A network model to predict ionic transport in porous materials
F. Henrique, P.J. Żuk, A. Gupta
PNAS, 2024
Abstract
Porous electrodes enhance the capacitance of electrochemical devices by maximizing surface area. However, the relationship between device performance and the porous material structure remains poorly understood. This study introduces a model to predict electrolyte transport in complex networks of slender pores. We derive modified Kirchhoff’s laws and equivalent circuit equations for electrolyte transport in charged confinement. Our framework accelerates numerical computations by six orders of magnitude without compromising accuracy. We leverage this model to investigate the influence of connections and pore size distribution on the charging time scale of electrical double layers and predict structure–property relationships. These findings hold potential for improving supercapacitor design and enabling 3D-printed microscale electrodes for wearable energy storage and supercapacitors in Internet-of-Things applications.