ANN-LIBS analysis of mixture plasmas: detection of xenon

0579026 - FZÚ 2024 RIV GB eng J - Journal Article
Saeidfirozeh, H. - Myakalwar, A. K. - Kubelík, Petr - Ghaderi, A. - Laitl, V. - Petera, L. - Rimmer, P. B. - Shorttle, O. - Heays, A.N. - Křivková, A. - Krůs, M. - Civiš, S. - Yanez, J. - Képeš, E. - Pořízka, P. - Ferus, M.
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
Institutional support: RVO:68378271
Keywords : artificial neural network method * characterising crucial physical plasma parameters * laser-induced breakdown spectra, * xenon
OECD category: Fluids and plasma physics (including surface physics)
Impact factor: 3.4, year: 2022
Method of publishing: Limited access
https://doi.org/10.1039/d2ja00132b

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.
Permanent Link: https://hdl.handle.net/11104/0347900