From Trial-and-Error to Closed-Loop Design: A Data-Driven Review on Biomass-Derived Carbon for Supercapacitors

Supercapacitors are vital for sustainable energy storage, and biomass-derived carbons are promising electrode materials owing to their low cost, renewability, and inherent hierarchical porosity. However, their electrochemical performance arises from a complex interplay of pore architecture, graphitization degree, and heteroatom doping, making trial-and-error optimization inefficient. This review adopts a data-driven perspective, systematically covering the entire value chain (from precursor selection and carbonization to pore engineering, surface modification, and characterization) while emphasizing how each variable can be quantified as input features for machine learning (ML). We discuss the mechanistic roles of structure, graphitization, and surface chemistry in regulating ion transport, electron conduction, and charge storage, and summarize common characterization (Raman, XRD, XPS, FTIR, BET) and electrochemical (CV, GCD, EIS) techniques. Critically, we highlight several problematic practices: the misleading “green” activation narrative, scalability limits of templating, non-linear trade-offs in chasing ultra-high surface area and heavy doping, oversimplified interpretation of the Raman I D / I G ratio, and deceptive reliance on three-electrode data for device-level predictions. To overcome these, we advocate for a closed-loop, data-driven design framework that integrates standardized databases, active learning, and inverse design to guide the rational development of biomass-derived carbon electrodes. This framework integrates ML not as an add-on but as the central engine connecting precursor–process–structure–performance relationships. Realizing the full potential of biomass-derived carbons demands a cultural shift from pursuing idealized metrics toward reproducibility, scalability, and rigorous device-relevant validation. By merging closed-loop data-driven discovery with mechanistic understanding, the field can effectively bridge the gap between laboratory findings and practical supercapacitor technologies.