Date of Award
Master of Science in Nuclear Engineering
Department of Engineering Physics
Abigail A. Bickley, PhD
A machine learning approach is taken to characterizing a group of synthetic uranium bearing particles. SEM images of these lab-created particles were converted into a binary representation that captured morphological features in accordance with a guide established by Los Alamos National Laboratory. Each particle in the dataset contains an association with chemical creation conditions: processing method, precipitation temperature and pH, calcination temperature are most closely tied to particle morphology. Additionally, trained classifiers are able to relate final products between particles, implying that morphological features are shared between particles with similar composition.
DTIC Accession Number
Gum, Daniel A., "A Machine Learning Approach to Characterizing Particle Morphology in Nuclear Forensics" (2020). Theses and Dissertations. 3598.