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<p>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.</p>

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X-ray fluorescence spectroscopy for rapid identification of cathode chemistry in lithium-ion battery recycling

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月2日

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  • Feihong Ren,
  • Vladimir Vidal,
  • Andréa Campos,
  • Florence Vacandio,
  • Bernard Angeletti,
  • Isabelle Giffard,
  • Perrine Chaurand,
  • Daniel Borschneck,
  • Suanto Syahputra,
  • Jérôme Rose,
  • Ismael Saadoune,
  • Clément Levard

摘要

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.