Deep Learning Classification of Li-Ion Battery Materials Targeting Accurate Composition Classification from Laser-Induced Breakdown Spectroscopy High-Speed Analyses
Source abstract
Abstract
Laser-induced breakdown spectroscopy (LIBS) is a valuable tool for the solid-state elemental analysis of battery materials. Key advantages include a high sensitivity for light elements (lithium included), complex emission patterns unique to individual elements through the full periodic table, and record speed analysis reaching 1300 full spectra per second (1.3 kHz acquisition rate). This study investigates deep learning methods as an alternative tool to accurately recognize different compositions of similar battery materials regardless of their physical properties or manufacturer. Such applications are of interest for the real-time digitalization of battery components and identification in automated manufacturing and recycling plant designs.
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Research context
OpenAlex topics: Laser-induced spectroscopy and plasma · Extraction and Separation Processes · Spectroscopy and Chemometric Analyses
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Laboratory of Chemistry and Electrochemistry of Solids, Department of Chemistry, Université de Montréal, 1375 Ave. Thérèse-Lavoie-Roux, Montréal, QC H2V 0B3, Canada
ELEMISSION Inc., 3410 Thimens Blvd, Montréal, QC H4R 1V6, Canada
Mila—Institut Québécois d’Intelligence Artificielle, Université de Montréal, 6666 Saint-Urbain Street, Montréal, QC H2S 3H1, Canada
Optics Lab, Institut National des Postes et Télécommunications, Avenue Allal Al Fassi, Rabat 10112, Morocco
Funding
Mitacs (Canada)and ELEMISSION Inc.
References (30)
Show source-provided references
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