Data-driven intelligent carbonization unifies diverse biomass into high-performance hard carbon negative electrodes.

Area of Science:

• Materials Science
• Electrochemistry
• Sustainable Energy

Background:

• High-performance hard carbons are crucial for sustainable batteries but are limited by biomass feedstock variability and complex processing-structure-performance relationships.
• Current methods struggle to efficiently control carbonization and microstructure for optimal electrochemical performance.

Purpose of the Study:

• To develop an accelerated and intelligent strategy for discovering and optimizing biomass-derived hard carbons.
• To establish a data-centric approach for manufacturing performance-unified hard carbon electrodes from diverse biomass resources.

Main Methods:

• Implementation of "intelligent carbonization" integrating programmable Joule heating (1000-2000°C, 10-60s) with machine learning.
• Mapping over 1000 synthetic pathways and analyzing multidimensional feature spaces.
• Identification of a performance-correlated factor predicting capacity, complementing traditional graphitic descriptors.

Main Results:

• Discovery of a novel performance-correlated factor for predicting hard carbon capacity.
• Production of advanced hard carbon with 369 mAh g⁻¹ reversible capacity, high rate capability, and excellent cycling stability (>5000 cycles at 3 A g⁻¹).
• Demonstration of low-cost, intelligent manufacturing of biomass-derived hard carbons with minimal energy input (0.1 kWh g⁻¹).

Conclusions:

• Intelligent carbonization significantly accelerates the optimization of biomass-derived hard carbons.
• The developed strategy enables the low-cost, large-scale production of unified hard carbon electrodes from diverse biomass.
• This approach paves the way for practical biomass valorization in sustainable energy storage solutions.