Issue 9, 2022

ANN-LIBS analysis of mixture plasmas: detection of xenon

Abstract

We developed an artificial neural network method for characterising crucial physical plasma parameters (i.e., temperature, electron density, and abundance ratios of ionisation states) in a fast and precise manner that mitigates common issues arising in evaluation of laser-induced breakdown spectra. The neural network was trained on a set of laser-induced breakdown spectra of xenon, a particularly physically and geochemically intriguing noble gas. The artificial neural network results were subsequently compared to a standard local thermodynamic equilibrium model. Speciation analysis of Xe was performed in a model atmosphere, mimicking gaseous systems relevant for tracing noble gases in geochemistry. The results demonstrate a comprehensive method for geochemical analyses, particularly a new concept of Xe detection in geochemical systems with an order-of-magnitude speed enhancement and requiring minimal input information. The method can be used for determination of Xe plasma physical parameters in industrial as well as scientific applications.

Graphical abstract: ANN-LIBS analysis of mixture plasmas: detection of xenon

Supplementary files

Article information

Article type
Paper
Submitted
19 4 2022
Accepted
01 7 2022
First published
05 7 2022

J. Anal. At. Spectrom., 2022,37, 1815-1823

ANN-LIBS analysis of mixture plasmas: detection of xenon

H. Saeidfirozeh, A. K. Myakalwar, P. Kubelík, A. Ghaderi, V. Laitl, L. Petera, P. B. Rimmer, O. Shorttle, A. N. Heays, A. Křivková, M. Krůs, S. Civiš, J. Yáñez, E. Képeš, P. Pořízka and M. Ferus, J. Anal. At. Spectrom., 2022, 37, 1815 DOI: 10.1039/D2JA00132B

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