Capturing catalytic behaviors under operational conditions is pivotal to gaining a mechanistic understanding and promoting the design of robust catalysts. The challenge lies in the difficulty of monitoring real-time surface dynamics driven by catalyst-environment interactions. Here, we introduce a framework based on density functional calculations and kinetic modeling. This framework significantly improves the accuracy of theoretical models’ descriptions of experimental observations by quantifying environmental impacts on surface phases and active sites. CO₂ hydrogenation over Pd-based catalysts is taken as a showcase. The observed selectivity variations of Pd and Pd-M bimetallic catalysts strongly correlate with hydrogen coverage maintained under typical CO₂ hydrogenation conditions. By reducing the amount of surface hydrogen, the selectivity tuned effectively from formic acid toward CO and methanol. This study not only deepens the comprehension of dynamics of active sites under active chemical conditions but also introduces an alternative opportunity for catalytic tuning by modulating catalyst-environment interactions.

中文翻译：

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通过调节催化剂-环境相互作用来微调催化选择性：在 Ple 基催化剂上进行 CO2 加氢


捕获操作条件下的催化行为对于获得机理理解和促进稳健催化剂的设计至关重要。挑战在于难以监测由催化剂-环境相互作用驱动的实时表面动力学。在这里，我们介绍了一个基于密度泛函计算和动力学建模的框架。该框架通过量化环境对表面相和活动地点的影响，显著提高了理论模型对实验观测描述的准确性。以 Pd 基催化剂上的 CO₂ 加氢为例。观察到的 Pd 和 Pd-M 双金属催化剂的选择性变化与在典型 CO₂ 加氢条件下保持的氢覆盖率密切相关。通过减少表面氢的量，选择性有效地从甲酸调整为 CO 和甲醇。这项研究不仅加深了对活性化学条件下活性位点动力学的理解，而且还通过调节催化剂-环境相互作用为催化调节引入了另一种机会。