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Radiochimica Acta

International Journal for chemical aspects of nuclear science and technology

Editor-in-Chief: Qaim, Syed M.


IMPACT FACTOR 2018: 1.339

CiteScore 2018: 1.20

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2193-3405
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Identifying surface morphological characteristics to differentiate between mixtures of U3O8 synthesized from ammonium diuranate and uranyl peroxide

Sean T. Heffernan
  • University of Utah Department of Civil and Environmental Engineering-Nuclear Engineering Program, 201 Presidents Circle, Salt Lake City, UT 84112, USA
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/ Nhat-Cuong Ly
  • University of Utah Scientific Computing and Imaging Institute, 72 S Central Campus Drive, Salt Lake City, UT 84112, USA
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/ Brock J. Mower
  • University of Utah Department of Civil and Environmental Engineering-Nuclear Engineering Program, 201 Presidents Circle, Salt Lake City, UT 84112, USA
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/ Clement Vachet
  • University of Utah Scientific Computing and Imaging Institute, 72 S Central Campus Drive, Salt Lake City, UT 84112, USA
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/ Ian J. Schwerdt
  • University of Utah Department of Civil and Environmental Engineering-Nuclear Engineering Program, 201 Presidents Circle, Salt Lake City, UT 84112, USA
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/ Tolga Tasdizen
  • University of Utah Scientific Computing and Imaging Institute, 72 S Central Campus Drive, Salt Lake City, UT 84112, USA
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/ Luther W. McDonald IV
  • Corresponding author
  • University of Utah Department of Civil and Environmental Engineering-Nuclear Engineering Program, 201 Presidents Circle, Salt Lake City, UT 84112, USA
  • 110 Central Campus Dr. Suite 2000, Salt Lake City, UT 84112, USA, Phone: +801-581-7768
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Published Online: 2019-05-24 | DOI: https://doi.org/10.1515/ract-2019-3140

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.

Keywords: Nuclear forensics; morphology; uranium oxide

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About the article

Received: 2019-03-18

Accepted: 2019-04-24

Published Online: 2019-05-24


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

This synthesis of uranyl peroxide, ADU, and their calcination products along with their subsequent analysis by p-XRD, and SEM were supported by the U.S. Department of Homeland Security, Funder Id: http://dx.doi.org/10.13039/100000180, Domestic Nuclear Detection Office, under Grant Award no. 2015-DN-077-ARI092. The Defense Threat Reduction Agency, 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, 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.


Citation Information: Radiochimica Acta, 20193140, ISSN (Online) 2193-3405, ISSN (Print) 0033-8230, DOI: https://doi.org/10.1515/ract-2019-3140.

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