Developing signtures in uranium oxides for use in nuclear forensic science

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Title Developing signtures in uranium oxides for use in nuclear forensic science
Publication Type thesis
School or College College of Engineering
Department Civil & Environmental Engineering
Author Heffernan, Sean T.
Date 2020
Description In the present research, 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 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 mixtures. Results from this study iv demonstrate the power of quantitative morphological analysis for determining the processing history of nuclear materials.
Type Text
Publisher University of Utah
Dissertation Name Master of Science
Language eng
Rights Management (c) Sean T. Heffernan
Format application/pdf
Format Medium application/pdf
ARK ark:/87278/s6nv6fan
Setname ir_etd
ID 1938974
Reference URL https://collections.lib.utah.edu/ark:/87278/s6nv6fan
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