Accelerating charging dynamics using self‐driven optimizing porous structures

Abstract

AbstractRelating the complex structures of electrodes to their charging dynamics is crucial for optimizing supercapacitors, which remains an experimental and theoretical challenge. Here, we construct a pore network model (PNM) that can be downward‐transformed into a well‐known transmission‐line model and a stack‐electrode model to describe the disordered porous structure of carbon‐based electrodes. A mathematical expression is derived using an equivalent circuit model of the PNM to quantify the relaxation times of the potential and concentration. The expression is then verified using numerical solutions based on the simplified Poisson–Nernst–Planck equations and experimental data. The structure of the PNM for experimental verification is directly extracted from a porous electrode reconstructed using a scanning electron microscopic image. A self‐driven optimization framework is proposed by coupling the derived expression with a genetic algorithm to generate an optimal porous structure that can be used to investigate the changing dynamics of the electrode. Our framework provides a general image–structure–performance optimization platform for understanding and accelerating charging dynamics in porous electrodes.