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ANN-LIBS analysis of mixture plasmas: detection of xenon
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SYSNO ASEP 0579026 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title ANN-LIBS analysis of mixture plasmas: detection of xenon Author(s) Saeidfirozeh, H. (CZ)
Myakalwar, A. K. (CL)
Kubelík, Petr (FZU-D) RID, ORCID
Ghaderi, A. (CA)
Laitl, V. (CZ)
Petera, L. (CZ)
Rimmer, P. B. (GB)
Shorttle, O. (GB)
Heays, A.N. (CZ)
Křivková, A. (CZ)
Krůs, M. (CZ)
Civiš, S. (CZ)
Yanez, J. (CL)
Képeš, E. (CZ)
Pořízka, P. (CZ)
Ferus, M. (CZ)Number of authors 16 Source Title Journal of Analytical Atomic Spectrometry. - : Royal Society of Chemistry - ISSN 0267-9477
Roč. 37, č. 9 (2022), s. 1815-1823Number of pages 10 s. Language eng - English Country GB - United Kingdom Keywords artificial neural network method ; characterising crucial physical plasma parameters ; laser-induced breakdown spectra, ; xenon Subject RIV BL - Plasma and Gas Discharge Physics OECD category Fluids and plasma physics (including surface physics) Method of publishing Limited access Institutional support FZU-D - RVO:68378271 UT WOS 000826342300001 EID SCOPUS 85134913156 DOI 10.1039/d2ja00132b Annotation 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. Workplace Institute of Physics Contact Kristina Potocká, potocka@fzu.cz, Tel.: 220 318 579 Year of Publishing 2024 Electronic address https://doi.org/10.1039/d2ja00132b
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