A theoretical and experimental approach for correlating nanoparticle structure and electrocatalytic activity.
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.
†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.
Rachel M
RM
Anderson
†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.
David F
DF
Yancey
†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.
Liang
L
Zhang
†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.
Samuel T
ST
Chill
†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.
Graeme
G
Henkelman
†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.
Richard M
RM
Crooks