Machine Learning-Guided Design of Biomass-Based Porous Carbon for Aqueous Symmetric Supercapacitors.

ABSTRACT: Biomass-derived porous carbon electrodes have attracted significant attention for high-performance supercapacitor applications due to their sustainability, cost-effectiveness, and tunable porosity. To accelerate the design and evaluation of these materials, it is essential to develop accurate and efficient strategies for optimizing their physicochemical and electrochemical properties. Herein, a machine learning (ML) approach is employed to analyze experimental data from previously reported sources, enabling the prediction of specific capacitance (F g^-1) based on various material characteristics and processing conditions. The trained ML model evaluates the influence of factors such as biomass type, electrolyte, activating agent, and key synthesis parameters, including activation

SUBMITTER: Minakshi M 

PROVIDER: S-EPMC12509479 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature