Chemical Sensors: Simulation and Modeling Volume 1: Microstructural Characterization and Modeling of Metal Oxides

Momentum Press is proud to bring to you Chemical Sensors: Simulation and Modeling Volume 1: Microstructural Characterization and Modeling of Metal Oxides, edited by Ghenadii Korotcenkov. This is the first of a new four-volume comprehensive reference work that provides computer simulation and modeling techniques in various fields of chemical sensing and the important applications for chemical sensing such as bulk and surface diffu-sion, adsorption, surface reactions, sintering, conductivity, mass transport, and interphase interactions. In this first volume, you will find background and guidance on:

• Fundamentalsofelectrochemistryforgassensors• Stabilizedzirconia-basedgassensors• Ion-sensitivefield-effecttransistor(ISFET)-basedchemicalsensors• Microfluidicchipsasnewplatformsforelectrochemicalsensing• Opticalandfiberopticchemicalsensors• Chemoluminscencechemicalsensingfornewapplicationsinwaterpollutioncontrol

Chemicalsensorsareintegraltotheautomationofmyriadindustrialprocessesandeverydaymonitoringofsuch activities as public safety, engine performance, medical therapeutics, and many more. This four-volume reference work covering simulation and modeling will serve as the perfect complement to Momentum Press’s 6-volume reference work, Chemical Sensors: Fundamentals of Sensing Materials and Chemical Sensors: Compre-hensive Sensor Technologies, which present detailed information related to materials, technologies, construction, andapplicationofvariousdevicesforchemicalsensing.Eachsimulationandmodelingvolumeinthepresentseries reviews modeling principles and approaches peculiar to specific groups of materials and devices applied for chemical sensing.

About the editorGhenadii KorotcenkovreceivedhisPh.D.inPhysicsandTechnologyofSemiconductorMaterialsandDe-vicesin1976,andhisHabilitateDegree(Dr.Sci.)inPhysicsandMathematicsofSemiconductorsandDielec-tricsin1990.Formanyyears,hewasaleaderoftheGasSensorGroup,andmanagerofvariousnationalandinternationalscientificandengineeringprojectscarriedoutintheLaboratoryofMicro-andOptoelectronics,TechnicalUniversityofMoldova.Currently,Dr.KorotcenkovisaresearchProfessorattheGwangjuInstituteofScienceandTechnology,RepublicofKorea.HisresearchhasincludedsignificantworkonSchottkybarri-ers,MOSstructures,nativeoxides,andphotoreceiversonthebaseofIII-Vscompounds.Hecontinueswithresearch in various aspects of materials sciences and surface science, with a particular focus on nanostructured metaloxidesandsolidstategassensordesign.Dr.Korotcenkovistheauthororeditorofelevenbooksandspe-cial issues, eleven invited review papers, seventeen book chapters, and more than 190 peer-reviewed articles. HisresearchactivitieshavebeenhonoredwiththeAwardoftheSupremeCouncilofScienceandAdvancedTechnologyoftheRepublicofMoldova(2004)andThePrizeofthePresidentsoftheUkrainian,BelarusandMoldovanAcademiesofSciences(2003),amongmanyothers.

ISBN: 978-1-60650-309-6

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CHEMICAL SENSORS VoLuME 1: MICrostruCturAL CHArACtErIzAtIon And ModELIng of MEtAL oxIdEs — Edited by ghenadii Korotcenkov, Ph.d., dr. sci.

A volume in the Sensors Technology SeriesEditedbyJoeWatsonPublished by Momentum Press®

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Contents

Preface xiii

about the editor xvii

contributors xix

1 Microstructural characterization of tin dioxide thin filMs 1J. WangZ. JiaoM. H. WuC.-H. ShekC. M. L. WuJ. K. L. LaiZ. W. Chen

1 Introduction 1

2 Growth and Nucleation of Thin Films 2

3 Multifractal Spectra of SEM Images of Thin Films 11

4 Microstructural Evolution of Nanocrystalline SnO2 Thin Films 16

5 Concluding Remarks 24

6 Acknowledgments 24

References 24

2 Microstructural evolution by in-situ teM observations and siMulations 29

Hiromi NakanoHidehiko Tanaka

1 Introduction 29

2 In-Situ TEM Observation Method 30

vi • contents

3 In-Situ Measurements of Phase Transitions of Metal Oxides 31

4 In-Situ Observation of Thermal Behavior of BaTiO3 Grains 35

5 In-Situ Observation of Grain Growth of Metal Oxides 385.1 Grain Growth of Spherical Grains of BaTiO3 385.2 Grain Growth of Faceted Grains of Ba5Nb4O15 405.3 New Theory of Grain Growth and Sintering for

Simulation 42

6 Summary 45


References 46

3 raMan sPectroscoPy of oxide Materials 49T. Pagnier

1 Introduction 49

2 Raman Spectroscopy Basics 502.1 The Raman Effect 502.2 Raman Spectrometers 532.3 Other Vibrational Spectroscopy Techniques 55

3 Structure Determination by Raman Spectroscopy 553.1 Basic Structural Characterization 553.2 Oxide Mixtures and Solid Solutions 59

4 The Nanostate: Nanopowders and Nanowires 594.1 SnO2 Nanopowders 604.2 WO3 Nanopowders 624.3 Other Oxides 634.4 Nanowires, Nanobelts, and Nanoribbons 634.5 Mixed Oxides 65

5 Synthesis of Nano-Objects 66

6 Interactions with Gaseous Species 67

7 Calculations of Raman Spectra 72

8 Conclusion 74

References 74

4 Microstructural and surface Modeling of sno2 using dft calculations 81

J. D. PradesA. CireraG. KorotcenkovB. K. Cho

contents • vii

1 Introduction 81

2 DFT-Based Approaches to Simulation and Modeling of Structural Properties and Surface Chemistry of Metal Oxides 83

2.1 Surface and Bulk Models 832.2 Bulk Stability 912.3 Surface Stability 952.4 Stability of Nanoparticles 1042.5 Chemisorption 108

3 DFT Models and Results Related to SnO2 Characterization 1233.1 SnO and SnO2 Bulk Properties 1243.2 SnO2 Surface Properties 1273.3 Molecular Adsorption 1373.4 Dopants and Their Role in Structural and Surface

Properties of the SnO2 145

4 Conclusion 148


References 149

5 density functional theory Modeling of zno for gas sensor aPPlications 163

M. J. S. Spencer

1 Introduction 163

2 Metal Oxides for Gas Sensing 1642.1 ZnO for Gas Sensing 167

3 Theoretical Methods for Studying Gas–Sensor Reactions 1693.1 Density Functional Theory 1693.2 Theoretical Approach to Studying ZnO Surfaces 170

4 Examples of DFT Studies of Gas–ZnO Sensor Reactions 1794.1 Nitrogen Oxides (NO, NO2, N2O) 1804.2 Ethanol (CH3CH2OH) 198

5 Conclusions and Outlook 209


References 210

6 Modeling interactions of Metal oxide surfaces with water 217L. VlcekP. GaneshA. Bandura

viii • contents

E. MamontovM. PredotaP. T. CummingsD. J. Wesolowski

1 Introduction 217

2 Metal Oxide–Water Interactions and Their Study 2192.1 General Oxide Properties and Surface Processes 2202.2 Experimental Methods 2232.3 Computer Simulations 2242.4 Theoretical Models 228

3 Electronic Structure and Surface Reactions 2293.1 TiO2 2293.2 SnO2 2333.3 ZnO 237

4 Thermodynamic and Structural Aspects of Adsorption 2404.1 TiO2 2404.2 SnO2 2434.3 ZnO 245

5 Dynamics of Adsorbed Water 2465.1 Fast Processes—Proton Motion 2475.2 Slow Processes—Molecular Motion 248

6 Perspectives 251


References 252

7 density functional theory study of water dissociative cheMisorPtion on Metal oxide surfaces 263

C. Zhou H. Cheng

1 Introduction 263

2 Catalytic Water Dissociation on Metal Oxide Surfaces 2652.1 Water Dissociative Chemisorption on Fe3O4(111) Surfaces 2662.2 Water Dissociative Chemisorption on Rutile-TiO2(110)

Surfaces 2752.3 Water Dissociative Chemisorption on

a-Al2O3(100) Surfaces 280

4 Summary 285


References 286

contents • ix

8 first-PrinciPles studies of hydrogen sPillover MechanisMs on Metal oxides 293

L. ChenM. YangH. Cheng

1 Introduction 293

2 General View of Spillover 2942.1 Mechanism of Hydrogen Spillover on Metal Oxide 2942.2 The Role of Noble Metals in the Spillover Effect 2952.3 The Role of Spillover in Chemical Sensors 2962.4 Experimental Confirmations of the Presence of

Spillover Effect 296

3 Computational Approach 297

4 Hydrogen Spillover in MoO3 297

5 Hydrogen Spillover in Al2O3 306

6 Hydrogen Spillover in WO3 309

7 Summary 312

References 313

9 adsorPtion and diffusion of adatoMs and sMall clusters on Metal oxide surfaces 317

R. FerrandoA. Fortunelli

1 Introduction 317

2 Theoretical Methods and Concepts in Metal/Oxide Interaction 3202.1 Theoretical Methods 3202.2 Concepts in Metal/Oxide Interaction 322

3 Diffusion of Isolated Adatoms 3243.1 Energy Barriers for Isolated Adatoms on Flat MgO(001) 3243.2 Long Jumps in Adatom Diffusion 3263.3 Diffusion in the Presence of Defects 329

4 Diffusion of Dimers, Trimers, and Tetramers 3304.1 Dimers 3304.2 Trimers 3334.3 Tetramers 334

5 Adsorption and Diffusion on Exotic (Ultrathin) Oxide Substrates 3385.1 Adsorption 3415.2 Diffusion 3475.3 Surface Nanopatterning and Template-Directed Assembly 350

x • contents

6 Conclusions 352

References 353

10 effect of size on the Phase stability of nanostructures 363S. LiQ. Jiang

1 Introduction and Motivation 363

2 Phase Stability and Structural Phase Transition 3672.1 Crystals and Crystalline Structures 3682.2 Factors Triggering Phase Transitions 3692.3 Size Effect on Phase-Transition Pressure and

Temperature of Nanostructures 3702.4 Research on the Thermodynamics of Solid Structural

Nanophase Transitions 3702.5 Effect of Surface: Surface Energy and Surface Stress 3712.6 Effect of Shape on Phase Stability 372

3 Theoretical Methods for Size-Dependent Phase Stability 3723.1 Simulation Techniques 3733.2 Thermodynamics 3743.3 Applications: Case Studies 382

4 Concluding Remarks 389

References 390

11 segregation-induced grain-boundary electrical Potential in ionic oxide Materials: siMulation aPProaches and Pending challenges 397

R. L. González RomeroJ. J. Meléndez MartínezF. L. Cumbrera HernándezD. Gómez García

1 Introduction: General Ideas About Segregation Effects in Oxide Materials 397

2 Modeling of Segregation to the Grain Boundaries: Analytical (or Continuum) Approach 398

3 Atomistic Approach: Molecular Dynamics Modeling 408

4 Mesoscopic Approach: Phase-Field Models 4144.1 Topological Evolution of a Collective of Many Grains 4154.2 Microstructural Evolution Coupled with Chemical

Segregation 417

contents • xi

5 Pending Problems and Future Prospects 418


References 419

index 425

xiii

PrefaCe

This series, Chemical Sensors: Simulation and Modeling, is the perfect comple-ment to Momentum Press’s six-volume reference series, Chemical Sensors: Fundamentals of Sensing Materials and Chemical Sensors: Comprehensive Sensor Technologies, which present detailed information about materials, technologies, fabrication, and applications of various devices for chemical sensing. Chemical sensors are integral to the automation of myriad industrial processes and every-day monitoring of such activities as public safety, engine performance, medical therapeutics, and many more.

Despite the large number of chemical sensors already on the market, selec-tion and design of a suitable sensor for a new application is a difficult task for the design engineer. Careful selection of the sensing material, sensor platform, technology of synthesis or deposition of sensitive materials, appropriate coatings and membranes, and the sampling system is very important, because those deci-sions can determine the specificity, sensitivity, response time, and stability of the final device. Selective functionalization of the sensor is also critical to achieving the required operating parameters. Therefore, in designing a chemical sensor, de-velopers have to answer the enormous questions related to properties of sensing materials and their functioning in various environments. This four-volume com-prehensive reference work analyzes approaches used for computer simulation and modeling in various fields of chemical sensing and discusses various phenomena important for chemical sensing, such as surface diffusion, adsorption, surface reactions, sintering, conductivity, mass transport, interphase inter actions, etc. In these volumes it is shown that theoretical modeling and simulation of the pro-cesses, being a basic for chemical sensor operation, can provide considerable assistance in choosing both optimal materials and optimal configurations of sensing elements for use in chemical sensors. The theoretical simulation and model ing of sensing material behavior during interactions with gases and liquid surroundings can promote understanding of the nature of effects responsible for high effectiveness of chemical sensors operation as well. Nevertheless, we have to understand that only very a few aspects of chemistry can be computed exactly.

xiv • preface

However, just as not all spectra are perfectly resolved, often a qualitative or ap-proximate computation can give useful insight into the chemistry of studied phe-nomena. For example, the modeling of surface-molecule interactions, which can lead to changes in the basic properties of sensing materials, can show how these steps are linked with the macroscopic parameters describing the sensor response. Using quantum mechanics calculations, it is possible to determine parameters of the energetic (electronic) levels of the surface, both inherent ones and those introduced by adsorbed species, adsorption complexes, the precursor state, etc. Statistical thermodynamics and kinetics can allow one to link those calculated surface parameters with surface coverage of adsorbed species corresponding to real experimental conditions (dependent on temperature, pressure, etc.). Finally, phenomenological modeling can tie together theoretically calculated characteris-tics with real sensor parameters. This modeling may include modeling of hot plat-forms, modern approaches to the study of sensing effects, modeling of processes responsible for chemical sensing, phenomenological modeling of operating char-acteristics of chemical sensors, etc.. In addition, it is necessary to recognize that in many cases researchers are in urgent need of theory, since many experimental observations, particularly in such fields as optical and electron spectroscopy, can hardly be interpreted correctly without applying detailed theoretical calculations.

Each modeling and simulation volume in the present series reviews model-ing principles and approaches particular to specific groups of materials and de-vices applied for chemical sensing. Volume 1: Microstructural Characterization and Modeling of Metal Oxides covers microstructural characterization using scanning electron microscopy (SEM), transmission electron spectroscopy (TEM), Raman spectroscopy, in-situ high-temperature SEM, and multiscale atomistic simulation and modeling of metal oxides, including surface state, stability, and metal oxide interactions with gas molecules, water, and metals. Volume 2: Conductometric-Type Sensors covers phenomenological modeling and computational design of conductometric chemical sensors based on nanostructured materials such as metal oxides, carbon nanotubes, and graphenes. This volume includes an over-view of the approaches used to quantitatively evaluate characteristics of sensitive structures in which electric charge transport depends on the interaction between the surfaces of the structures and chemical compounds in the surroundings. Volume 3: Solid-State Devices covers phenomenological and molecular model-ing of processes which control sensing characteristics and parameters of various solid-state chemical sensors, including surface acoustic wave, metal-insulator-semiconductor (MIS), microcantilever, thermoelectric-based devices, and sensor arrays intended for “electronic nose” design. Modeling of nanomaterials and nano-systems that show promise for solid-state chemical sensor design is analyzed as well. Volume 4: Optical Sensors covers approaches used for modeling and simu-lation of various types of optical sensors such as fiber optic, surface plasmon resonance, Fabry-Pérot interferometers, transmittance in the mid-infrared region,

preface • xv

luminescence-based devices, etc. Approaches used for design and optimization of optical systems aimed for both remote gas sensing and gas analysis chambers for the nondispersive infrared (NDIR) spectral range are discussed as well. A descrip-tion of multiscale atomistic simulation of hierarchical nanostructured materials for optical chemical sensing is also included in this volume.

I believe that this series will be of interest of all who work or plan to work in the field of chemical sensor design. The chapters in this series have been prepared by well-known persons with high qualification in their fields and therefore should be a significant and insightful source of valuable information for engineers and researchers who are either entering these fields for the first time, or who are al-ready conducting research in these areas but wish to extend their knowledge in the field of chemical sensors and computational chemistry. This series will also be interesting for university students, post-docs, and professors in material science, analytical chemistry, computational chemistry, physics of semiconductor devices, chemical engineering, etc. I believe that all of them will find useful information in these volumes.

G. Korotcenkov

xvii

about the editor

Ghenadii Korotcenkov received his Ph.D. in Physics and Technology of Semiconductor Materials and Devices in 1976, and his Habilitate Degree (Dr.Sci.) in Physics and Mathematics of Semiconductors and Dielectrics in 1990. For a long time he was a leader of the scientific Gas Sensor Group and manager of various national and international scientific and engineering projects carried out in the Laboratory of Micro- and Optoelectronics, Technical University of Moldova. Currently, Dr. Korotcenkov is a research professor at the Gwangju Institute of Science and Technology, Republic of Korea.

Specialists from the former Soviet Union know Dr. Korotcenkov’s research results in the field of study of Schottky barriers, MOS structures, native oxides, and photoreceivers based on Group III–V compounds very well. His current research interests include materials science and surface science, focused on nanostructured metal oxides and solid-state gas sensor design. Dr. Korotcenkov is the author or editor of 11 books and special issues, 11 invited review papers, 17 book chapters, and more than 190 peer-reviewed articles. He holds 18 patents, and he has presented more than 200 reports at national and international conferences.

Dr. Korotcenkov’s research activities have been honored by an Award of the Supreme Council of Science and Advanced Technology of the Republic of Moldova (2004), The Prize of the Presidents of the Ukrainian, Belarus, and Moldovan Academies of Sciences (2003), Senior Research Excellence Awards from the Technical University of Moldova (2001, 2003, 2005), a fellowship from the International Research Exchange Board (1998), and the National Youth Prize of the Republic of Moldova (1980), among others.

xix

Jian Wang (Chapter 1)Shanghai Applied Radiation InstituteSchool of Environmental and Chemical EngineeringShanghai UniversityShanghai 200444, People’s Republic of China

Zheng Jiao (Chapter 1)Shanghai Applied Radiation Institute and Institute of Nanochemistry and NanobiologySchool of Environmental and Chemical EngineeringShanghai UniversityShanghai 200444, People’s Republic of China

Minghong Wu (Chapter 1)Shanghai Applied Radiation Institute and Institute of Nanochemistry and NanobiologySchool of Environmental and Chemical EngineeringShanghai UniversityShanghai 200444, People’s Republic of China

Chan-Hung Shek (Chapter 1)Department of Physics and Materials Science City University of Hong KongKowloon Tong, Hong Kong

C. M. Lawrence Wu (Chapter 1)Department of Physics and Materials Science City University of Hong KongKowloon Tong, Hong Kong

Contributors

xx • contributors

Joseph K. L. Lai (Chapter 1)Department of Physics and Materials ScienceCity University of Hong KongKowloon Tong, Hong Kong

Zhiwen Chen (Chapter 1)Shanghai Applied Radiation Institute School of Environmental and Chemical EngineeringShanghai UniversityShanghai 200444, People’s Republic of China andDepartment of Physics and Materials ScienceCity University of Hong KongKowloon Tong, Hong Kong

Hiromi Nakano (Chapter 2)Cooperative Research Facility Center Toyohashi University of Technology Tempaku, Toyohashi, Aichi 441-8580, Japan

Hidehiko Tanaka (Chapter 2)National Institute for Materials ScienceInternational Center for Materials NanoarchitectonicsTsukuba, Ibaraki 305-0044, Japan

Thierry Pagnier (Chapter 3)Laboratoire d’Electrochimie et de Physicochimie des Matériaux et Interfaces (LEPMI) Grenoble Institute of Technology 38402 Saint-Martin-d’Hères, France

J. Daniel Prades (Chapter 4)Departament d’Electrònica, MIND-IN2UBUniversitat de Barcelona08028 Barcelona, Spain

Albert Cirera (Chapter 4)Departament d’Electrònica, MIND-IN2UBUniversitat de Barcelona08028 Barcelona, Spain

contributors • xxi

Ghenadii Korotcenkov (Chapter 4)Department of Material Science and EngineeringGwangju Institute of Science and TechnologyGwangju, 500-712, Republic of KoreaandTechnical University of MoldovaChisinau 2004, Republic of Moldova

Beongki Cho (Chapter 4)Department of Material Science and Engineering and Department of Nano Bio Materials and ElectronicsGwangju Institute of Science and TechnologyGwangju 500-712, Republic of Korea

Michelle J. S. Spencer (Chapter 5)Department of Chemistry La Trobe Institute for Molecular Science La Trobe University Bundoora, Victoria 3086, AustraliaandApplied Physics, School of Applied Sciences RMIT UniversityVictoria 3001, Australia

Lukas Vlcek (Chapter 6)Chemical Sciences Division Oak Ridge National LaboratoryOak Ridge, Tennessee 37831, USA

Panchapakesan Ganesh (Chapter 6)Center for Nanophase Materials Sciences Oak Ridge National LaboratoryOak Ridge, Tennessee 37831, USA

Andrei Bandura (Chapter 6)Department of Quantum Chemistry The St. Petersburg State University198504 Petrodvorets, Russia

Eugene Mamontov (Chapter 6)Chemical & Engineering Materials Division Oak Ridge National LaboratoryOak Ridge, Tennessee 37831, USA

xxii • contributors

Milan Predota (Chapter 6)Institute of Physics and Biophysics University of South BohemiaCeske Budejovice, 37005 Czech Republic

P. T. Cummings (Chapter 6)Department of Chemical and Biomolecular Engineering Vanderbilt UniversityNashville, Tennessee 37235, USAandCenter for Nanophase Materials SciencesOak Ridge National LaboratoryOak Ridge, Tennessee 37831, USA

D. J. Wesolowski (Chapter 6)Chemical Sciences DivisionOak Ridge National LaboratoryOak Ridge, Tennessee 37831, USA

Chenggang Zhou (Chapter 7)Department of Chemistry National University of SingaporeSingapore 117543andFaculty of Materials Science and ChemistryChina University of Geosciences WuhanWuhan 430074, People’s Republic of China

Hansong Cheng (Chapter 7)Department of ChemistryNational University of SingaporeSingapore 117543

Liang Chen (Chapter 8)Ningbo Institute of Materials Technology and EngineeringChinese Academy of SciencesNingbo 315201, People’s Republic of China

Ming Yang (Chapter 8)Sustainable Energy LaboratoryChina University of GeosciencesWuhan 430074, People’s Republic of China

contributors • xxiii

Hansong Cheng (Chapter 8)Department of Chemistry National University of SingaporeSingapore 117543

Riccardo Ferrando (Chapter 9)Dipartimento di FisicaUniversità di Genova16146 Genova, Italy

Alessandro Fortunelli (Chapter 9)CNR-IPCFIstituto per i Processi Chimico-Fisici del Consiglio Nazionale delle Ricerche Pisa 56124, Italy

Shuang Li (Chapter 10)USTC—CityU Joint Advanced Research CentreSuzhou 215123, People’s Republic of Chinaand Department of Physics and Materials ScienceCity University of Hong KongHong Kong SAR, People’s Republic of China

Qing Jiang (Chapter 10)Key Laboratory of Automobile Materials, Ministry of Educationand School of Materials Science and Engineering Jilin UniversityChangchun 130022, People’s Republic of China

Robert Luis González Romero (Chapter 11)Departamento de Física de la Materia Condensada Universidad de Sevilla41080 Sevilla, Spain

Juan José Meléndez Martínez (Chapter 11)Departamento de Física Universidad de Extremadura06006 Badajoz, Spain

Francisco Luis Cumbrera Hernández (Chapter 11)Departamento de Física de la Materia CondensadaUniversidad de Sevilla41080 Sevilla, Spain

xxiv • contributors

Diego Gómez García (Chapter 11)Departamento de Física de la Materia CondensadaUniversidad de Sevilla41080 Sevilla, Spain

1DOI: 10.5643/9781606503119/ch1

Chapter 1

Microstructural characterization of tin DioxiDe thin filMs

J. WangZ. JiaoM. h. WuC.-h. ShekC. M. L. WuJ. K. L. LaiZ. W. Chen

1. IntroductIon

Semiconductor oxides are fundamental to the development of smart and functional materials, devices, and systems (Wang 1998; Pan 2001; Chen 2006a). These oxides have two unique structural features, mixed cation valences and an adjustable oxygen deficiency, which are the bases for creating and tuning many novel material properties, from chemical to physical (Chen et al. 2004). Because of the increasing importance of air pollution and the need to monitor concentration levels of gases such as CO, CO2, NOx, O3, SO2, etc., the development of many kinds of sensors and control systems has been jolted into action in recent years (Dai 2002; Ng 2003; Wang 2003; Cheng 2004). Microstructure variations will inevitably change their physical and chemical properties. Therefore, study of the microstructure of the

2 • CheMiCaL SenSorS – SiMULation and ModeLinG: VoLUMe 1

grains can help us to uncover certain nanoscale features and to explain the corresponding experimental results. With the continuous reduction of sizes and the increased demands for performance of optical, electronic, and magnetic devices, the control of thin-film structures is becoming increasingly significant (Chen et al. 2010).

Many materials have been reported to be usable for design of metal oxide sensors. It has been established that materials in different structural states can be used in these resistivetype gas sensors (Sberveglieri 1992; Korotcenkov 2008). The surface conductance of a semiconducting oxide is affected by the concentration of ambient gases. Resistive gas sensors are based on this principle, and the nature of the sensing mechanism is related to the electrical response of gas sensors to reactive gases. The change in the sensor resistance provides an indication of the gas concentration (Chen et al. 2006b). These sensors can be quite versatile, as they may be used to detect oxygen, flammable gases, and common toxic gases. Their mechanism of operation is complex, however, involving interactions between gaseous molecules and defects on the surface and grain boundaries. In practice, nanocrystalline and polycrystalline materials have found their greatest application in solidstate gas sensors because of their small crystallite size, inexpensive design technology, and stability of both structural and electrophysical properties (Schierbaum 1992; Kohl 2001). Therefore, it is necessary to establish the role of morphology and crystallographic structure in gassensing effects, and the structural parameters of the oxides and the sensor response.

The main focus of this review is to explore the growth process and the microstructural characteristics of thin films using tin oxides.

2. Growth and nucleatIon of thIn fIlms

The study of thin-film growth has been characterized by the application of surfacescience methods to understand growth at the atomic level. Understanding the physics of thin-film nucleation and growth in synthesis processes is very important both theoretically and technologically (Chen et al. 2008). There has been widespread interest in understanding the influence of the nanometer size scale, the surface and interface, and the nucleation and growth on the materials’ chemical and physical properties. Over the years, many thin-film growth techniques have been based on pulsedbeam deposition, such as pulsed laser deposition and pulsed magnetron sputtering deposition. The pulsed laser deposition technique stands out by its unique features, such as its extremely high instantaneous deposition rate and the highly energetic ablated species, which permit the growth of nanostructured thin films that exhibit unprecedented properties (Hinnemann et al. 2001). Therefore, investigation of the micro and nanostructure characteristics of thin films, by means of x-ray diffraction (XRD) and high-resolution transmission

MiCroStrUCtUraL CharaCteriZation oF tin dioXide thin FiLMS • 3

electron microscopy (HRTEM), is an essential step toward a better understanding of how the growth conditions influence the thin-film nanostructure, which, in turn, determines the film sensing performance (Chen 2003; Serventi 2003).

Pulsed laser deposition (PLD) is a growth technique in which photonic energy is coupled to the bulk starting material via electronic processes (Nagano 1984; Chrisey1994). The rationale for using PLD lies primarily in its pulsed nature, the possibility of carrying out surface chemistry far from thermal equilibrium, and under favorable conditions. Above a certain power density, significant material removal occurs in the form of an ejected luminous plume. The threshold power density needed to produce such a plume depends on the target material, its morphology, and the laser pulse wavelength and duration, but might be of the order of 10–500 MW cm−2 for ablation using ultraviolet (UV) excimer laser pulses of 10ns duration. Material from the plume is then allowed to recondense on a substrate, where thin-film growth occurs. The physical processes in PLD are highly complex and interrelated. They depend on the laser pulse parameters and the properties of the target material (Chrisey et al. 1994).

To investigate the scaling behavior of PLD, we consider a variant of the model. In contrast to that model, the duration of a pulse is assumed to be zero (Combe et al. 1998), and we neglect the transient enhancement of the mobility of freshly deposited atoms. However, the model is defined as a solid-on-solid growth model on a square lattice of L × L sites with integer heights representing the configuration of the adsorbed layer. This model is controlled by three parameters: the pulse intensity I, the diffusion constant D, and the average flux density of incoming particles F. In particular, the control parameters of PLD are the pulse intensity I, which is the number of particles deposited in one pulse per unit area, and the diffusiontodeposition ratio D/F. The average deposition rate is given by F = I/Δt, where Δt is the time interval between two pulses. The intensity is measured in monolayers (ML), and D/F is dimensionless, as the lattice constant is set to unity (Hinnemann 2001; Chen 2011).

Figure 1.1 shows kinetic Monte Carlo simulations of pulsed laser deposition (Figure 1.1a) and molecular-beam epitaxy (MBE) (Figure 1.1b) in the submonolayer regime. It can be seen in Figure 1.1a that the surface morphology depends sensitively on the pulse intensity.

In MBE, usually the surface is covered with many small islands, whereas for PLD there are a few large islands spaced relatively far apart. However, if the power density per pulse is reduced, we would expect PLD to produce the same island morphology as MBE. This is indeed the case, although the two situations are not exactly the same, as in PLD the deposition takes place at fixed times whereas in MBE it is probabilistic and therefore the time intervals between two depositions have a Poisson distribution. This difference only influences the island morphology at high D/F, while the finite-size effect is setting in. For simulations presented here, only the D/F ratio below the finite-size region is considered in performing Monte Carlo


simulations (Chen et al. 1998). The nucleation density for various pulse intensities was investigated using a square lattice system of size 400 × 400 for Monte Carlo simulation. The island distance is measured at 0.2 ML coverage, when the island density reaches its maximum but coalescence has not yet set in. For small densities, we recall the well-known power law for the island distance in MBE,

γµ ( / )Dl D F (1.1)

where the exponent γ depends on the dimensions of the surface, the island dimension, and the critical nucleus i*; that is, the smallest stable island contains i* + 1 atoms. For a twodimensional surface, compact islands, and a critical nucleus of i* = 1, one obtains γ = 1/6. One should note that here the islands are not compact but fractal patterns, as can be seen in Figure 1.1. This is due to the fact that the edge diffusion is not considered in the simulations. The exponent γ can be determined from simulations if one monitors the number of nucleation events in a layer -µ 2( )Dl as a function of D/F. However, for PLD with large densities, the island distance obeys a different power law:

-µ vDl I (1.2)

figure 1.1. Kinetic Monte Carlo simulations of pulsed laser deposition and molecular-beam epitaxy in the submonolayer regime. (a) a simulated pLd-grown surface with D/F = 2 × 108 and I = 0.01 ML. (b) a simulated MBe-grown surface with D/F = 2 × 108. The figure shows typical configurations after deposition of 0.05 ML. (reproduced with permission from Chen et al. 2008. Copyright 2008 taylor & Francis.)

1

Metal Oxide Characterization Figure 1


where γ γ= - = +/(1 2 ) 1/(2 )fv d and, more generally γ = +1/(4 )fd if they have the fractal dimension df. In this regime the island distance is independent of the parameter D/F, since the adatoms do not make use of their diffusion probability. When they find an island, they attach to it in a much shorter time compared with that of diffusion between two depositions. So, the two regimes described by Eqs. (1.1) and (1.2) are separated by a crossover at a certain density, for which the number of deposited atoms is of the same order of magnitude as the adatom density. Since the average adatom density n in MBE scales as γ- +µ 1 2( / )n D F , the critical density demonstrates the same scaling behavior:

γ- +µ 1 2( / )cI D F (1.3)

The qualitative difference between PLD and MBE for I > Ic is shown in Figure 1.1. There are many nucleations at an early stage, although the effective flux of incoming particles is the same in both cases. The scaling behavior, namely, the fractal dimension, is measured using the boxcounting method. The fractal dimension values for the PLD and MBE were decided to be 1.28 ± 0.03 and 1.62 ± 0.03, respectively. Comparing the fractal dimension of PLD with MBE, we found that the fractal dimension obtained by PLD is smaller than that of MBE (Chen et al. 2008). A simple schematic diagram, shown in Figure 1.2, illustrates a key difference in the nature of coalescence events between pulsed and continuous depositions, although in a real system the same difference may unfold in a more complex

2


figure 1.2. Schematic diagrams showing the difference between the dynamics of coalescence in pulsed laser deposition (pLd) and molecular-beam epitaxy (MBe). (a) pLd (pulsed delivery): concurrent coalescence. (b) MBe (continuous delivery): sequential coalescence. (reproduced with permission from Chen et al. 2008. Copyright 2008 taylor & Francis.)


way. Consider four droplets, A, B, C, and D, deposited/formed simultaneously, for instance, in a pulsed process, or one after another (in the sequence A, B, C, and then D) in the continuous process. As shown in Figure 1.2a (pulsed case), the concurrent multiple droplet coalescence leads to a single large droplet. On the other hand, the outcome in Figure 1.2b (continuous deposition case) shows that the initial coalescence of A and B would produce a large droplet with no overlap with the subsequently arriving/forming droplets C and D. Therefore, it is possible to use a simple model to reveal the differences of coalescence dynamics between pulsed laser deposition and molecularbeam epitaxy.

Figures 1.3a and 1.3b show XRD patterns of the cassiterite SnO2 bulk and the asprepared SnO2 thin films, respectively. All reflections can be indexed with

3


figure 1.3. Xrd patterns of Sno2: (a) Sno2 bulk material; (b) the as-prepared Sno2 thin film. (reproduced with permission from Chen et al. 2005. Copyright 2005 elsevier.)


a tetragonal unit cell of SnO2, and the lattice parameters are close to a = 4.737 Å and c = 3.185 (Zheng et al. 1996). XRD results showed that the average grain size of the asprepared nanocrystalline SnO2 was about 12 nm. The crystallographic planes identified on the XRD patterns as presented in Figure 1.3b are typical of a pure polycrystalline SnO2 phase, in terms of peak positions and relative peak intensities (ICDD, 1995). It is noted that a broad peak centered around 2h = 30° was clearly visible in the XRD pattern of the PLD SnO2 thin films (see Figure 1.3b). We believe that this broad peak is due to the presence of an amorphous SnO phase. This was clearly demonstrated in previous work (Dolbec et al. 2002), where PLD SnO2 thin films were grown under vacuum (laser influence of ~4.6 J/cm2) over a wide range of deposition temperatures (from room temperature to 600°C), and the as-deposited film consisted of both a polycrystalline SnO2 phase and an amorphous SnO (a-SnO) phase (Godbole 1990; Phillips 1996). The XRD signature of aSnO is a broad hump centered around 2h = 29.8°, which corresponds to the most intense reflection of the SnO phase (Chen et al. 2005a), in agreement with the XRD patterns shown in Figure 1.3b. Moreover, for PLD SnO2 thin films as thick of 4 mm (in our experiment), more than 98% of the XRD signal originates from the film, thereby eliminating any relevant consideration of a possible contribution of the glass substrate to the broad component of the XRD pattern shown in Figure 1.3b. The broad peak that characterizes glass is known to be centered near 2h = 26°. The presence, therefore, of such an amorphous SnO phase in the films suggests that the growth conditions (i.e., under vacuum at room temperature) are in fact not appropriate for a complete crystallization of the pure SnO2 phase. This conclusion accords with previous results (Dolbec et al. 2002), where it was demonstrated that a suitable oxygen background pressure is a prerequisite for the growth of pure polycrystalline SnO2 phase.

To explain the nanostructures of metal oxide nanocrystallites, one should investigate a possible mechanism to explain the microstructural behavior. High-resolution transmission electron microscopy (HRTEM) investigation of thin films that can be used as elements of conductance sensors can give useful information about the local composition at dislocation cores. HRTEM images show that each nanocrystallite is connected mainly with two or three neighbors through necks; however, grain boundary contacts cannot be ignored. A more detailed analysis can be made based on the highly magnified HRTEM image in Figure 1.4. Figures 1.4a and 1.4b show highresolution transmission electron micrographs of as prepared SnO2 thin films. Besides the amorphous SnO phase, several nanocrystallites are clearly visible. In Figure 1.4a, the SnO2 nanoparticle is clearly crystalline and equiaxed (see the two white circles, A and B). However, nanocrystalline SnO2 grains are often overlapped and connected with two or more neighbors through necks (Figure 1.4a, white arrows). The overlapped parts display more edge dislocations. In Figure 1.4b, besides the nanocrystallites A and B, it is found that the nanocrystalline SnO2 grains are overlapped and connected with four neighbors


through necks (see A, B, C, and D). The overlapped parts contain a high density of extended defects, such as twin boundaries, exhibited in the neighborhood of the white circle (Chen et al. 2005a). These results indicate that the thin films are composed of both polycrystalline SnO2 and amorphous SnO phases. Since the phenomenon of gas sensitivity is related to electron transport, the geometry and nature of the contact areas between SnO2 particles is especially important. The grainrotation process is directly related to the reduction of surface energy, which is for the purpose of minimizing the area of highenergy faces (Moldovan et al. 2002). Figure 1.5 presents typical microstructural details of asdeposited tin oxide thin films, revealing that the as-deposited films have been partially crystallized even without heat treatment at high temperatures. The presence of crystalline SnO2 with a rutile structure was confirmed by selected-area electron-diffraction pattern (SAED) analysis. The inset at the bottom left-hand corner displays the SAED pattern taken from the film, showing the polycrystalline diffraction patterns of nanocrystallite which can be identified as rutile SnO2 structure. For instance, one, two, three, and four diffraction rings in the inset at the bottom lefthand corner have a lattice spacing of 2.369 Å, 2.119 Å, 1.498 Å, and 1.322Å, which have been identified to be the (200), (210), (210), and (202) reflections of rutile-type SnO2, which is a tetragonal phase with the space group P42/mnm and lattice parameters a = 4.737 Å and c = 3.185 Å, respectively (Baur 1956; Von Allmen 1995; Chen 2005b). This low-magnification HRTEM image of the as-deposited thin film in Figure 1.5 shows randomly oriented particles, as well as several clusters where the SnO2 grains are overlapped and connected with two or three neighbors through necks, formed by grains with the same crystallographic orientation (indicated by white arrows). As can be seen in Figure 1.5, the presence of clusters is quite common, suggesting the largescale formation of such morphologies.

4


figure 1.4. high-resolution transmission electron micrographs of as-prepared Sno2 thin films pre-pared by the pLd method. (reproduced with permission from Chen et al. 2005. Copyright 2005 elsevier.)


Figure 1.6a shows that the grain cluster is composed of several primary nanocrystallites without grain boundaries, which offers direct evidence that coalescence occurs when two or more neighbor grains assume the same orientation, resulting in a single nanocluster. Slight misorientations (indicated by the white arrows) are visible in the image of a cluster composed of several primary SnO2 nanocrystallites (e.g., four nanocrystallites, indicated by one, two, three, and four regions), shown in Figure 1.6a. These misorientations or defects originate from imperfect attachment among several nanocrysallites, resulting in edge and screw dislocations. A substantial driving force for the crystal rotation to achieve perfectly coherent grain–grain boundaries is expected; however, some rotation may be metastable and preserved by the formation of a coincident site array. Figure 1.6b shows the initial stage of the graingrowth process. The white arrow in the circle indicates the formation of necks between grains. An analysis of Figure 1.6b suggests that

5


figure 1.5. Low-magnification HRTEM image and SAED pattern of the SnO2 particles prepared by pulsed laser deposition at room temperature. arrows indicate the presence of clusters showing that the Sno2 grains are overlapped and connected with two or three neighbors through necks. (reproduced with permission from Chen et al. 2005. Copyright 2005 Springer-Verlag.)


6


figure 1.6. High-magnification HRTEM images of SnO2 nano crystallites: (a) Sno2 cluster com-posed of several nanocrystallites; (b) the initial stage of the grain-growth process. (reproduced with permission from Chen et al. 2005. Copyright 2005 Springer-Verlag.)

the first step in the grain-growth process is the formation of necks. After this step, a rotation may occur to decrease the angle of misorientation, changing the grains’ orientation. When the grains assume the same orientation, i.e., a coherent grain–grain boundary, the grain boundary must migrate toward the smaller particle, resulting in a single larger nanocrystallite. It can be seen that the SnO2 particles are well crystallized, but the grain interiors often appear to be free from defects. Therefore, it is suggested that the formation processes of nucleation, grain rotation, coalescence, and growth occur when two or more particles collide (Penn and Banfield 1998).

Figure 1.7 shows that the formation processes of nucleation, grain rotation, coalescence, and growth, which occur when two or more particles collide, promote attachment between particles. Figure 1.7a shows the nucleation process of SnO2

nanocrystallites. Figure 1.7b displays the grainrotation process after nucleation. This process of attachment is followed by a process of rotation, which leads to a low-energy configuration, thereby forming a coherent grain boundary and eliminating the common grain boundary, resulting in a single larger nanocrystallite—that is, the coalescence process (see Figure 1.7c). Figure 1.7d shows the growth process of SnO2 nanocrystallites (Lal et al. 1991).


3. multIfractal spectra of sem ImaGes of thIn fIlms

Since the middle of the 1980s, the relationships among the fractal formation of thin films, the annealing temperature, the annealing time, the thickness of films, the electron emission, etc., have been investigated thoroughly. The concept of fractal geometry has proved useful in describing structures and processes in experimental systems (Musolino 1999; Hu 2002). In the past few years, multifractals have been found to have very important applications in electron microscopy. The microstructure evolution of SnO2 thin films has been investigated using x-ray diffraction, scanning electron microscopy (SEM), and atomic force microscopy, and this formation of significant fractal features is rather unusual.

Multifractal spectra of SEM images are calculated by the box-counting method. The images can be divided into many boxes of size (l × l), and we let e = l/L (L = 512, ε < 1). Pij(ε) is the average deposition probability of the film in box (i, j ). The height distribution probability Pij(ε) is determined as

7


figure 1.7. Schematic representation of the formation processes showing (a) nucleation, (b) grain rotation, (c) coalescence, and (d) growth of nanoclusters in as-deposited thin film. Arrows in (b) illustrate the rotation between grains. (reproduced with permission from Chen et al. 2005. Copyright 2005 Springer-Verlag.)


ε =å

( ) ijij

ij

hP

h (1.4)

where hij is the average height of box (i, j ) of size e measured from the datum planes corresponding to the Si (100) substrate surface. Pij (ε) can be described as a multifractal as

αε ε( )~ijP (1.5)

α

α ε ε- ( )( ) ~ fN (1.6)

where a is the singularity of the subset of probabilities, Nα(ε) is the number of boxes of size ε with the same probability, and α( )f is the fractal dimension of the a subset. The dependence of α( )f on a is the multifractal spectrum. For a random multifractal, α( )f can be calculated by the partition function method using a computer code (Huang 1989; Li 1995, 1996; Wang 1995; Chen 2001). The partition function cq(a) is defined and expressed as a power law in terms of ε with an exponent τ(q), where q is the moment order (–∞ < q < +∞):

τχ ε ε εº =å ( )( ) ( )q qq ijP (1.7)

τ(q) can be obtained from the slope of the ln cq(a)–ln ε curve. α( )f can be obtained by performing Legendre transformation as follows:

[ ]τ

α =( )d q

dq (1.8)

and

α α τ= -( ) ( )f q q (1.9)

In real calculations, the value of q cannot be infinite. One can take the maximum |q| when the a and α( )f approach to the saturated values with the increase of q. The maximum |q| depends on the probability, and the narrower the probability distribution, the larger the |q| were used. The width of the multifractal spectrum is Δa, and the difference between the fractal dimensions of the maximum probability subset (a = amin) and the minimum one (a = amax) is [ ]∆ ∆ α α= -min max( ) ( )f f f f (Wang 1995; Penn 1998).

Figure 1.8 presents SEM images of SnO2 thin films prepared on Si (100) substrate at temperatures of (A) 300°C, (B) 350°C, (C) 400°C, and (D) 450°C, respectively. The


SEM observations indicated that all thin films produced under different substrate temperatures exhibited selfsimilar fractal patterns with increase in average fractal size measurement on the fractal regions. It can be seen that the fractal patterns have an open and loose structure with increasing substrate temperature. The average sizes of the fractal patterns (or clusters) are about 0.307 mm (see Figure 1.8A), 0.906 mm (see Figure 1.8B), 1.202 mm (see Figure 1.8C), and 1.608 mm (see Figure 1.8D). The average sizes of the fractal clusters for four thin films were estimated by measurement on the fractal regions. The measuring procedure was as follows: for each SEM image, we chose 10 fractal patterns at random to get an average value. The average sizes of the fractal patterns were obtained by averaging the values of SEM images with different orientations. It was found that the average sizes of the fractal clusters increase with increasing substrate temperature (Ohta and Honjo 1988).

Figure 1.9 shows the ln cq(a)–ln ε curves of the SnO2 thin film obtained at a Si substrate temperature of 500°C. To display the ln cq(a)–ln ε curves clearly, only 13 ln cq(a)–ln ε curves with intervals of 40 |q| are plotted. The box sizes ε are taken as 1/512, 1/256, 1/128, 1/64, 1/32, 1/16, 1/8, 1/4, 1/2, and 1. The correlation coefficients of the ln cq(a)–ln ε curves at q = ±280 are 1.0000. The linearity of

figure 1.8. SeM images of Sno2 thin films prepared on Si (100) substrate at temperatures of (A) 300°C, (B) 350°C, (C) 400°C, and (d) 450°C. (reproduced with permission from Chen et al. 2010. Copyright 2010 american Chemical Society.)

8



ln cq(a)–ln ε is very important for the multifractal, and its linear range is called the scaling range. Regular multifractals have strict linearity in the ln cq(a)–ln ε plot at all moments q, and this linearity can approach zero infinitely (ln ε → −∞). It can be seen that the linearity of the ln cq(a)–ln ε curves is excellent at all q moments. It is expected that the range will be wider if the number of pixels in the SEM images is increased. The multifractal analysis can therefore be used to describe the SnO2 thin film deposition quantitatively.

Figure 1.10 shows the multifractal spectra α( )f of the SEM images. It can be seen that the α( )f of the Si substrate hooks to the right, while those of the three SnO2 thin films hook to the left. The plotted results indicate that the widths and the shapes of the multifractal spectra are completely different. The width of the α( )fof the Si substrate is smallest. The higher the substrate temperature, the wider is the spectrum of α( )f , and the larger the amax. Table 1.1 shows the para meters of the multifractal spectra of the specimens. It can be seen that Δa increases with increasing substrate temperature, indicating that the height probability became more nonuniform. Since ε < 1, amin presents the maximum probability ( αε

minmaxp ) and amax presents the maximin probability ( αε

maxminp ). Therefore,

the Δa can be used to describe the range of the probability due to max min/ .P P ∆αε-

The larger the Δa, the wider is the probability distribution, and the larger the 9


figure 1.9. ln cq(a)–ln e plots of Sno2 thin films obtained at a substrate temperature of 500oC. (reproduced with permission from Chen et al. 2005. Copyright 2005 elsevier.)


10


figure 1.10. Multifractal spectra f(a) of the substrate and three Sno2 thin films. (Reproduced with permission from Chen et al. 2005. Copyright 2005 elsevier.)

difference between the highest and the lowest growth probabilities. It is found that the Δf of the multifractal spectra of the SnO2 thin films are all greater than zero, and increase as the Si substrate temperature increases, while the Δf of the multifractal spectra of the substrate are less than zero. While αmin( )f represents the number of the boxes of the same maximum probabilities αε α ε ε-=

min( )min min( ) ( ) fNp N and

αmax( )f reflects the number of the minimum one αε α ε ε-=

max( )max max( ) ( ) fNp N , Δf

describes the ratio of the number of the maximum probability and that of the minimum one: ∆ε ε ε-=max min( )/ ( ) fNp Np . Consequently, Δf > 0 means that the chance

SubStrate temperature (°C) amax f (amax) amin f (amin) Da Df

Room temperature 2.043 2.169 2.033 1.590 0.010 –0.579400 2.059 0.530 2.008 1.107 0.051 0.577450 2.070 0.500 2.005 1.114 0.065 0.614500 2.084 0.666 2.011 1.694 0.073 1.028

Source: Reproduced with permission from Chen et al. 2005. Copyright 2005 Elsevier.

table 1.1. parameters of the multifractal spectra of the specimens


of the height distribution of the deposits lying at the highest sites is greater than that at the lowest sites, and vice versa.

In summary, the multifractal spectra α( )f show that the higher the substrate temperature, the wider the spectrum, and the larger the ∆ ∆ α α= -min max[ ( ) ( )]f f f f . The nonuniformity of the height distribution increases with increasing substrate temperature, and the liquid droplets of SnO2 thin films are formed on previous thin films. These results show that the SEM images can be characterized by the multifractal spectra. With an increase of substrate temperature, the higher longrange temperature field may promote new nuclei and subsequent growth, which leads to the fractal growth of the fine branches and a lower fractal density. This fractal structure may lead to improvement in the design of gas sensors for the monitoring of environmental pollutants.

4. mIcrostructural evolutIon of nanocrystallIne sno2 thIn fIlms

The low-dimensional systems such as thin films demonstrate a variety of physical, chemical, and functional properties which are different from their bulk counterparts (Chen et al. 2005c). The influence of grain size on material properties is especially remarkable for polycrystalline thin films. In the past several years, the interface and surface microstructure of nanomaterials have been extensively and intensively investigated. Various types of interface structure models have also been proposed for nanostructured materials, such as the gaslike model, order and extended order models, etc. (Chopra 1993; Wang 1994). Commonly, the peculiar properties of nanomaterials are explained in terms of the interface and surface structures, while the effects from the microstructure of the grains are neglected. As a matter of fact, for different preparation methods, the microstructure of nanomaterials can be nanocrystallite, nanoamorphous grain, or nanocluster agglomerations with some crystalline features. Since the grains are the basic components of nanomaterials, changes in their microstructure should inevitably change the physical and chemical properties. Therefore, study of the microstructure of grains can help us to discover the general structure of nanomaterials and explain the corresponding properties.

Raman techniques can reveal local structural information of amorphous and poorly crystallized samples. Usually, disordering induces spectral changes with reference to the bulk crystal spectra and their vibrational state densities (Zhu et al. 1987). Tin oxide gives rise to well-defined Raman spectra, and the dynamics of rutilelike compounds have been extensively studied (Katiyar 1971; Gervais 1985). However, for nanocrystallites, the surface atoms represent a non-negligible fraction of atoms and give rise to specific spectral changes. The corresponding frequencies of the optical modes have been calculated theoretically and measured


experimentally (Stao and Asari 1995). When the SnO2 grain size is small, the Raman spectrum is modified, at least partially. Several authors have reported that the existence of some Raman peaks is closely related to grain size (Xie et al. 1994). Figures 1.11a, 1.11b, and 1.11c show xray diffraction patterns of commercial SnO2 bulks, and SnO2 thin films as-prepared and annealed at 300°C for 30 min, respectively. Tin dioxide has a tetragonal rutile crystalline structure (known in its mineral form as cassiterite) with point group 14

4hD and space group P42/mnm (Wang et al. 1994). The unit cell consists of two metal atoms and four oxygen atoms. Each metal atom is situated amidst six oxygen atoms which approximately form the corners of a regular octahedron. Oxygen atoms are surrounded by three tin atoms which approximate the corners of an equilateral triangle.

The average grain sizes were calculated using the TEM micrograph and the Scherrer formula λ β θ= / cos ,D K respectively, where D is the diameter of the nanoparticles, K = 0.9, λ(Cu Ka) = 1.5406 Å, and β is the full-width half- maximum of the diffraction lines. Both results show that the average grain sizes of the as prepared and the annealing SnO2 nanoparticles are about 8 and 12 nm, respectively. Figure 1.12 shows a TEM bright-field image and the corresponding selected-area electron diffraction (SAED) pattern (inset) of the as-prepared SnO2 thin film. The particle sizes observed in the micrographs are larger than those estimated from XRD data, indicating significant agglomeration of the particles. As seen in the TEM bright-field image, there are many small particles of roughly spherical shape. The contrast of the particles in different regions of the TEM image indicates different densities, which may be related to the grain sizes. The polycrystalline diffraction rings of SAED pattern (inset in Figure 1.12) also demonstrate the characteristics of typical tetragonal SnO2 thin film (d200 = 2.37 Å, d210 = 2.12 Å, d310 = 1.50 Å, and d202 = 1.32 Å). The interplanar spacings in keeping with those of the tetragonal rutile structure are shown in Table 1.2. These indicate that the SnO2 average grain size is smaller than that of the bulk material. The crystallites close to the Bragg orientations are recognizable by their dark contrast. For the tetragonal structure, the lattice parameters can be calculated by

=+ +2 2 2 2 2

1

[( )/ ] ( / )hkld

h k a l c (1.10)

where h, k, and l are all integers, (hkl ) is the lattice plane index, and a and c are lattice constants. For a real crystal, the calculated values of a and c are the same based on different crystal planes. However, the presence of a large number of vacant lattice sites and local lattice disorders may lead to obvious reduction (or even disappearance) of intensities in the XRD peaks of the some lattice planes [e.g., the lattice planes (200), (220), (002), (310), etc., in Figure 1.11b]. Therefore, these results imply destroyed periodicities in some crystal planes and a significant distortion of the rutile lattice. We have calculated the lattice parameters with


11


figure 1.11. X-ray diffraction patterns of Sno2 thin films: (a) commercial bulk SnO2; (b) as-prepared Sno2 thin film; (c) annealed SnO2 thin film. (Reproduced with permission from Chen et al. 2004. Copyright 2004 american physical Society.)


12


figure 1.12. TEM bright-field image of as-prepared nanocrystalline SnO2 thin film, showing the fine grains of Sno2 thin films; selected-area electron diffraction (SAED) pattern (inset) corresponding to the tetragonal rutile structure, showing the microstructural characteristics of the polycrystalline Sno2 thin film. (Reproduced with permission from Chen et al. 2004. Copyright 2004 American Physical Society.)

mccarthy and Welton 1989, commercial bulk, aS-prepared thin film,Sample dhkl (Å) dhkl (Å) dhkl (Å)

110 3.347 3.3608 3.3422101 2.6427 2.6536 2.6347200 2.369 2.3720 2.3660111 2.3094 210 2.1189 211 1.7641 1.7667 1.7603220 1.6750 1.6823 1.6738002 1.5934 1.5964 1.5901301 1.4984 1.4932221 1.4829112 1.4392301 1.4155

Source: Reproduced with permission from Chen et al. 2004. Copyright 2004 American Physical Society.

table 1.2. relationships of interplanar spacing (dhkl) of index (hkl ) in the direct lattice for different samples


different samples using Eq. (1.10). From Table 1.3, we can easily notice the relationship of the grain sizes to the change of lattice parameters. However, we found that there is an increase in a and a decrease in c for the asprepared SnO2 thin films prepared by the PLD method. This implies that the as-prepared thin films may exhibit a large number of oxygen vacancies, vacancy clusters, and local lattice disorder, which may lead to an increase in a and a decreases in c.

Figures 1.13a, 1.13b, and 1.13c show the roomtemperature Raman spectra of commercial SnO2 bulk materials, asprepared, and annealed at 300°C for 30min nanocrystalline SnO2 thin films, respectively. In general, SnO2 with the rutile structure belongs to the space group 14

4 ,hD of which the normal lattice vibration at the G point of the Brillouin zone is given as follows on the basis of group theory (Traylor et al. 1971):

1 1 2 2 3 1 4 2 5 1 2

4 1 5

(1 ) (1 ) (1 ) (1 ) (1 ) (1 )

2 ( ) 3 ( )g g g g g u

u u

A A B B E A

B E

Γ Γ Γ Γ Γ Γ Γ

Γ Γ

+ + + + - -

- +

= + + + + +

+ +

(1.11)

To investigate the characteristics of Raman spectra, Figure 1.14 depicts the symmetries of the optic modes of the rutile structure for zero wave vector. Among these vibrations, the symmetry A2g and B1u modes are optically inactive, while symmetry A2u and Eu modes are infrared active. The remaining optic modes, A1g, B1g, B2g, and Eg, are Raman active in first order with the polarizability tensors (Peercy and Morosin 1973):

αæ ö÷ç ÷ç ÷ç ÷= ç ÷ç ÷ç ÷÷çè ø

1

0 0( ) 0 0

0 0g

aA a

b

1

0 0( ) 0 0

0 0 0g

cB cα

æ ö÷ç ÷ç ÷ç ÷= -ç ÷ç ÷ç ÷÷çè ø

(1.12)

2

0 0( ) 0 0

0 0 0g

fB fα

æ ö÷ç ÷ç ÷ç ÷= ç ÷ç ÷ç ÷÷çè ø

0 0( ) 0 0

0g

dE d

d dα

æ ö÷ç ÷ç ÷ç ÷= ç ÷ç ÷ç ÷÷çè ø

mccarthy andSample Welton 1989 aS-prepared thin film commercial bulkS

a110 4.733 4.753 4.727a110 4.738 4.744 4.732Δa −0.005 0.009 −0.005c101110(Å) 3.186 3.198 3.173c101200(Å) 3.184 3.201 3.172Δc 0.002 −0.003 0.001

Source: Reproduced with permission from Chen et al. 2004. Copyright 2004 American Physical Society.

table 1.3. relationships of lattice parameters for different samples


13


figure 1.13. room-temperature raman spectra of (a) Sno2 bulk material, (b) as-prepared nano-crystalline Sno2 thin film, and (c) annealed SnO2 thin film. (Reproduced with permission from Chen et al. 2004. Copyright 2004 american physical Society.)


figure 1.14. Symmetries of the optic modes of the tetragonal rutile structure for zero wave vector. Labeling is in the notation of Mulliken (in parentheses, of Koster). doubly degenerate modes are indicated by a ‘‘(2)’’ preceding the Mulliken symbol. (reproduced with permission from Chen et al. 2004. Copyright 2004 american physical Society.)

14



It can be seen from Figure 1.13b that the Raman intensity is strongest for the mode A1g at 636.3 cm−1, followed by the mode B2g at 780.9 cm−1 and the mode Eg at 474.1 cm−1. The data for the A1g, B2g, and Eg modes are in agreement with those observed in the previous report (Diéguez et al. 2001). Thus, the Raman spectra show the typical features of the rutile phase for the asprepared SnO2 thin film. However, the variation of the mode B1g could not be measured in the present experiment. The mode B1g appears quite often with smaller nanoparticles, but is difficult to locate due to its very low intensity with respect to the other modes [e.g., 0.001I (A1g)] and the existence of the lowfrequency bands (Peercy and Morosin 1973). The remarkable aspect of the Raman spectrum for the asprepared SnO2 thin film (see Figure 1.13b) is the appearance of a new Raman peak at about 516.1 cm−1. This new Raman peak has not been observed in previous reports. To explore whether this new Raman peak comes from the nanocrystalline SnO2, the asprepared SnO2 thin film was annealed at 300°C for 30 min. Figure 1.13c shows the Raman spectrum of the nanocrystalline SnO2 thin film annealed at 300°C for 30 min. The remarkable aspect of the Raman spectrum is that this new Raman peak disappears in annealing SnO2 thin film. This result agrees well with the theoretical predication of the vibrational modes in the highfrequency region of 542–486 cm−1 that have been investigated by Diéguez et al. (2001). In theory, this vibrational mode can be attributed to disorder activation of the Raman forbidden mode (Abello et al. 1998). The appearance of this new peak indicates that it arises either as a consequence of reduced particle dimensions (when prepared by PLD techniques) or due to the conversion from amorphous to crystalline material.

It is known that, in a disordered crystal, there is a correlation between preventing atoms from vibrating in phase and preventing their displacement by imperfections modifying the space symmetry. Changes in the local symmetry of the crystal produce changes in some of the components of the polarizability tensor, even the normally forbidden vibration modes—i.e., due to the loss in longrange order, all phonons are optically possible and the Raman spectrum should resemble the phonon density of the states. In an intermediate case, a shift of the classical modes accompanied by broadening and the appearance of some SnO2 forbidden modes should be observed. This is indeed what occurs with modes A1g, B2g, Eg, and A2u. However, the band at 516.1 cm−1 cannot fit any of the SnO2 Raman forbidden modes. The appearance of this new mode as a consequence of the coupling between modes also cannot explain the high contribution of this band observed in the spectrum of the smaller nanoparticles. Furthermore, it is well known that nanometersized crystalline materials have a crystalline component forming the crystallite nucleus and an interfacial component consisting of all the atoms situated at the grain boundaries between particles or at their surfaces. The latter usually have several atomic layers in which atoms are slightly displaced from the exact position that would be suggested by the crystalline structure of the material.


5. concludInG remarks

This chapter has introduced in detail the current state of synthesis, characterization, and microstructure of tin oxides. It has been clearly shown that tin oxide nanocrystals are a promising strategic material for fabricating unexpected nanostructures with controllable size and morphology and adjustable properties. With recent advances in the field of metal oxide nanofabrication techniques, smart arti-ficial tin oxide nanocrystals should be drawing more attention and finding many uses in a variety of applications, including optics, electronics, solar cells, and sensing. Some of these properties are tunable based on the adjustment of the related fabrication processes and the structural parameters of the nanostructures. Due to these interesting and tunable properties, some fabricated nanostructures are close to being incorporated into micro/nanodevices. Some challenges still remain, however, in clarifying the intricate aspects of these thin films and the applications of SnO2 thin films.

6. acknowledGments

The work described in this chapter was financially supported by the Shanghai Pujiang Program (Project Number 10PJ1404100), China, Key Innovation Fund of Shanghai Municipal Education Commission (Project Number 10ZZ64), National Natural Science Foundation of China (Project Numbers 11074161 and 20871081, Key Project Number 40830744), Science and Technology Commission of Shanghai Municipality (Project Numbers 10JC1405400, 09530501200, and 09XD1401800), and Shanghai Leading Academic Discipline Project (Project Number S30109). This work was also supported by a grant from the City University of Hong Kong (Project Number 7002657).

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