Finite element simulation of biomass-derived carbon electrodes for hybrid supercapacitors using COMSOL multiphysics

The growing demand for sustainable energy storage has intensified interest in biomass-derived carbons as eco-friendly and cost-effective electrode materials for supercapacitors. In this study, a finite element simulation framework was developed in COMSOL Multiphysics 6.2 to investigate the hybrid charge storage behaviour of pine-derived carbon electrodes. The model integrates porous matrix double-layer capacitance with Butler-Volmer electrochemical kinetics under cyclic voltammetry conditions, enabling a realistic description of electrode-electrolyte interactions. The results demonstrates that the hierarchical pore architecture of pine carbons supports both electric double-layer capacitance and pseudocapacitance, with the relative contributions dynamically shifting depending on operating conditions. This hybrid mechanism was successfully captured through potential distribution, ion transport, and current response profiles, providing insights that are difficult to isolate experimentally. This study demonstrates the application of a coupled porous matrix double-layer capacitance Butler-Volmer simulation to biomass-derived electrodes, effectively bridging the gap between Cyclic Voltammetry and predictive modeling. By providing mechanistic clarity and a robust quantitative framework, it establishes pine-derived carbon as a sustainable and high-performing electrode material, while advancing simulation guided design strategies for next-generation supercapacitors. Fig. 1. Schematic representation of electrochemical simulation workflow for supercapacitor modeling. • Finite element simulation of pine-derived carbon electrodes is presented. • Hybrid EDLC-pseudocapacitive storage captured under cyclic voltammetry. • Porous matrix double-layer model coupled with Butler-Volmer kinetics. • Simulation reveals potential and ion transport in hierarchical porosity. • Framework bridges experimental CV results with predictive modeling.