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<p>Carbon-based electrode materials are widely recognized for their outstanding electrical conductivity, large specific surface area, and robust chemical stability, thereby rendering them highly suitable for electric double-layer capacitors (EDLCs) that rely on electrostatic charge storage. Among these materials, biomass-derived activated carbon (AC)—especially from agroforestry waste—has gained prominence as a potential candidate due to its cost-effectiveness, abundant availability, and simple synthesis processes. Numerous experimental investigations have explored the synthesis of AC from diverse biomass sources. Also, the research is now increasingly shifting toward predictive modeling to understand and optimize AC properties for supercapacitor applications. Recent efforts focus on forecasting key performance metrics such as yield, heteroatom content, and specific capacitance based on structural and compositional parameters, including pore architecture, chemical constituents, graphitization level, testing conditions, and electrolyte choice. This review presents a detailed analysis of AC fabrication techniques, the influence of processing variables on material performance, and the growing role of machine learning (ML) in predicting electrochemical behavior. Particular emphasis is placed on linking synthesis conditions with structural characteristics and their impact on specific capacitance.</p>

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Biomass-derived activated carbon: a review on process parameters, material properties, and machine learning approaches for supercapacitor

  • 期刊论文

  • 发表日期：

    2026年1月6日

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  • Pranay Singh,
  • Himanshu Sekhar Panda

摘要

Carbon-based electrode materials are widely recognized for their outstanding electrical conductivity, large specific surface area, and robust chemical stability, thereby rendering them highly suitable for electric double-layer capacitors (EDLCs) that rely on electrostatic charge storage. Among these materials, biomass-derived activated carbon (AC)—especially from agroforestry waste—has gained prominence as a potential candidate due to its cost-effectiveness, abundant availability, and simple synthesis processes. Numerous experimental investigations have explored the synthesis of AC from diverse biomass sources. Also, the research is now increasingly shifting toward predictive modeling to understand and optimize AC properties for supercapacitor applications. Recent efforts focus on forecasting key performance metrics such as yield, heteroatom content, and specific capacitance based on structural and compositional parameters, including pore architecture, chemical constituents, graphitization level, testing conditions, and electrolyte choice. This review presents a detailed analysis of AC fabrication techniques, the influence of processing variables on material performance, and the growing role of machine learning (ML) in predicting electrochemical behavior. Particular emphasis is placed on linking synthesis conditions with structural characteristics and their impact on specific capacitance.