Authors: Raymond Gasper
Source: Scholarworks (University of Massachusetts Amherst) (article, 2021-04-12)
Field: Materials Science — Materials Chemistry

Computational Modeling of the Structure and Catalytic Behavior of Graphene-Supported Pt and PtRu Nanoparticles Computer modeling has the potential to revolutionize the search for new catalysts for specific applications primarily via high-throughput methodologies that allow researchers to scan through thousands or millions of potential catalysts in search of an optimal candidate. To date, the bulk of the literature on computational studies of heterogeneous catalysis has focused on idealized systems with near-perfect crystalline surfaces that are representative of macroscopic catalysts. Advancing the frontier to nanoscale catalysis, in particular, heterogeneous catalysis on nanoclusters, requires consideration of low-symmetry nanoparticles with realistic structures including the attendant complexity arising from under-coordination of catalyst atoms and dynamic fluxionality of clusters. In this thesis, we focus on understanding structure–property–function relationships of Platinum and Platinum-Ruthenium alloy nanoclusters on defective graphene supports, which are highly effective catalysts for methanol fuel cells. In particular, we focus on understanding the interplay between support defects and the electronic structure of supported nanoclusters, and the consequent impact on the thermodynamics and kinetics of the methanol decomposition reaction (MDR), a reaction of interest for renewable energy technologies such as direct-methanol fuel cells. Using density functional theory (DFT) modeling, we first investigate the adsorption and reaction thermodynamics of MDR intermediates on defective graphene-supported Pt13 nanoclusters with realistic, low-symmetry morphologies. We find that the support-induced shifts in catalyst electronic structure correlate well with an overall change in adsorption behavior of MDR intermediates. The reaction thermodynamics are modified by the support interaction to more favorable reaction free energies, suggesting greater catalytic activity. We also show that adsorption energy predictors established for traditional heterogeneous catalysis studies of MDR on macroscopic crystalline facets are equally valid on catalyst nanoclusters (supported or otherwise) with irregular, low-symmetry surface morphologies. To understand the kinetics of MDR on graphene-supported Pt13 clusters, we implement and apply a microkinetic model within a batch reactor setup. The microkinetic model predicts high activity for the MDR over nanoparticles that interact strongly with support defects, in comparison to larger nanoparticles that are only weakly influenced by the support which exhibit much lower activity; these results agree with fuel-cell level experimental results. We also find that the support effect induces changes in the most favorable reaction pathway, and in the populations of dominant surface species under realistic reaction conditions. Our studies provide molecular-level insights into experimental observations of enhanced catalytic activity of graphene-supported Pt nanoclusters for MDR and suggest promising avenues for further tuning of catalytic activity through computer-aided-engineering of catalyst−support interactions. An associated problem with modeling supported nanoclusters involves being able to generate, at the outset, realistic structures of nanoparticles. Using an empirical-potential-based genetic algorithm (developed by my colleague Dr. Hongbo Shi) and DFT modeling, we identify low-energy structures of Pt nanoparticles over the range of 10-100 atoms. We then show that there exists a size window (40−70 atoms) over which Pt nanoclusters bind CO weakly, the binding energies being comparable to those on Pt(111) or Pt(100) facets. The size-dependent adsorption energy trends are, however, distinctly non-monotonic and are not readily captured using traditional descriptors such as d-band energies or (generalized) coordination numbers of the Pt binding sites. Instead, by applying machine-learning algorithms (collaborative work with Dr. Hongbo Shi), we show that multiple descriptors, broadly categorized as structural and electronic descriptors, are essential for qualitatively capturing the CO adsorption trends. Our approach allows for building quantitatively predictive models of site-specific adsorbate binding on realistic, low-symmetry nanostructures, which is an important step in modeling reaction networks as well as for rational catalyst design in general. We also extend the Pt-C empirical potential to the Pt-Ru-C system that will allow for future studies of supported Pt-Ru nanoclusters that are among the best known catalysts for MDR. Developing the Pt-Ru-C empirical potential was based on previously established potentials for the Pt-C and Ru-C system. Achieving an accurate Pt-Ru-C potential required careful benchmarking against experimental and DFT data, resulting in targeted adjustment of the Pt-Ru and Ru-C bond parameters. Nanoparticle ; Catalysis ; Graphene ; Nanotechnology ; Materials science ; Computer science ; Chemistry ; Organic chemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science