Scholarlink

<p>Ethylene oxide (EO) is a vital chemical intermediate produced exclusively via silver-catalyzed epoxidation. A central challenge lies in maximizing selectivity towards EO against competing combustion reactions. This review summarizes recent advancements in understanding the dynamic nature of silver catalysts under working conditions. We highlight the transition from idealized ultra-high vacuum models to working catalysts with in-situ characterization and advanced computational simulations. Key insights include the in-situ identification of active sites under working conditions, the evolution of theoretical approaches from static calculations to machine-learning-driven global optimization and molecular dynamics, facet and size effects, and the mechanistic role of promoters in enhancing selectivity. Future efforts must focus on the integration of in-situ spectroscopy and machine-learning-accelerated simulations to bridge the pressure and materials gaps, paving the way for the rational design of next-generation catalysts.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

From single crystals to working catalysts: integrating in-situ spectroscopy and computational modeling to bridge the pressure gap in ethylene epoxidation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月25日

查看全文
  • Yuyao Qin,
  • Jin Qu,
  • Zhihui Song,
  • Jiawei Zhao,
  • Li Jin

摘要

Ethylene oxide (EO) is a vital chemical intermediate produced exclusively via silver-catalyzed epoxidation. A central challenge lies in maximizing selectivity towards EO against competing combustion reactions. This review summarizes recent advancements in understanding the dynamic nature of silver catalysts under working conditions. We highlight the transition from idealized ultra-high vacuum models to working catalysts with in-situ characterization and advanced computational simulations. Key insights include the in-situ identification of active sites under working conditions, the evolution of theoretical approaches from static calculations to machine-learning-driven global optimization and molecular dynamics, facet and size effects, and the mechanistic role of promoters in enhancing selectivity. Future efforts must focus on the integration of in-situ spectroscopy and machine-learning-accelerated simulations to bridge the pressure and materials gaps, paving the way for the rational design of next-generation catalysts.