Scanning Transmission Electron Microscopy: Advanced Characterization Methods for Materials Science Applications [Paperback]

Chapter 1 Practical Aspects of Quantitative and High-Fidelity STEM Data Recording Chapter 2 Machine Learning for Electron Microscopy Chapter 3 Application of Advanced Aberration-Corrected Transmission Electron Microscopy to Material Science: Methods to Predict New Structures and Their Properties Chapter 4 Large Dataset Electron Diffraction Patterns for the Structural Analysis of Metallic Nanostructures

Scanning Transmission Electron Microscopy 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 scanning transmission electron microscopy (STEM). It also highlights strategies to record and interpret large electron diffraction datasets for the analysis of nanostructures.

This book:

• 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 multiframe 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