In:
Journal of Analytical Atomic Spectrometry, Royal Society of Chemistry (RSC), Vol. 37, No. 9 ( 2022), p. 1815-1823
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.
Type of Medium:
Online Resource
ISSN:
0267-9477
,
1364-5544
Language:
English
Publisher:
Royal Society of Chemistry (RSC)
Publication Date:
2022
detail.hit.zdb_id:
1484654-8
detail.hit.zdb_id:
54176-X
SSG:
11
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