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Cited by 2Catalysis for Biomass ConversionElectrocatalysts for Energy ConversionCO2 Reduction Techniques and Catalysts

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

Zhao, Pengwei, Zhang, Yefan, Chen, Bin, Zhao, Weipeng, Chen, Zhuo, Guan, Linjie, Sun, Shengzhenzhi, Zhang, Shuya +4 more·2026·ACS Nano
DOI 10.1021/acsnano.6c00124
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Abstract

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.

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Crossref · 10.1021/acsnano.6c00124

Cites

46 of 46 references verified0 citation errors · 0 not checkable

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  • Catalysis and adsorption tasks formed the largest application block, represented by six studies on electrocatalyst design, adsorption-configuration search, biomass-valorization catalysis, MOF catalyst optimization, and single-atom-alloy discovery.

    “Catalysis and adsorption tasks formed the largest application block, represented by six studies on electrocatalyst design, adsorption-configuration search, biomass-valorization catalysis, MOF catalyst optimization, and single-atom-alloy discovery [19,20,27,28,30,34].”

    From From Scientific Copilots to Tool-Grounded Autonomy: AI Agents in Simulation-Driven Materials Discovery (2026) · cites this paper as [27]

  • A second group of studies used hybrid human-in-the-loop designs, where the agentic layer guided design or analysis but final judgment remained partly manual.

    “A second group of studies used hybrid human-in-the-loop designs, where the agentic layer guided design or analysis but final judgment remained partly manual [20,22,27,30,31].”

    From From Scientific Copilots to Tool-Grounded Autonomy: AI Agents in Simulation-Driven Materials Discovery (2026) · cites this paper as [27]

  • In other papers, the workflow could be reconstructed only partially from supporting information, and prompt logs, failure traces, or decision histories remained incomplete.

    “In others, the workflow could be reconstructed only partially from supporting information, and prompt logs, failure traces, or decision histories remained incomplete [17,22,24,26,27,29,33,34].”

    From From Scientific Copilots to Tool-Grounded Autonomy: AI Agents in Simulation-Driven Materials Discovery (2026) · cites this paper as [27]

  • Hybrid human-in-the-loop systems accounted for four of the six experimentally validated papers, which suggests that human oversight appears to be retained more often where the downstream consequences of an incorrect decision are more costly or irreversible, although the small sample precludes strong inference.

    “Hybrid human-in-the-loop systems accounted for four of the six experimentally validated papers, which suggests that human oversight appears to be retained more often where the downstream consequences of an incorrect decision are more costly or irreversible, although the small sample precludes strong inference [20,22,27,30,31].”

    From From Scientific Copilots to Tool-Grounded Autonomy: AI Agents in Simulation-Driven Materials Discovery (2026) · cites this paper as [27]

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Authors
Zhao, P., Zhang, Y., Chen, B., Zhao, W., Chen, Z., Guan, L., Sun, S., Zhang, S., Li, Y., Peng, W., Zhang, Q., & Fan, X.
Affiliations
Tianjin University (CN)
Corresponding author
  • Zhang, Qicheng
  • Fan, Xiaobin
ORCID
Venue
ACS Nano
Publisher
American Chemical Society (ACS)
Published
2026-03-25
Volume
20
Issue
13
Pages
10640-10650
ISSN
  • 1936-0851 (ISSN-L)
  • 1936-086X
DOI
10.1021/acsnano.6c00124
OpenAlex
W7140671949
Cited by
2
References
46
Citations by year
2026: 2
FWCI
3.47
Type
journal article
Language
en
Open access
closed
Licence
Locations
  • ACS Nano (publishedVersion)
  • PubMed (publishedVersion)
Funders
  • Key Technologies Research and Development Program (2022YFA1504000)
  • Innovative Research Group Project of the National Natural Science Foundation of China (22121004)
  • China Postdoctoral Science Foundation (GZC20241205)
  • National Natural Science Foundation of China (22508297)
  • Haihe Laboratory of Sustainable Chemical Transformations
Topics
  • Physical Sciences › Engineering › Biomedical Engineering › Catalysis for Biomass Conversion
  • Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Electrocatalysts for Energy Conversion
  • Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › CO2 Reduction Techniques and Catalysts
Keywords
nickel-based electrocatalysts, built-in electric field, 5-hydroxymethylfurfural electrooxidation, manganese doping, flow electrolyzer, electrocatalyst stability, electronic structure engineering

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