A network model to predict ionic transport in porous materials

Henrique, Filipe; Żuk, Paweł J.; Gupta, Ankur

A network model to predict ionic transport in porous materials

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.

Description

Understanding the dynamics of electric-double-layer (EDL) charging in porous media is essential for advancements in next-generation energy storage devices. Due to the high computational demands of direct numerical simulations and a lack of interfacial boundary conditions for reduced-order models, the current understanding of EDL charging is limited to simple geometries. Here, we present a network model to predict EDL charging in arbitrary networks of long pores in the Debye–Hückel limit without restrictions on EDL thickness and pore radii. We demonstrate that electrolyte transport is described by Kirchhoff’s laws in terms of the electrochemical potential of charge (the valence-weighted average of the ion electrochemical potentials) instead of the electric potential. By employing the equivalent circuit representation suggested by these modified Kirchhoff’s laws, our methodology accurately captures the spatial and temporal dependencies of charge density and electric potential, matching results obtained from computationally intensive direct numerical simulations. Our network model provides results up to six orders of magnitude faster, enabling the efficient simulation of a triangular lattice of five thousand pores in 6 min. We employ the framework to study the impact of pore connectivity and polydispersity on electrode charging dynamics for pore networks and discuss how these factors affect the time scale, energy density, and power density of capacitive charging. The scalability and versatility of our methodology make it a rational tool for designing 3D-printed electrodes and for interpreting geometric effects on electrode impedance spectroscopy measurements.

Citation

F. Henrique, P.J. Żuk, A. Gupta, A network model to predict ionic transport in porous materials, Proc. Natl. Acad. Sci. U.S.A. 121 (22) e2401656121, https://doi.org/10.1073/pnas.2401656121 (2024).