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<p>Catalytic N<sub>2</sub>O decomposition in the presence of O<sub>2</sub> is a key process for addressing environmental challenges, such as greenhouse gas emissions and ozone layer depletion. However, the identification of efficient catalysts for this reaction remains challenging owing to the limitations of conventional methods. In this study, we employ a machine learning approach designed to accelerate the discovery of effective direct N<sub>2</sub>O decomposition catalysts. Starting with 51 catalysts and conducting 37 cycles of a closed-loop discovery system (machine-learning prediction + experiment), 633 catalysts are experimentally tested. Over 10 multi-elemental catalysts exhibiting superior activity are identified, surpassing the performance of the originally identified best catalyst. Among them, Rh(1)–Pd(2)/ZrO<sub>2</sub>_EP exhibits the highest catalytic performance for N<sub>2</sub>O decomposition. Through control experiments and a combination of ex situ and in situ characterizations, we identify the essential role of each component within the catalyst system.</p>

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Data-driven catalyst design for direct catalytic N2O decomposition

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月28日

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  • Chenxi He,
  • Shinya Mine,
  • Yuan Jing,
  • Tsz Lok Wan,
  • Jialei Zhang,
  • Junxian Qin,
  • Ningqiang Zhang,
  • Koichiro Taketoshi,
  • Akihiko Anzai,
  • Ryo Toyoshima,
  • Hiroshi Kondoh,
  • Ichigaku Takigawa,
  • Ken-ichi Shimizu,
  • Takashi Toyao

摘要

Catalytic N2O decomposition in the presence of O2 is a key process for addressing environmental challenges, such as greenhouse gas emissions and ozone layer depletion. However, the identification of efficient catalysts for this reaction remains challenging owing to the limitations of conventional methods. In this study, we employ a machine learning approach designed to accelerate the discovery of effective direct N2O decomposition catalysts. Starting with 51 catalysts and conducting 37 cycles of a closed-loop discovery system (machine-learning prediction + experiment), 633 catalysts are experimentally tested. Over 10 multi-elemental catalysts exhibiting superior activity are identified, surpassing the performance of the originally identified best catalyst. Among them, Rh(1)–Pd(2)/ZrO2_EP exhibits the highest catalytic performance for N2O decomposition. Through control experiments and a combination of ex situ and in situ characterizations, we identify the essential role of each component within the catalyst system.