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 re...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Batteries |
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| Online Access: | https://www.mdpi.com/2313-0105/8/11/231 |
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| author | Marie-Chloé Michaud Paradis François R. Doucet Steeve Rousselot Alex Hernández-García Kheireddine Rifai Ouardia Touag Lütfü Ç. Özcan Nawfal Azami Mickaël Dollé |
| author_facet | Marie-Chloé Michaud Paradis François R. Doucet Steeve Rousselot Alex Hernández-García Kheireddine Rifai Ouardia Touag Lütfü Ç. Özcan Nawfal Azami Mickaël Dollé |
| author_sort | Marie-Chloé Michaud Paradis |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T19:16:35Z |
| format | Article |
| id | doaj.art-d59793b6421b48a896fceb87d8eb61ea |
| institution | Directory Open Access Journal |
| issn | 2313-0105 |
| language | English |
| last_indexed | 2024-03-09T19:16:35Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Batteries |
| spelling | doaj.art-d59793b6421b48a896fceb87d8eb61ea2023-11-24T03:45:14ZengMDPI AGBatteries2313-01052022-11-0181123110.3390/batteries8110231Deep Learning Classification of Li-Ion Battery Materials Targeting Accurate Composition Classification from Laser-Induced Breakdown Spectroscopy High-Speed AnalysesMarie-Chloé Michaud Paradis0François R. Doucet1Steeve Rousselot2Alex Hernández-García3Kheireddine Rifai4Ouardia Touag5Lütfü Ç. Özcan6Nawfal Azami7Mickaël Dollé8Laboratory of Chemistry and Electrochemistry of Solids, Department of Chemistry, Université de Montréal, 1375 Ave. Thérèse-Lavoie-Roux, Montréal, QC H2V 0B3, CanadaELEMISSION Inc., 3410 Thimens Blvd, Montréal, QC H4R 1V6, CanadaLaboratory of Chemistry and Electrochemistry of Solids, Department of Chemistry, Université de Montréal, 1375 Ave. Thérèse-Lavoie-Roux, Montréal, QC H2V 0B3, CanadaMila—Institut Québécois d’Intelligence Artificielle, Université de Montréal, 6666 Saint-Urbain Street, Montréal, QC H2S 3H1, CanadaELEMISSION Inc., 3410 Thimens Blvd, Montréal, QC H4R 1V6, CanadaLaboratory of Chemistry and Electrochemistry of Solids, Department of Chemistry, Université de Montréal, 1375 Ave. Thérèse-Lavoie-Roux, Montréal, QC H2V 0B3, CanadaELEMISSION Inc., 3410 Thimens Blvd, Montréal, QC H4R 1V6, CanadaOptics Lab, Institut National des Postes et Télécommunications, Avenue Allal Al Fassi, Rabat 10112, MoroccoLaboratory of Chemistry and Electrochemistry of Solids, Department of Chemistry, Université de Montréal, 1375 Ave. Thérèse-Lavoie-Roux, Montréal, QC H2V 0B3, CanadaLaser-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.https://www.mdpi.com/2313-0105/8/11/231laser-induced breakdown spectroscopyLi-ionartificial intelligencedeep learningactive materialsolid-state electrolyte |
| spellingShingle | Marie-Chloé Michaud Paradis François R. Doucet Steeve Rousselot Alex Hernández-García Kheireddine Rifai Ouardia Touag Lütfü Ç. Özcan Nawfal Azami Mickaël Dollé Deep Learning Classification of Li-Ion Battery Materials Targeting Accurate Composition Classification from Laser-Induced Breakdown Spectroscopy High-Speed Analyses Batteries laser-induced breakdown spectroscopy Li-ion artificial intelligence deep learning active material solid-state electrolyte |
| title | Deep Learning Classification of Li-Ion Battery Materials Targeting Accurate Composition Classification from Laser-Induced Breakdown Spectroscopy High-Speed Analyses |
| title_full | Deep Learning Classification of Li-Ion Battery Materials Targeting Accurate Composition Classification from Laser-Induced Breakdown Spectroscopy High-Speed Analyses |
| title_fullStr | Deep Learning Classification of Li-Ion Battery Materials Targeting Accurate Composition Classification from Laser-Induced Breakdown Spectroscopy High-Speed Analyses |
| title_full_unstemmed | Deep Learning Classification of Li-Ion Battery Materials Targeting Accurate Composition Classification from Laser-Induced Breakdown Spectroscopy High-Speed Analyses |
| title_short | Deep Learning Classification of Li-Ion Battery Materials Targeting Accurate Composition Classification from Laser-Induced Breakdown Spectroscopy High-Speed Analyses |
| title_sort | deep learning classification of li ion battery materials targeting accurate composition classification from laser induced breakdown spectroscopy high speed analyses |
| topic | laser-induced breakdown spectroscopy Li-ion artificial intelligence deep learning active material solid-state electrolyte |
| url | https://www.mdpi.com/2313-0105/8/11/231 |
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