Settings

Now showing1 - 1 of 1
  • Some of the metrics are blocked by your 
    consent settings
    Item type:Publication, Access status: with files ,
    Machine learning-driven optimization of biochar-based supercapacitors for sustainable energy storage: mechanisms, trends, and perspectives
    (Elsevier, 2026-03-31)
    Sajjad, Fatima
    ;
    Iftikhar, Rashid
    ;
    Inam, Muhammad Ali
    ;
    Zadran, Saifullah
    ;
    Haider, Salman
    Global transition toward sustainable and efficient energy storage has intensified research into supercapacitors, valued for their rapid charge-discharge capability, high power density, and long operational lifespan. Among carbonaceous materials, biomass-derived biochar has emerged as a low-cost, renewable, and structurally tunable precursor for electrode design. This review provides a structured and critical synthesis of biochar-based supercapacitor strategies, systematically categorizing heteroatom doping, nanocomposite engineering, and hybrid architecture within an integrated electrochemical performance framework. Unlike existing literature that predominantly summarizes modification approaches, this review emphasizes cross-study comparability, mechanistic attribution, and device-level relevance to clarify transferable design principles. A consolidated analysis of recent machine learning (ML) applications reveals a paradigm shift from empirical optimization to data-driven predictive design. Benchmarking against reported datasets, descriptors, and model architectures, we confirm that tree-based ensemble and neural-network models (e.g., XGBoost, LightGBM, ANN) consistently achieve high predictive accuracy (R² > 0.9), while also identifying current limitations in data standardization and descriptor harmonization. The review critically evaluates how ML can move beyond correlation towards interpretable and transferable optimization under realistic device constraints.Finally, a converging research roadmap is proposed, prioritizing standardized reporting, device-level benchmarking, and constraint-aware ML integration to accelerate scalable implementation. By integrating materials engineering, data-driven modeling, and practical deployment considerations, this review establishes a comprehensive framework for advancing sustainable biochar-based supercapacitors toward real-world energy storage applications.
    Publicationtype: Review Article
      31  20