are changed, which can reflect certain properties of the system [7]. The SPR-based optical gas sensors have several advantages including simplicity, high reliability, room temperature operation, and fast response. Some workers have explored SPR technique for gas sensing applications to exploiting a sensitive film based on the metal film in Kretschmann configuration. SPR sensors using palladium as sensing metal have demonstrated to detect hydrogen due to intense adsorption of H2 molecules in Pd surface. The detection of target gases using SPR technique usually requires a suitable sensitive layer that is coated on the surface of noble metal, which can produce surface plasmon wave (SPW) propagates at the interface of metal/dielectric layer. Changing the refractive index of sensitive layer based on the presence of gas will result in the change in the SP dispersion relation. The change in refractive index of the sensing layer exposure to target gas is directly related to the concentration of target gas molecules. Thus, in order to obtain highly sensitive and

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selective sensor, optimization of sensing film is very important and requiring more attentions [8]. Transition metal oxide thin films have attractive technological importance for sensing applications. Among these transition metal oxides, zinc oxide (ZnO), as a wide band gap (3.37 eV) II–VI group semiconductor material, has been investigated extensively and exhibited great potentials for gas sensing than other applications. Do et al. [9] reported the surface plasmon enhanced ultra-violet emission of Au-decorated ZnO structures for superior gas sensing performances. Pure and Audecorated sub-micrometer ZnO spheres were successfully grown on glass substrates by simple chemical bath deposition and photoreduction methods. A series of characterization techniques, including scanning electron microscopy (SEM), transmission electron microscopy (TEM), energy-dispersive X-ray spectroscopy (EDS), UV–vis absorption, and photoluminescence (PL) spectra, was used to verify the incorporation of plasmonic Au nanoparticles (NPs) on the ZnO film. In addition, the results of time-resolved photoluminescence (TRPL) spectra indicated that a typical surface plasmonic effect existed in the interface with a fast rate of charge transfer from Au nanoparticles to the sub-micrometer ZnO sphere, which suggested the strong possibility of the application in efficient catalytic devices. As shown in Fig. 7.6, the NO2 sensing ability of as-deposited ZnO films was investigated with different gas concentrations at an optimized sensing temperature (about 120 °C). Surface decoration of plasmonic Au nanoparticles provided an enhanced sensitivity (141 times) with excellent response (τ Res = 9 s) and recovery time (τ Rec = 39 s). Thus, the enhanced gas sensing performance and photocatalytic degradation processes are attributed to not only the surface plasmon resonance but also dependent on a Schottky barrier between plasmonic Au and ZnO structures (Fig. 7.7). A highly sensitive and efficient SPR gas sensor (prism/Au/ZnO) has been fabricated for CO gas detection using ZnO sensing layer as a substrate (Fig. 7.8) [10]. The optimized ZnO thin film of 200 nm grown on substrate at 250 °C exhibits an enhanced and stable sensing response toward CO gas in a wide concentration range of 0.5–100 ppm (Fig. 7.9). The developed SPR sensor shows a quick response (1 s) and high sensitivity (0.091/ppm) toward CO gas, and the selectivity studies also indicate that the developed SPR sensor is highly sensitive toward CO, showing negligible interference with other gases (NH3 , CO2 , NOx , LPG, H2 ). Thepudom et al. [11] developed a PEC CPF sensor with a photoelectrode composed of gold-coated grating substrate/TiO2 /P3 HT/AuNPs (Fig. 7.10). The propagating SPR on the gold grating, which corresponded well with the absorption peak of the P3 HT film, enhanced the short-circuit photocurrent for highly sensitive detection of CPF. Moreover, loading of Au NPs further increased the short-circuit photocurrent due to the localized surface plasmon effect. The modified hybrid plasmonic photoanode was enhanced compared with photoanodes which without surface plasmon excitation. By the synergetic effect of both types of plasmons, the shortcircuit photocurrent was amplified to a µA scale, which is notably higher than the signal obtained using a previously reported method, and several tens of nA-level signals are obtained in all CPF concentration (Fig. 7.11). Moreover, CPF can be

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Fig. 7.6 a Response of all sensors upon exposure to 10 ppm NO2 at different operating temperatures. b Dynamic transient of resistance in response to NO2 at 120 °C. c Response–recovery characteristics and d the responses of all sensors for different target gases. Reprinted with permission [9]. Copyright 2018, licensee Beilstein-Institute Fig. 7.7 Schematic illustration of the mechanism for the enhanced gas sensing and photoactivity reported for the Au nanoparticle/ZnO structures. Reprinted with permission [9]. Copyright 2018, licensee Beilstein-Institute

detected at a very low concentration (~7.5 nM) using this system, and it demonstrates that this novel method can offer a multifunctional photovoltaic effect that can be used in several PEC-based sensing applications. Besides, Sharma et al. [12] designed a novel fiber optic hexachlorobenzene (HCB) sensor using nanohybrid of graphene nanoplatelets (GNP) supported with tin oxide nanoparticles (SnO2 NPs) based on SPR effect. In the nanohybrid, presynthesized SnO2 nanoparticles can be decorated on GNP, and synergistic effects are attributed

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Fig. 7.8 a SPR reflectance data obtained for the prism/Au/ZnO structure on exposure of CO gas of different concentration, b variation of SPR dip angle and Rmin for the SPR sensing system with different concentration of CO gas (calibration curve). Reprinted with permission [10]. Copyright 2017, Materials Research Society

Fig. 7.9 a Transient response of SPR sensor (prism/Au/ZnO system having ZnO film grown at 250 °C) toward and b the calibration curve of the SPR sensor toward CO gas. Reprinted with permission [10]. Copyright 2017, Materials Research Society

to enhance the chemical reactivity for HCB detection. GNP/SnO2 nanohybrid owing to its synergistic properties such as high conductivity, surface area, reactivity, and catalytic behavior promises excellent sensitivity and selectivity to detect HCB. With optimized parameters, the proposed probe achieves good sensing performance to detect HCB concentration in a wide range from 0 to 10–2 g/L. A red shift of 74 nm in peak absorbance wavelength is observed for this concentration range. The limit of detection for this sensor is near 8.7 × 10–13 g/L, which is smaller than the values reported in the literature for sensors utilizing various other techniques. Furthermore, the HCB concentration in real samples is tested using proposed probe, and the results are close to the values reported in the literature. In addition, the interference of other analytes on the performance of the sensor is also compared and discussed, and the novel sensors can be used for online monitoring and remote sensing.

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Fig. 7.10 Schematic diagrams of a the fabricated photoelectrode with a grating structure, b the PEC cell for CPF measurements, and c the CPF sensing mechanism. Reprinted with permission [11]. Copyright 2017, Elsevier Fig. 7.11 Plots of the short-circuit photocurrent of the fabricated PEC cells with various compositions as a function of the CPF concentration. The short-circuit current was measured at an optimal incident angle of 50°. Reprinted with permission [11]. Copyright 2017, Elsevier

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In fact, noble metal–metal oxide nanohybrids play an ever-increasing role in photocatalytic applications. Zhang et al. [13] prepared a ZnO–Ag nanoparticle by a modified polymer-network gel method, which could apply for room temperature light-assisted NO2 gas sensing. Since a heterojunction produced between ZnO and Ag nanoparticles, abundant surface oxygen vacancies can be formed, where the sensitivities of the sensors to NO2 gas (0.5–5 ppm) under various light (λ = 520– 365 nm) illumination conditions are enhanced in comparison with those in pure ZnO sensor. In addition, the SPR effect is beneficial for the excellent visible-light performance of this ZnO–Ag nanostructure. More importantly, by tuning the working wavelength using different LED light sources, an optimized sensitivity could be obtained. When blue-green LED (470 nm, 75 mW/cm2 ) is used, the 3 mol.% Ag–ZnO sensor can show the highest sensitivity as well as superior stability and selectivity, and the effect of humidity on the sensor performance is also discussed in detail.

7.3 Pulse-Driven Gas Sensing Recently, pulse-driven microgas sensors, as an influential low-power consuming sensor, have aroused great attentions among researchers from multiinterdisciplinary fields. The metal oxides-based pulse-driven gas sensors based on microheaters and electrodes are mounted through microelectronic-mechanical system (MEMS) techniques that heat the microheater repeatedly every second. Briefly, this sensor can heat for one second and then allow to cooling for one second following with heating, and these steps can be repeated continuously. In this driving mode, catalytic activation on the surfaces of these nanoparticles can only proceed during the heating-on phase, while flammable gases penetrate into the sensing layer during the heating-off phase. Schematic diagrams that depict this pulse-driven gas diffusion behavior in the sensing layer using SnO2 nanoparticles (NPs) are displayed in Fig. 7.12. It can be seen that this pulse-driven gas sensor operated by tucking the target gas into the sensing layer. In the previous work, Sasahara et al. reported the catalytic combustion sensor in the pulse-heating mode toward VOC gases that shows a large response peak before reaching a steady state. The pulse-driven gas sensor appears to combine the required two key factors, namely activation of the gas-oxidation process and gas penetration into the sensing layer, facilitating improvements in the sensitivity toward flammable gases and decreasing the concentration detection limits. Furthermore, Suematsu et al. [14] presented a heater-switching, pulse-driven, microgas sensor composed of a microheater and a sensor electrode. It was fabricated based on Pd–SnO2 clustered nanoparticles as the gas sensing material. It was found that the sensor can heat and cool repeatedly by adjusting the voltage of the microheater. Moreover, the VOC gases penetrate into the interior of the sensing layer within its unheated state. The utility factor of the pulse-driven sensor was greater than that of a conventional, continuously heated sensor. As a result, the response of the sensor to toluene was enhanced, and the sensor responded to toluene even at levels of 1 ppb.

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Fig. 7.12 Schematic diagrams of the gas diffusion behavior in the heating-off and heating-on phases. Reprinted with permission [14]. Copyright 2018, American Chemical Society

In addition, according to the relationship between its response and concentration of toluene, the pulse-driven sensor in this study can detect toluene at concentrations of 200 ppt and even lower. Therefore, the combination of a pulse-driven microheater and a suitable material designed to detect toluene resulted in an improvement of sensor response and facilitated ppt-level toluene detection. This sensor may play a key role in the development of medical diagnoses based on human breath. Similarly, Yang et al. also fabricated a microspheric directly heated gas sensor based on the mesoporous Ag2 O/SnO2 composites and investigated its gas sensing performance. The results indicated that the mesoporous Ag2 O/SnO2 -based gas sensor exhibited excellent selectivity, high response, and good stability toward H2 S at 100 °C, where a pulse-driven method was introduced to enhance the sensitivity to H2 S [15]. Under pulse-driving, the response of the sensor to 300 ppb H2 S was 5.7, which was approximately two times higher than that under constant current, and the limit of detection (LOD) was improved to 50 ppb (Fig. 7.13). The high sensing performance of the sensor was attributed to the composition and structure of mesoporous Ag2 O/SnO2 and the pulse-driven mode. Ruiz et al. [16] developed a pulse-driven miniature sensor mounted with aggregated Pd/SnO2 nanoparticles for the detection of volatile organic compound gases. A small number of suspended droplets of nanoparticles were deposited on a microheater integrated with microelectrodes to form a microsensing film. The microsensor responded well to hydrogen and toluene even when the heater was driven in pulsed heating mode. In particular, the sensor responded quickly to toluene within 0.1 s when the heater was turned on, indicating that its combustion reaction and diffusion occurred efficiently in the microfilm. The work showed that the pulse-driven microsensor as a battery-operated portable gas sensor has a broad application prospect. Triantafyllopoulou et al. [17] reported the fabrication and characterization of lowpower SnO2 gas sensors based on nanoporous silicon microhotplates for the detection of toxic gases, including CO and NO. The response of the SnO2 -based gas sensors was

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Fig. 7.13 a Response of mesoporous Ag2 O/SnO2 pulse-driven sensor with different duty cycle and cycle time toward 300 ppb H2 S. b Response of mesoporous Ag2 O/SnO2 sensor with pulse and constant currents as functions of H2 S concentration. Reprinted with permission [15]. Copyright 2016, Elsevier

measured for various gas concentrations (100–500 ppm). Analysis was performed in isothermal operation mode, keeping constant of the microhotplate temperature and in pulsed temperature mode by applying voltage pulses to the heater. In both cases, the response of the sensors increased as the temperature and the concentration of the detecting gases increased (Fig. 7.14). Through comparison between the two different operation modes, it can be seen that the sensors exhibit higher sensitivity in pulsed temperature mode. Adopting this technique, a significant reduction of power consumption can be achieved. Moreover, the sensors show significant selectivity in detecting NO, and therefore, the discrimination between the two gases can be readily achieved. Ruiz et al. [18] prepared Cr–WO3 , Cr–TiO2, and Pd–SnO2 nanoparticles by a sol– gel method, and the additive-modified metal oxides nanomaterials were deposited on hotplate platforms by microdropping technique. Gas test of the sensitive layers to

Fig. 7.14 a Sensor’s sensitivity toward CO for pulsed temperature operation mode, compared to isothermal mode, b NO sensitivity for sensors operating in pulsed temperature mode, in comparison with stationary mode. Reprinted with permission [17]. Copyright 2008, WILEY

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ammonia was performed in stationary and pulse-driven mode of operation. The pulsedriven mode can promote the regeneration of the surface yielding an enlargement of the sensor response and a decrease of the transient time. The improved sensing response to ammonia was particularly marked for the Cr–WO3 -based gas sensor.

7.4 Field-Effect Transistor Gas Sensors The importance of metal oxide semiconductor (MOS) and field-effect transistor (FET)-based gas sensors has been widely recognized due to their extended practical applications for gas detection. It has been confirmed that the characteristics of gas sensors were dependent on the sensitivity of the metal oxide and catalytic materials. MOS sensors are one of the most widely available gas sensors. FET type gas sensors exhibit many unique advantages, compared with traditional ones, including their reduced shape, size, and lower production cost. Nevertheless, the processing parameters and reproducibility require improvement to satisfy their applications. For example, Zhou et al. [19] developed a new interlocking p + n FET circuit for Mn-doped ZnO nanoparticles (MZO) to detect the acetone gas at low concentration, even close to 1.8 ppm (Fig. 7.15). It is noteworthy that MZO in this interlocking amplification circuit shows a low voltage signal of 2 ppm acetone. In other words, the response to acetone from 1 to 2 ppm increases by ~1233%, which can differentiate diabetic patients from healthy people. Moreover, the response to 2 ppm acetone is hardly influenced by high relative humidity of 85%. Meanwhile, MZO in this interlocking circuit showed excellent acetone selectivity compared to formaldehyde, acetaldehyde, toluene, and ethanol, suggesting a promising technology for the widespread qualitative screening of diabetes. Importantly, this interlocking circuit is also applicable to other types of metal oxide semiconductor gas sensors. The resistance jump of p- and n-FETs induced by the change of their gate voltages is considered as the transilient response produced from this interlocking circuit. MOS-FET H2 sensors can operate at room temperature or elevated temperatures, approximately up to 150 °C, to improve the sensor response and reduce the effects of changes, including temperature and relative stickiness. This high-temperature heating extends the power consumption for the sensor operation and limits their applications in battery-powered sensors [20]. Some innovative frameworks for decreasing the power consumption of MOS-FET H2 sensors have been studied recently. Yokosawa et al. [21] reported specific heating of the catalytic metal layer during H2 exposure. The formation of a MOS-FET with an air opening between the gate metal and the protection layer (suspended gate) allows gas molecules to migrate freely to both surfaces, and then, H2 measurement is possible even at room temperature. Park et al. [22, 23] fabricated the two-sided gates with Pd-doped Si nanowires (SiNW sensor). The as formed FET has agate length of 1 m and channel width of 100 nm

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Fig. 7.15 a Design scheme of the traditional circuit and the interlocking p + n field-effect transistor (FET) circuit for metal oxide (MOX) acetone sensor. b Output voltage of Mn-doped ZnO (MZO) to acetone from 0.5 to 3 ppm in the traditional electric circuit and the interlocking p + n FET circuit (RL is 7.1 MW) under a humidity of 25%. Reprinted with permission [19]. Copyright 2018, MDPI

(Fig. 7.16a). As shown in Fig. 7.16b, in order to improve the sensitivity and selectivity of hydrogen, Pd nanoparticles were deposited (~1 nm) on the top surface of the Si nanowire. The transfer characteristics display a decrement of channel current and increment of threshold voltage that indicates the improved withdrawal of electrons from the channel after palladium decoration. The past few decades have witnessed a research boom in Cu-benzenehexathio (Cu-BHT) owing to its ultra-high electrical conductivity and crystal defect Cu2c . However, the compact structure and small specific surface area of Cu-BHT with two-dimensional kagome lattice severely limit its practical applications in sensing and catalysis. To solve the above problems, Wang et al. [24] conceived and fabricated Cu-BHT nanotubes (Cu-BHT-NTs) by simple homogeneous reaction, which not only displayed a larger specific surface area, but also possessed a higher proportion

Fig. 7.16 Pd-decorated SiNW-FET sensor with local side-gates: a schematic of the SiNW-FET for H2 sensing and b variation in transfer characteristics for pristine Si nanowire and Pd-decorated Si nanowire. Reprinted with permission [22]. Copyright 2014, AIP Publishing LLC

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of crystal defects (66.6%), compared with the conventional nanorod-like structure. The Cu-BHT-NTs materials can be successfully integrated as a DPPTT/Cu-BHTNTs heterojunction field-effect transistor (OFET) sensor with a sensitivity of up to 13,610%, a minimum detection limit of 5 ppb, and special selectivity for nitric oxide (NO) gases (Fig. 7.17). Systematic theoretical analysis was also performed, and the results indicated that the Cu2c site in Cu-BHT-NT promoted electron transfer from heterostructure to NO molecules, proving that the strong binding interaction between Cu-BHT-NTs and NO molecule induced such high sensitivity and selectivity. In addition, a fully flexible sensor device based on heterojunction OFET sensor was also successfully constructed, which can meet the needs of conveniently wearable gas sensor, opening up a new way for developing next generation of wearable intelligent electronics.

Fig. 7.17 The performance of heterojunction OFET-based sensor. a Schematic of the OFET gas sensor. b Sensing response of heterojunction devices with different volume ratios to different concentrations of NO (5, 10, and 10 ppm). c Response–recovery curve with different concentrations of NO, ranging from 5 to 1 ppm. d Repeatability of the response to 10 ppb of NO. e Response–recovery time curves at 10 ppb of NO. f, g Ambient stability study in the presence of 10 ppb NO. h Selectivity of the sensors. Reprinted with permission [24]. Copyright 2022, Wiley

References

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19. Zhou X, Wang J, Wang Z, Bian Y, Wang Y, Han N, Chen Y (2018) Transilient. Response to acetone gas using the interlocking p-n field-effect transistor. Circuit Sens 18:1914. https://doi. org/10.3390/s18061914 20. Scharnagl K, Karthigeyan A, Burgmair M, Zimmer M, Doll T, Eisele I (2001) Low temperature hydrogen detection at high concentrations: comparison of platinum and iridium. Sens Actuators B 80:163–168. https://doi.org/10.1016/S0924-4247(01)00672-0 21. Yokosawa K, Saitoh K, Nakano S, Goto Y, Tsukada K (2008) FET hydrogen-gas sensor with direct heating of catalytic metal. Sens Actuators B 130:90–99. https://doi.org/10.1016/j.snb. 2007.07.084 22. Ahn J, Yun J, Choi Y, Park I (2014) Palladium nanoparticle decorated silicon nanowire fieldeffect transistor with side-gates for hydrogen gas detection. Appl Phys Lett 104:013508. https:/ /doi.org/10.1063/1.4861228 23. Sharma B, Sharma A, Kim J (2018) Recent advances on H2 sensor technologies based on MOX and FET devices: a review. Sens Actuators B 262:758–770. https://doi.org/10.1016/j.snb.2018. 01.212 24. Wang L, Chen X, Yi Z, Xu R, Dong J, Wang S, Zhao Y, Liu Y (2022) Facile synthesis of conductive metal-organic frameworks nanotubes for ultrahigh-performance flexible NO sensors. Small Methods 6:2200581. https://doi.org/10.1002/smtd.202200581

Chapter 8

Sensing Devices of Semiconducting Metal Oxide Gas Sensors

Over the past decades, various semiconducting gas sensors have been developed and even present in the market. However, it is difficult to classify semiconducting metal oxides gas sensors due to their different work principles [1, 2]. This chapter describes the fundamental aspects of the various semiconducting gas sensors that have been developed or proposed so far. Generally speaking, a semiconducting gas sensor is composed of a receptor and a transducer. The former is provided with materials or a materials system which upon interacting with a target gas, either induces a change in its own properties (work function, dielectric constant, electrode potential, mass, etc.) or emits heat or light. The transducer is a device to transform such an effect into an electrical signal (i.e., sensor response). The construction of a sensor is determined by the transducer used, with the receptor appearing to be implanted within it. From this perspective, a semiconducting gas sensor can be defined as a sensor in which a semiconducting material is used as a receptor and/or transducer. The gas sensors can be classified based on their sensing methods of two types [3–5], i.e., the methods based on the variation of electrical properties and the methods based on variation in other properties. Furthermore, materials like semiconducting metal oxides (SMOs), carbon nanotubes, and polymers are able to show response to target gases based on variation in electrical properties. The gas sensors can be also classified according to the type of transducer adopted, including resistor [6, 7], diode [8, 9], metal– insulator-semiconducting (MIS) capacitor [10, 11], metal–insulator-semiconducting field-effect transistor (MIS FET) [12, 13], and oxygen concentration cell [14, 15]. Among above-mentioned different sensor types, chemiresistors or so-called resistive gas sensing elements are most commonly applied. In chemiresistors, semiconducting metal oxides are typically used as gas sensing materials, which change their electrical resistance when oxidizing or reducing gases are contacted. These sensors are often called “semiconducting metal oxide gas sensors”, which can be further divided into two types based on their various mechanisms, i.e., surface-sensitive and bulk-sensitive sensors. This section is devoted to surface-sensitive resistor sensors. A continuing demand for better sensors which are expected to be cheaper, faster, more sensitive, selective, and stable compared with the conventional sensing devices has © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Deng, Semiconducting Metal Oxides for Gas Sensing, https://doi.org/10.1007/978-981-99-2621-3_8

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greatly boosted the development in the gas sensing field toward high-performance nanosensors [16]. Microfabrication techniques based on integrated circuit (IC) technology are used for fabricating small three-dimensional devices. These techniques, known as micromachining, have enabled the reliable, producible, and massive fabrication of new chemical gas sensors with small size and low-cost. Recent developments in microfabrication techniques employing very-large-scale integration (VLSI) technology are very attractive for fabrication of metal oxide-semiconducting (MOS) gas sensors with further decreased size and improved efficiency. Numerous efforts toward metal oxide-semiconducting FET (MOS-FET) gas sensors based on Si or SiC channels have been also made for the purpose of miniaturization. Since Janata et al. first proposed the suspended gate FET, and the sensing schemes of FET-type sensors based on sensing materials such as ZnO or SnO2 nanowires have been studied extensively [17], and they are actively being put to practical applications. In this chapter, the structure of devices, sensing materials, and working principles of traditional MOS, MOS-MEMS, and MOS-FET gas sensors are discussed and elucidated in detail.

8.1 Resistor-Type Sensors Conductometric gas sensors or chemiresistors based on semiconducting metal oxides are actually one of the most widely investigated family of gas sensors. They have attracted considerable interest in both the fundamental and industry fields due to their outstanding features, such as low-cost and production flexibility, operation simplicity, wide application in various fields with large number of detectable gases. The initial momentum was provided by the findings of metal oxide gas reaction effects by Bardeen for Ge [18] and a little later by Heiland for ZnO [19]. As a milestone, Taguchi et al. pioneered the commercialization of semiconducting sensors based on metal oxides, leading to an important industrial product (Taguchi-type sensors) [20]. Nowadays, many companies in the world are involved in commercialization of this type of sensors, such as Figaro (Japan), Nissha FIS (Japan), UST (China), City Tech (UK), Alphasense (UK), and Honeywell (American). Their applications span from simple explosive or toxic gases alarms to air intake control in cars to components in complex chemical sensor systems [21]. Gas sensing properties have been accounted for by three basic factors, namely, receptor function, transducer function, and utility factor, as schematically shown in Fig. 8.1 [21–23]. The first one is related with how each metal oxide crystal responds to the stimulant gas, which is directly relevant the intrinsic properties of a specific semiconducting metal oxide, associating with an ideally specific interaction of the surface with the target analyte. The second one is about how the sensing response of each crystal is transduced into device resistance, referring to an effective transduction of this molecular information into a macroscopically accessible signal, that is, the change of the electrical resistance. The third one describes how the device response (resistance change) is attenuated in an actual porous sensing body due to the consumption of the stimulant gas during its diffusion inside [24]. Accordingly,

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Fig. 8.1 Three basic factors controlling semiconducting gas sensors. Reprinted with permission from Ref. [21]. Copyright 2009, Elsevier

for a given type of base material, the sensor property sensitively depends on the structural features, the presence and state of catalytically active surface dopants, and the working temperature.

8.1.1 Device Structure and Fabrication of Resistor-Type Gas Sensors Sensor devices are fabricated into a resistor in which a porous stack of the sensing materials is attached with a heater and a resistance measuring probe (usually a pair of metal electrodes). As shown in Figs. 8.2a, c, the assembled device was schematically illustrated: Originally fabrication was a sintered ceramic tubes printed with Au electrodes and Pt wires; then, a Ni–Cr alloy coil was inserted into the tube served as a heater to control the operating temperature of the sensors, and this was followed by a thin alumina tube within a heavy coating [25]. The fabrication of a high-quality gas sensor starts with the preparation of a fine powder of semiconducting oxide (crystallite size around 10 nm in diameter) through what is known as a “wet” process. For example, in order to obtain the ZnObased resistor-type sensors, the ZnO materials are slightly ground with an adhesive terpineol in an agate mortar to form a slurry suspension. The slurry suspension is then coated onto the surface of ceramic tubes with a pair of Au electrodes to form thick films, and a small Ni–Cr alloy coil is placed through the tube as a heater to provide the working temperature of the gas sensor. The side-heating type devices are aged at optimum working temperature for one week to improve the stability before testing. Figure 8.2b shows the working principle of the gas sensing measurement system and structure of the sensor. A load resistor (RL) is connected with the gas sensors in order to adjust the voltage on the sensor within the optimal range. The circuit voltage (V c ) was set at 5 V, and the output voltage (V out ) was the terminal voltage of the load resistor. In a typical sensing process, the test gas was injected into a chamber and diluted with air. Then, the test gas can react with the

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Fig. 8.2 a Photo of the assembled device. b The electric circuit of gas sensing measurements. c Sketch of the structure of side-heated mesoporous ZnO-based gas sensor. Reprinted with permission from Ref. [25]. Copyright 2016, The Royal Society of Chemistry

adsorbed oxygen species on the sensing layers and release free electrons, leading to decrease the resistance and the voltage of the sensor, while the V out was increased. In practical applications, each device is bonded to the connector pins and put inside a metal cap with a hole(s) on top, in order to avoid the risk of triggering gas explosions. In addition, an adsorbent such as active carbon (often referred to as a “filter”) is placed in a layer immediately behind the hole in order to remove unpleasant gases. The continuous demand for high-performance sensors that are cheaper, faster, more sensitive, selective, and stable compared with the conventional ones has been boosting the developments in the gas sensing field toward advanced nanosensors. As a microversion of this structure, microelectromechanical system (MEMS) sensor is currently under extensive research and development [26–29], which we will describe later.

8.1.2 Sensing Materials Among different sensor types, conductometric gas sensors or chemiresistors are one of the most investigated family of sensors. A surface-sensitive resistor sensor works on a very simple principle. Upon exposure to a target gas in air at an elevated

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temperature, its resistance either decreases or increases as a function of the partial pressure of the gas. Among many metal oxides, n-type oxides (such as SnO2 , In2 O3 , WO3 , ZnO, and γ-Fe2 O3 ) and p-type oxides (NiO, CuO, Co3 O4 , Cr2 O3 , and Mn3 O4 ) exhibit significant gas sensing properties [30–32]. When an n-type oxide is used, under normal atmospheric conditions and typical working temperatures ranging from 200 to 400 °C, it has an electron-depleted surface. Electron depletion at the surface is due to the adsorption of atmospheric oxygen as O2 − or O− species which tie up electronic carriers. The electrondepleted surface is highly gas sensitive. The reducing gases like CO or H2 can react with the surface and remove the chemisorbed oxygen, and thus the depletion region decreases, while oxidizing gases like NO2 can cause an increase of the depletion region. Apart from such redox-active gases, CO2 and water vapor have been known to affect the resistance to a greater or lesser degree. Its action principle is based on that the above-targeted gases can affect the concentration of oxygen species owing to dissociation behavior on the surface of metal oxides. Thus, exploitation of the effects of CO2 has led to the development of a semiconducting CO2 sensor [33]. In practice, semiconducting tin oxide (SnO2 ) sensors are widely used for detection of various pollutants and combustible gases. The advantages of these sensors are high sensitivity, simple fabrication, low weight, and low price. However, the major problem associated with SnO2 -based sensors is their low selectivity. The sensitivity and selectivity of these sensors may be further improved by introducing suitable additives such as noble metals (e.g., Pd and Pt), transition metal oxides (such as La2 O3 , Nd2 O3 , and SrO) and non-metallic element (e.g., Si, N, and P) to form composite materials. For instance, ceria has been used to increase the sensitivity to H2 S and improve the CO selectivity. Using noble metals and other types of dopants is a typical approach to improve the selectivity of chemical gas sensors by changing the atomic and electronic structure of sensing materials or the pathway of surface reaction of target gases on sensing layers [34, 35] Typical metal oxides composite for improved sensing to specific gases are listed below, such as SnO2 –PdO (CO, propane, etc.), SnO2 Pt and/or PdO (methane), SnO2 –Co3 O4 (CO), SnO2 –CuO (H2 S), SnO2 –Ag2 O (H2 ), In2 O3 –PdO (CO, odorant gases), WO3 –Pt (CO), WO3 –Au (NH3 ), WO3 –Si (acetone), WO3 –Ce (H2 S), SnO2 –La2 O3 –Pt (ethanol), SnO2 –CaO (ethanol), In2 O3 – Fe2 O3 (ozone), SnO2 –Fe2 O3 (NO2 ), TiO2 –Cr2 O3 (NO) [36–45].

8.2 MEMS Platforms Gas Sensors Microelectromechanical systems (MEMS) technology has allowed for the integration of the gas sensor, heating element, and temperature sensor on a standard Si wafer into standard complementary metal oxide-semiconducting (CMOS) circuitry. Nowadays, it has been developed greatly for realizing various types of physical sensors and actuators. CMOS or CMOS-MEMS technology has been currently employed in sensor production in a multitude of applications for miniaturization of the devices,

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low power consumption, faster sensor response, batch fabrication at industrial standards, low-cost, and better sensitivity as well. On the other hand, nanomaterials employed in the fabrication of sensing device have gained increasing attention due to their enormously increased surface area-to-volume ratio compared to their bulk counterparts, which has provided opportunities to reduce the operating temperature of metal oxide-semiconducting gas sensors [46–53]. The applications of MEMS microheater devices are expanding rapidly, because they are the key components in a gas sensor system including humidity as well as toxic gas sensors. In this section, both kinds of micromachined substrates, closedmembrane type and the suspended-membrane type, are discussed. The deposition of the sensing layer is complicated by the mechanical fragility of the micromachined substrates. Different approaches used for the formation of the sensing layer such as thin-film and thick-film deposition techniques are summarized and discussed. Finally, the gas sensing function of the sensitive layer is analyzed and various ways for extracting the information are presented with respect to the improvement of sensor performance brought by this new approach.

8.2.1 Device Structure and Fabrication of MEMS Gas Sensors One of the disadvantages of traditional semiconducting metal oxide gas sensors is the low gas sensitivity due to the limited surface-to-volume ratio. In addition, the ceramic thin film gas sensors are usually operated at temperatures exceeding 500 °C in order to improve sensitivity. High operating temperature (≥300 °C) is drawback for most of the gas sensor systems (Taguchi type) because it ultimately results in high power consumption. A low power consumption is a fundamental requirement for a sensor system with an acceptable battery lifetime, especially in field application, and an elevated temperature with uniform temperature distribution throughout the sensing layer is a necessary requirement as it often enhances the sensitivity of the sensor. For this reason, the MEMS microheater is one of the key components for the chemical gas sensors in raising the required temperature with temperature uniformity [54–59]. The temperature uniformity mainly depends on the film materials and on the geometry of the microheater. Recent trends on application of metal oxide gas sensors require good thermal and mechanical performance such as good mechanical stability at high temperatures [60–68]. For these objectives, the thermal characteristics of the microhotplate have to be well known and optimized, mainly with respect to power consumption, transient response, and uniform temperature distribution, by controlling the heat losses, dielectric materials, and heater configuration. Researchers have developed the microhotplate structure using different metals and metal alloys in combination with different heater structures [69, 70]. Whatever may be the issue, the key aspect is that the heater material should sustain the high temperature without damage with low thermal expansion and the membrane should be a good dielectric

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with low thermal conductivity. There are two types of membranes, namely, closed membrane and suspended membrane. In the closed-membrane type, there is a silicon membrane underneath the heater area, which serves as the heat distributor among the membrane and also stabilizes it mechanically. A typical process flow is sketched in Fig. 8.3 [71]. The closedmembrane-type sensor is formed by anisotropic etching of silicon from the backside. Wet etchants like KOH or EDP2 are generally used. Appropriate etch stops for those etchants are silicon nitride, silicon oxide, or boron-doped silicon. For the formation of the membrane, two different strategies are known. The first, the more popular one, uses silicon oxide and/or silicon nitride as membrane and insulation materials to obtain membranes of typical thickness between 1 and 2 mm. The second, lately demonstrated, uses nitrided porous silicon of thickness between 25 and 30 mm, which can be obtained by silicon anodization and following nitridation. In the suspended-membrane type, spider-like support beams carry the hotplate with the sensing layer as shown in Fig. 8.4 [71]. Under the hotplate, the substrate is etched off by producing a microcavity to provide a good thermal isolation. In this case, etching is performed from the front side using standard etchant or by sacrificial etching. The secondary advantage is that no photolithography is required from the backside. It is the reason for the popularity of this latter approach. Recently, a slightly modified approach has been reported by Dusco et al. [69]. Instead of etching by a concentrated KOH solution, the silicon substrate is made porous by HF in the first step, and then, the porous silicon is etched by a diluted (1–2%) KOH solution. This device exhibits a high stability against mechanical shocks. The air can flow around the small hotplate and it does not need to flow around the whole chip as in the case of the membrane-type solution. Another advantage of this structure is that this type of sensor exhibits much lower power consumption due to the exceptionally low thermal mass of the membrane. Though there are several aforementioned positive aspects, this type of membrane cannot be proven well for mechanical instability as it is supported by only four beams.

8.2.2 Sensing Materials Microelectromechanical systems (MEMS) technology has recently become popular for the miniaturization of sensors. Gas sensors based on different kinds of semiconducting metal oxide fabricated by MEMS technology have the advantage of small size, low weight, high performance, easy mass production, and low-cost. Many studies have employed MEMS technology in manufacturing various gas sensors. For instance, Fau et al. [71] have published a report on nanosized SnO2 -sensitive layer on a silicon platform for sensing CH4 and CO where the minimum methane concentration measured was 200 ppm. Mitzner [72] and others have reported on methane sensing using ZnO but with a prolonged recovery. As above mentioned, one of the critical technologies to fabricate chemiresistive type microsensors lies in that the sensing materials need to be precisely loaded

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Fig. 8.3 Schematic illustration about the process flow for the formation of a closed-membrane-type gas sensor. Reprinted with permission from Ref. [71]. Copyright 2001, Elsevier

onto the designated microregion, which is still a difficult task, especially for mass production this type of sensor. A special work is mentioned here [73, 74]; in this work, patterned self-assembly monolayer (SAM) with super-hydrophobic characteristic, which is prior grown onto the non-sensing region of the microchip, is used to guide the sensing material spontaneously flow onto the hydrophilic sensing area. The optical and SEM images of as-fabricated sensor are shown in Figs. 8.5a, b, respectively. It is clear that only sensing electrodes are exposed and all the other wires are covered with a SiO2 layer. Therefore, the heating circuit and sensing circuit are isolated inplane. As the stress of each layer is optimally designed and controlled, the thermal mismatch stress introduced by the composite layers is quite small and the plate keeps flat in the high-temperature experiment. Pd nanodots modified mesoporous-In2 O3 is

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Fig. 8.4 Schematic illustration of a suspended-membrane-type gas sensor. Reprinted with permission from Ref. [71]. Copyright 2001, Elsevier

successfully loaded onto the desired microsensing area of a microheating-plate sensor [74]. The sensitivity of the sensor to 200 ppm hydrogen is evaluated and shown in Fig. 8.5c, where stepped temperature of 100–350 °C is achieved by heating the hotplate at an I increment of 50 °C. In the relatively lower temperature range from 100 to 200 °C, the response increases with the measurement temperature and reaches the maximum response signal at 200 °C. When the temperature further increases to above 200 °C, the response decreases quickly. As shown in Fig. 8.5d, the sensor is sequentially exposed to H2 with varied concentrations from 0.5 to 100 ppm and the sensor response increases along with increasing gas concentration. The sensor shows ultra-high response to 0.5 ppm H2 and the response is about 0.01. For 100 ppm H2 , the response signal is as high as 0.37 and the noise of floor is about 0.33%. It can be seen that the sensor response increases along with hydrogen concentration increasing. There is a logarithmic linear relationship between sensing response and gas concentration, and the fitting curve is plotted in Fig. 8.5e. As depicted in Fig. 8.5f, the response signal to 100 ppm H2 is 0.36 while the noise of floor is recorded as 1%. The result obviously indicates that the precise upload of sensing materials can efficiently reduce the noise and is helpful to promote the sensor resolution [74]. Recently, another key problem for fabricating high-performance sensors based on MEMS microheater is how to improve the response sensitivity of the device and meanwhile maintain the stability and consistency of the device. Differ from the traditional gas sensor based on sintered ceramic tube, MEMS sensors have ultrasmall sensitive area and are also difficult to integrate, and thus it requires more harsh conditions and environments for the production process of sensitive materials. For example, Zhu et al. synthesized ordered monolayer SnO2 nanobowls on the MEMS

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Fig. 8.5 MEMS sensor chip with a suspended heating plate a is optical image and b is SEM image of as-fabricated microplate sensor chip. Sensing area is surrounded by ring heater. The wires are isolated by a layer of SiO2 , and electrodes are exposed for chemiresistance connection. c Temperature-dependent sensing response to 200 ppm H2 of the same sensor. 200 °C is the best working temperature. d Sensor response to H2 at 200 °C, with the concentrations in the range of 0.5–100 ppm. Inset picture shows that the sensor is resoluble to 0.5 ppm H2 . e The logarithmic linear relationship between sensing response and gas concentration. f Responses of the sensor with SAM (red) and without SAM (black) to 100 ppm H2 . The inset figure is sensor baselines which show the noise-floor of the sensors. Reprinted with permission from Ref. [74]. Copyright 2018, Elsevier

microheater via the interface assembly between polystyrene (PS) spheres and tin precursor. In addition, one-dimensional linear ZnO nanorods were also fabricated on the wall of the nanobowls through ALD deposition and hydrothermal growth (Fig. 8.6). Benefit from its high specific surface area and unique heterostructure, the obtained SnO2 @ZnO nanobowls exhibited ultra-high sensing response toward H2 S gas (Ra /Rg = 6.24 for 1 ppm) and excellent stability. Moreover, this technology can not only maintain high sensitivity but also exhibit the possibility of wafer-level

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preparation, which shows great application prospect. Interestingly, the sensitization mechanism was analyzed via revealing the band structure, and it found that the electrons could transfer from tin dioxide to zinc oxide and create a contact barrier, which was attributed to the lower work function of SnO2 (4.9 eV) than ZnO (5.2 eV). Thus, it can result in a higher resistance in air and a greater change in resistance when exposed to H2 S gas [75].

Fig. 8.6 Highly sensitive and stable MEMS sensor toward H2 S gas based on SnO2 @ZnO nanobowls. Reprinted with permission from ref. [75]. Copyright 2020, Springer Nature

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Similarly, Wu et al. synthesized SnO2 –ZnO core–shell nanosheets modified with Pt nanoparticles (SnO2 –ZnO–Pt) toward H2 S sensor via atomic layer deposition, hydrothermal method, and sputter coating. More importantly, the sensitive material of SnO2 –ZnO–Pt is in situ formed on microelectromechanical system (MEMS) devices, and thus it is expected to be a high-performance gas sensor with superior sensitivity, high selectivity, good reproducibility, and low power consumption (Fig. 8.7). For instance, it could exhibit high sensitivity (Ra /Rg = 30.43) and excellent selectivity toward 5 ppm H2 S at 375 °C, and its rate of resistance change was about 29.43, which was about 24 times than pure SnO2 nanosheets (~1.25) and 9 times than SnO2 –ZnO core–shell nanosheets (~3.43) sensors, respectively. The excellent sensing performance is mainly attributed to the formation of heterojunction, catalytic sensitization effect, and increased surface area via Pt nanoparticles modification. Hence, the proposed SnO2 –ZnO–Pt nanosheets have great potential as high-performance sensing materials for H2 S gas sensors [76]. Furthermore, Zhu et al. prepared carbon nanoparticles modified mesoporous αFe2 O3 (C–d–mFe2 O3 ) nanorods on the MEMS substrate through facile hydrothermal reaction and one-step pyrolysis in air, and it could be considered as excellent sensitive materials to detect acetone gas, which showed high sensitivity and stability (Fig. 8.8). MEMS sensor ensures low power consumption, small size, and high integration, and the obtained C–d–mFe2 O3 nanorods exhibit good thermal stability and excellent acetone sensing performance, including favorable response (Ra /Rg = [email protected] ppm), high selectivity, fast response/recovery (10/27 s), and low detection limit (500 ppb) at 225 °C. Furthermore, acetone sensors have remarkable long-term stability and repeatability even reserved in air for more than 10 months. The enhanced acetone sensing performance can be attributed to the large specific surface area of mesoporous α-Fe2 O3 nanorods, the highly conductive carbon nanoparticles on the surface, and the formation of α-Fe2 O3 /C heterojunction, which further confirmed through density functional theory calculation. Thus, the competitive performance of C–d-mFe2 O3 nanorods’ gas sensor makes it of great practical application potential in environmental harmful acetone monitoring [77].

8.3 Field-Effect Transistor-Type Gas Sensors Miniaturization is one of the ultimate goals in the development of field-effect transistor-type gas sensor based on semiconducting nanowire [78, 79]. In this section, the typical FET gas sensors by taking semiconducting nanowires as typical examples are discussed [80, 81]. Individual nanowires or nanowire network films are usually used as the active detecting elements. The parameters of FET gas sensors can be modulated by changing the concentration or types of the target gases, and it is strongly dependent on the features of the gas molecules. For metal oxides, the adsorption and desorption behavior of gas molecules on the surface of sensitive materials can affect the concentration of carrier, and thus the sensing performance can be adjusted via the above sensing mechanism. As shown in Fig. 8.9, partial free electrons can

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Fig. 8.7 MEMS sensor toward H2 S gas based on SnO2 –ZnO core–shell nanosheets modified with Pt nanoparticles. Reprinted with permission from Ref. [76]. Copyright 2022, American Chemical Society

be captured at the surface of metal oxides in oxidizing gases, and thus a decrease in carrier concentration leads to an increase in resistance, while reducing gases are opposite to oxidizing gases. Different from the traditional conductance gas sensor, FET gas sensors can not only change the sensitivity by adjusting the grid voltage, but also realize pattern recognition by threshold voltage and sub-threshold swing [82]. In addition, with the help of microfabrication techniques, ultra-small nanowire FET gas sensors can be fabricated to be integrated into smart systems. The design of gas sensors and the gas sensing mechanism based on semiconducting nanowire FETs are briefly discussed in this section. It is worth noting that the term “FET gas sensor” may also refer to metal oxide-semiconducting FET (MOS-FET) gas sensors based

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Fig. 8.8 MEMS sensor toward H2 S gas with high sensitivity and stability based on α-Fe2 O3 /C nanorods. Reprinted with permission from Ref. [77]. Copyright 2022, Elsevier

on Si or SiC channels [83–85]. These sensors work on a different sensing mechanism of catalytic interaction between metal gate and gas molecules.

8.3.1 Structure and Fabrication of Nanowire FET Gas Sensors According to the working mechanisms and device structures, semiconducting nanowire gas sensors can be mainly categorized into conductometric and field-effect transistor (FET) types. As shown in Fig. 8.10, the gate is separated from the nanowire by a dielectric layer on back, top, or all around the nanowire, named as back-gate, topgate, or surrounding-gate FET respectively [81, 86]. Conductometic sensors are based on resistance changes which are induced by the exposure of the sensing elements

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Fig. 8.9 Schematic diagram of FET nanowires’ gas sensors. a In an oxidizing atmosphere, partial free electrons are fixed on the surface of the nanowires. b In a reducing atmosphere, some of the captured electrons are released into the conduction band. c and d Corresponding energy band diagram. Reprinted with permission from Ref. [82]. Copyright 1993, Elsevier

to target gases. A local heater is usually required to raise the temperature of the sensing material to improve its reactivity. This type of devices is a natural evolution of Taguchi-type metal oxide gas sensors by replacing the sensing components from metal oxide grain films to nanowire films. In contrast, FET gas sensors are based on the changes of the FET parameters due to the exposure of the sensing channels to target gases. Apart from the current variation, changes in other FET parameters, such as threshold voltage and sub-threshold swing, could be also used to identify the sensing processes. Such a principle and mechanism are different from the conductometric gas sensors where only the resistance changes are used to account for gas sensing. The FET gas sensors have attracted features as follows. Firstly, they are able to work at room temperature. By contrast, the conductometric sensors are usually working at 200–400 °C. Secondly, they enable the application of a variety of sensing materials, thus bring more possibility to generate a sensing surface for sensitive and selective sensing. Thirdly, due to their ultra-small dimensions and compatibility with microfabrication technology, the FET gas sensors are suitable for the fabrication of sensor arrays for more powerful sensing capability [87]. Several comprehensive reviews are already available about gas sensors based on semiconducting nanowires, especially the conductometric-type gas sensors [88–94]. The fabrication of metal oxide nanowire-based FET gas sensors is usually dependent on the way that the nanowires are obtained by top-down approach, such as lithographically defined Si nanowires, and nanowire fabrication is compatible with the subsequent device making processes [95]. In contrast, if the nanowires are

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Fig. 8.10 Schematics of NW-FET. a 3D view of a back-gate NW-FET. b Cross-section of cylindrical nanowire back-gate FET. c Cross-section of belt-like nanowire back-gate FET. d Crosssection of top-gate NW-FET. Reprinted with permission from Ref. [81]. Copyright 2012, The Royal Society of Chemistry

produced by the bottom-up approach, the nanowires have to be transferred onto the final device substrate, and such as fabrication process is not so controllable and needs to be improved. The simplest way is by dispersing the nanowires into a liquid medium and then dropping the suspending solution on the substrate. This approach gives nanowires lying on the substrate without any control over their dispersion and alignment.

8.3.2 Sensing Materials The sensitivity of metal oxide nanowire-based FET gas sensors is directly related to the radius of the nanowires, and it can be derived based on the classical Drude model. In this model, it is assumed that the current density in the nanowires is uniform, and the relation between the conductance “G” of the nanowires and the radius “r” and length “l” can be expressed by Eq. (8.1), where ne and μe are represented electron density and mobility, respectively. Electron density ne can be further calculated from Eq. (8.2), where n0 is standing for the conductivity before exposure to the targeted gas, while N s and α are represented the surface adsorption density and char transfer coefficient of gas molecules, respectively. Thus, the resistance change rate can also be expressed as Eq. (8.3), and it is obvious that the resistance change rate is linear with 1/r. It indicates that the sensitivity of gas sensors will be improved with the reduction of the radius of the nanowires. G=

πr 2 n e eμe l

(8.1)

2α Ns γ

(8.2)

ne = n0 −

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G 2 α Ns = ∗ G0 γ n0

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(8.3)

Various semiconducting nanowires, especially metal oxides of SnO2 , ZnO,. and V2 O5 , have been intensively investigated in FET gas sensors [96, 97]. In this section, the discussions are divided into four parts, according to the type of materials that are used in FET gas sensors based on SnO2 , ZnO, In2 O3 and other oxide nanowires, respectively. SnO2 -based gas sensors are the predominant solid-state sensors used nowadays [98, 99]. In 1962, Taguchi applied for a Japanese patent on porous SnO2 ceramic materials-based gas sensors, which has been considered as the beginning of SnO2 gas sensors [100]. Later on, SnO2 -based gas sensors were put onto the market in 1968 after several years’ effort. It has been reported that much larger change of resistance can be realized by decreasing the diameter of the SnO2 nanocrystals when switching test gas atmosphere to air during measurement [101]. In addition, the successful synthesis of single-crystalline SnO2 nanowires has made it possible to investigate this new material in FET gas sensors with the help of microfabrication technology, and to date, lots of researches have been done about the FET gas sensors based on SnO2 nanowires [102]. For example, Moskovits et al. reported that in a typical SnO2 -nanowire FET gas sensor (Fig. 8.11a), the rate and extent of oxidation or reduction reaction taking place at the surface nanowires could be modified by changing the electron concentration in the nanowires through tuning the gate voltage [103], which is a characteristic feature of nanowire FET gas sensors. The responses of the device to three different gas atmospheres (N2 , N2 + O2 , and N2 + O2 + CO) were investigated. As shown in Fig. 8.11b, when the gas only contains N2 , oxygen can thermal desorption from the surface of the nanowire to form surface vacancies. The electrons captured by the adsorbed oxygen are thermally excited into the conduction band and the current is high. When 10 mL/min of oxygen was added to the 100 mL/min N2 flow (at time t 1 in Fig. 8.11b), the source–drain current decreases drastically and eventually reaches a new steady state. At some time t 2 after that, CO is introduced into in the gas mixture (100 mL/min N2 , 10 mL/min O2 , and 5 mL/ min CO). CO can react with SnO2 to produce CO2 , leaving behind oxygen vacancies. It creates new donor states, which cause an increase in conductance. At the same time, oxygen adsorption– desorption takes place, and this also affects the number of vacancies. The overall reaction is the catalytic oxidation of CO to CO2 at the tin oxide surface. By changing the number of electrons available for oxygen surface chemistry, the oxidation rate and the extent of oxygen adsorption can be modulated. When the gate voltage is appropriately tuned and selected, the sensitivity of the device can be extremely high. It showed that manipulating the carrier concentration inside a nanowire affects the chemical reactivity of its surface and is very important for nanowire FET gas sensors. Additionally, the sensing ability of pristine SnO2 nanowires can be further enhanced by surface modification with metal nanoparticles, because decoration of catalytic metal nanoparticles on oxides can effectively improve the activity of oxide surfaces [104–106].

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Fig. 8.11 a Schematic illustration of the nanowire FET gas sensor. b Evolution of the nanowire conductance in three different gas conditions, at time t 1 , N2 was changed to a mixture of N2 + O2 , and at time t2 to a mixture of N2 + O2 + CO. The response curves of the device were drastically tuned by gate potentials, especially at the decaying and rising regions. Reprinted with permission from Ref. [103]. Copyright 2004, The American Chemical Society

As one of the most prominent semiconducting metal oxides, ZnO has been also intensively studied for its versatile physicochemical properties and potential applications in electronics, optoelectronics, and piezoelectronics. ZnO is a direct band gap semiconductor (3.37 eV at room temperature), with a stable wurtzite crystal structure and polar surfaces. A variety of nanostructured devices, such as ultraviolet lasers, light-emitting diodes, photodetectors and chemical sensors, have been fabricated to utilizing ZnO nanowires due to these unique properties. Similar to SnO2 nanowires, in the field of gas sensor, ZnO nanowires have been also employed to fabricate FET gas sensors [90]. It is well known that ZnO nanowires have a large amount of surface defects, mainly oxygen vacancies, which can adsorb gas species and act as scattering and trapping centers. These defects and chemical species have important influences on the performance of ZnO NW-FETs. Typically, Zhou et al. fabricated

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ZnO nanowire-based chemical sensors, and these sensors showed a detection limit of 60 ppb for TNT molecules at room temperature [107]. Figure 8.12a shows a schematic diagram of a ZnO nanowire FETs sensor. The SEM image (inset of Fig. 8.12a) reveals the dimension of a single ZnO nanowire sensor with the channel length of 2 mm. The I–V curves (at V g = V 0 ) obtained under the different TNT concentrations from 60 to 1.36 ppm are shown in Fig. 8.12b. With the increasing of TNT concentration, the device conductance was monotonically suppressed from 3 (in air) to 0.5 mS (in 1.36 ppm TNT). Figure 8.12c plots the changes in ZnO nanowire conductance normalized against the initial conductance. Seven cycles have been successively recorded, corresponding to seven different TNT/air concentrations ranging from 60 to 1.36 ppm, respectively. As the increasing of TNT concentration, more pronounced conductance modulation was observed. As depicted in Fig. 8.12d, the sensitivity follows the equation of S = 1/(A + B/C), where A = 0.086, B = 16.74, and C refers to the concentration. At lower concentrations, the chemical sensor exhibits a linear dependence between the normalized sensor response and the TNT concentration, while the surface coverage tends to saturate and hence leads to the saturation response observed in Fig. 8.12d. This is a common phenomenon in metal oxide NW-FET gas sensors. Similarly, Fan et al. found that ZnO NW-FET had different characteristics in response to NH3 at different temperatures, and NH3 molecule would increase the resistance of ZnO nanowire at room temperature, while it would reduce the resistance of ZnO nanowire at high temperature (500 K). It was attributed to the changes in the Fermi energy level of ZnO and the chemical potential of NH3 molecules. At room temperature, the Fermi level of ZnO is higher than the chemical potential of NH3 molecules, and electrons can flow from ZnO to NH3 molecules, resulting in increased resistance of ZnO nanowire. At high temperature, the Fermi level of ZnO is lower than the chemical potential of NH3 molecules, leading to the decrease of resistance of ZnO nanowire [108]. It is worth noting that high-performance ZnO NW-FETs have been demonstrated by Cha et al. with self-aligned planar gate electrodes and well-defined nanosize air gap dielectric [109]. These unique ZnO NW-FETs exhibits excellent performance with a transconductance of 3.06 mS, an on–off ratio of 106 , and a field-effect mobility of 928 cm2 · V−1 · s−1 which is the highest value for ZnO NW-FETs without any specific treatment like passivation. Furthermore, In2 O3 is another popular material for gas sensors, and there are several works about In2 O3 nanowire FET gas sensors [110, 111]. Typically, Shen et al. reported the synthesis of self-assembled In2 O3 nanowires, where single-kinked In2 O3 nanostructure-based field-effect transistors were fabricated, and mobilities higher than 200 cm2 /(V · s) were obtained [110]. The inset of Fig. 8.13a was a schematic illustration of a typical device, and a SEM image of the single nanowire device was shown in the inset of Fig. 8.13b. Figure 8.13a is the drain current (I DS ) versus source–drain voltage (V DS ) curves of a typical device. Linear current versus voltage is obtained, indicating very good Ohmic contacts. From the curves, it can be seen that the conductance increases gradually with increased gate voltage ranging from −40 to 40 V, indicating typical n-type semiconducting behaviors. The I DS −V G curve is also measured for the same device (Fig. 8.13b), and it also implies that kinked In2 O3 nanostructures are n-type semiconductors. The transconductance values, gm,

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Fig. 8.12 a Schematic illustration of a ZnO NW-FET structure. b I ds –V ds curves taken in air and at different TNT concentrations. c Sensing response of a ZnO nanowire chemical sensor to TNT. d Normalized conductance change against TNT concentrations (C), which was fitted using S = 1/ (A + B/C). Reprinted with permission from Ref. [107]. Copyright, 2010 WILEY–VCH

derived from the equation gm = dI/dV G , can be calculated from the linear region of V G shown in Fig. 8.13b. On the basis of the above equations, the mobility was calculated to be around 243 cm2 /(V · s), indicating its promising application in fabricating high-performance electronic devices.

8.4 Conclusions Semiconducting metal oxides-based gas sensors are becoming more and more important in the future. The continuous advances in nanoscience and nanotechnology will boost the development of semiconducting gas sensor materials. Nanosized semiconducting structures (nanoporous particles, nanofibers, nanotubes, nanodots, etc.) with a well-defined material composition and morphology provide a better chance for us to construct stable and reproducible gas sensors. Semiconducting gas sensors have so far been developed on the basis of experience and intuition. Tremendous efforts have been devoted to discovering new sensing materials, new ways of materials processing, new types of device, new targets for gas sensing, and so on, putting emphasis on gas sensing performances. There are also many subjects of research

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Fig. 8.13 Current–voltage data recorded from a FET built on a single kinked In2 O3 nanowire. a I DS –V DS curves measured at different gate voltages with a step of 5 V. b I DS –V G transfer curves. Inset in panel b is a SEM image of the fabricated device. Reprinted with permission from Ref. [110]. Copyright, 2011 American Chemical Society

which are worth challenging in order to progress the innovation of semiconducting gas sensors.

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84. Hong Y, Kim CH, Shin J, Kim KY, Kim JS, Hwang CS, Lee LH (2016) Highly selective ZnO gas sensor based on MOSFET having a horizontal floating-gate. Sens Actuators B 232:653– 659. https://doi.org/10.1016/j.snb.2016.04.010 85. Sysoev VV, Goschnick J, Schneider T, Strelcov H, Kolmakov A (2007) A gradient microarray electronic nose based on percolating SnO2 nanowire sensing elements. Nano Lett 7(10):3182– 3188. https://doi.org/10.1021/nl071815 86. Huang H, Liang B, Liu Z, Wang X, Chen D, Shen G (2012) Metal oxide nanowire transistors. J Mater Chem 22(27):13428–13445. https://doi.org/10.1039/C2JM31679J 87. Kandasamy S, Wlodarski W, Holland A, Nakagomi S, Kokubun Y (2007) Electrical characterization and hydrogen gas sensing properties of an-ZnO/p-SiC Pt-gate metal semiconductor field effect transistor. Appl Phys Lett 90(6):064103. https://doi.org/10.1063/1.2450668 88. Huang J, Wan Q (2009) Gas sensors based on semiconducting metal oxide one-dimensional nanostructures. Sensors 9(12):9903–9924. https://doi.org/10.3390/s91209903 89. Chen X, Wong CKY, Yuan CA, Zhang G (2013) Nanowire-based gas sensors. Sens Actuators B 177:178–195. https://doi.org/10.1016/j.snb.2012.10.134 90. Lao CS, Liu J, Gao P, Zhang L, Davidovic D, Tummala R, Wang ZL (2006) ZnO nanobelt/ nanowire Schottky diodes formed by dielectrophoresis alignment across Au electrodes. Nano Lett 6(2):263–266. https://doi.org/10.1021/nl052239p 91. Li C, Zhang D, Liu X, Han S, Tang T, Han J, Zhou C (2003) In2 O3 nanowires as chemical sensors. Appl Phys Lett 82(10):1613–1615. https://doi.org/10.1063/1.1559438 92. Mubeen S, Moskovits M (2011) Gate-tunable surface processes on a single-nanowire fieldeffect transistor. Adv Mater 23(20):2306–2312. https://doi.org/10.1002/adma.201004203 93. Dattoli EN, Davydov AV, Benkstein KD (2012) Tin oxide nanowire sensor with integrated temperature and gate control for multi-gas recognition. Nanoscale 4(5):1760–1769. https:// doi.org/10.1039/c2nr11885h 94. Chang SP, Li CW, Chen KJ, Chang SJ, Hsu CL, Hsueh TJ, Hsueh HT (2012) ZnO-nanowirebased extended-gate field-effect-transistor pH sensors prepared on glass substrate. Sci Adv Mater 4(11):74–1178. https://doi.org/10.1166/sam.2012.1410 95. Fan Z, Lu JG (2005) Gate-refreshable nanowire chemical sensors. Appl Phys Lett 86(12):123510. https://doi.org/10.1063/1.1883715 96. Yu HY, Kang BH, Pi UH, Park CW, Choi SY, Kim GT (2005) V2 O5 nanowire-based nanoelectronic devices for helium detection. Appl Phys Lett 86(25):253102. https://doi.org/10. 1063/1.1954894 97. Park J, Kim Y, Kim G-T, Ha JS (2011) Facile fabrication of SWCNT/SnO2 nanowire heterojunction devices on flexible polyimide substrate. Adv Funct Mater 21:4159–4165. https://doi. org/10.1002/adfm.201101470 98. Gopel W, Schierbaum KD (1995) SnO2 sensors-current status and future prospects. Sens Actuators B 26:1–12. https://doi.org/10.1016/0925-4005(91)80207-z 99. Barsan N, Schweizer-Berberich M, Gopel W (1999) Fundamental and practical aspects in the design of nanoscaled SnO2 gas sensors: a status report. Fresenius J Anal Chem 365:287–304. https://doi.org/10.1007/s002160051490 100. Chen PC, Shen G, Zhou C (2008) Chemical sensors and electronic noses based on 1-D metal oxide nanostructures. IEEE Trans Nanotechnol 7:668–682. https://doi.org/10.1109/TNANO. 2008.2006273 101. Xu CN, Tamaki J, Miura N, Yamazoe N (1991) Grain-size effects on gas sensitivity of porous SnO2 —based elements. Sens Actuators B Chem 3:147–155 102. Freer EM, Grachev O, Duan X, Martin S, Stumbo DP (2010) High-yield self-limiting singlenanowire assembly with dielectrophoresis. Nat Nanotechnol 5(7):525–530. https://doi.org/ 10.1038/NNANO.2010.106 103. Zhang Y, Kolmakov A, Chretien S, Metiu H, Moskovits M (2004) Control of catalytic reactions at the surface of a metal oxide nanowire by manipulating electron density inside it. Nano Lett 4:403–407. https://doi.org/10.1021/nl034968f 104. Zhang J, Liu X, Wu S, Xu M, Guo X, Wang S (2010) Au nanoparticle-decorated porous SnO2 hollow spheres: a new model for a chemical sensor. J Mater Chem 20:6453–6459. https://doi. org/10.1039/C0JM00457J

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Chapter 9

Integration Technologies in Gas Sensor Application

Metal oxide semiconductor (MOS) is a set of widely used gas sensing material that provides high sensitivity to a wide variety of gases. However, considering the complexity of actual environment, the lack of selectivity toward analyte mixture has become one of the major issues in practical application of MOS sensors. Their crosssensitivity may fail the MOS sensors to distinguish a certain gas out of the complex atmosphere and lead to false alarms [1, 2]. In view of their high sensitivity and lowcost, enhancing the selectivity of MOS sensors thus shows great practical significance in sensor industries. The efforts have been made in solving cross-sensitivity issue generally followed two methodologies: One is to narrow down the cross-sensitivity of MOS material itself as we mentioned before by doping or changing microstructures and so on [3–6]. The other method that we are going to discuss in this chapter is to construct arrays of multiple MOS sensors with different cross-sensitivity profiles. Similar to the human olfactory system, the sensors arrays give response patterns to analyte gases instead of a single response value, followed by the pattern analysis step, and finally output an overall result. Therefore, such sensors’ array devices are also called the electronic nose (e-nose).

9.1 E-nose: Sensors’ Array In 1964, Wilkens and Hartman built a multisensors device by using various combinations of electrodes to propose the possibility that some different electrode-coating substances could be capable of giving differential responses to different mixtures [7]. The first intelligent electronic smell-sensing systems appeared in 1982 by Persaud and Dodd. Gardner used pattern recognition techniques to discriminate the output of electronic smell sensors [8]. The term “electronic nose” was introduced in 1988 by Gardner and Bartlett, as “an instrument which comprises an array of electronic chemical sensors with partial specificity and appropriate pattern recognition system, capable of recognizing simple or complex gases” [9]. Much effort has been devoted © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Deng, Semiconducting Metal Oxides for Gas Sensing, https://doi.org/10.1007/978-981-99-2621-3_9

299

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9 Integration Technologies in Gas Sensor Application

to sensor design along with the technological process, and the first commercially available devices showed up in 1993 (AlphaMOS). Since then, rapid development of sensor arrays has bring along various intelligent gas sensing systems which are not limited to the array of chemiresistive MOS sensors, but the combination of a wider arrange of devices including quartz crystal microbalance (QCM) sensors, surface acoustic waves (SAW) sensors, conducting polymer (CP) sensors, and so on [2, 10, 11]. Some of the most widely used commercial e-noses are summarized in Table 9.1, along with their technologies and manufacturers. Unlike other analytical instruments such as flame ionization detector (FID) or gas chromatograph-mass spectrometer (GC–MS) which are designed to detect and identify individual components of a gas mixture, an e-nose entails chemical sensors in an array rather to detect the chemical signature, or fingerprint of gaseous mixtures as a whole in parallel. The basic definition of an e-nose device provided by Gardner and Bartlett claims several requisites components including: an aroma delivery system that transfers the volatile aromatic molecules from the source material to the sensor array system, a chamber with fixed temperature and humidity where sensors are housed, an electronic transistor which converts the chemical signal into an amplified and conditioned electrical signal, then a digital converter to transform the signal from electrical to digital, and a computer microprocessor to do the statistical analysis of the digital signal and displays the output. A schematic flow describing the detection mechanism of e-nose is shown in Fig. 9.1. One of the key issues in e-nose system design is sensor selection. In a qualified e-nose, each sensor in the array should provide different selectivity profiles to target analytes according to the application demand. In order to achieve effective identification or classification of gaseous mixture, sensors should also meet several other dispensable requirements including (1) proper sensitivity that can maximize overall instrument sensitivity, (2) fast response and recovery, (3) good repeatability, (4) high stability to temperature and humidity. The lack of any above conditions may lead to false-negative determinations that fail to detect target gases or false-positive indications that overestimate analytes’ concentration. Therefore, it is always important to select proper sensor in the array according to specific situations and periodically calibrate sensors monitors to maintain effectivity and accuracy of output data from e-nose devices. In addition to analyzing a mixture of various gases by employing multiple sensors with different selectivity profiles, this concept of sensors integration can also be used to expand detection limit through combining sensors with similar selectivity but different measuring ranges. Due to their complementary measuring ranges, the sensor array can reach a wide detection limit even from ppb level to a hundred percent, meanwhile ensuring a high accuracy. The commercial TGS series gas sensors (Figaro Engineering, JP) are most widely used in e-nose sensor arrays. The Cyranose 320 (Cyrano Science, Pasadena, CA, USA) is a portable electronic-nose system whose component technology consists of 32 individual polymer sensors blended with carbon black composite and configured as an array. Airsense PEN2 and PEN3 (Airsense Analytics GmbH, Schwerin, Germany) e-noses contain a very small and portable ten metal oxide semiconductor

9.1 E-nose: Sensors’ Array Table 9.1 Some commercially available electronic noses, models, and technologies

301

Manufacturer

Products

Technology

Airsense Analytics

i-Pen, PEN2, PEN3

MOS

GDA 2

MOS, EC, IMS, PID

Alpha MOS

FOX 2000, 3000, MOS 4000 RQ Box, Prometheus

MOS, EC, PID, MS

Applied Sensor

Air quality module

MOS

Chemsensing

ChemSensing Sensor array

Colorimetric optical

CogniScent Inc

ScenTrak

Dye polymer sensors

CSIRO

Cybernose

Receptor-based array

Dr. Födisch AG

OMD 98, 1.10

MOS

Forschungszentrum

SAGAS

SAW

Gerstel GmbH Co

QSC

MOS

GSG Mess- und

MOSES II

Modular gas sensors

Illumina Inc

oNose

Fluorescence optical

Microsensor

Hazmatcad, Fuel SAW Sniffer,

Systems Inc

SAW MiniCAD mk II

Osmetech Plc

Aromascan A32S

Sacmi

EOS 835, Ambiente

Scensive Technol

Bloodhound ST214

Conducting polymers

Smiths Group plc

Cyranose 320

Carbon black-polymers

Sysca AG

Artinose

MOS

Conducting polymers

Technobiochip

LibraNose 2.1

QMB

RST Rostock

FF2, GFD1

MOS, QMB, SAW

Reprinted from Ref. [10]. Copyright 2011, with permission from MDPI AG, Basel, Switzerland

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Fig. 9.1 Schematic flow of electronic-nose construction

(MOS) gas sensor arrays with a small-volume measuring chamber. It can be linked with an adsorbent trapping unit or a headspace autosampler for laboratory analyses.

9.2 Statistical Analysis Techniques The digital output of an e-nose system is usually a group of data or pattern which contains signals from every sensor in the array. Effective information is not able to be analyzed from an individual signal of each sensor, but only can be acquired from the whole combination. Therefore, the output signals of e-nose sensors have to be interpreted with proper pattern recognition (PARC) techniques. The main purpose of data analysis methods is to emphasize the similarities and differences between the components of the data set through reduction of dimensionality. This is usually done in the following processes: data acquisition and preprocessing, feature extraction, classification, and decision-making (Fig. 9.2) [12]. First, sensing data are collected by transducer and converted into an electrical signal, and the output is a vector in pattern space, where this process involves the conversion of analog and digital signals. Second, features are extracted from the input pattern and converted from pattern space into feature space which can be considered as a reduction of dimensionality. Then, features are selected and classified according to the similarity (usually is measured by a distance function defined on pairs of patterns) and finally go through decisionmaking strategy. During the gas sensing process, the adsorption energy, catalytic reaction energy barrier, and desorption energy of gas molecules are usually different due to the different properties of gas molecules and the intermediate product. Thus, the response value, response–recovery time, slope of response curve, and slope of recovery curve of the same gas-sensitive device to different gas molecules are different, and it also means that the response signal of gas sensing device contains the fingerprint information of gas sensing process (Fig. 9.3) [13]. How to extract the fingerprint information is directly related to the accuracy of pattern recognition. In order to solve the above problems, Ogbeide et al. proposed a novel feature extraction method based on dynamic response curve, and it could realize the classification and concentration detection of NO2 , NH3 and H2 O. In addition, except for the collection of gas-sensitive signal basic information, other parameters such as sensitivity (S max ) and response time (T res ) can be calculated from fitting formula (9.1) based on response–recovery curves, where γ res(rec) , C res(rec) , and τ res(rec) are represented as offset parameter, time

9.2 Statistical Analysis Techniques

303

Fig. 9.2 Signal processing and pattern recognition in the electronic nose. Reprinted from Ref. [12]. Copyright 2011, with permission from American Institute of Physics

fitting constant, and constant fitting parameter of response–recovery process, respectively. Furthermore, the integrated area Ares(rec) of response and recovery times was extracted, and a total of ten characteristic parameters were identified accurately. Fitting curve = γres(rec) + Cres(rec) exp−t/τres(rec)

(9.1)

Commercially available analysis techniques fall into three main categories: graphical analyses, multivariate data analyses (MDAs), and network analyses. Depending on whether the target outputs are supplied during the learning stage, these methods can be divided into supervised techniques and unsupervised (Fig. 9.4). Unsupervised methods are generally used in exploratory data analysis without prior references of known samples, intending to discriminate different samples rather than to identify them. On the contrary, supervised techniques are used to specifically identify gases according to previously developed database which is containing properties or characteristics of known (sets of) samples. The selection of an appropriate statistical analysis method depends on the number of objects and their variables, the complexity of the problem, and the computational capabilities of the software. Graphical analysis involves bar chart, polar plots, and hierarchical cluster analysis (HCA). Graphical methods provide dimensionally reduced data for visual analysis. As the simplest method, this option is suitable when visually comparing unknown analyte to a single specified reference. But in most cases when multiple references are used, data analysis can become much more complicated and thus an alternative approach may be necessary. Among multivariate data analysis (MDA), principal component analysis (PCA), discriminant function analysis (DFA), and cluster analysis (CA) are mostly used

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9 Integration Technologies in Gas Sensor Application

Fig. 9.3 a Feature extraction diagram; b Parallel coordinate map for extracting features. Reprinted from Ref. [13]. Copyright 2006, with permission from Springer Nature

[13]. The principle of MDA technique is to reduce high dimensionality in a multivariate problem where variables are partly correlated. Therefore, it is an effective data analysis approach in e-nose when sensors have overlapping sensitivities. PCA is the most widely used unsupervised and chemometric method which allows a visualization of all information in the pattern by reducing the dimensionality of numerical data sets in a multivariate problem [14–16]. This method helps to observe the differences and similarities among different samples and find out which variables contribute most to the difference, as a primary evaluation of the between-class similarity. DFA techniques are supervised, probabilistic and have been widely used and proven successful in many applications. There are two types, linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA). QDA often fits the data better than LDA but has more parameters to estimate (Fig. 9.5) [13]. In addition, the classes in LDA are considered to have normal distribution and equal dispersion. In order to optimize the resolution between classes, LDA maximizes the variance between categories and minimizes the variance within categories. CA technique is used to separate data into specific groups depending on the similarities or distances between points in the dataset [17, 18]. As the classification threshold was incrementally lowered, samples were linked together and aggregate into larger clusters of increasingly dissimilar elements. Therefore, the results of

9.2 Statistical Analysis Techniques

305

Fig. 9.4 Grouping of data analysis methods. ANN: artificial neural network. LDA: linear discriminant analysis. QDA: quadratic discriminant analysis. FDA: discriminant factor analysis. SVM: support vector machine. SIMCA: soft independent modeling of class analogies. K-NN: k-nearest neighbors. MLP: multilayer perceptron. PNN: probabilistic neural network. RBF: radial basis function neural network. LVQ: learning vector quantization. ARTMAP: adaptive resonant theory map. SOM: self-organizing map. ART: adaptive resonant theory neural network. HCA: hierarchical cluster analysis

306

9 Integration Technologies in Gas Sensor Application

Fig. 9.5 a Typical polar plot of sensing data; b dendrogram illustrating HCA data clustering. Reprinted from Ref. [13]. Copyright 2006, with permission from Springer

hierarchical clustering methods are often displayed by dendrogram where distances between clusters are observed to determine similarity in observations. This method computes the distance from each individual data point to the center of each cluster (centroid) using the pooled within-group covariance matrix. Network analysis such as artificial neural network (ANN) consists of a set of interconnected processing algorithms functioning in parallel. ANN is one of the most evolved analytical methods utilized in commercial e-noses statistical software packages. ANN is composed of a set of very simple calculation units that start out from a dataset and transform it into a set of response values. ANN offers a chemometric technique of great potential for the treatment of the signals generated by electronic noses based on sensors that afford nonlinear responses. ANN is based on the cognitive process of the human brain on a simplified and abstract level [19]. The result of ANN data analysis is usually in the form of a percentage match of identification elements in the sample with those of aroma patterns from known sources in the reference library. Of all the various artificial neural network (ANN) structures, the multilayer perceptron (MLP) is the most used.

9.3 Temperature-Gradient Approach A common property of MOS gas sensor is that operating temperature has a strong impact on the sensing performance since temperature changing can lead to great differences in physical and chemical properties of MOS, analyst adsorption and desorption behavior, and even surface reaction process. This unique property brings about an alternative strategy to induce analytical orthogonality into sensing system by creating a gradient in operating temperature. Sysoev et al. reported a gradient microarray e-nose based on SnO2 nanowire using KAMINA platform. To differentiate the response of the sensors, the lateral variation of the surface temperature of the film was enabled by four meander-shaped

9.3 Temperature-Gradient Approach

307

Pt heaters on the rear side of each substrate (Fig. 9.6) [20]. Results showed that although the isothermal (without thermal gradient) patterns of the sensor array could already sufficiently differentiate signals for various gases, when a temperature gradient was additionally applied, the discrimination significantly increased and the average Mahalanobis distance was even more than twice of the isothermal case. In addition to spatial thermal gradient, temporal changing of operating temperature can also induce orthogonality to signal patterns. Three WO3 sensors (film, nanowire50, nanowire100) were integrated by Benkstein et al. with microscale conductometric platforms featuring millisecond dynamic temperature control [21]. A polysilicon heater with dynamic pulsed-temperature program was used to control

Fig. 9.6 a KAMINA microarray chip SnO2 nanowire sensor; b IR image of the chip under application of temperature gradient, 520 K (green area)–600 K (red area) along the electrode array; c LDA analysis of the conductivity patterns of SnO2 nanowire-based microarray at exposure to the target gases (2–10 ppm concentration range). The classification spheres correspond to normal distribution of data at 0.9999 confidence level. The microarray operates under (i) homogeneous heating at 580 K (const. T areas inside the ellipse) and (ii) temperature gradient at 520–600 K (grad. T areas). Reprinted from Ref. [20]. Copyright 2007, with permission from American Chemical Society

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Fig. 9.7 Dynamic temperature program. Conductance measurements are made at each open circle (ramp) and filled square (base). Reprinted from Ref. [21]. Copyright 2007, with permission from Elsevier

the operating temperature from 60 to 480 °C with 30 °C increments (Fig. 9.7). Three sensors exhibited different profiles toward analytes under different temperatures (Fig. 9.8a). According to PCA analysis, temperature gradient introduces variance along a different direction (arrows indicating sensor temperature from low to high) (Fig. 9.8b). Orthogonality and similarity studies shown in Fig. 9.8c indicate different correlation bands under different atmospheres (most apparent for NO2 ). The capability to perform rapid temperature changes during sensor operation enables elucidation of the differences between their responses.

9.4 Integrated Manufacturing of Electronic Nose In general, e-noses consist of a combination of metal oxide gas sensors, and thus it is required that its manufacturing technology should be as universal as possible to meet the synthesis requirements of various metal oxides. Moreover, gas sensors are still in great demand for high sensitivity, excellent long-term stability, and high selectivity, as well as convenient mass production techniques, and these technologies should be applicable to the microelectronics industry, which can fabricate tiny, inexpensive, repeatable, and fully functional sensing devices. Interestingly, chemical vapor deposition (CVD), atomic layer deposition (ALD), and inclined angle deposition (OAD) have high application value in this field. Typically, Hwang et al. prepared monocrystalline TiO2 spiral array based on OAD technology, and the difference with CVD technology was that the regular pattern prepared by OAD technology had higher specific surface area. Meanwhile, its stable monocrystalline structure can effectively guarantee its long-term stability, and highly inclined steam is combined with controlled substrate rotation during bevel deposition, which can accelerate it to create arrays of various three-dimensional nanostructures, such as spiral, diagonal bar, vertical column, zigzag, and square spiral. Subsequently, the authors further

9.5 Electronic-Nose Applications

309

Fig. 9.8 a Mean ramp measurements of the three different WO3 sensors to three analytes: CH3 OH (40 µmol/mol), CO (200 µmol/mol), and NO2 (1 µmol/mol). b Projection of the six-dimensional sensor response for the data in Fig. 9.6a, with varied analyte concentrations included, along the first three principal components (variance captured = 97.12%). The arrows show the trend with increasing ramp temperature (the different patterning is to aid in tracing their paths). c Results from the statistical analysis of the conductometric measurements made using temperature programs (60–480 °C). Reprinted from Ref. [21]. Copyright 2009, with permission from Elsevier

extended it to other metal oxides, including tin oxide and tungsten oxide, and gassensitive layers of six different structural materials are also constructed to achieve excellent sensitivity and selectivity toward H2 , CO, and NO2 [22].

9.5 Electronic-Nose Applications E-noses have become commercially available in recent years and are now widely applied to various fields ranging from food processing, environmental monitoring, agriculture, industrial manufacturing, microbial pathogen detection, and medical diagnostic applications. Despite the wide applications, e-nose systems have to be designed specifically to some specific applications. The architectural design, constituent sensors in the array, and data analysis model of an e-nose for a specific condition are all decided by the requirement of the composition of target analyte gases and operating environment. A proper selection of an appropriate e-nose system for

310

9 Integration Technologies in Gas Sensor Application

a particular application must involve an evaluation of systems on a case-by-case basis. Some key considerations involved in e-nose selection for a particular application should include assessments of the selectivity and sensitivity range of individual sensor arrays for particular target analyte gases (likely present in samples to be analyzed) and the number of unnecessary (redundancy) sensors with similar sensitivities, as well as sensor accuracy, reproducibility (preciseness), response speed, recovery rate, robustness, and overall performance.

9.5.1 Applications in Food Industry The largest proportion of electronic-nose systems is in food industry where e-nose is mainly applied in food quality control through monitoring the gases. The volatile mixture released from food typically gives a lot characteristic information about its flavor and quality. Therefore, e-nose systems have become an effective kind of instrument to evaluate food quality that covers many links in the food production chain: quality assurance of raw and manufactured products, maturity monitoring of fruits, contamination detection in packaging, shelf-life determination, fishprocessing inspection, fermentation processes inspection, early stage of biodeterioration detection, alcoholic beverages classification, and so on. Table 9.2 gives some recent researches studying MOS e-noses for food and beverage application, which are not limited to the listed ones. Fruits: Plenty researches have demonstrated that the maturity level of fruits can be indicated by the aroma emitted by fruits. Many volatile compounds are naturally formed by enzymes found in the intact tissue of fruits and vegetables. Cheeses: In cheeses, quality, flavor, and taste are closely connected to the ripening process which depends on the growth of bacteria, lipid degradation and oxidation, and proteolysis. Meat: The majority of publications of foodstuff analysis by e-nose instrument are related to meat and fish products [34–36]. Most of them are connected with quality control to monitor quality, shelf-life, spoilage, and taints. Recently, the research group of Professor Liu Huan from Huazhong University of Science and Technology developed the new technology of intelligent e-nose through cooperation research from the gas-sensitive material to recognition algorithm and is proposing an olfactory algorithm based on the whole gas-sensitive process of semiconductor sensor (All-Feature Olfactory Algorithm, AFOA). In addition, the high sensitivity, high reliability, portable intelligent e-nose has been constructed, which has successfully improved the recognition accuracy of complex gases. The team used a variety of self-developed MOS gas-sensitive materials as artificial gas receptors, and different types of olfactory receptor cells were simulated by MOS gas sensor unit, where the charge transfer caused by gas–solid interfacial reaction was converted into resistance change output. The e-nose uses an array of six nonspecific MOS gas sensors as major device, and it provides more learnable features for the subsequent recognition algorithm. In addition, the sensitive materials in these

9.5 Electronic-Nose Applications

311

Table 9.2 E-noses for food and beverage applications Sample

Analysis purpose Sensors

Data analysis method

References

Peanut

Detection of Aspergillus spp. contamination levels

Fox 3000 (Alpha MOS, Toulouse, France)

PCA, PC-LDA

[22]

Apple juices

Determine the character and the quality

PEN3 (Airsense Analytics PCA GmbH, Schwerin, Germany)

[23]

Sunflower oil

Determination of frying disposal time

Fox 4000 (Alpha MOS, Toulouse, France)

Fuzzy logic analysis

[24]

Soy sauce

Halal food certification

Smart Nose 300 (Smart Nose Inc., Marin-Epagnier, Switzerland)

DFA

[25]

Paddy

Paddy quality assessment

TGS 880, TGS 822, TGS 826, TGS 2602, TGS 2620, TGS 2600 (Figaro Engineering, Japan)

Multiple linear regression (MLR)

[26]

Meat

Evaluation of bacterial population

TGS 821, TGS 822, TGS 825, TGS 826, TGS 2600, TGS 2602, TGS 2610, TGS 2620 (Figaro Engineering, Japan)

PCA, backpropagation neural network (BPNN)

[27]

Meat

Rapid prediction of ochratoxin A-producing strains of Penicillium

ISE Nose 2000 (SoaTec S.r.l., Parma, Italy)

DFA

[28]

Ginsengs

Determine the 16 TGS sensors (Figaro chemical Engineering, Japan) constituents of different ginsengs

Support vector machine (SVM)

[29]

Honey

Classify botanical Heracles (Alpha Mos, origin and Toulouse, France) equipped determine with a GC adulteration

PCA, Partial least squares discriminant analysis (PLS-DA)

[30]

Honey

Botanical origin Fox 4000 (ALPHA MOS, identification and Toulouse, France) quality determination

PCA, DFA

[31]

Wine

Evaluation of oxygen exposure levels and polyphenolic content

PLS-DA

[32]

14 MOS sensors

(continued)

312

9 Integration Technologies in Gas Sensor Application

Table 9.2 (continued) Sample

Analysis purpose Sensors

Data analysis method

References

Coffee

Quality control

PCA, ANN

[33]

SP-12A, SP-31, SP-AQ3, ST-31 (FIS Inc.); TGS-813, TGS-842, TGS-823, TGS-800 (Figaro Engineering, Japan)

MOS gas sensors are mainly including SnO2 quantum dots, SnO2 nanowire, SnO2 nanoparticles, In2 O3 quantum dots, NiO nanoparticles, and WO3 quantum dots [23]. Since MOS gas sensors are cross-sensitive to various gases, advanced gas recognition algorithm is very important for the performance improvement and application expansion of e-nose (Fig. 9.9). Inspired by the human sense of smell, the team used artificial neural networks to simulate the olfactory bulb, the olfactory cortex of the brain, and the complex connections between them. The self-developed AFOA realized the extraction and analysis of complete information in the response–recovery process between the MOS gas sensor array and a variety of gas molecules. Based on the above technology, intelligent e-nose integrated with MOS gas sensor array can identify five kinds of Chinese liquor (CGJ, BYBJX, BYBNX, STJ, MTWZ) with high sensitivity (94.1%) and rapid sensing rate (within 2 min). This work demonstrates a smart e-nose based on six MOS gas sensors with a deep learning-based gas recognition algorithm: AFOA, which can recognize target gases in complex environments. In addition, it also presents a low-cost, portable, and evolvable collaborative design and manufacturing route for e-nose, which has a broad application prospect.

Fig. 9.9 E-nose artificial olfactory system (a) versus human olfactory system (b). Reprinted from Ref. [22]. Copyright 2013, with permission from Royal Society of Chemistry

9.5 Electronic-Nose Applications

313

Artificial senses such as e-noses have a great potential for further improving the selectivity of single gas sensors, which has aroused great attentions in monitoring harmful gases. For example, Wang et al. demonstrated the doping effect of gallium on In2 O3 nanotubes (NT), and a four-component sensor array for the detection of trimethylamine (TMA) was fabricated (Fig. 9.10). It showed that all gallium-doped/alloyed In2 O3 (Ga-In2 O3 ) sensors exhibited improved sensitivity and selectivity toward TMA at operating temperatures of 240 °C. Among these sensitive materials, 5 mol% of Ga-doped In2 O3 -based gas sensor exhibited optimal sensing response in the concentration range of 0.5 – 100 ppm, and in addition, it showed ultralow detection limit (13.83 ppb). Benefiting from the excellent sensing performance, the 5 mol% – Ga-doped In2 O3 materials were used to fabricate a four-component sensor array, and the array has a unique response mode under variable gas background. Hence, gas-sensitive data were used to train backpropagation neural networks (BPNNs), radial basis function neural networks (RBFNNs), and linear regression based on principal component analysis (PCA-LR) to distinguish different gases with high accuracy and predict the concentration of target gases in different gases and gas mixtures. Furthermore, for the classification of six gases (three single gases and three binary gas mixtures) and the prediction of TMA concentrations in the presence of different concentrations of TMA and acetone, the accuracy reached 92.85% and 99.14%, respectively [24].

9.5.2 Applications in Environmental Monitoring Air quality control has always been one of the most concerned issues in daily life. The applications of e-nose in environmental monitoring cover a broad range of field including indoor air quality control, industrial process monitoring, and automotive exhaust emissions detection. Considering the complexity of environment atmosphere, e-noses are currently the most reliable approach to quantify as well as classify gases in real-time. Microbial volatile organic compounds (MVOCs) are one of the most common indoor air pollutions. Molds were reported to produce a wide range of volatile organic compounds including alcohols, ketones, esters, and sulfur compounds [37]. The volatile production is highly species dependent, and traditionally, it was used to classify fungi to species level. Schiffman et al. conducted an e-nose consisting of 15 MOS sensors to detect and classify bacteria and fungi, and the e-nose was capable of discriminating five fungi with up to 96% accuracy [37]. A couple of MOS sensors were employed by Helli et al. to evaluate two industrial and environmental gases (H2 S and NO2 ), alone or mixed (Fig. 9.11) [38]. Using discriminant factorial analysis, the sensor array can correctly recognize the composition of analytes, but only in dry atmosphere. The accuracy is largely influenced by humidity and the presence of CO2 . This brings out the greatest difficulties that most

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Fig. 9.10 a Schematic diagram of the perceptual pathway of the artificial olfactory system and the sensor array. b Six gas classification results based on BPNN model. c–f SEM images of pure In2 O3 NT (c), 1% Ga-In2 O3 NTs (d), 5% Ga-In2 O3 NTs (e), and 10% Ga-In2 O3 NTs (f). Reprinted from Ref. [24]. Copyright 2022, with permission from Wiley

MOS sensors are easily affected by the variations of environment conditions, especially humidity and temperature. Therefore, the reliability of MOS sensors toward environment conditions has become a focus of research. In addition to air, e-noses can be also used for analysis of water and soil by detecting volatiles coming out from the sample [39, 40]. The studies conducted in this field are mainly based on the principle of the headspace analysis up to now [41]. Considering the need for identification and rescue of trapped people in extreme environments without visual input, inspired by the “touch and smell fusion” perception of the natural star-nosed mole, Tao Hu’s team from Shanghai Institute of Microsystems and Information Technology, Chinese Academy of Sciences fused the MEMS flexible sensor array of smell and touch with multimodal machine learning

9.5 Electronic-Nose Applications

315

Fig. 9.11 FDA results for gas nature discrimination in a dry group and b humid group. Reprinted from Ref. [38]. Copyright 2004, with permission from Elsevier

algorithm to build an intelligent manipulator that mimics the star-nosed mole with touch and smell integration. Thanks to the excellent performance of silicon-based MEMS gas sensor (sensitivity 1 order of magnitude more than human) and pressure sensor (detection limit 1 order of magnitude more than human), after touching the object with the fingers of the manipulator, the key features such as local morphology, material hardness, and overall contour can be accurately obtained. In addition, the palm can smell the “fingerprint” of the object synchronously, and further through the bionic BOT machine learning neural network real-time processing, finally complete the identification of human body, confirm the location, judge the buried state, remove obstacles and closed-loop rescue (Fig. 9.12) [42]. Through the field investigation in the first-line fire rescue unit, the real restoration and construction of the human body covered by rubble pile burial scene, and the recognition accuracy of 11 typical objects including human body reached 96.9%, which was 15% higher than that of single sensor. Compared with the previously reported single-touch (548 sensors) perception, this work achieved better recognition by using only 1/7 of the number of sensors through 70 touch and 6 smell synesthesia. Moreover, the reduced sensor scale and sample size are more suitable for rapid response and application in complex environment and resource limited conditions. Besides, facing the interference gas or partial device damage, it often encountered in the actual rescue and the system still maintains a good accuracy (>80%) through the multimodal sensing complementarity and the rapid adjustment of the neural network.

9.5.3 Applications in Respiratory Diseases From ancient times, exhaled gas has attributed to diagnose diseases. Until now, over 3000 volatile organic compounds (VOCs) have been identified in human breath, and many of them are directly related to some diseases (Table 9.3). These VOCs

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Fig. 9.12 Biomimetic tactile and olfactory-related intelligent sensory system. Reprinted from Ref. [42]. Copyright 2022, with permission from Springer Nature

are produced during metabolic processes, as well as during disease processes in the airways or elsewhere in the body [43]. The concentration variation of certain gases often indicate corresponding pathological changes, such as lung cancer, asthma, diabetes, and cystic fibrosis. Until now, many breath compounds have been proven related to disease. For example, acetone has been found more abundant in diabetics’ breath due to the abnormal carbohydrate metabolism [44–46]; nitric oxide can be measured as an indicator of asthma and COPD [47–49]; decane, 4-methyoctane, undecane, aldehydes, benzene and its derivatives, 1-butanol for lung cancer [17, 50, 51]; carbonyl sulfide, carbon disulfide, and isoprene are related to liver diseases [52, 53]. Gas chromatography and mass spectrometry (GC–MS) is universally accepted as the standard test for exhaled breath analysis, because it allows large spectrumspecific identification of VOCs, aiding to find new pathophysiological pathways [43, 54, 55]. However, the usage of GC–MS in breath VOCs analysis is still limited because it is expensive, complex, and time consuming, especially for early-stage clinical diagnosis. Thus, meeting the requirement of fast, accurate, non-invasive, and low-cost for early diagnosis still remains challenging. As a powerful tool for the analysis of complex gases, e-nose ensures a fast detection by recognizing the fingerprint of a mixture rather than identify individual components, making them a promising option to tentative diagnosis. Many medical researchers have published experimental data in the last ten years to demonstrate the feasibility of using the electronic nose to diagnose human diseases and to identify many different pathogenic microorganisms through the detection of the VOCs [10]. Gibson et al. first classified 12 different bacteria and human-pathogenic yeasts with a high precision of 93.4% [56].

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Table 9.3 Application of electronic nose in early detection of some diseases Associated disease Asthma Chronic Obstructive Pulmonary Disease (COPD)

Related gases

E-nose

References

NH3 , NO

SnO2 and WO3 sensors

[53]

–

Cyranose 320

[54]

–

Four MOS sensors (Figaro, Japan)

[55]

–

Lung cancer Decane, Ethylbenzene, propylbenzene, etc.

Seven QMB sensors

[56]

Cyranose 320

[57]

Cyranose 320

[58] [59]

–

Cyranose 320

Formaldehyde

Four differently doped SnO2 sensors [60]

Decane, 4-methyoctane, undecane, aldehydes, benzene and its derivatives, 1-butanol, etc.

Eight TGS-type MOS sensors, four electrochemical gas sensors, one catalytic combustion-type sensors, one hot-wire gas sensor

[61]

Chronic – liver disease

Seven QMB sensors

[62]

Respiratory infections

DiagNose, C-it, Zutphen, Netherlands

[63]

–

Diabetes

Acetone

Cyrano 320

[64]

Six TGS-type MOS sensors, three temperature-modulated MOS sensors, one CO2 sensor, one temperature–humidity sensor

[65]

12 TGS-type MOS sensors

[66]

Researchers in Poland developed an e-nose device based on commercial VOC gas sensors, and it showed the testing results of the novel coronavirus pneumonia (COVID-19) at the local hospital. The researchers took into account technical problems identified in the study that affected the test results, and they believe that the research could help to advance the development of new technologies to limit the spread of COVID-19 and similar viral infections. The researchers conducted the experiment in a local hospital ward dedicated to treating people infected with COVID19, where breath samples were collected in one group of hospitalized patients and one control group (Fig. 9.13). The researchers examined 56 respiratory samples (33 from patients with severe COVID-19, 17 from healthy controls, and 6 from ambient air). The researchers investigated the patients of different ages, genders, and underlying medical conditions [57]. It stressed that the findings showed a higher detection rate in older age groups. In addition, there were differences in humidity in the breath samples analyzed, and the average humidity of breath samples from people infected with COVID-19 was

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Fig. 9.13 a Newly developed e-nose uses the damp end of the last wave of exhaled air to analyze gas samples. b Illustration of collection of breath samples using BioVOC™. Reprinted from Ref. [57]. Copyright 2022, with permission from Springer Nature

higher than that of healthy volunteers. In the researchers’ exploratory study, they focused on possible corrections to improve detection accuracy and anti-jamming against fluctuating environmental factors. The researchers identified the most reliable gas sensor parameters that could be applied to the test process, greatly reducing the detection time, which further strengthened the demands of sensors with good selectivity and high sensitivity. Interestingly, the concentration of single VOC marker for COVID-19 infection is tens of ppb levels. Nevertheless, the presence of a large VOC mixture in exhaled air has a significant impact on the application of commercial gas sensors to detect COVID-19 infections. There are still some unsolved questions before the e-nose can be used to detect COVID-19. Firstly, researchers are difficult to determine how the inevitable time drift of resistive gas sensors affects the detection rate of e-noses and whether they need to be calibrated repeatedly during intensive operations. They believe that more effective and faster cleaning procedures for gas sensors (e.g., ultraviolet (UV) irradiation or pulsed heating) should be introduced. Secondly, the efficiency of testing for the tested person who has a cold, seasonal influenza and other lung diseases exhibits great potential. Finally, preconcentrator technology was used to increase VOC concentration and reduce the requirement for gas sensors, and the technology could increase the cost of e-noses and make them bulkier. The above questions contain great challenge toward introducing mass screening for COVID-19 and monitoring its medical effectiveness, and the method produces faster and cheaper results, but it is not as accurate as the gold-standard PCR test.

9.6 Conclusions and Outlook

319

Typically, Yan et al. designed a breath analysis test device with ten gas sensors, and the non-invasive detection of diabetes patients was achieved by measuring acetone gas concentration. The authors selected 203 breath samples from 36 patients and selected the features with the largest sample information through sequential advance method (SFS). Due to the small size of samples, the simple regression model may have the problem of over-fitting, and thus the author adopted the method of combining global training with local training. The global training can use as many samples as possible, while ignoring the variance between different patients, so the accuracy may be low. However, the local training only uses the samples of a single patient, which can be corrected by using the samples of other patients after the training is completed, where the accuracy achieved 79.3% [58]. Formaldehyde is a potential respiratory marker for lung cancer. In the process of detecting formaldehyde in exhaled air, it is often interfered by a variety of gases, such as acetone, ammonia, and ethanol. Andreas et al. constructed four kinds of porous tin dioxide sensors doped with Pt, Si, Pd, and Ti to achieve the specific detection of formaldehyde in gas mixture, respectively. Tin oxides with various doping components were deposited on silicon-based substrates through flame spray pyrolysis, and different doping components could induce different selectivities. It has found that in the presence of interfering gases, the regression curve appeared typical shift, so the author adopted multiple linear regression method, and even in the presence of high acetone, ammonia, and ethanol, formaldehyde could still be detected [59]. E-noses point out a new direction a non-invasive diagnostic technology. However, there are still limitations to the widespread use of e-noses in medical diagnosis. The most obvious drawbacks are the lack of standardized sample collection and data analysis system, as well as relatively small numbers of the populations subjected to e-nose evaluation [67–73]. In addition, the exhaled breath shows non-negligible individual difference in the aspect of gaseous component and concentration, which can cause great impact to diagnostic accuracy [59, 74].

9.6 Conclusions and Outlook The development of e-nose has achieved remarkable success in recent decades. As a powerful tool for detection or classification of single or complicated gases, the sensing process of e-noses is rapid and non-invasive which just adapt to the new demands of gas sensing application fields including food industry, environmental monitoring, medical diagnosis, and so on. The concept of integrating multiple sensors combining pattern recognition system can add new dimensions to sensing signals, significantly covering traditional shortage of cross-selectivity of MOS sensors. However, e-nose technique is still facing challenges, mostly from the time drift and poor humidity tolerance of some sensors. To solve these problems, periodic correction is usually employed, as well as the developing of new sensing materials with better sensing performance. With the progress of big data and Internet of things (IoT), e-nose provides promising prospect as a cutting-edge technique.

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57. Kwiatkowski A, Borys S, Sikorska K, Drozdowska K, Smulko J (2022) Clinical studies of detecting COVID-19 from exhaled breath with electronic nose. Sci Rep 12:15990. https://doi. org/10.1038/s41598-022-20534-8 58. Yan K, Zhang D (2014) Blood glucose prediction by breath analysis system with feature selection and model fusion. Annu Int Conf IEEE Eng Med Biol Soc. 9:6406. https://doi.org/ 10.1109/embc.2014.6945094 59. Güntner AT, Koren V, Chikkadi K, Righettoni M, Pratsinis SE (2016) E-Nose sensing of lowppb formaldehyde in gas mixtures at high relative humidity for breath screening of lung cancer? ACS Sens 1(5):528–535. https://doi.org/10.1021/acssensors.6b00008 60. Moon HG, Jung Y, Han SD, Shim Y-S, Jung W-S, Lee T, Lee S, Park JH, Baek S-H, Kim J-S, Park H-H, Kim C, Kang C-Y (2018) All villi-like metal oxide nanostructures-based chemiresistive electronic nose for an exhaled breath analyzer. Sens Actuators B 257:295–302. https:// doi.org/10.1016/j.snb.2017.10.153 61. Dragonieri S, Schot R, Mertens BJ, Le Cessie S, Gauw SA, Spanevello A, Resta O, Willard NP, Vink TJ, Rabe KF, Bel EH, Sterk PJ (2007) An electronic nose in the discrimination of patients with asthma and controls. J Allergy Clin Immunol 120(4):856–862. https://doi.org/10. 1016/j.jaci.2007.05.043 62. Vries R, Brinkman P, van der Schee MP, Fens N, Dijkers E, Bootsma SK, de Jongh FH, Sterk PJ (2015) Integration of electronic nose technology with spirometry: validation of a new approach for exhaled breath analysis. J Breath Res 9(4):046001. https://doi.org/10.1088/1752-7155/9/4/ 046001 63. Incalzi RA, Pennazza G, Scarlata S, Santonico M, Petriaggi M, Chiurco D, Pedone C, D’Amico A (2012) Reproducibility and respiratory function correlates of exhaled breath fingerprint in chronic obstructive pulmonary disease. PLoS ONE 7(10):e45396. https://doi.org/10.1371/jou rnal.pone.0045396 64. Shafiek H, Fiorentino F, Merino JL, Lopez C, Oliver A, Segura J, de Paul I, Sibila O, Agusti A, Cosio BG (2015) Using the electronic nose to identify airway infection during COPD exacerbations. PLoS ONE 10(9):e0135199. https://doi.org/10.1371/journal.pone.0135199 65. Thriumani R, Zakaria A, Hashim YZH, Jeffree AI, Helmy KM, Kamarudin LM, Omar MI, Shakaff AYM, Adom AH, Persaud KC (2018) A study on volatile organic compounds emitted by in-vitro lung cancer cultured cells using gas sensor array and SPME-GCMS. BMC Cancer 18(1):362. https://doi.org/10.1186/s12885-018-4235-7 66. Tirzite M, Bukovskis M, Strazda G, Jurka N, Taivans I (2017) Detection of lung cancer in exhaled breath with an electronic nose using support vector machine analysis. J Breath Res 11(3):036009. https://doi.org/10.1088/1752-7163/aa7799 67. Li W, Liu H, Xie D, He Z, Pi X (2017) Lung cancer screening based on type-different sensor arrays. Sci Rep 7(1):1969. https://doi.org/10.1038/s41598-017-02154-9 68. De Vincentis A, Pennazza G, Santonico M, Vespasiani-Gentilucci U, Galati G, Gallo P, Vernile C, Pedone C, Antonelli Incalzi R, Picardi A (2016) Breath-print analysis by e-nose for classifying and monitoring chronic liver disease: a proof-of-concept study. Sci Rep 6:25337. https:/ /doi.org/10.1038/srep25337 69. Schnabel RM, Boumans ML, Smolinska A, Stobberingh EE, Kaufmann R, Roekaerts PM, Bergmans DC (2015) Electronic nose analysis of exhaled breath to diagnose ventilatorassociated pneumonia. Respir Med 109(11):1454–1459. https://doi.org/10.1016/j.rmed.2015. 09.014 70. Arasaradnam RP, Quraishi N, Kyrou I, Nwokolo CU, Joseph M, Kumar S, Bardhan KD, Covington JA (2011) Insights into ‘fermentonomics’: evaluation of volatile organic compounds (VOCs) in human disease using an electronic ‘e-nose.’ J Med Eng Technol 35(2):87–91. https:/ /doi.org/10.3109/03091902.2010.539770 71. Yan K, Zhang D, Wu D, Wei H, Zhu G (2014) Design of a breath analysis system for diabetes screening and blood glucose level prediction. IEEE Trans Biomed Eng 61(11):2787. https:// doi.org/10.1109/TBME.2014.2329753 72. Guo D, Zhang D, Li N, Zhang L, Yang J (2010) A novel breath analysis system based on electronic olfaction. IEEE Trans Biomed Eng 57(11):2753. https://doi.org/10.1109/TBME. 2010.2055864

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Chapter 10

Applications of Semiconducting Metal Oxide Gas Sensors

In recent years, semiconducting metal oxide (SMO) material-based gas sensors have been widely applied in the house, factory, hospitals, and laboratory. It provides a promising option for rapid and sensitive semiquantitative detection. It attracts enormous attention not only due to its low-cost, simplicity of fabrication, compact size, and its excellent gas sensing characteristic including high sensitivity, outstanding selectivity, fast response/recovery and low detection limits (< ppm levels), and an unfavorable wide range of detectable gases. The detection gas of metal oxide semiconductor-based gas sensor covers volatile organic compounds, toxic gases, some dangerous gases, and specific gases, making it an effective alternative in gas alarm, environmental monitoring, food hygiene quarantine, and medical diagnosis. Herein, the ongoing study of SMO-based gas sensors is summarized according to the classification of gas species. The representative SMO sensors in the past 15 years were reviewed, and their sensing properties were listed in tables. Important advances in sensitivity, selectivity, and operating temperature are discussed, and readers can refer to the literature for more details.

10.1 Sensors for Volatile Organic Compounds (VOCs) Gas Volatile organic compounds (VOCs) are organic chemicals with high vapor pressure at room temperature, and their boiling point is usually low resulted from their high vapor pressure, which make them easy to evaporate from liquid or solid into the environment. In general, VOCs can be produced naturally or artificially. One of the main sources is factory or laboratory emissions. Some of VOCs not only cause environment pollution but also directly harm to human health. For example, alcohols and aromatic hydrocarbons can stimulate the mucous membranes and upper respiratory tracts. Some of these gases, such as benzenes and formaldehyde, have been proved carcinogenic. Therefore, it is necessary to develop gas sensors for early and fast detection of VOCs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Deng, Semiconducting Metal Oxides for Gas Sensing, https://doi.org/10.1007/978-981-99-2621-3_10

325

326

10 Applications of Semiconducting Metal Oxide Gas Sensors

10.1.1 Sensors for Ethanol Ethanol (C2 H5 OH) is a colorless, inflammable, and volatile liquid with the boiling point of 78.3 °C. It is one of the most commonly and widely used as a solvent, which has a great number of applications in food industries, pharmaceutical industries, and chemical factories. Usually, the exposure to ethanol vapor is not dangerous, but it can lead to headache, drowsiness, irritation of eyes, and difficulty in breathing. Moreover, the quantitative detection of ethanol vapor at ppm levels is not only of medical but also of social importance because alcohol consumption is one of the main causes to traffic accidents. A number of metal oxides investigating for ethanol sensing have been summarized in Table 10.1. Among these materials, tin oxide and zinc oxide are the most representative SMO-based sensor materials for ethanol. For example, Chen et al. reported a large-scale synthesis of single-crystalline SnO2 nanorods with diameters of 4–15 nm via a hydrothermal route [1]. The crystalline SnO2 nanorods-based sensors exhibit response of 4.2–31.4 to 10–300 ppm ethanol at the working temperature of 300 °C. Highly porous and nanostructured SnO2 thin film was produced via a combustion chemical vapor deposition process, and the SnO2 -based gas sensors possessed a sensitivity of 1075–500 ppm ethanol vapor at an operating temperature of 300 °C [2]. A multilayer SnO2 nanoplates-based sensor was fabricated by Li et al. through thermal evaporation and condensation followed annealing [3]. The sensitivities of multilayer SnO2 nanoplates were over double higher than that of the single-layer SnO2 nanoplates for 50 ppm ethanol at 350 °C, due to the larger surface area of multilayer nanoplates. Liu et al. reported that core–shell SnO2 microsphere showed an excellent sensitivity to 10–50 ppm ethanol at 260 °C, which was higher than the response of hollow SnO2 nanoparticles [4]. ZnO is another metal oxide usually applied for ethanol sensing. The ZnO nanorods were synthesized by a simple low-temperature hydrothermal method, and the ZnO nanorods-based sensor showed considerable response to ethanol even at the low concentration of 1 ppm [5]. To further enhance the sensing performance, thin film gas sensors based on 8-nm-diameter and exposed-{0001}-facet ZnO nanorods self-aligning along the ceramic tube are fabricated by a facile solution-processing technique. This materials possess higher sensitivity and faster response speed (less than 10 s) than that of the exposed-{1010}-facet ZnO nanorods-based sensors [6]. The authors proposed that it was mainly due to the high exposure of {10-10}-facet which could improve the oxygen adsorption. Flower-like hierarchical ZnO prepared by bisolvents was applied for ethanol, and the detection concentration was down to 10 ppb, which might be attributed to the specific hierarchical structure [7]. The porous structure is beneficial for improving surface area and the diffusion of gas, thus enhancing gas sensing performance. Zhou et al. synthesized ordered mesoporous ZnO by using PEO-bPS as structure-directing agent [8]. The prepared mesoporous ZnO shows excellent ethanol sensing performance with high sensitivity (66–50 ppm ethanol, 350 °C), a fast response (6 s) and recovery (7 s), and high selectivity (Fig. 10.1). Apart from SnO2 and ZnO, the classical n-type SMOs, other metal oxides for ethanol sensing have been extensively studied by researchers, including other n-type SMOs such as

10.1 Sensors for Volatile Organic Compounds (VOCs) Gas

327

In2 O3 [9], V2 O5 [10], TiO2 [11], and p-type SMOs, such as CuO [12, 13] and NiO [14]. As is known that noble metal modification is a good way to enhance the sensing performance. It is effective that metal oxide can be regarded as the main carrier and is sensitive to ethanol. Hwang et al. reported that Ag decorated SnO2 nanowires exhibited a 3.7-fold enhancement in gas response to 100 ppm of C2 H5 OH at the working temperature of 450 °C compared to pure SnO2 nanowires [15]. The other examples are Pt/SnO2 [16] and Pd/SnO2 [17]. Au-modified ZnO sensor can response toward 50 ppm of ethanol at 325 °C with the response/recovery time in 5 and 20 s, respectively [18]. Pd is also reported to enhance the ZnO ethanol-sensing performance [19]. Au@In2 O3 core–shell nanostructure exhibited an excellent response of 36.14–100 ppm ethanol and high selectivity at a low operating temperature of 160 °C [20]. The response of Au@In2 O3 -based sensors was about 1.5 times higher than that of the sensor based on pristine In2 O3 . Other noble metal/metal oxide composites, such as Au/WO3 [21], Ag/Fe2 O3 [22], Ag/TiO2 [23], and Rh/In2 O3 [24], have been studied for ethanol sensing. Generally, Au and Ag are the most effective modifier among noble metals for ethanol sensing. Ren et al. [25] proposed a one-step multicomponent co-assembly approach to prepare a Pt nanoparticle-decorated Si-doped WO3 nanowires interwoven into 3D mesoporous super-structures (Pt/Si–WO3 NWIMSs), which employed amphiphilic poly(ethylene oxide)-block-polystyrene (PEO-b-PS) as the structure-directing agent, and Keggin polyoxometalates (H4 SiW12 O40 ) as the tungsten source, as well as hydrophobic (1,5-cyclooctadiene)dimethylplatinum(II) as Pt precursor. The resulting Pt/Si–WO3 NWIMSs was used to construct semiconductor gas sensors, presenting excellent sensing properties toward ethanol at low temperatures (100 °C) with high sensitivity (S = 93@50 ppm), low detection limit (0.5 ppm), rapid response–recovery rate (17/7 s), outstanding selectivity, and good long-term stability (Fig. 10.2). Furthermore, the in-situ infrared spectroscopy, gas chromatography-mass spectrometry (GC–MS) analysis, and DFT theoretical calculations were carried out to deeply investigate the chemical process of ethanol sensing on the surface of Pt/Si–WO3 NWIMSs and the internal mechanism of its charming sensing performance. An intelligent gas sensor module based on Pt/Si–WO3 NWIMSs had been developed in this work, which enabled communicate with smart phones through Bluetooth connection, realizing real-time, and accurate monitoring of ethanol in the environment. Doping is another efficient strategy to improve the sensing performance. The dopant such as In and Sb can modulate the electrical properties of the metal oxide. Wan et al. reported the synthesis of Sb-doped SnO2 nanowires, and it was applied in ethanol sensor [26]. The Sb-doped SnO2 -based sensors showed the fewest response and recovery time (1 s and 5 s to 10 ppm ethanol), which were much less than pure SnO2 (longer than 10 min) since Sb doping was favored to the absorption of oxygen molecules. In-doped ZnO nanowires prepared by Li et al. exhibited excellent sensitivity (about 27 toward 100 ppm ethanol concentration) and fast response and recovery time (shorter than 2 s) [27].

450 300

50 300

100 10

100 5

50 100

Nanorods

Sphere

Nanowire

Nanowire

Nanowire

Nanowire

Nanoparticle

Nanorods

Nanorods

Nanorods array

Mesoporous film

Nanopillar

Mesoporous microsphere

Flower-like microspheres 10 100

Nanoplate

Flower-like nanorods

Nanowire

Flower-like structure

Nanorods

Nanorods

SnO2

SnO2

SnO2 /Pt

SnO2 /Ag

Sb–SnO2

La2 O3 –SnO2

SiO2 –SnO2

ZnO

ZnO

ZnO

ZnO

ZnO

ZnO

ZnO

ZnO

ZnO–Au

ZnO–PdO

Fe2 O3 /ZnO

In2 O3 50

500

50

100

100

200

50

100

500

50

330

220

320

325

300

350

350

370

370

320

300

400

300

450

200

260

300

350

300

SnO2

500

Film

SnO2

31/8 – /– ~1/~1 9/20 – /– 5/100 1/5 1/110 – /– 54/61 22/10 ~10/~10 6/7 10/20 4/6 ~17/~12 – /– 5/20 1/7 – /– 6/11

1075a 3.78a 31.4a 60.5a ~8400c 228.1a ~25/15a 57.3a 318a ~35a ~42a ~70a 66a 18.29a 2.2a ~4c 176.8a ~7a 35.4a 22.1a 11.3a

(continued)

[35]

[30]

[19]

[18]

[34]

[7]

[33]

[32]

[8]

[6]

[31]

[5]

[28]

[29]

[25]

[15]

[16]

[4]

[1]

[3]

[2]

Gas concentration (ppm) Operating temperature (°C) Response Response /recovery time (s) References

Structure

Materials

Table 10.1 Gas sensing properties of SMO sensors for ethanol

328 10 Applications of Semiconducting Metal Oxide Gas Sensors

50

200 100

100 100

Core–shell nanostructure

Thin film

Nanobelt

Thin film

Nanorods

Hemispheres

Nanobelt

Nanobelt

Nanobelt

Nanobelt

Core–shell

Au@In2 O3

TiO2

Ag–TiO2

CuO

CuO

NiO

V2 O5

V2 O5 /Ti

V2 O5 /Fe

V2 O5 /Sn

Ag@α–Fe2 O3

b

a /Rg or Rg /Ra ) ([Ra − Rg ]/Ra , %) c (I /I ) g a

a (R

100

Hollow sphere

In2 O3 –Rh

100

100

1000

12.5

500

100

100

Nanowires

In2 O3 ~1/~80 4/2 5/52 ~2/~2 31/52 42/51 – /– 32/30 49/85 36/64 37/126 5.5/16

36.14a 535%b 46.153a 2.2a 9.8a 5a 1.7a 2.0a 2.3a 3.1b 6a

∼30

250

200

200

200

200

300

210

180

200

371 160

10/20

2a 4748a

370

[22]

[10]

[14]

[12]

[13]

[23]

[11]

[20]

[24]

[9]

Gas concentration (ppm) Operating temperature (°C) Response Response /recovery time (s) References

Structure

Materials

Table 10.1 (continued)

10.1 Sensors for Volatile Organic Compounds (VOCs) Gas 329

330

10 Applications of Semiconducting Metal Oxide Gas Sensors

Fig. 10.1 a Response–recovery curve and b relationships between ethanol concentration and response of the mesoporous ZnO sensor and non-mesoporous ZnO sensor to ethanol vapors of different concentrations. c Dynamic response–recovery curve of the mesoporous ZnO and nonmesoporous ZnO to 50 ppm ethanol. Reprinted with permission from Ref. [8]. Copyright 2016, Royal Society of Chemistry

Fig. 10.2 a Optical image and schematic illustration of the gas sensor device. b Diagram of the interaction between ethanol molecules and Pt nanoparticles decorated tungsten oxide nanowires. c Responses to 50 ppm ethanol at different work temperatures and d responses to ethanol of different concentration (0.5–500 ppm) at 100 °C of Pt/Si–WO3 NWIMSs with different Pt content (wt%). e Dynamic response–recovery curves at different concentration (0.5–500 ppm) at 100 °C. f Response–recovery times to 50 ppm ethanol. g Responses to different gases of 50 ppm. h Cycle performances to 50 ppm ethanol. i Long-term stability of the gas sensor. Reprinted with permission from Ref. [25]. Copyright 2021, American Chemical Society

10.1 Sensors for Volatile Organic Compounds (VOCs) Gas

331

In addition, oxide additives, such as SiO2 , La2 O3 , and Fe2 O3 , can be also used to functionalize SnO2 and ZnO semiconducting oxide as ethanol sensors. Tricoli et al. produced the SiO2 -doped SnO2 particles by flame spray pyrolysis (FSP) [28]. The addition of 0–4 wt% of silica can well restrict the sinter neck size, leading to enhance the sensitivity and low down the detect limit to a low concentration of ethanol (100 ppb). Hieu et al. coated La2 O3 layer on the SnO2 nanowires to obtain high sensitivity and high selectivity to C2 H5 OH [29]. Fe2 O3 -decorated ZnO nanowires exhibited high response to ethanol compared to that of pure ZnO as well as high selectivity. The improved ethanol sensing performance in these cases is attributed to the formation of heterojunctions [30].

10.1.2 Sensors for Acetone Acetone, as a potential biomarker in breath analysis, has attracted growing attention in recent years. Table 10.2 summarizes the acetone sensing properties of different metal oxides. ZnO, In2 O3 , α-Fe2 O3, and WO3 are the most investigated metal oxides due to their promising potentiality for acetone detection. The studies for pure metal oxides for acetone sensing are few. Vomiero et al. produced single-crystalline nanowires of In2 O3 and it response to acetone at 400 °C [36]. Crystalline meso- and macroporous Co3 O4 nanorods reported by Nguyen et al. were used for effective acetone sensors [37]. The Co3 O4 nanorods-based sensors exhibit highest response to acetone comparing to ethanol and benzene and a fast response and a recovery time of one minute. WO3 is recently found to be a sensitive metal oxide for acetone. Chen et al. fabricated the WO3 nanoplate sensors with a high and stable sensitive response to acetone vapors (42 for 1000 ppm), low detect limit (2 ppm), and short response and recovery times (3–10 s and 12–13 s to 2–1000 ppm) at the working temperature of 300 °C [38]. Considering acetone as a biomarker in the detection of diabetics exhale, the development of acetone sensors with low detection limits and high anti-humidity has become a research hotspot in recent years. Similar to ethanol sensors, noble metals are applied to modify metal oxides to enhance acetone sensing performance. Karmaouia et al. synthesized In2 O3 NPs with 2 wt% Pt metal NPs (2–3 nm) on the surface, which exhibited a detection limit as low as 10 ppb or less [39]. Furthermore, this In2 O3 /Pt-based sensors can response toward 0.29 ppm acetone at 200 °C in 75% humidity, showing potential application in exhalation gas detection. Shin et al. prepared Pt nanoparticles functionalized hierarchical SnO2 fibers with an excellent response (Rair /Rgas – 1 = 0.72 to low acetone concentration of 120 ppb), fast acetone response (