Scanning Transmission Electron Microscopy
Advanced Characterization Methods for Materials Science Applications
- Available for pre-order. Item will ship after December 22, 2020
Scanning Transmission Electron Microscopy: Advanced Characterization Methods for Materials Science Applications The information comprised in this book is focused on discussing the latest approaches in the recording of high fidelity quantitative annular dark field (ADF) data. It showcases the application of machine learning in electron microscopy and the latest advancements in image processing and data interpretation for materials notoriously difficult to analyze using (S)TEM. It also highlights strategies to record and interpret large electron diffraction data sets for the analysis of nanostructures.
- Discusses existing approaches for experimental design in the recording of high fidelity quantitative ADF data.
- Presents the most common types of scintillator-photomultiplier ADF detectors, along with their strengths and weaknesses. Proposes strategies to minimize the introduction of errors from these detectors and avenues for dealing with residual errors.
- Discusses the practice of reliable multi-frame imaging, along with the benefits and new experimental opportunities it presents in electron dose or dose-rate management.
- Focuses on supervised and unsupervised machine learning for electron microscopy.
- Discusses open data formats, community driven software and data repositories.
- Proposes methods to process information at both global and local scales, and discusses avenues to improve the storage, transfer, analysis, and interpretation of multidimensional data sets.
- Provides the spectrum of possibilities to study materials at the resolution limit by means of new developments in instrumentation.
- Recommends methods for quantitative structural characterization of sensitive nanomaterials using electron diffraction techniques and describes strategies to collect electron diffraction patterns for such materials.
This book helps academics, researchers, and industry professionals in materials science, chemistry, physics, and related fields to understand and apply computer-science derived analysis methods to solve problems regarding data analysis and interpretation of materials properties.
Table of Contents
Part 1. Nanoscale Imaging and Analysis Using High Angle Annular Dark Field Scanning Transmission Electron Microscopy (HAADF-STEM). 1. Quantitative Analysis of Nanomaterials Using HAADF-STEM. 2. Machine-Learning Derived Concepts for Analyzing, Classification and Interpretation of Multidimensional Data. Part 2. Advanced (S)TEM Imaging and Spectroscopy Methods for the Determination for Structure-Property Relationship in Nanomaterials. 3. Determination of Novel Structures in Complex Oxides Using Advanced STEM and Spectroscopy Techniques. 4. Identification of Crystal Structure of Nanomaterials Using Electron-Diffraction Techniques and Nanoinformatics.
Dr. Alina Bruma received her PhD degree in Nanoscale Physics from The University of Birmingham, UK in 2013. Dr. Bruma completed several postdoctoral stages at the Laboratory of Crystallography and Materials Science (CRISMAT-CNRS) France, University of Texas at San Antonio, USA and The National Institute of Standards and Technology, USA before moving to the American Institute of Physics Publishing in 2019. Her research has been focused on the study of crystalline structure of materials and the determination of their structure-property relationship using transmission electron microscopy and electron diffraction. Dr Bruma is also the Chairman of The Electron Diffraction sub-committee at the International Center for Diffraction Data (ICDD).