Kavli Affiliate: Feng Wang
| First 5 Authors: Qi You, Yan Sun, Feng Wang, Jun Cheng, Fujie Tang
| Summary:
Understanding the solvation structure of electrolytes is critical for
optimizing the electrochemical performance of rechargeable batteries, as it
directly influences properties such as ionic conductivity, viscosity, and
electrochemical stability. The highly complex structures and strong
interactions in high-concentration electrolytes make accurate modeling and
interpretation of their “structure-property" relationships even more
challenging with spectroscopic methods. In this study, we present a machine
learning-based approach to predict dynamic $^7$Li NMR chemical shifts in
LiFSI/DME electrolyte solutions. Additionally, we provide a comprehensive
structural analysis to interpret the observed chemical shift behavior in our
experiments, particularly the abrupt changes in $^7$Li chemical shifts at high
concentrations. Using advanced modeling techniques, we quantitatively establish
the relationship between molecular structure and NMR spectra, offering critical
insights into solvation structure assignments. Our findings reveal the
coexistence of two competing local solvation structures that shift in dominance
as electrolyte concentration approaches the concentrated limit, leading to
anomalous reverse of $^7$Li NMR chemical shift in our experiment. This work
provides a detailed molecular-level understanding of the intricate solvation
structures probed by NMR spectroscopy, leading the way for enhanced electrolyte
design.
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