Abstract
Efficient identification of cathode chemistry in end-of-life lithium-ion batteries is essential for enabling effective battery recycling. Current approaches often rely on battery disassembly or time-consuming testing, limiting their practical use at scale. Here we report a rapid classification strategy based on X-ray fluorescence spectroscopy combined with statistical analysis. A reference dataset was established from high-quality elemental spectra collected from more than 100 end-of-life lithium-ion batteries. Statistical grouping was used to define cathode categories, which were validated by selective disassembly and complementary chemical analysis. The trained classification model was then applied to newly acquired spectra collected within seconds per battery, enabling fast identification without additional disassembly. The approach achieves high prediction accuracy across the studied dataset and demonstrates the feasibility of rapid cathode identification for battery recycling applications.
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Data availability
Processed data are included in the article and Supplementary Information. Additional raw data are available from the corresponding author upon reasonable request.
Code availability
The data processing and statistical analysis scripts used in this study are provided as Supplementary Information (Supplementary Script 1). The scripts rely on existing open-source packages and were used without modification.
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Acknowledgements
This work was supported by the European project RESTART (LEAP RE program). The LEAP Re project has received funding from the European Union’s Horizon 2020 research and Innovation Program under grant agreement 963530. The authors are grateful to 1001 PILES BATTERIES (Aix-en-Provence, France) for providing the spent batteries. This work benefited from the MATRIX platform (CEREGE, France), funded by the French “Investissements d’Avenir” program managed by the French National Research Agency through Nano-ID (EQUIPEX ANR-10-EQPX39-01) and IMAGINE2 (EQUIPEX ANR-21-ESRE-0043). Matrix is member of the REGEF French research infrastructure.
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Feihong Ren designed and carried out most of the experiments, collected the end-of-life battery samples, performed data processing, prepared the figures and tables, and wrote and revised the manuscript. Vladimir Vidal, Perrine Chaurand, and Daniel Borschneck provided guidance on the XRF platform and were responsible for its maintenance. Andréa Campos performed the SEM–EDS analyses, contributed to the discussion of the results, and reviewed the manuscript. Florence Vacandio provided guidance on the electrochemical analyses, contributed to the discussion of the results, and reviewed the manuscript. Suanto Syahputra assisted with the electrochemical measurements. Bernard Angeletti and Isabelle Giffard conducted the ICP–OES analyses and processed the corresponding data. Jérôme Rose and Ismael Saadoune contributed to project discussions and manuscript review. Clément Levard supervised the first author, contributed to the experimental design, discussed the results, and reviewed and revised the manuscript.
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Communications Engineering thanks Jiadong Yu, Misaki Katayama and Joseph Jegan Roy for their contribution to the peer review of this work. Primary Handling Editors: [Jiangong Zhu] and [Rosamund Daw]. A peer review file is available.
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Ren, F., Vidal, V., Campos, A. et al. X-ray fluorescence spectroscopy for rapid identification of cathode chemistry in lithium-ion battery recycling. Commun Eng (2026). https://doi.org/10.1038/s44172-026-00618-3
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DOI: https://doi.org/10.1038/s44172-026-00618-3
