Banisalman Mosab Jaser, Lee Hong Woo, Koh Heeyeun, Han Sang Soo
Computational Science Research Center, Korea Institute of Science and Technology (KIST), 5 Hwarangno 14-gil, Seongbuk-gu, Seoul 02792, Republic of Korea.
ACS Appl Mater Interfaces. 2021 Apr 21;13(15):17577-17585. doi: 10.1021/acsami.1c01947. Epub 2021 Apr 9.
In computational catalysis, density-functional theory (DFT) calculations are usually utilized, although they suffer from high computational costs. Thus, it would be challenging to explicitly predict the catalytic properties of nanoparticles (NPs) at the nanoscale under solvents. Using molecular dynamics (MD) simulations with a reactive force field (ReaxFF), we investigated the catalytic performance of Ni-Pt NPs for the direct synthesis of hydrogen peroxide (HO), in which water solvents were explicitly considered along with the effects of the sizes (1.5, 2.0, 3.0, and 3.5 nm) and compositions (NiPt, NiPt, and NiPt) of the NPs. Among the Ni-Pt NPs, 3.0 nm NPs show the highest activity and selectivity for the direct synthesis of HO, revealing that the catalytic performance is not well correlated with the surface areas of NPs. The superior catalytic performance results from the high H dissociation and low O dissociation properties, which are correlated with the numbers of NiNiPt-fcc and NiNi-bridge sites on the surface of Ni-Pt NPs, respectively. The ReaxFF-MD simulations propose the optimum composition (NiPt) of 3.0 nm Ni-Pt NPs, which is also explained by the numbers of NiNiPt-fcc and NiNi-bridge sites. Furthermore, from the ReaxFF-MD simulations, the direct synthesis of HO for the Ni-Pt NPs can be achieved not only with the Langmuir-Hinshelwood mechanism, which has been conventionally considered, but also with the water-induced mechanism, which is unlikely to occur on pure Pd and Pd-based alloy catalysts; these results are supported by DFT calculations. These results reveal that the ReaxFF-MD method provides significant information for predicting the catalytic properties of NPs, which could be difficult to provide with DFT calculations; thus, it can be a useful framework for the design of nanocatalysts through complementation with a DFT method.
在计算催化中,通常会使用密度泛函理论(DFT)计算,尽管其计算成本很高。因此,在溶剂存在的情况下明确预测纳米尺度下纳米颗粒(NP)的催化性能具有挑战性。我们使用具有反应力场(ReaxFF)的分子动力学(MD)模拟,研究了Ni-Pt纳米颗粒在过氧化氢(HO)直接合成中的催化性能,其中明确考虑了水溶剂以及纳米颗粒的尺寸(1.5、2.0、3.0和3.5纳米)和组成(NiPt、NiPt和NiPt)的影响。在Ni-Pt纳米颗粒中,3.0纳米的纳米颗粒在HO直接合成中表现出最高的活性和选择性,这表明催化性能与纳米颗粒的表面积没有很好的相关性。优异的催化性能源于高H解离和低O解离特性,它们分别与Ni-Pt纳米颗粒表面的NiNiPt-fcc和NiNi-桥位数量相关。ReaxFF-MD模拟提出了3.0纳米Ni-Pt纳米颗粒的最佳组成(NiPt),这也可以通过NiNiPt-fcc和NiNi-桥位数量来解释。此外,从ReaxFF-MD模拟中可以看出,Ni-Pt纳米颗粒的HO直接合成不仅可以通过传统上认为的朗缪尔-欣谢尔伍德机制实现,还可以通过水诱导机制实现,而水诱导机制在纯Pd和Pd基合金催化剂上不太可能发生;这些结果得到了DFT计算的支持。这些结果表明,ReaxFF-MD方法为预测纳米颗粒的催化性能提供了重要信息,而这可能是DFT计算难以提供的;因此,它可以成为通过与DFT方法互补来设计纳米催化剂的有用框架。