ArticleNature communications2026
Data-driven intelligent carbonization unifies diverse biomass into high-performance hard carbon negative electrodes.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
4 citing papers in PubMed.
- 900°C thermal-force coupling carbonization tailoring hard carbon microstructures enables high performance sodium-ion pouch batteries.Nature communications · 2026Article
- Beyond Isolated Optimization: A Holistic Review Across the Pre-Mid Post-Treatment Chain for Hard Carbon in Sodium-Ion Battery.Nano-micro letters · 2026Review
- Precursor-Dependent Initial Coulombic Efficiency of Hard Carbon Anodes for Sodium-Ion Batteries: A Comparative Review.Materials (Basel, Switzerland) · 2026Review
- Data-driven intelligent carbonization unifies diverse biomass into high-performance hard carbon negative electrodes.Nature communications · 2026Article
Corrections and comments
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Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
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
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Registered trials
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