Abstract
Quantitative isotopic, elemental, and morphological data collected for five uranium oxide materials were subjected to advanced statistical processing. Materials were initially distinguished based on quantitative isotopic values and trace elemental analyses. Then, for the first time, quantitative morphological data was incorporated using advanced, custom software tools. Chemical and physical distinctions allowed for differentiation with 95% confidence levels in predicted class assignments for similar sample types. This study indicates that there is significant potential in applying statistical analysis processing to the detection and exploitation of quantitative morphology signatures within nuclear materials, both individually and in addition to more traditional data.
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