Exploring Machine-Learning-Enabled Libs Towards Forensic Trace Attributive Analysis of Fission Products in Surrogate High-Level Nuclear Waste

Joshua Nyairo Onkangi,Hudson Kalambuka Angeyo

Journal of Applied Spectroscopy(2024)

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摘要
We investigated the utility of machine-learning-enabled LIBS for direct rapid analysis of selected fission products (FPs), namely, Y, Sr, Rb, and Zr in surrogate high-level nuclear waste mimicking three hypothetical but realistic scenarios: post-detonation glass debris, post-detonation powders, and microliter liquid drops from a radiological crime scene (RCS). Artificial neural network calibration strategies for trace quantitative analysis of the FPs in these materials were developed and achieved >95% prediction for all sample types. Owing to a lack of appropriate certified reference materials synthetic reference standards materials were used to perform method validation to accuracies ˃91%. Based on the spectral responses of the FPs, principal component analysis successfully differentiated nuclear from non-nuclear waste, demonstrating the method’s potential for RCS nuclear forensic and attributive analysis.
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关键词
laser-induced breakdown spectroscopy,artificial neural networks,principal component analysis,machine learning,nuclear forensics and attribution,high-level nuclear waste,fission products
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