Production of mixed element actinide reference particulates to support nuclear safeguards using THESEUS, an aerosol-based particulate synthetic methodology.

The THermally Evaporated Spray for Engineered Uniform particulateS (THESEUS) production platform was developed to generate highly uniform mixed actinide oxide particles. The particulate synthesis platform builds on previous efforts and utilizes an aerosol-based technology to generate, calcine, characterize, and aggregate a monodisperse oxide phase particle product. In this study, particles comprised of uranium oxide, incorporated with varying compositions of thorium, were produced. Th/U test materials with 232Th concentrations between 1 ppm and 10%, ratioed to 238U, were successfully generated with in situ calcination at 600 °C and characterized by in situ aerodynamic particle size spectrometry and ex situ microanalytical methods. Populations of monodisperse particulates (geometric standard deviation - GSD < 1.15) with an average diameter near 1 µm were generatated and micro-Raman spectroscopy of individual particles identified U3O8 as the primary material phase for the range of Th/U samples analyzed. Single particle measurements and automated particle analyses by secondary ion mass spectrometry (SIMS) were performed. Uniform inter-particle elemental and isotopic homogeneity for uranium and thorium isotopes was characterized by SIMS, and a 232Th/238U relative sensitivity factor of 0.53 was determined. SIMS results demonstrated differences in the 232Th/238U profiling behavior for Th/U particulates with increased Th content (>1%). Despite the observed profiling behavior, single particle measurements of the 10% Th sample indicate inter-particle homogeneity. This work represents the first systematic study of Th/U microparticulate reference materials generated and intended for nuclear safeguards applications and serves as a demonstration of THESEUS to support a sustained capability for the production mixed-element particulate reference materials.

Fully convolutional neural network for removing background in noisy images of uranium bearing particles.

A fully convolutional neural network (FCN) was developed to supersede automatic or manual thresholding algorithms used for tabulating SIMS particle search data. The FCN was designed to perform a binary classification of pixels in each image belonging to a particle or not, thereby effectively removing background signal without manually or automatically determining an intensity threshold. Using 8000 images from 28 different particle screening analyses, the FCN was trained to accurately predict pixels belonging to a particle with near 99% accuracy. Background eliminated images were then segmented using a watershed technique in order to determine isotopic ratios of identified particles. A comparison of the isotopic distributions of an independent data set segmented using the neural network with a commercially available automated particle measurement (APM) program developed by CAMECA was performed. This comparison highlighted the necessity for effective background removal to ensure that resulting particle identification is not only accurate, but preserves valuable signal that could be lost due to improper segmentation. The FCN approach improves the robustness of current state-of-the-art particle searching algorithms by reducing user input biases, resulting in an improved absolute signal per particle and decreased uncertainty of the determined isotope ratios.