Investigation of process history and phenomenology of plutonium oxides using vector quantizing variational autoencoder

TL;DR

This paper employs a vector quantizing variational autoencoder to quantitatively analyze plutonium oxide particles from SEM images, enabling prediction of synthesis process parameters and revealing key influences on particle morphology.

Contribution

It introduces a novel pipeline using VQ-VAE for quantitative particle morphology analysis and process parameter prediction in plutonium oxide synthesis.

Findings

Achieved over 80% accuracy in predicting process parameters from single particles.

Demonstrated improved classification accuracy with multiple particles, often with as few as four.

Identified strike order and oxalic acid feedstock as primary factors influencing particle morphology.

Abstract

Accurate, high throughput, and unbiased analysis of plutonium oxide particles is needed for analysis of the phenomenology associated with process parameters in their synthesis. Compared to qualitative and taxonomic descriptors, quantitative descriptors of particle morphology through scanning electron microscopy (SEM) have shown success in analyzing process parameters of uranium oxides. We utilize a VQ-VAE to quantitatively describe plutonium dioxide (PuO2) particles created in a designed experiment and investigate their phenomenology and prediction of their process parameters. PuO2 was calcined from Pu(III) oxalates that were precipitated under varying synthetic conditions that related to concentrations, temperature, addition and digestion times, precipitant feed, and strike order; the surface morphology of the resulting PuO2 powders were analyzed by SEM. A pipeline was developed to extract and quantify useful image representations for individual particles with the VQ-VAE to perform multiple classification tasks simultaneously. The reduced feature space could predict process parameters with greater than 80% accuracies for some parameters with a single particle. They also showed utility for grouping particles with similar surface morphology characteristics together. Both the clustering and classification results reveal valuable information regarding which chemical process parameters chiefly influence the PuO2 particle morphologies: strike order and oxalic acid feedstock. Doing the same analysis with multiple particles was shown to improve the classification accuracy on each process parameter over the use of a single particle, with statistically significant results generally seen with as few as four particles in a sample.