Modeling Morphology and Catalytic Activity of Nanoparticle Ensembles Under Reaction Conditions
Modeling Morphology and Catalytic Activity of Nanoparticle Ensembles Under Reaction Conditions
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DOI：
10.1021/acscatal.0c01005
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发表时间：
2020-06-05
期刊：
影响因子：
12.9
通讯作者：
Mpourmpakis, Giannis
中科院分区：
文献类型：
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作者：
Cheula, Raffaele;Maestri, Matteo;Mpourmpakis, Giannis
虽然纳米颗粒催化剂在反应条件下获得不同的尺寸和形状，但多相催化的计算建模通常基于明确定义的晶面。在这里，我们结合联合收割机密度泛函理论（DFT）计算与玻尔兹曼统计描述合奏的纳米粒子获得不同的形态下的反应条件（温度和气相化学势）和各自的活性位点的分布。我们将我们的方法应用于Rh催化纳米颗粒，我们解决了亚稳态纳米结构对整体CO解离催化活性的贡献。重要的是，我们展示了催化趋势如何改变时，占一个合奏的纳米粒子相比，一个单一的，化学稳定的纳米粒子。因此，我们的工作启发催化剂形态的统计表示建模结构敏感的反应的影响。
Although nanoparticle catalysts obtain different sizes and shapes under reaction conditions, computational modeling in heterogeneous catalysis is usually based on well-defined crystallographic planes. Herein, we combine density functional theory (DFT) calculations with Boltzmann statistics to describe ensembles of nanoparticles obtaining different morphologies under reaction conditions (temperature and gas-phase chemical potential) and their respective distribution of active sites. We apply our methodology on Rh catalytic nanoparticles, and we address the contribution of metastable nanostructures on the overall CO dissociation catalytic activity. Importantly, we demonstrate how catalytic trends can change when accounting for an ensemble of nanoparticles compared to a single, thermodynamically stable nanoparticle. Thus, our work enlightens the impact of statistical representation of catalyst morphology on modeling structure-sensitive reactions.