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

Area of Science:

• Electrochemistry
• Materials Science
• Artificial Intelligence

Background:

• Electrochemical synthesis is a sustainable method for chemical production.
• Artificial intelligence (AI) can accelerate electrocatalyst design for efficient processes.

Purpose of the Study:

• To develop an AI-agent-assisted strategy for designing a nickel-based catalyst with a built-in electric field (BEF).
• To optimize the electrooxidation of 5-hydroxymethylfurfural (HMF) to 2,5-furandicarboxylic acid (FDCA).

Main Methods:

• An AI agent identified manganese (Mn) doping to create a BEF in a nickel-based catalyst.
• The catalyst's electronic structure and interfacial microenvironment were optimized.
• Electrochemical performance was evaluated in batch and flow electrolyzers.

Main Results:

• The Mn-Ni(OH)2 catalyst achieved >99% HMF conversion, Faradaic efficiency, and FDCA selectivity at 700 mA cm⁻² and 1.45 V vs RHE.
• The catalyst demonstrated stable activity over 45 cycles.
• Flow electrolyzer tests yielded current densities of 0.5 and 1 A cm⁻² at specific cell voltages over 100 hours.

Conclusions:

• The AI-assisted dual-regulation strategy effectively engineered catalyst electronic structure and interfacial microenvironment.
• The built-in electric field (BEF) enhances charge transfer and mass transport for sustainable electrosynthesis.
• This approach bridges catalyst design and process optimization for green chemistry.