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Please use this identifier to cite or link to this item: http://hdl.handle.net/2289/8684
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dc.contributor.authorAmogh, M S-
dc.contributor.authorXavier, Sebin Sebastian-
dc.contributor.authorJose, Jeena Rose-
dc.contributor.authorPhilip, Reji-
dc.contributor.authorBiju, P R-
dc.date.accessioned2026-03-02T11:15:28Z-
dc.date.available2026-03-02T11:15:28Z-
dc.date.issued2026-04-
dc.identifier.citationSpectrochimica Acta Part B: Atomic Spectroscopy, 2026, Vol. 238 (4), AR No. 107480en_US
dc.identifier.issn0584-8547-
dc.identifier.urihttp://hdl.handle.net/2289/8684-
dc.descriptionRestricted Access.en_US
dc.description.abstractLaser Induced Breakdown Spectroscopy (LIBS) is widely used for rapid material characterization and quantitative analysis due to its versatility and speed. Linking LIBS spectra to precise quantitative measurements is challenging because of nonlinear effects arising from surface variations, matrix interactions, and self-absorption. As a result, precise quantitative analysis has been a persistent challenge for the LIBS community. We perform stoichiometric evaluation of BaSrTiO₃ (BST) samples with varying compositions using a stationary-state plasma approach. BST is notable for its high dielectric performance and is employed in applications such as non-volatile memories, pyroelectric sensors, and electro-optic devices. We simulate the LIBS spectra using a two-zone plasma model under Local Thermodynamic Equilibrium (LTE) and fit them to experimental spectra to extract stoichiometry, electron density, and plasma temperature. A Controlled Random Search (CRS) algorithm is used to optimize the fit to find the stoichiometry accurately. Further, an Artificial Neural Network (ANN) is trained exclusively on synthetic spectra representing the constituent elements, and when applied to experimental spectra, it accurately predicts the stoichiometry of samples not seen during training. To our knowledge, this work is the first to employ a two-zone stationary-state plasma model to generate synthetic training data for an ANN, which is then successfully applied to predict elemental compositions from experimental spectra. Our findings show that a simple ANN based on one-dimensional plasma modelling enables rapid in-situ classification and stoichiometric analysis, reducing reliance on large experimental spectral datasets for machine learning. This approach could simplify the complexities associated with experimental spectral requirements for training and predicting elemental concentration using ML models.en_US
dc.language.isoenen_US
dc.publisherSpectrochimica Acta Part B: Atomic Spectroscopyen_US
dc.relation.urihttps://doi.org/10.1016/j.sab.2026.107480en_US
dc.rights© 2026 Elsevier B.V.en_US
dc.subjectLIBSen_US
dc.subjectCF-LIBSen_US
dc.subjectLaser produced plasmasen_US
dc.subjectArtificial intelligenceen_US
dc.subjectArtificial neural networken_US
dc.subjectMachine learningen_US
dc.titleLIBS of BaSrTiO3 compounds using ANN based on a stationary state plasma modelen_US
dc.typeArticleen_US
dc.additionalSupplementary Material: https://www.sciencedirect.com/science/article/pii/S0584854726000315?pes=vor&utm_source=clarivate&getft_integrator=clarivate#ec0005en_US
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