Raman composition analysis of electrolyte solvent mixtures from industrial lithium-ion battery (LIB) recycling

Abstract

We report a Raman spectroscopy method for the quantitative analysis of electrolyte solvent mixtures from industrial lithium-ion battery recycling processes. Compositions of the electrolyte solvent mixtures were evaluated using classical least squares (CLS) and partial least squares (PLS) regression, which were compared in terms of accuracy, robustness, and applicability. CLS regression outperformed PLS, achieving mean absolute deviations of 0.006 mol/mol for binary and 0.018 mol/mol for quinary mixtures. Importantly, the approach includes a straightforward strategy to assess low-abundance compounds, providing an illustrative method to determine whether trace compounds should be included or excluded in the quantitative model. Having developed the method with 310 synthesized mixtures of common organic carbonates spanning binary to quinary systems, we successfully quantified real condensate samples from two different battery recycling shredders with markedly different fluorescence backgrounds. Shifted Excitation Raman Difference Spectroscopy (SERDS) in the near infrared spectral region enabled reliable extraction of Raman signals and subsequent robust mixture quantification, by also effectively suppressing etaloning.