Eco-friendly synthesis coupled with predictive analytics: developing hierarchical lignin-derived ordered mesoporous carbon for advanced supercapacitors

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 Zn²⁺ as cross-linking and pore-forming agents, followed by KHCO₃ 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 Zn²⁺ additive concentration, carbonization temperature, and subsequent KHCO₃ 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.