Journal
JOURNAL OF NUCLEAR MATERIALS
Volume 588, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.jnucmat.2023.154779
Keywords
Nuclear forensics; Particle morphology; Scanning electron microscopy; Machine learning; Particle segmentation
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This article introduces a method for identifying the processing history of unknown nuclear materials in nuclear forensics using particle morphology. By measuring the morphology of solid materials using scanning electron microscopes and combining robust image analysis and classification methods, morphology analysis can be conducted quickly and accurately. This method can be applied to intercepted nuclear materials and the detection of trace amounts of nuclear materials.
Particle morphology is an emerging signature that has the potential to identify the processing history of unknown nuclear materials. Using readily available scanning electron microscopes (SEM), the morphology of nearly any solid material can be measured within hours. Coupled with robust image analysis and classification methods, the morphological features can be quantified and support identification of the processing history of unknown nuclear materials. The viability of this signature depends on developing databases of morphological features, coupled with a rapid data analysis and accurate classification process. With developed reference methods, datasets, and throughputs, morphological analysis can be applied within days to (i) interdicted bulk nuclear materials (gram to kilogram quantities), and (ii) trace amounts of nuclear materials detected on swipes or environmental samples. This review aims to develop validated and verified analytical strategies for morphological analysis relevant to nuclear forensics.
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