4.6 Article

Rapid quantitative analysis of trace elements in plutonium alloys using a handheld laser-induced breakdown spectroscopy (LIBS) device coupled with chemometrics and machine learning

Journal

ANALYTICAL METHODS
Volume 13, Issue 30, Pages 3368-3378

Publisher

ROYAL SOC CHEMISTRY
DOI: 10.1039/d1ay00826a

Keywords

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Funding

  1. Defense Threat Reduction Agency's Nuclear Technologies Directorate
  2. LANL by Plutonium Sustainment and Material Recycle and Recovery

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This study presents the first reported quantification of trace elements in plutonium using a portable laser-induced breakdown spectroscopy (LIBS) device, and demonstrates the enhancement of sensitivity and precision through chemometric analysis. Evaluation of different analytical methods showed that partial least squares regression was superior in determining the content of iron and nickel in plutonium metal, with LoDs of 15 and 20 ppm, respectively. These findings are critical for identifying undesirable trace elements in plutonium components for applications such as fabricating radioisotope thermoelectric generators or nuclear fuel.
We present the first reported quantification of trace elements in plutonium via a portable laser-induced breakdown spectroscopy (LIBS) device and demonstrate the use of chemometric analysis to enhance the handheld device's sensitivity and precision. Quantification of trace elements such as iron and nickel in plutonium metal via LIBS is a challenging problem due to the complex nature of the plutonium optical emission spectra. While rapid analysis of plutonium alloys has been demonstrated using portable LIBS devices, such as the SciAps Z300, their detection limits for trace elements are severely constrained by their achievable pulse power and length, light collection optics, and detectors. In this paper, analytical methods are evaluated as a means to circumvent the detection constraints. Three chemometric methods often used in analytical spectroscopy are evaluated; principal component regression, partial least-squares regression, and artificial neural networks. These models are evaluated based on goodness-of-fit metrics, root mean-squared error, and their achievable limits of detection (LoDs). Partial least squares proved superior for determining content of iron and nickel in plutonium metal, yielding LoDs of 15 and 20 ppm, respectively. These results of identifying the undesirable trace elements in plutonium components are critical for applications such as fabricating radioisotope thermoelectric generators or nuclear fuel.

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