ANN-LIBS analysis of mixture plasmas: detection of xenon

Ghaderi, Amirhossein; Laitl, Vojtech; Heays, Alan N.; Civis, Svatopluk; Kepes, Erik

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.

Más información

Título según WOS: ANN-LIBS analysis of mixture plasmas: detection of xenon
Título de la Revista: JOURNAL OF ANALYTICAL ATOMIC SPECTROMETRY
Volumen: 37
Número: 9
Editorial: ROYAL SOC CHEMISTRY
Fecha de publicación: 2022
Página de inicio: 1815
Página final: 1823
DOI:

10.1039/d2ja00132b

Notas: ISI