Marie-Chloé Michaud Paradis
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Marie-Chloé Michaud Paradis has a total of 8 co-authors. Here are the top co-authors by number of shared publications:
- Alex Hernandez-Garcia
AI Institute Mila Quebec
1 shared publication - François R. Doucet
ELEMISSION Inc., 3410, Thimens blvd., Montréal, QC H4R 1V6, Canada
1 shared publication - Kheireddine Rifai
ELEMISSION Inc., 3410, Thimens blvd., Montréal, QC H4R 1V6, Canada
1 shared publication - Lütfü-Çelebi Özcan
ELEMISSION Inc., 3410, Thimens blvd., Montréal, QC H4R 1V6, Canada
1 shared publication - Mickael Dollé
Département de Chimie Université de Montréal Montréal Québec Canada
1 shared publication - Nawfel Azami
Optics Laboratory, INPT, Avenue Allal Al Fassi, Rabat, Morocco
1 shared publication
Latest Publications
- Open Access
Deep Learning Classification of Li-Ion Battery Materials Targeting Accurate Composition Classification from Laser-Induced Breakdown Spectroscopy High-Speed Analyses
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.
Open AccessDeep Learning Classification of Li-Ion Battery Materials Targeting Accurate Composition Classification from Laser-Induced Breakdown Spectroscopy High-Speed Analyses
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.