Tuesday, 11 October 2022: 14:30
Room 219 (The Hilton Atlanta)
The demand for batteries is rapidly growing across a range of technologies. The increasingly diverse use cases for batteries require various capabilities, particularly requirements for high energy densities, that are currently unmet by traditional Li-ion batteries. Electrolyte stability proves to be a bottleneck for battery advancement towards energy dense chemistries beyond Li-ion, including metal anodes. In situ spectroscopy tools, such as Fourier transform infrared (FTIR) spectroscopy, Raman spectroscopy, and X-ray spectroscopy, have provided insight into critical molecular-level interactions in batteries during cycling. These in situ tools have yielded continuous improvement of electrolyte properties. Spectroscopy datasets, however, contain many nuances that challenge meaningful human understanding. Artificial intelligence and chemometric tools can be coupled with in situ spectroscopy to find relevant interpretations of spectral datasets and elucidate complex molecular phenomena. In this research, an analysis was performed on FTIR spectroscopy data from an electrolyte composed of LiPF6 in ethylene carbonate (EC) and ethyl methyl carbonate (EMC) to discern solvation behavior using principal component analysis (PCA) and a convolutional neural network (CNN). PCA pinpointed exact band locations of solvation shifting behavior in the IR spectra and improved understanding of the relationship between lithium concentrations and peak changes. The CNN was trained with spectral datasets of electrolytes with known lithium concentrations and then could predict lithium concentrations from spectral datasets with extraordinarily high accuracy. Additionally, the CNN interpreted FTIR spectral datasets from a graphite half-cell with EC/EMC/LiPF6 electrolyte and accurately determined the lithium concentration in the bulk electrolyte. The CNN also observed lithium depletion events (up to 10% lithium depletion) in the graphite anode during fast-charging cycles of the galvanostatic intermittent titration technique. This research breaks new ground on using advanced computational tools for in situ spectroscopic analysis of battery electrolytes and demonstrates an improved understanding of complex molecular-level phenomena in electrolytes.