AI-Agent-Guided Design of Dual-Scale Modulated Nickel-Based Catalyst with Built-In Electric Field for Enhanced Biomass Electrooxidation

Le résumé fourni par la source

Electrochemical synthesis offers a sustainable route for chemical production, and integrating artificial intelligence (AI) into electrocatalyst design promises to accelerate the development of efficient processes. Herein, we report an AI-agent-assisted strategy for the design of a nickel-based catalyst with a built-in electric field (BEF) for the electrooxidation of 5-hydroxymethylfurfural (HMF) to 2,5-furandicarboxylic acid (FDCA). The AI-agent autonomously identified Mn doping as a means to create a BEF that simultaneously optimizes the electronic structure and the interfacial microenvironment. These synergistic effects enable the resulting Mn–Ni(OH) 2 catalyst to achieve a current density exceeding 700 mA cm –2 at 1.45 V vs RHE, with HMF conversion, Faradaic efficiency, and FDCA selectivity all surpassing 99%. Notably, after 45 cycles, the activity of Mn–Ni(OH) 2 remains stable. When assembled into a flow electrolyzer, current densities of 0.5 and 1 A cm –2 are achieved at cell voltages of 1.735 and 2.162 V, respectively, over a duration of 100 h. Characterization and simulation results reveal that the BEF enhances charge transfer by modulating the Ni e g * orbital and accelerates mass transport by disrupting the interfacial hydrogen-bond networks. This AI-assisted, dual-regulation strategy bridges the gap between catalyst electronic structure engineering and interfacial microenvironment design for sustainable electrosynthesis applications.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.