Determination of lithium concentration in black mass using laser-induced breakdown spectroscopy hand-held...

Lithium has become one of the most strategic materials in the industry, given its wide use for the realization of efficient energy storage devices and for improving the chemical and physical characteristics of advanced ceramic and glass materials. Its widespread use in the last decades has also posed problems for its recovery and recycling, mostly from exhaust Li-ion batteries, which are mechanically treated to obtain the black mass. The black mass is a carbon-based material derived from the cathodic and anodic components of the batteries, containing several metals, as for example cobalt and nickel, along with lithium in varying quantities. Therefore, it is important to develop fast and accurate techniques for determining the black mass composition. This information is also essential for optimizing the extraction processes of lithium or other metals from the black mass. In this paper, we employ automated data processing using an Artificial Neural Network (ANN) to analyze spectra acquired from black mass equivalent materials with a commercial hand-held Laser-Induced Breakdown Spectroscopy (LIBS) instrument, enabling the determination of lithium content from a minimal set of spectral features. Additionally, we compare the results obtained for real black mass with reference values from other elemental analysis techniques. The combination of the ANN algorithm speed and robustness with the reliability of the LIBS instrument demonstrates the feasibility of accurate determination of the lithium content in black mass with a minimum treatment of the samples and on very different matrices.