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Hierarchical lignin-derived ordered mesoporous carbon (HOMC) was significant for advanced supercapacitors. However, achieving controllable fabrication and optimizing electrochemical behavior were challenging. In this work, an eco-friendly HOMC was synthesized using lignin as carbon precursors and Zn 2+ as cross-linking and pore-forming agents, followed by KHCO 3 activation, eliminating the need for toxic phenolic resins and acid treatments for metal removal. Machine learning technology, specifically an Artificial Neural Network (ANN) model, was utilized to assist the experimental design and prediction. The ANN model suggested an ideal hierarchical structure and optimized oxygen level, achieved through the adjustment of Zn 2+ additive concentration, carbonization temperature, and subsequent KHCO 3 activation to maximize capacitance. The HOMC electrode, with a micropore-to-mesopore ratio (S micro /S meso ) of 1.01 and an oxygen content of 8.81 at%, acquired a specific capacitance of 362 F·g −1 at 0.5 A·g −1 in 6 mol·L −1 KOH electrolyte. The assembled HOMC//HOMC supercapacitor could afford a high energy density of 33.38 Wh·kg −1 with a corresponding specific power density of 300 W·kg −1 in TEATFB/PC electrolyte. Meanwhile, the long-term cycle stability of 94.33% was achieved after 20,000 cycles. This work provides an ANN-assisted strategy for the synthesis of HOMC, highlighting its potential to valorize biomass and agricultural waste in sustainable energy storage solutions. Hierarchical ordered mesoporous carbon (HOMC) was fabricated and activated using an eco-friendly synthetic method with walnut shell derived lignin as carbon percussor, and Zn 2+ as crosslinking agent. The HOMC demonstrated high performance for supercapacitors with the assistance of machine learning. • Green synthesis of hierarchical ordered mesoporous carbon from lignin. • Machine learning assisted preparation and prediction. • Rational design of micro-mesoporous distribution and oxygen content. • HOMC exhibited high performance for supercapacitors.
Zhao et al. (Fri,) studied this question.