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

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