Autonomous Raman Spectroscopy for Investigating Lithium Salt Degradation Processes

Lithium salts such as Li₂O, LiOH, and Li₂CO₃ play a critical role in the production of high-quality lithium-ion and lithium-metal battery materials. To address the challenge of continuous monitoring of these salts in atmospheric environments, we developed an autonomous Raman spectroscopy system integrated with machine learning. This Python-based system captures Raman spectra and predicts the relative concentrations of degradation products in real-time, while dynamically adjusting data collection intervals. Our findings reveal that Li₂O forms a surface layer of Li₂CO₃ and LiOH, with the inner layers remaining largely unaltered, whereas LiOH degrades by forming LiOH●H₂O and Li₂CO₃. This automated approach significantly accelerates the monitoring of degradation processes, offering valuable mechanistic insights that enhance storage protocols and quality control in the manufacturing of battery materials.