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Document Type

Open Access Dissertation

Degree Name

Doctor of Philosophy (PhD)

Degree Program

Chemical Engineering

Year Degree Awarded

2017

Month Degree Awarded

September

First Advisor

Ashwin Ramasubramaniam

Second Advisor

Scott M. Auerbach

Subject Categories

Catalysis and Reaction Engineering | Materials Chemistry | Other Engineering Science and Materials | Other Materials Science and Engineering | Structural Materials

Abstract

Fuel cells have been demonstrated to be promising power generation devices to address the current global energy and environmental challenges. One of the many barriers to commercialization is the cost of precious catalysts needed to achieve sufficient power output. Platinum-based materials play an important role as electrocatalysts in energy conversion technologies. In order to improve catalytic efficiency and facilitate rational design and development of new catalysts, structure–function relationships that underpin catalytic activity must be understood at a fundamental level.

First, we present a systematic analysis of CO adsorption on Pt nanoclusters in the 0.2-1.5 nm size range with the aim of unraveling size-dependent trends and developing predictive models for site-specific adsorption behavior. Using an empirical-potential-based Genetic Algorithm (GA) and DFT modeling, we 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 (111) or (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, we show that multiple descriptors, broadly categorized as structural and electronic descriptors, are essential for qualitatively capturing the CO adsorption trends. Nevertheless, attaining quantitative accuracy requires further refinement and we propose the use of an additional descriptor – the fully-frozen adsorption energy – that is a computationally inexpensive probe of CO–Pt bond formation. With these three categories of descriptors, we achieve an absolute mean error in CO adsorption energy prediction of 0.12 eV, which is similar to the underlying error of DFT adsorption calculations. 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.

Thereafter, to understand support effects on the activity of Pt nanoclusters, we employ a combination of empirical potential simulations and DFT calculations to investigate structure–function relationships of small PtN (N = 2-80) clusters on model carbon (graphene) supports. A bond-order empirical potential is employed within a GA to go beyond local optimizations in obtaining minimum-energy structures of PtN clusters on pristine as well as defective graphene supports. Point defects in graphene strongly anchor Pt clusters and also appreciably affect the morphologies of small clusters, which are characterized via various structural metrics such as the radius of gyration, average bond length, and average coordination number. A key finding from the structural analysis is that the fraction of potentially active surface sites in supported clusters is maximized for stable Pt clusters in the size range of 20-30 atoms, which provides a useful design criterion for optimal utilization of the precious metal. Through selected ab initio studies, we find a consistent trend for charge transfer from small Pt clusters to defective graphene supports resulting in the lowering of the cluster d-band center, which has implications for the overall activity and poisoning of the catalyst. The combination of a robust empirical potential-based GA for structural optimization with ab initio calculations opens up avenues for systematic studies of supported catalyst clusters at much larger system sizes than are accessible to purely ab initio approaches.

Finally, we present a self-consistent charge density-functional tight-binding (SCC-DFTB) parameterization for PtRu alloys, which is developed by employing a training set of alloy cluster energies and forces obtained from Kohn-Sham DFT calculations. Extensive simulations of a testing set of PtRu alloy nanoclusters show that this SCC-DFTB scheme is capable of capturing cluster formation energies with high accuracy relative to DFT calculations. The new SCC-DFTB parameterization is employed within a GA to search for global minima of PtRu clusters in the range of 13-81 atoms and the emergence of Ru-core/Pt-shell structures at intermediate alloy compositions is systematically demonstrated. Our new SCC-DFTB parameterization enables computationally inexpensive modeling and exploration of structure–function relationships for Pt-Ru clusters that are among the best-performing catalysts in numerous energy applications.

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