ANN-LIBS analysis of mixture plasmas: detection of xenon. Issue 9 (18th July 2022)

• Record Type: Journal Article
• Authors: Saeidfirozeh, Homa; Myakalwar, Ashwin Kumar; Kubelík, Petr; Ghaderi, Amirhossein; Laitl, Vojtěch; Petera, Lukáš; Rimmer, Paul B.; Shorttle, Oliver; Heays, Alan N.; Křivková, Anna; Krůs, Miroslav; Civiš, Svatopluk; Yáñez, Jorge; Képeš, Erik; Pořízka, Pavel; Ferus, Martin
• Is Part Of: Journal of analytical atomic spectrometry. Volume 37:Issue 9(2022)
• Page Start: 1815
• Page End: 1823
• Publication Date: 2022-07-18
• Subjects: Atomic spectra -- Periodicals; Atomic absorption spectroscopy -- Periodicals; 543.0858
• DOI: 10.1039/d2ja00132b
• Languages: English
• ISSNs: 0267-9477

Abstract

Abstract : We developed an artificial neural network method for characterising crucial physical plasma parameters ( i.e., temperature, electron density) in a fast and precise manner that mitigates common issues arising in evaluation of LIBS spectra. 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.