Mapping capability of linear correlation statistics for characterization of complex materials using laser-induced breakdown spectroscopy
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Mapping capability of linear correlation statistics for characterization of complex materials using laser-induced breakdown spectroscopyData
2022-09-22Cita bibliográfica
MATEO, M. P. and NICOLAS, G., 2022. Mapping capability of linear correlation statistics for characterization of complex materials using laser-induced breakdown spectroscopy. [online]. 2022. Vol. 1227, p. 340260. DOI 10.1016/j.aca.2022.340260. Available from: https://www.sciencedirect.com/science/article/pii/S0003267022008315
Resumo
[Abstract]: In this work, the capability of linear correlation statistics for chemical mapping by laser-induced breakdown spectroscopy (LIBS) has been studied for the first time for the characterization of samples with compositional changes on surface and in depth. For that purpose, a corrosion layer of varied spread has been caused in brass samples by chemical treatment and afterwards analyzed by LIBS. Correlation depth profiles, two-dimensional (2D) correlation maps and three-dimensional (3D) correlation maps have been generated from LIBS data to contrast the results obtained from treated and non-treated samples and zones. Conventional LIBS maps based on signal intensity have also been generated for comparison. The conclusions of this study demonstrate the capability and benefits of using the linear correlation method for 3D mapping by LIBS of samples with non-uniform composition. In this sense, the proposed methodology has allowed to determine the location of the corroded regions in the analyzed volume, even in the non-treated zones also affected by the byproducts originated from the chemical attack, in contrast to conventional LIBS mapping based on signal representation.
Palabras chave
Laser induced breakdown spectroscopy
Depth profiling
3D mapping
Linear correlation coefficient
Corrosion
Brass
Depth profiling
3D mapping
Linear correlation coefficient
Corrosion
Brass
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Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
ISSN
0003-2670