Rapid Nuclear Forensics Analysis via Machine-learning-enabled Laser-induced Breakdown Spectroscopy (Libs)

Abstract

Nuclear forensics (NF) is an analytical methodology that involves analysis of intercepted nuclear and radiological materials (NRM) so as to establish their nuclear attribution. The critical challenge in NF currently is the lack of suitable microanalytical methodologies for direct, rapid, minimally invasive detection and quantification of NF signatures. Laserinduced breakdown spectroscopy (LIBS) has the potential to overcome these limitations with the aid of machine-learning (ML) techniques. In this paper, we report the development of ML-enabled LIBS methodology for rapid NF analysis and attribution in support of nuclear security. The atomic uranium lines at 385.464 nm, 385.957 nm, and 386.592 nm were identified as NF signatures of uranium for rapid qualitative detection of trace uranium concealed in organic binders and uranium-bearing mineral ores. The limit of detection of uranium using LIBS was determined to be 34 ppm. A multivariate calibration strategy for the quantification of trace uranium in cellulose and uranium-bearing mineral ores was developed using an artificial neural network (ANN, a feed forward back-propagation algorithm) and spectral feature selection: (1) uranium lines (348 nm to 455 nm), (2) uranium lines (380 nm to 388 nm), and (3) subtle uranium peaks (UV range). The model utilizing category 2 was able to predict the 48 ppm of uranium with a relative error prediction (REP) of 10%. The calibration model utilizing subtle uranium peaks, that is, category 3, could predict uranium in the pellets prepared from certified reference material (CRM) IAEA-RGU-1, with an REP of 6%. This demonstrates the power of ANN to model noisy LIBS spectra for trace quantitative analysis. The calibration model we developed predicted uranium concentrations in the uranium-bearing mineral ores in the range of 54–677 ppm. Principal component analysis (PCA) was performed on the LIBS spectra (200–980 nm) utilizing feature selection of the uranium-bearing samples collected from different regions of Kenya clustered into groups related to their geographic origins. The PCA loading spectrum revealed that the groupings of these samples were mainly due to rare earth elements, namely, cerium, dysprosium, praseodymium, promethium, neodymium, and samarium. ML-enabled LIBS therefore has utility in field NF analysis and attribution of uranium in NRM under concealed conditions