Abstract
Sustainable batteries necessitate high-performance hard carbon negative electrodes derived from abundant biomass. However, realizing their full potential is significantly limited by the inherent diversity of biomass feedstocks, the intricate control over carbonization and resulting microstructures, and the complex interplay between processing, structure, and electrochemical performance. Here, we introduce “intelligent carbonization”, a strategy integrating programmable Joule heating (1000-2000 °C, 10-60 s) with machine learning to substantially accelerate the discovery and optimization of biomass-derived hard carbons. By mapping over 1000 synthetic pathways and decoding the multidimensional feature space, we reveal a performance-correlated factor that serves as a crucial predictor of capacity, complementing conventional graphitic descriptors (in-plane crystallite size/ interlayer spacing). By a minimal energy input (0.1 kWh g−1), our strategy converts biochar into advanced hard carbon delivering 369 mAh g−1 reversible capacity, high rate capability, and improved cycling stability (>5000 cycles at a specific current of 3 A g−1). This data-centric approach allows low-cost and intelligent manufacturing of diverse biomass resources into performance-unified hard carbon negative electrodes, thereby paving the way for practical and large-scale biomass valorization towards sustainable energy storage solutions.
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All data are available within the main text and Supplementary Files, or available from the corresponding authors. Source data are provided with this paper.
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Code is available on CodeOcean capsule60: (https://doi.org/10.24433/CO.8253729.v4).
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Acknowledgements
This work is supported by the National Natural Science Foundation of China (52125601 [H.Y.], 52425706 [J.X.], 52521008 [Y.Y.]), the Key R&D Program of Hubei province (2024BCB091 [Y.Y.]), the Interdisciplinary Research Program of HUST (2025JCYJ002 [Y.Y.]), the State Key Laboratory of Advanced Electromagnetic Technology (AET 2025KF003 [Y.Y.]), and the State Key Laboratory of Coal Combustion (FSKLCCA2602 [Y.Y.]). We acknowledge the support of the Analytical and Testing Center, Huazhong University of Science and Technology. Jianbao Gao acknowledged the financial support by the Youth Fund of the National Natural Science Foundation of China (52401047 [J.G.]), the China Postdoctoral Science Foundation (2023M741244 [J.G.]) and the Postdoctor Project of Hubei Province (2024HBBHCXB012 [J.G.]).
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J.C., H.Z., and Y.R. conducted the experiments and prepared the manuscript. J.C., Y.R., and J.G. carried out the machine learning. J.C. conducted the material characterizations, electrochemical tests, and mechanistic characterizations. C.L. and J.Z. (Jiale Zhao) performed the TEM tests. J.Z. (Jiawen Zeng) and C.F. contributed to the low-temperature electrochemical tests. Z.W., J.W., B.S., and Y.H. provided advice and disscussed the results. H.Y., J.X., and Y.Y. supervised and directed this project, and all authors contributed to the manuscript.
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Cui, J., Rao, Y., Gao, J. et al. Data-driven intelligent carbonization unifies diverse biomass into high-performance hard carbon negative electrodes. Nat Commun (2026). https://doi.org/10.1038/s41467-026-70411-5
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DOI: https://doi.org/10.1038/s41467-026-70411-5
