ANN-LIBS analysis of mixture plasmas: detection of xenon

0559890 - ÚFCH JH 2023 RIV GB eng J - Článek v odborném periodiku
Saeidfirozeh, Homa - Myakalwar, A. K. - Kubelík, Petr - Ghaderi, A. - Laitl, Vojtěch - Petera, Lukáš - Rimmer, P. B. - Shorttle, O. - Heays, Alan - Křivková, Anna - Krůs, Miroslav - Civiš, Svatopluk - Yanez, J. - Kepes, E. - Pořízka, P. - Ferus, Martin
ANN-LIBS analysis of mixture plasmas: detection of xenon.
Journal of Analytical Atomic Spectrometry. Roč. 37, č. 9 (2022), s. 1815-1823. ISSN 0267-9477. E-ISSN 1364-5544
Grant CEP: GA ČR(CZ) GA21-11366S; GA MŠMT EF16_019/0000778
Institucionální podpora: RVO:61388955 ; RVO:61389021
Klíčová slova: ANN-LIBS analysis * xenon * planetary processes
Obor OECD: Physical chemistry; Fluids and plasma physics (including surface physics) (UFP-V)
Impakt faktor: 3.4, rok: 2022
Způsob publikování: Omezený přístup

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.
Trvalý link: https://hdl.handle.net/11104/0333017