Research
Investigation of Morphological
Characteristics of U-Materials
Funded by DNDO-ARI
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  5. Publication from the work
  We are investigating the morphological and microstructural properties of uranium ores and uranium oxides using a variety of characterization techniques as a signature for identification of provenance and process history of uranium materials used in the production of nuclear weapons. We have systematically divide the nuclear fuel cycle into distinct periods where unique physiochemical signatures are hypothesized to exist. The physiochemical signatures and changes in microstructure are monitored using a suite of high-resolution scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray fluorescence (XRF), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS). The Morphological Analysis for Material Attribution (MAMA) software developed at Los Alamos National Laboratory (LANL) is being used to quantify the morphological features from the images generated. In addition to characterizing forensic signatures that can be related to the material’s origin and history, we are also comparing the specific information that can be generated from each morphological characterization technique, the utility (time, skill, and cost), the reliability, and the robustness of the information.  
Project Team Members
  • Luther McDonald IV
  • Bryony Richards
  • Christy Ruggerio
  • Adam Olsen
  • Ian Schwerdt
  • Sean Heffernan
Identification of Morphological and Oxygen Isotopic Signatures of
Uranium Oxides
Funded by DTRA
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  2. HVFS
Oxygen ratios from meteoric water are incorporated in uranium materials during synthesis, and oxidation and/or hydration during storage under temperature and relative humidity. We are investigating the 18O/16O isotope ratio as a signature for identification of provenance and process history of uranium materials used in the production of nuclear weapons. The utility of 18O/16O ratios for assessing process history cannot be realized without a fundamental understanding of how O-atoms are incorporated in U materials and what role microstructural signatures may have in these reactions. Thus, we are evaluating the rate of incorporating mixed oxygen isotopes in high purity U-oxides to simulate 18O/16O ratio changes within a sample following relocation to a new geographical region. To accurately monitor temporal changes in the 18O/16O ratios, an oxygen isotope standard is being developed to calibrate measurements using of state-of-the-art instrumentation. Bulk 18O/16O ratios will be measured using a high vacuum fluorination system (HVFS) with isotope ratio mass spectrometry (IRMS) and basic measurements will be conducted using secondary ion mass spectrometry (SIMS). 
Project Team Members
  • Luther McDonald IV
  • Bryony Richards
  • Marianne Wilkerson
  • Dave Podlesak
  • Mike Klosterman
  • Erik Abbott
Machine Learning and Signature Analysis of Nuclear Forensics Data
Funded by DNDO-ARI
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The development of uranium oxide physical and chemical signatures is critical to the field of nuclear forensic analysis. Qualitative morphological parameters provide supplementary information in nuclear forensic investigations, but tell a limited portion of an unknown sample’s story. There is a major need for quantitative parameters that can rapidly determine whether differences between an unknown sample and a standard are statistically significant. It would be even more beneficial if these parameters could elucidate not just the starting material speciation, but the processing conditions experienced by the sample. Accounting for storage and temporal effects further accounts for the total process history of an unknown sample. In the future, one could see a quantitative morphological database that expedites the attribution process. We propose to develop a data processing pipeline that streamlines the analysis of complex nuclear forensics data and statistically correlates data across multiple techniques. This pipeline will build upon existing computational tools in the nuclear forensics community, such as the Morphological Analysis for Material Attribution (MAMA) software developed by Los Alamos National Laboratory (LANL), to characterize particle morphologies. The proposed fundamental science in an academic setting coupled with technical guidance from Pacific Northwest National Laboratory (PNNL) will instruct and inform the next generation of nuclear scientists and engineers.
Project Team Members
  • Tolga Tasdizen (PI)
  • Luther McDonald IV
  • Ayla Khan
  • Ian Schwerdt