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.

我们研究了支持机器学习的 LIBS 在直接快速分析替代高放核废料中选定的裂变产物 (FP)（即 Y、Sr、Rb 和 Zr）的效用，模拟了三种假设但现实的场景：爆炸后来自放射性犯罪现场 (RCS) 的玻璃碎片、爆炸后粉末和微升液滴。开发了用于对这些材料中 FP 进行痕量定量分析的人工神经网络校准策略，并对所有样品类型实现了 >95% 的预测。由于缺乏适当的认证参考材料，使用合成参考标准材料进行方法验证，准确度为 ˃91%。基于 FP 的光谱响应，主成分分析成功区分了核废物和非核废物，证明了该方法在 RCS 核法证和属性分析中的潜力。