02/12/2026
By Dongming Xie
Date: Thursday, Feb. 19, 2026
Time: 3:30-4:45 p.m.
Location: Ball Hall 210
Abstract:
Beyond lithium-ion batteries have attracted significant research interest in the last couple of years due to their significantly higher theoretical specific capacities and earth-abundant active materials. Lithium-sulfur (Li-S) batteries are one of the most commonly studied examples, as the cathode active material, sulfur, is abundant, inexpensive, and has a high capacity. However, several mechanisms hinder the Li-S battery performance: the depletion of the lithium metal or the electrolyte in the cell with cycling, the polysulfide shuttle mechanism due to the transport of the soluble reaction intermediates between the cathode and the anode, and the passivation of the cathode surface due to the precipitation of the insoluble product Li2S are some examples of these mechanisms that lead to fast capacity fade and thus limited cycle life of the Li-S batteries. Due to the highly complex reaction and degradation mechanisms in the cell, the performance of the Li-S battery depends significantly on the materials and cell design. Mathematical modeling coupled with electrochemical characterization is essential to better understand this critical link between the key materials and cell design parameters -electrolyte-to-sulfur (E/S) ratio, carbon-to-sulfur (C/S) ratio, sulfur loading, and carbon and electrolyte properties- and the Li-S battery performance. In our research group, we couple electrochemical characterization (e.g., electrochemical impedance spectroscopy, galvanostatic cycling) and modeling (e.g., electrochemical modeling, cell- and system-level performance modeling, machine learning) to investigate the critical link between materials, cell design, and Li-S battery performance. Moreover, we develop machine learning models to guide the design of other critical battery chemistries beyond Li-ion, such as lithium-oxygen, sodium-ion, and potassium-ion batteries.