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<p>Ruthenium oxides (RuO<sub><i>x</i></sub>) are promising alternatives to iridium catalysts for the oxygen-evolution reaction in proton-exchange membrane water electrolysis but lack stability in acid. Alloying with other elements can improve stability and performance but enlarges the search space. Material acceleration platforms combining high-throughput experiments with machine learning can accelerate catalyst discovery, yet predicting and co-optimizing synthesizability, activity and stability remain challenging. A predictive featurization workflow that links a hypothesized catalyst to its actual single- or mixed-phase synthesis and acidic oxygen-evolution reaction properties has not been reported. Here we report a hierarchical workflow, termed mixed acceleration, integrating theoretical and experimental descriptors to predict synthesis, activity and stability. Guided by mixed acceleration through 379 experiments, we identified seven ruthenium-based oxides surpassing the Pareto frontier of activity and stability. The most balanced composition, Ru<sub>0.</sub><sub>5</sub>Zr<sub>0.</sub><sub>1</sub>Zn<sub>0.</sub><sub>4</sub>O<sub><i>x</i></sub>, achieved an overpotential of 194 mV at 10 mA cm<sup>−2</sup> with a ruthenium dissolution rate 12 times lower than that of RuO<sub>2</sub>.</p><p></p>

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Stable acidic oxygen-evolving catalyst discovery through mixed accelerations

  • 期刊论文

  • 发表日期：

    2026年1月6日

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  • Yang Bai,
  • Kangming Li,
  • Ning Han,
  • Jiheon Kim,
  • Runze Zhang,
  • Suhas Mahesh,
  • Ali Shayesteh Zeraati,
  • Brandon R. Sutherland,
  • Kelvin Chow,
  • Yongxiang Liang,
  • Sjoerd Hoogland,
  • Jianan Erick Huang,
  • David Sinton,
  • Edward H. Sargent,
  • Jason Hattrick-Simpers

摘要

Ruthenium oxides (RuOx) are promising alternatives to iridium catalysts for the oxygen-evolution reaction in proton-exchange membrane water electrolysis but lack stability in acid. Alloying with other elements can improve stability and performance but enlarges the search space. Material acceleration platforms combining high-throughput experiments with machine learning can accelerate catalyst discovery, yet predicting and co-optimizing synthesizability, activity and stability remain challenging. A predictive featurization workflow that links a hypothesized catalyst to its actual single- or mixed-phase synthesis and acidic oxygen-evolution reaction properties has not been reported. Here we report a hierarchical workflow, termed mixed acceleration, integrating theoretical and experimental descriptors to predict synthesis, activity and stability. Guided by mixed acceleration through 379 experiments, we identified seven ruthenium-based oxides surpassing the Pareto frontier of activity and stability. The most balanced composition, Ru0.5Zr0.1Zn0.4Ox, achieved an overpotential of 194 mV at 10 mA cm−2 with a ruthenium dissolution rate 12 times lower than that of RuO2.