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Meeting 2021 TMS Annual Meeting & Exhibition
Symposium Data Science and Analytics for Materials Imaging and Quantification
Presentation Title Quantitative X-ray Fluorescence Nanotomography
Author(s) Mingyuan Ge, Xiaojing Huang, Hanfei Yan, Wilson K. S. Chiu, Kyle S Brinkman, Yong S Chu
On-Site Speaker (Planned) Mingyuan Ge
Abstract Scope The Hard X-ray Nanoprobe at NSLS-II provides nanoscale 3D multi-modality imaging capability which is extremely useful in studying materials with complex internal structures. An important scientific problem is to investigate phase/grain boundaries of multi-component materials during or after material processing such as sintering, which generally has profound impacts on material’s property and functionality. X-ray fluorescent (XRF) nanotomgraphy is well-suited for the task. However, accurate quantification of XRF tomography is hampered by a well-known self-absorption problem. In this work, we present an approach to tackle the problem and apply to investigate the phase separation in a mixed ionic and electronic conducting ceramic material (Ce1-xGdxO2), a modal system generates broad interests in high temperature membranes and solid fuel cell applications. We will show how accurate absorption correction leads to a discovery of new phase in this system and providing key information in elucidating the structure evolution during the material synthesis process.
Proceedings Inclusion? Planned:

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

Advancements in EBSD Using Machine Learning
Computer Vision and Machine Learning for Microstructural Characterization and Analysis
Convolutional neural network-assisted recognition of nanoscale L12 ordered structures in face-centred cubic alloys
Deep Neural Network Facilitated Complex Imaging of Phase Domains
Dictionary Indexing of EBSD Patterns Assisted by Convolutional Neural Network
High Dimensional Analysis of Abnormal Grain Growth under Dynamic Annealing Conditions
Improved EBSD Indexing through Non-Local Pattern Averaging
Materials Characterization in 3D Using High Energy X-ray Diffraction Microscopy: Irradiated and Deformed Materials
Microstructure Image Segmentation with Deep Learning: from Supervised to Unsupervised Methods
Quantitative EBSD Image Analysis and Prediction via Deep Learning
Quantitative X-ray Fluorescence Nanotomography
Resolving Pseudosymmetry in Tetragonal ZrO2 Using EBSD with a Modified Dictionary Indexing Approach
Understanding Powder Morphology and Its Effect on Flowability Through Machine Learning in Additive Manufacturing
Understanding the Keyhole Dynamics in Laser Processing Using Time-resolved X-ray Imaging Coupled With Computer Vision and Data Analytics

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