Abstract
This study aims to determine forensic signatures for processing history of UO2 based on modifications in intermediate materials within the uranyl peroxide route. Uranyl peroxide was calcined to multiple intermediate U-oxides including Am-UO3, α-UO3, and α-U3O8 during the production of UO2. The intermediate U-oxides were then reduced to α-UO2 via hydrogen reduction under identical conditions. Powder X-ray diffractometry (p-XRD) and X-ray photoelectron spectroscopy (XPS) were used to analyze powders of the intermediate U-oxides and resulting UO2 to evaluate the phase and purity of the freshly synthesized materials. All U-oxides were also analyzed via scanning electron microscopy (SEM) to determine the morphology of the freshly prepared powders. The microscopy images were subsequently analyzed using the Morphological Analysis for Materials (MAMA) version 2.1 software to quantitatively compare differences in the morphology of UO2 from each intermediate U-oxide. In addition, the microscopy images were analyzed using a machine learning model which was trained based on a VGG 16 architecture. Results show no differences in the XRD or XPS spectra of the UO2 produced from each intermediate. However, results from both the segmentation and machine learning proved that the morphology was quantifiably different. In addition, the morphology of UO2 was very similar, if not identical, to the intermediate material from which it was prepared, thus making quantitative morphological analysis a reliable forensic signature of processing history.
Funding source: U.S. Department of Homeland Security
Award Identifier / Grant number: 2015-DN-077-ARI092
Funding source: Defense Threat Reduction Agency
Award Identifier / Grant number: HDTRA1-16-1-0026
Funding source: U.S. Department of Homeland Security
Award Identifier / Grant number: 2016-DN-077-ARI102
Funding statement: This synthesis of uranyl peroxide and its calcination products along with their subsequent analysis by p-XRD, XPS, and SEM were supported by the U.S. Department of Homeland Security, Domestic Nuclear Detection Office, Funder Id: http://dx.doi.org/10.13039/100000180, under Grant Award no. 2015-DN-077-ARI092. The Defense Threat Reduction Agency, Funder Id: http://dx.doi.org/10.13039/100000774, under Grant Award no. HDTRA1-16-1-0026 supported the quantitative image analysis via MAMA. The machine learning analysis was supported by the U.S. Department of Homeland Security, Domestic Nuclear Detection Office, Funder Id: http://dx.doi.org/10.13039/100000180, under Grant Award no. 2016-DN-077-ARI102. This work made use of University of Utah Shared facilities of the Surface Analysis and Nanoscale Imaging Group sponsored by the College of Engineering, Health Sciences Center, Office of the Vice President for Research, and the Utah Science Technology and Research (USTAR) Initiative of the State of Utah. This work made use of the Materials Characterization Lab at the University of Utah.
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Supplementary Material
The online version of this article offers supplementary material (https://doi.org/10.1515/ract-2018-3065).
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