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Classification and authentication of materials using prompt gamma ray activation analysis

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Abstract

Prompt gamma ray activation analysis (PGAA) is a non-destructive nuclear measurement technique that quantifies isotopes present in a sample. Here, we use PGAA spectra to train different types of models to elucidate how discriminating these spectra are for various classes of materials. We trained discriminative models for closed set scenarios, where all possible material classes are known. We also trained class models to address open set conditions, where this enumeration is impossible. After appropriate pre-processing and data treatments, all such models performed nearly perfectly on our dataset, suggesting PGAA spectra may serve as powerful nuclear fingerprints for robust material classification.

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Data available from authors upon reasonable request.

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Acknowledgements

Certain equipment, instruments, software, or materials, commercial or non-commercial, are identified in this paper to specify the experimental procedure adequately. Such identification is not intended to imply recommendation or endorsement of any product or service by NIST, nor is it intended to imply that the materials or equipment identified are necessarily the best available for the purpose. Contribution of the National Institute of Standards and Technology, not subject to US Copyright.

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Authors and Affiliations

  1. Chemical Sciences Division, National Institute of Standards and Technology, Gaithersburg, MD, 20899-8320, USA

    Nathan A. Mahynski, Jacob I. Monroe, David A. Sheen, Rick L. Paul, H. Heather Chen-Mayer & Vincent K. Shen

  2. Ralph E. Martin Department of Chemical Engineering, University of Arkansas, Fayetteville, AR, 72701, USA

    Jacob I. Monroe

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  1. Nathan A. Mahynski
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  2. Jacob I. Monroe
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Correspondence to Nathan A. Mahynski.

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Mahynski, N.A., Monroe, J.I., Sheen, D.A. et al. Classification and authentication of materials using prompt gamma ray activation analysis. J Radioanal Nucl Chem 332, 3259–3271 (2023). https://doi.org/10.1007/s10967-023-09024-x

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