Identifying surface morphological characteristics to differentiate between mixtures of U${}_3$O${}_8$ synthesized from ammonium diuranate and uranyl peroxide

In the present study, surface morphological differences of mixtures of triuranium octoxide (U${}_3$O${}_8$), synthesized from uranyl peroxide (UO${}_4$) and ammonium diuranate (ADU), were investigated. The purity of each sample was verified using powder X-ray diffractometry (p-XRD), and scanning electron microscopy (SEM) images were collected to identify unique morphological features. The U${}_3$O${}_8$ from ADU and UO${}_4$ was found to be unique. Qualitatively, both particles have similar features being primarily circular in shape. Using the morphological analysis of materials (MAMA) software, particle shape and size were quantified. UO${}_4$ was found to produce U${}_3$O${}_8$ particles three times the area of those produced from ADU. With the starting morphologies quantified, U${}_3$O${}_8$ samples from ADU and UO${}_4$ were physically mixed in known quantities. SEM images were collected of the mixed samples, and the MAMA software was used to quantify particle attributes. As U${}_3$O${}_8$ particles from ADU were unique from UO${}_4$, the composition of the mixtures could be quantified using SEM imaging coupled with particle analysis. This provides a novel means of quantifying processing histories of mixtures of uranium oxides. Machine learning was also used to help further quantify characteristics in the image database through direct classification and particle segmentation using deep learning techniques based on Convolutional Neural Networks (CNN). It demonstrates that these techniques can distinguish the mixtures with high accuracy as well as showing significant differences in morphology between the mixtures. Results from this study demonstrate the power of quantitative morphological analysis for determining the processing history of nuclear materials.