Rachel B. Getman

DFT-Based Coverage-Dependent Model of Pt-Catalyzed NO Oxidation

A coverage‐dependent, mean‐field microkinetic model of catalytic NO oxidation, NO+0.5 O2⇌NO2, at a Pt(111) surface has been developed, based on large supercell density functional theory (DFT) calculations. DFT is used to determine the overall energetics and activation energies of candidate reaction steps as a function of surface coverage. Surface coverage is found to have a significant but non‐uniform effect on the energetics, pathways, and activation energies of reaction steps involving formation or cleavage of ONO and OO bonds, and inclusion of this coverage dependence is essential for obtaining a qualitatively correct representation of the catalysis. Correlations are used to express all reaction parameters in terms of a single coverage variable θ and steady‐state solutions to the resultant mean‐field models are obtained in the method of DeDonder relations. At conditions representative of NO oxidation catalysis, the surface coverage is predicted to be 0.25≤θ<0.4 ML and to be controlled by equilibrium between gas‐phase NO and NO2 and chemisorbed O. O2 dissociative adsorption (O2(g)→2O*) is rate limiting in the model. The DFT‐based mean‐field model captures many features of the experimentally observed catalysis, and its short‐comings point the way toward more robust models of coverage‐dependent kinetics.

Stepwise adsorption in a mesoporous metal–organic framework: experimental and computational analysis

Stepwise adsorption in a metal-organic framework with both micro- and meso-pores is caused by adsorbates first filling the micropores, then adsorbing along the mesopore walls, and finally filling the mesopores.

A first-principles investigation of the effect of Pt cluster size on CO and NO oxidation intermediates and energetics.

As catalysis research strives toward designing structurally and functionally well-defined catalytic centers containing as few active metal atoms as possible, the importance of understanding the reactivity of small metal clusters, and in particular of systematic comparisons of reaction types and cluster sizes, has grown concomitantly. Here we report density functional theory calculations (GGA-PW91) that probe the relationship between particle size, intermediate structures, and energetics of CO and NO oxidation by molecular and atomic oxygen on Pt(x) clusters (x = 1-5 and 10). The preferred structures, charge distributions, vibrational spectra, and energetics are systematically examined for oxygen (O(2), 2O, and O), CO, CO(2), NO, and NO(2), for CO/NO co-adsorbed with O(2), 2O, and O, and for CO(2)/NO(2) co-adsorbed with O. The binding energies of oxygen, CO, NO, and of the oxidation products CO(2) and NO(2) are all markedly enhanced on Pt(x) compared to Pt(111), and they trend toward the Pt(111) levels as cluster size increases. Because of the strong interaction of both the reactants and products with the Pt(x) clusters, deep energy sinks develop on the potential energy surfaces of the respective oxidation processes, indicating worse reaction energetics than on Pt(111). Thus the smallest Pt clusters are less effective for catalyzing CO and NO oxidation in their original state than bulk Pt. Our results further suggests that oxidation by molecular O(2) is thermodynamically more favourable than by atomic O on Pt(x). Conditions and applications in which the Pt(x) clusters may be effective catalysts are discussed.

A modelling approach for MOF-encapsulated metal catalysts and application to n-butane oxidation.

Metal nanoparticles (NP) encapsulated by metal-organic frameworks (MOFs) are novel composite materials that have shown promise as regioselective catalysts. The regioselectivity in these materials arises from steric constraints imposed by the porous MOF structure, which limit the way molecules approach and interact with the metal surface. Here we introduce a conceptually simple DFT approach to model reactions under such steric constraints. This approach is computationally efficient and accounts for the steric constraints imposed by a MOF pore in a general way. The adsorption of reactants, intermediates, and products associated with oxidation of n-butane to 1-butanol (and 2-butanol) on clean and oxygen-covered palladium surfaces is investigated with (and without) the constraints of a pore. Reaction energies are calculated, and we find that the thermodynamic favorability of the intermediate reactions is affected by the presence of steric constraints, oxygen coverage, and the exposed crystal surface of the metal. Based on these results, the Pd(111) surface with 0.25 ML oxygen coverage and steric constraints (which could be provided by a suitable MOF) seems promising to favor the desired sequence of reactions that would lead to the conversion of n-butane to 1-butanol.

Sinter-Resistant Platinum Catalyst Supported by Metal-Organic Framework

Single atoms and few-atom clusters of platinum are uniformly installed on the zirconia nodes of a metal-organic framework (MOF) NU-1000 via targeted vapor-phase synthesis. The catalytic Pt clusters, site-isolated by organic linkers, are shown to exhibit high catalytic activity for ethylene hydrogenation while exhibiting resistance to sintering up to 200 °C. In situ IR spectroscopy reveals the presence of both single atoms and few-atom clusters that depend upon synthesis conditions. Operando X-ray absorption spectroscopy and X-ray pair distribution analyses reveal unique changes in chemical bonding environment and cluster size stability while on stream. Density functional theory calculations elucidate a favorable reaction pathway for ethylene hydrogenation with the novel catalyst. These results provide evidence that atomic layer deposition (ALD) in MOFs is a versatile approach to the rational synthesis of size-selected clusters, including noble metals, on a high surface area support.

Molecular simulations of physical and chemical adsorption under gas and liquid environments using force field- and quantum mechanics-based methods

Here we review our simulations of adsorption on metal–organic frameworks (MOFs) and platinum (Pt) catalysts, focusing on the modelling methods required to understand these two very different systems. MOFs are porous, crystalline materials with large surface areas, which are promising for a variety of adsorption applications. We review our simulations of gas uptake in PCN-53 (porous coordination network) as well as gas storage in MOFs functionalised with metal alkoxide sites. While fluid–solid interactions in both systems can be modelled quite well using algebraic force fields, the alkoxide sites in the functionalised MOFs require specialised versions, in order to describe the stronger adsorption energies. We discuss grand canonical Monte Carlo (GCMC) simulations of both systems. Pt is a common catalyst, and simulations have proven quite useful for providing molecular level details to understand its functionality. This involves understanding adsorption phenomena, which often requires quantum mechanical calculations. We describe our periodic boundary condition density functional theory (DFT) simulations of Pt-catalysed NO oxidation, focusing on adsorbate geometries and coverage effects. Finally, we describe one of the current ‘grand challenges’ in molecular simulations of adsorption, modelling catalytic activity in aqueous phase, which requires a combination of algebraic force fields, DFT and GCMC.

A DFT and MD study of aqueous-phase dehydrogenation of glycerol on Pt(1 1 1): comparing chemical accuracy versus computational expense in different methods for calculating aqueous-phase system energies

Abstract Glycerol, which is one of the most abundant by-products in biodiesel production, can be converted into H2 through aqueous-phase reforming (APR). Dehydrogenation is one of the main processes in glycerol APR. In this work, we use computational methods to study Pt(1 1 1)-catalysed glycerol dehydrogenation under aqueous conditions. There are 84 intermediates and 250 possible reactions in the dehydrogenation network. Inclusion of the liquid environment adds computational expense, especially if we are to study all the reaction intermediates and reactions under explicit water solvation using quantum methods. In this work, we present a method that can be used to efficiently estimate reaction energies under explicit solvation with reasonable accuracy and computational expense. The method couples a linear scaling relationship for obtaining adsorbate binding energies with Lennard-Jones + Coulomb potentials for obtaining water–adsorbate interaction energies. Comparing reaction energies calculated with this approach to reaction energies obtained from a more extensive approach that attains quantum-level accuracy (published previously by our group), we find good correlation (R2 = 0.84) and reasonable accuracy (the mean absolute error, MAE = 0.28 eV).

Using Gas-Phase Clusters to Screen Porphyrin-Supported Nanocluster Catalysts for Ethane Oxidation to Ethanol

We demonstrate the use of gas phase metal hydroxide clusters to identify descriptors and generate scaling relationships for predicting catalytic performances of porphyrin-supported metal hydroxide catalysts. Using the gas phase clusters for these purposes takes just 5 % of the time that would have been required if the porphyrin-supported models had been used.Graphical Abstract

Optimizing Open Iron Sites in Metal–Organic Frameworks for Ethane Oxidation: A First-Principles Study

Activation of the C-H bonds in ethane to form ethanol is a highly desirable, yet challenging, reaction. Metal-organic frameworks (MOFs) with open Fe sites are promising candidates for catalyzing this reaction. One advantage of MOFs is their modular construction from inorganic nodes and organic linkers, allowing for flexible design and detailed control of properties. In this work, we studied a series of single-metal atom Fe model systems with ligands that are commonly used as MOF linkers and tried to understand how one can design an optimal Fe catalyst. We found linear relationships between the binding enthalpy of oxygen to the Fe sites and common descriptors for catalytic reactions, such as the Fe 3d energy levels in different reaction intermediates. We further analyzed the three highest-barrier steps in the ethane oxidation cycle (including desorption of the product) with the Fe 3d energy levels. Volcano relationships are revealed with peaks toward higher Fe 3d energy and stronger electron-donating group functionalization of linkers. Furthermore, we found that the Fe 3d energy levels positively correlate with the electron-donating strength of functional groups on the linkers. Finally, we validated our hypotheses on larger models of MOF-74 iron sites. Compared with MOF-74, functionalizing the MOF-74 linkers with NH2 groups lowers the enthalpic barrier for the most endothermic step in the reaction cycle. Our findings provide insight for catalyst optimization and point out directions for future experimental efforts.

Combining HPC and Big Data Infrastructures in Large-Scale Post-Processing of Simulation Data: A Case Study

Advances in scientific software and computing infrastructure have enabled researchers across disciplines to simulate and model highly complex systems. At the same time, these increases in simulation duration and scale have led to significant growths in the sizes of output data, which can be as much as hundreds of gigabytes or more. While there exist solutions to assist with most standard post-simulation analytics, researchers must develop their own code to support customized analytical tasks. Given the nature of these output data, most naive in-house sequential codes end up being inefficient, and in most cases, time-consuming. In this paper, we propose a solution to this issue by transparently combining the strengths of a high-performance computing cluster and a big data infrastructure to support an end-to-end scientific workflow. More specifically, we present a case study around the design of a research computing environment at Clemson University where these two computing systems are integrated and accessible from one another. This environment allows simulation data to be automatically transferred across systems and complex analytical tasks on these data to be developed using the Hadoop/Spark frameworks. Results show that a hybrid workflow for molecular dynamics simulation can provide significant performance improvements over a traditional workflow. Furthermore, code complexity of Hadoop/Spark solutions is shown to be less than that of a traditional solution.