Sumaria Vaidish, Nguyen Luan, Tao Franklin Feng, Sautet Philippe
Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, California 90094, United States.
Department of Chemical and Petroleum Engineering, University of Kansas, Lawrence, Kansas 66045, United States.
J Am Chem Soc. 2023 Jan 11;145(1):392-401. doi: 10.1021/jacs.2c10179. Epub 2022 Dec 22.
Heterogeneous catalysis is key for chemical transformations. Understanding how catalysts' active sites dynamically evolve at the atomic scale under reaction conditions is a prerequisite for accurately determining catalytic mechanisms and predictably developing catalysts. We combine in situ time-dependent scanning tunneling microscopy observations and machine-learning-accelerated first-principles atomistic simulations to uncover the mechanism of restructuring of Pt catalysts under a pressure of carbon monoxide (CO). We show that a high CO coverage at a Pt step edge triggers the formation of atomic protrusions of low-coordination Pt atoms, which then detach from the step edge to create sub-nano-islands on the terraces, where under-coordinated sites are stabilized by the CO adsorbates. The fast and accurate machine-learning potential is key to enabling the exploration of tens of thousands of configurations for the CO-covered restructuring catalyst. These studies open an avenue to achieve an atomic-scale understanding of the structural dynamics of more complex metal nanoparticle catalysts under reaction conditions.
多相催化是化学转化的关键。了解催化剂的活性位点在反应条件下如何在原子尺度上动态演变，是准确确定催化机理和可预测地开发催化剂的先决条件。我们结合原位时间分辨扫描隧道显微镜观察和机器学习加速的第一性原理原子模拟，以揭示一氧化碳（CO）压力下铂催化剂的重构机制。我们表明，铂台阶边缘的高CO覆盖率会触发低配位铂原子的原子突起的形成，这些原子突起随后从台阶边缘脱离，在台面上形成亚纳米岛，在那里低配位位点通过CO吸附物得以稳定。快速准确的机器学习势是能够探索数万种CO覆盖的重构催化剂构型的关键。这些研究为在反应条件下实现对更复杂金属纳米颗粒催化剂结构动力学的原子尺度理解开辟了一条途径。
