Enumerating Active Sites on Metal Nanoparticles: Understanding the Size Dependence of Cobalt Particles for CO Dissociation

Abstract

Detailed understanding of structure sensitivity, a central theme in heterogeneous catalysis, is important to guide the synthesis of improved catalysts. Progress is hampered by our inability to accurately enumerate specific active sites on ubiquitous metal nanoparticle catalysts. We employ herein atomistic simulations based on a force field trained with quantum-chemical data to sample the shape of cobalt particles as a function of their size. Algorithms rooted in pattern recognition are used to identify surface atom arrangements relevant to CO dissociation, the key step in the Fischer–Tropsch (FT) reaction. The number of step-edge sites that can catalyze C–O bond scission with a low barrier strongly increases for larger nanoparticles in the range of 1–6 nm. Combined with microkinetics of the FT reaction, we can reproduce experimental FT activity trends. The stabilization of step-edge sites correlates with increasing stability of terrace nanoislands on larger nanoparticles.

Structure Sensitivity of CO2 Conversion over Nickel Metal Nanoparticles Explained by Micro-Kinetics Simulations

“…Edge and vertex energies were not taken into account. The local structures of the surface atoms of each slab model and each constructed nanoparticle were determined by pattern recognition algorithm based on the common neighbor analysis (CNA) method. , The pattern recognition is based on a preestablished library of common crystal terminations of fcc, hcp, and single crystal (SC) bulk crystals . The activity of each Wulff-constructed metal nanoparticle was calculated as a linear combination of the activity of the exposed facets, with the following formula A = prefix∑ i n i α i where A is the total activity of a nanoparticle, n i is the number of surface atoms of facet i , and α i is the theoretical activity per atom of facet i at 640 K.…”

“…Edge and vertex energies were not taken into account. The local structures of the surface atoms of each slab model and each constructed nanoparticle were determined by pattern recognition algorithm based on the common neighbor analysis (CNA) method. , The pattern recognition is based on a preestablished library of common crystal terminations of fcc, hcp, and single crystal (SC) bulk crystals . The activity of each Wulff-constructed metal nanoparticle was calculated as a linear combination of the activity of the exposed facets, with the following formula where A is the total activity of a nanoparticle, n i is the number of surface atoms of facet i , and α i is the theoretical activity per atom of facet i at 640 K.…”

“…To enumerate the active sites on these nanoparticles, an atomic pattern recognition algorithm based on the common neighbor analysis method was used. 46 Fortunately, atomic fingerprints of each of the studied surface facets were found to be unique. This allows us to efficiently determine the surface density of the different types of active sites as a function of the nanoparticle size, the result of which is depicted in a bar chart in Figure 10 and in scatter-and line plots in Section K of the Supporting Information.…”

“…45,46 The pattern recognition is based on a preestablished library of common crystal terminations of fcc, hcp, The results are reported in Supporting Information (1) Section G, (2) Section I, and (3) Section J. and single crystal (SC) bulk crystals. 46 The activity of each Wulffconstructed metal nanoparticle was calculated as a linear combination of the activity of the exposed facets, with the following formula…”

Modeling CoCu Nanoparticles Using Neural Network-Accelerated Monte Carlo Simulations

“…Using the obtained core–shell structures when investigating the size effect, we calculated the DFT single point energies of these structures, the results of which are shown in Figure . Comparing the DFT single point energies of the Co 1 Cu 1 subclusters, the energies of core–shell structures are always lower.…”