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Exploring Machine-Learning-Enabled LIBS Towards Forensic Trace Attributive Analysis of Fission Products in Surrogate High-Level Nuclear Waste
Abstract
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.
About the Authors
J. N. Onkangi
University of Nairobi
Kenya
Kenya
Joshua Nyairo Onkangi
Department of Physics
Nairobi
H. K. Angeyo
University of Nairobi
Kenya
Kenya
Hudson Kalambuka Angeyo
Department of Physics
Nairobi
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Review
For citations:
Onkangi J.N., Angeyo H.K. Exploring Machine-Learning-Enabled LIBS Towards Forensic Trace Attributive Analysis of Fission Products in Surrogate High-Level Nuclear Waste. Zhurnal Prikladnoii Spektroskopii. 2023;90(6):965.
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