Nuclear proliferomics: A new field of study to identify signatures of nuclear materials as demonstrated on alpha-UO3

• Published in: Talanta (Oxford) 2018-08, Vol.186, p.433-444
• Main Authors: Schwerdt, Ian J., Brenkmann, Alexandria, Martinson, Sean, Albrecht, Brent D., Heffernan, Sean, Klosterman, Michael R., Kirkham, Trenton, Tasdizen, Tolga, McDonald IV, Luther W.
• Format: Article
• Language: English
• Subjects: Electron microscopy; Machine learning; Morphology; Nuclear forensics; Nuclear proliferomics; Nuclear safeguards
• ISSN: 0039-9140; 1873-3573
• DOI: 10.1016/j.talanta.2018.04.092

The use of a limited set of signatures in nuclear forensics and nuclear safeguards may reduce the discriminating power for identifying unknown nuclear materials, or for verifying processing at existing facilities. Nuclear proliferomics is a proposed new field of study that advocates for the acquisition of large databases of nuclear material properties from a variety of analytical techniques. As demonstrated on a common uranium trioxide polymorph, α-UO3, in this paper, nuclear proliferomics increases the ability to improve confidence in identifying the processing history of nuclear materials. Specifically, α-UO3 was investigated from the calcination of unwashed uranyl peroxide at 350, 400, 450, 500, and 550 °C in air. Scanning electron microscopy (SEM) images were acquired of the surface morphology, and distinct qualitative differences are presented between unwashed and washed uranyl peroxide, as well as the calcination products from the unwashed uranyl peroxide at the investigated temperatures. Differential scanning calorimetry (DSC), UV–Vis spectrophotometry, powder X-ray diffraction (p-XRD), and thermogravimetric analysis-mass spectrometry (TGA-MS) were used to understand the source of these morphological differences as a function of calcination temperature. Additionally, the SEM images were manually segmented using Morphological Analysis for MAterials (MAMA) software to identify quantifiable differences in morphology for three different surface features present on the unwashed uranyl peroxide calcination products. No single quantifiable signature was sufficient to discern all calcination temperatures with a high degree of confidence; therefore, advanced statistical analysis was performed to allow the combination of a number of quantitative signatures, with their associated uncertainties, to allow for complete discernment by calcination history. Furthermore, machine learning was applied to the acquired SEM images to demonstrate automated discernment with at least 89% accuracy. [Display omitted] •Discovery of several unique morphological signatures for α-UO3 as a function of calcination temperature.•Quantification of surface morphology features as an indicator of process history.•Quantification of amorphous content as a novel pre-detonation nuclear forensics signature in α-UO3.•Utilization of machine learning in image analysis to classify SEM images by temperature.•Coupling of multiple quantitative signatures with their associated uncertainties to discern