AI-Driven Nanostructured Electrocatalysts for Efficient Green Hydrogen Production and Sustainable Fuel Generation

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:AI最適化と材料科学の融合による触媒開発の加速手法として、水素研究に新たな方法論を提供する。

🏢実務担当者:水素製造事業者は、AI活用による触媒開発期間短縮と効率向上の可能性を評価できる。

🏛政策担当者:グリーン水素のコスト低減に向けた技術革新の支援政策の根拠となる。

<title>Abstract</title> <p>Green hydrogen is gaining importance as one of the most potential clean energy carriers in achieving carbon neutrality, and this will help reduce dependence on fossil fuels. Unfortunately, most of the state-of-art electrocatalysts for water splitting exhibit relatively low electrochemical activity and high cost, which restricts their commercial application. We propose an AI-Driven Nanostructured Electrocatalyst Optimization Framework (AINEOF) for efficient green hydrogen production and sustainable fuel generation. The proposed framework combines artificial intelligence and nanoscale material design to optimize the composition, morphology, electronic structure, and kinetic reaction parameters for HER and OER catalysts. We employ Bayesian optimization with a hybrid deep neural network to predict the optimal nanostructure parameters by minimizing computational and experimental resources. Designed transition-metal-based nanostructures with built-in defect sites and heterostructure interfaces are the most effective catalysts that greatly facilitate catalytic reaction activity and charge transfer efficiency. The simulation results show that the HER overpotential of the proposed framework is merely 41 mV when current density increases to 10 mA cm0(2)(-1), which is much lower than that of conventional catalysts(78 mV); and meanwhile, it leads to a reduction of OER overpotential to 236 mV (32.8% relative improvement). AI optimization illustrated a 67.4% reduction in catalyst discovery time and an enhancement of hydrogen production activity from 86.1% to 96.8%, while its Faradaic efficiency improved from 92.82% to over 99.2%. In addition, this nanostructured electrocatalyst shows good long-term durability with 95.6% of activity retention after continuous electrolysis for 100 h. The total water-splitting system can reach an energy conversion efficiency of 84.9%, a current density of 1.62 A cm⁻² and a hydrogen production rate of 8.84 mmol h⁻¹ cm⁻², surpassing all state-of-art methods available to date. Results show that the combination of artificial intelligence and nanostructured catalyst engineering is a powerful and sustainable route toward next-generation green hydrogen and renewable fuel technologies.</p>

• Research Square https://doi.org/10.21203/rs.3.rs-10279610/v1first seen 2026-08-04 04:33:58