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

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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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