CO2 Adsorptions on d-Block-Metal-Doped Nickel Nanoparticles: Unexpected Adsorption Configurations Predicted by Machine Intelligence,The Journal of Physical Chemistry C

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CO2 Adsorptions on d-Block-Metal-Doped Nickel Nanoparticles: Unexpected Adsorption Configurations Predicted by Machine Intelligence
The Journal of Physical Chemistry C ( IF 3.3 ) Pub Date : 2021-08-31 , DOI: 10.1021/acs.jpcc.1c07133
Shiru Lin ₁ , Chong Teng ₁ , Junwei Lucas Bao ₁

Affiliation

Catalytic CO₂ conversion to valuable chemicals on metallic nanoparticles is widely studied in industry. A key step in the thermal activation of adsorbed CO₂ is C–O bond scission, which depends sensitively on surface-bound CO₂ geometry. However, most of the prior studies used either linear or bent CO₂ as the precursor state without considering which geometry is the energetically most stable on a specific metallic surface. The dopant effects on CO₂ adsorption geometries were omitted. Herein, we constructed a training set that contains the most stable CO₂ adsorption geometries on 12 doped Ni nanoparticles using the Ni (111) facet and trained a classification model for efficient and reliable identification of CO₂ adsorption geometry, which predicted unexpected adsorption configurations that were not identified in previous catalytic mechanistic studies. We explored the physical origins of the preferred adsorption geometry and found that the amount of charge transfer between CO₂ and the metallic surface plays a critical role.

中文翻译：

d 块金属掺杂镍纳米颗粒上的 CO2 吸附：机器智能预测的意外吸附配置

在金属纳米颗粒上催化 CO ₂转化为有价值的化学品在工业中得到了广泛研究。吸附的 CO ₂热活化的关键步骤是 C-O 键断裂，它敏感地取决于表面结合的 CO ₂几何形状。然而，大多数先前的研究使用线性或弯曲的 CO ₂作为前体状态，而没有考虑哪种几何形状在特定金属表面上能量最稳定。省略了掺杂剂对 CO ₂吸附几何形状的影响。在这里，我们构建了一个训练集，其中包含最稳定的 CO ₂使用 Ni (111) 面对 12 个掺杂的 Ni 纳米粒子进行吸附几何形状，并训练分类模型以有效可靠地识别 CO ₂吸附几何形状，该模型预测了在以前的催化机理研究中未发现的意外吸附配置。我们探索了首选吸附几何结构的物理起源，发现 CO ₂和金属表面之间的电荷转移量起着关键作用。

更新日期：2021-09-16

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