Identifying surface morphological characteristics to differentiate between mixtures of U₃O₈ synthesized from ammonium diuranate and uranyl peroxide

Abstract

In the present study, surface morphological differences of mixtures of triuranium octoxide (U3O8), synthesized from uranyl peroxide (UO4) 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 U3O8 from ADU and UO4 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. UO4 was found to produce U3O8 particles three times the area of those produced from ADU. With the starting morphologies quantified, U3O8 samples from ADU and UO4 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 U3O8 particles from ADU were unique from UO4, 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.

Determining the Composition of a Mixed Material with Synthetic Data

“…A tertiary mixture was prepared of U 3 O 8 from ADU, UH, and SDU. Each mixture was prepared by aliquoting approximately 40 mg of each U 3 O 8 component into a small PTFE vial containing a Teflon-coated stir bar, followed by 15 min of agitation with a Vortex mixer at the medium intensity setting as described by Heffernan et al (2019). Table 1 lists the measured mass and weight% of each mixture.…”

“…We abbreviate these sets of mixtures as ADU-UO 4 and ADU-UH-SDU, respectively, for the rest of the paper. For mixtures of ADU-UO 4 , we utilized images from Heffernan et al (2019). These images were acquired at a resolution of 1,024 × 884 with a horizontal field width (HFW) of 5.11 μm, which represents the scale across the width of the image.…”

“…These images were acquired at a resolution of 1,024 × 884 with a horizontal field width (HFW) of 5.11 μm, which represents the scale across the width of the image. The details of how the ADU-UO 4 mixtures were prepared and imaged can be found in Sections 2.1 and 2.2 in Heffernan et al (2019).…”

“…Matson et al (2019) utilized CNNs to categorize structures of carbon nanotubes and nanofibers. Similarly, Hanson et al (2019) and Heffernan et al (2019) also used CNNs for the task of categorizing the characteristics of materials. In addition to using CNNs for the task of classification, CNNs have also been deployed for other tasks such as estimating optimal operational parameters (such as the focus setting) during the image acquisition of scanning electron microscopy (SEM) images (Yang et al, 2020), segmenting structures characterized in SEM images (Ly et al, 2019;Pazdernik et al, 2020), denoising the drifted microscopic images (Vasudevan & Jesse, 2019), and reconstructing sparse SEM images (Trampert et al, 2019).…”

“…Determining the composition of a mixed material is of interest in many fields (Sarkar et al, 2009;Samad et al, 2014;Rossen & Scrivener, 2017;Heffernan et al, 2019). For instance, composite metal oxides show tremendous benefits in absorption, separation, and photosensitive operations over single metal oxides in catalytic and electrocatalytic processes.…”