Machine Learning-Based Stoichiometric Analysis of BaSrTiO<sub>3</sub> (BST) via Synthetic LIBS Spectra Generated from a Two-Zone Plasma Model
Laser Induced Breakdown Spectroscopy (LIBS) analysis is a versatile tool for effectively classifying and quantifying materials. The connection between LIBS spectral data and quantitative measurements is often complex and nonlinear, influenced by factors such as sample surface condition, matrix effects and self-absorption [1], [2]. This work introduces a novel strategy employing a two-zone stationary state plasma model to address these challenges by generating synthetic training datasets for machine learning-based stoichiometric analysis.
Authors
- P.R. Biju (ORCID: https://orcid.org/0000-0003-4620-4749)
- Reji Philip (ORCID: https://orcid.org/0000-0003-3836-470X)
- Jeena Rose Jose (ORCID: https://orcid.org/0000-0002-3784-0877)
- Sebin Sebastian Xavier
- M S Amogh
Institutions
- Raman Research Institute (IN)
- Mahatma Gandhi University (IN)
Publication Details
- Published
- 2025-06-23
- DOI
- https://doi.org/10.1109/cleo/europe-eqec65582.2025.11110899
- Primary Topic
- Laser-induced spectroscopy and plasma
- Type
- article
- Field-Weighted Citation Impact
- 0.00