†Department of Chemistry, ‡Texas Materials Institute, and §Institute for Computational and Engineering Sciences, The University of Texas at Austin, 105 E. 24th St., Stop A5300, Austin, Texas 78712-1224, United States.
Acc Chem Res. 2015 May 19;48(5):1351-7. doi: 10.1021/acs.accounts.5b00125. Epub 2015 May 4.
The objective of the research described in this Account is the development of high-throughput computational-based screening methods for discovery of catalyst candidates and subsequent experimental validation using appropriate catalytic nanoparticles. Dendrimer-encapsulated nanoparticles (DENs), which are well-defined 1-2 nm diameter metal nanoparticles, fulfill the role of model electrocatalysts. Effective comparison of theory and experiment requires that the theoretical and experimental models map onto one another perfectly. We use novel synthetic methods, advanced characterization techniques, and density functional theory (DFT) calculations to approach this ideal. For example, well-defined core@shell DENs can be synthesized by electrochemical underpotential deposition (UPD), and the observed deposition potentials can be compared to those calculated by DFT. Theory is also used to learn more about structure than can be determined by analytical characterization alone. For example, density functional theory molecular dynamics (DFT-MD) was used to show that the core@shell configuration of Au@Pt DENs undergoes a surface reconstruction that dramatically affects its electrocatalytic properties. A separate Pd@Pt DENs study also revealed reorganization, in this case a core-shell inversion to a Pt@Pd structure. Understanding these types of structural changes is critical to building correlations between structure and catalytic function. Indeed, the second principal focus of the work described here is correlating structure and catalytic function through the combined use of theory and experiment. For example, the Au@Pt DENs system described earlier is used for the oxygen reduction reaction (ORR) as well as for the electro-oxidation of formic acid. The surface reorganization predicted by theory enhances our understanding of the catalytic measurements. In the case of formic acid oxidation, the deformed nanoparticle structure leads to reduced CO binding energy and therefore improved oxidation activity. The final catalytic study we present is an instance of theory correctly predicting (in advance of the experiments) the structure of an effective DEN electrocatalyst. Specifically, DFT was used to determine the optimal composition of the alloy-core in AuPd@Pt DENs for the ORR. This prediction was subsequently confirmed experimentally. This study highlights the major theme of our research: the progression of using theory to rationalize experimental results to the more advanced goal of using theory to predict catalyst function a priori. We still have a long way to go before theory will be the principal means of catalyst discovery, but this Account begins to shed some light on the path that may lead in that direction.
本账目所描述的研究目标是开发高通量基于计算的筛选方法，以发现催化剂候选物，并使用适当的催化纳米粒子进行后续实验验证。树状聚合物包裹的纳米粒子（DENs）是直径为 1-2nm 的具有良好定义的金属纳米粒子，可作为模型电催化剂。理论与实验的有效比较要求理论和实验模型完美地相互映射。我们使用新颖的合成方法、先进的表征技术和密度泛函理论（DFT）计算来接近这一理想。例如，可以通过电化学欠电位沉积（UPD）合成具有良好定义的核@壳 DENs，并且可以将观察到的沉积电位与通过 DFT 计算得到的沉积电位进行比较。理论还用于了解更多结构信息，而这些信息仅凭分析表征是无法确定的。例如，密度泛函理论分子动力学（DFT-MD）被用于表明 Au@Pt DENs 的核@壳结构经历了表面重构，这极大地影响了其电催化性能。对 Pd@Pt DENs 的单独研究也揭示了重组，在这种情况下，从核壳结构反转成 Pt@Pd 结构。了解这些类型的结构变化对于建立结构与催化功能之间的相关性至关重要。事实上，这里描述的工作的第二个主要重点是通过理论和实验的结合来关联结构和催化功能。例如，前面描述的 Au@Pt DENs 系统用于氧还原反应（ORR）以及甲酸的电氧化。理论预测的表面重构增强了我们对催化测量的理解。在甲酸氧化的情况下，变形的纳米颗粒结构导致 CO 结合能降低，从而提高了氧化活性。我们提出的最后一个催化研究是理论正确预测（在实验之前）有效 DEN 电催化剂的结构的一个实例。具体来说，使用 DFT 来确定 AuPd@Pt DENs 中合金核的最佳组成用于 ORR。随后通过实验证实了这一预测。这项研究突出了我们研究的主要主题：使用理论来合理化实验结果，以更高级的目标是使用理论来预先预测催化剂功能。在理论成为催化剂发现的主要手段之前，我们还有很长的路要走，但本账目开始揭示了可能朝着这一方向发展的一些线索。
