The general interest in the implementation of renewable energies, particularly in the development of lithium-ion batteries, makes lithium a key element to be analyzed. We are working to develop a tool to determine if the isotopic effect of lithium has an impact on battery aging.
For this purpose, we propose an analytical procedure for isotope ratio determination based on the monitoring of the lithium isotope components through their spin-orbital coupling and its isotope shift for the electronic transition 22P←22S around 670.788 nm. For that, it is used a high-resolution continuum source atomic absorption spectrometer (HR-CS-AAS) coupled to a double echelle modular spectrometer (DEMON) and spectral data analysis by a decision-tree-based ensemble machine learning (ML) algorithm.
The optical resolution was improved from 140,000 to 790,000 to better deconvolution the Li isotopic components in the atomic spectrum. A set of samples with 6Li isotope amount fractions ranging from 0.06 to 0.99 mol mol−1 was employed for the training of a scalable tree boosting ML algorithm (XGBoost). The procedure was validated for the isotope ratio determination of a set of stock chemicals (Li2CO3, LiNO3, LiCl, and LiOH) and a BAM candidate reference material, LiNi1/3Mn1/3Co1/3O2 (NMC111) cathode material. The precision obtained ranged from 0.2 ‰ to 0.4 ‰.
Finally, the built ML model was applied to determine the isotope ratio of a broad set of reference materials with HR-CS-AAS and compared with multi-collector inductively coupled plasma mass spectrometry (MC-ICP-MS). The results are metrologically comparable compatible.
 A. Winckelmann et al. Anal. Chem., 93 (2021) 10022-10030, DOI: 10.1021/acs.analchem.1c00206.