In silico characterization of nanoparticles.

Nanoparticles (NPs) make for intriguing heterogeneous catalysts due to their large active surface area and excellent and often size-dependent catalytic properties that emerge from a multitude of chemically different surface reaction sites. NP catalysts are, in principle, also highly tunable: even small changes to the NP size or surface facet composition, doping with heteroatoms, or changes of the supporting material can significantly alter their physicochemical properties. Because synthesis of size- and shape-controlled NP catalysts is challenging, the ability to computationally predict the most favorable NP structures for a catalytic reaction of interest is an in-demand skill that can help accelerate and streamline the material optimization process. Fundamentally, simulations of NP model systems present unique challenges to computational scientists. Not only must considerable methodological hurdles be overcome in performing calculations with hundreds to thousands of atoms while retaining appropriate accuracy to be able to probe the desired properties. Also, the data generated by simulations of NPs are typically more complex than data from simulations of, for example, single crystal surface models, and therefore often require different data analysis strategies. To this end, the present work aims to review analytical methods and data analysis strategies that have proven useful in extracting thermodynamic trends from NP simulations.

An Atomistic View of Platinum Cluster Growth on Pristine and Defective Graphene Supports.

Density functional theory (DFT) is used to systematically investigate the electronic structure of platinum clusters grown on different graphene substrates. Platinum clusters with 1 to 10 atoms and graphene vacancy defect supports with 0 to 5 missing C atoms are investigated. Calculations show that Pt clusters bind more strongly as the vacancy size increases. For a given defect size, increasing the cluster size leads to more endothermic energy of formation, suggesting a templating effect that limits cluster growth. The opposite trend is observed for defect-free graphene where the formation energy becomes more exothermic with increasing cluster size. Calculations show that oxidation of the defect weakens binding of the Pt cluster, hence it is suggested that oxygen-free graphene supports are critical for successful attachment of Pt to carbon-based substrates. However, once the combined material is formed, oxygen adsorption is more favorable on the cluster than on the support, indicating resistance to oxidative support degradation. Finally, while highly-symmetric defects are found to encourage formation of symmetric Pt clusters, calculations also reveal that cluster stability in this size range mostly depends on the number of and ratio between PtC, PtPt, and PtO bonds; the actual cluster geometry seems secondary.

Assessment of the Accuracy of Density Functionals for Calculating Oxygen Reduction Reaction on Nitrogen-Doped Graphene.

Experimental studies of the oxygen reduction reaction (ORR) at nitrogen-doped graphene electrodes have reported a remarkably low overpotential, on the order of 0.5 V, similar to Pt-based electrodes. Theoretical calculations using density functional theory have lent support to this claim. However, other measurements have indicated that transition metal impurities are actually responsible for the ORR activity, thereby raising questions about the reliability of both the experiments and the calculations. To assess the accuracy of the theoretical calculations, various generalized gradient approximation (GGA), meta-GGA, and hybrid functionals are employed here and calibrated against high-level wave-function-based coupled-cluster calculations (CCSD(T)) of the overpotential as well as self-interaction corrected density functional calculations and published quantum Monte Carlo calculations of O adatom binding to graphene. The PBE0 and HSE06 hybrid functionals are found to give more accurate results than the GGA and meta-GGA functionals, as would be expected, and for a low dopant concentration, 3.1%, the overpotential is calculated to be 1.0 V. The GGA and meta-GGA functionals give a lower estimate by as much as 0.4 V. When the dopant concentration is doubled, the overpotential calculated with hybrid functionals decreases, while it increases in GGA functional calculations. The opposite trends result from different potential-determining steps, the *OOH species being of central importance in the hybrid functional calculations, while the reduction of *O determines the overpotential obtained in GGA and meta-GGA calculations. The results presented here are mainly based on calculations of periodic representations of the system, but a comparison is also made with molecular flake models that are found to give erratic results due to finite size effects and geometric distortions during energy minimization. The presence of the electrolyte has not been taken into account explicitly in the calculations presented here but is estimated to be important for definitive calculations of the overpotential.

Elastic Collision Based Dynamic Partitioning Scheme for Hybrid Simulations.

In hybrid simulations, such as the QM/MM approach, the system is partitioned into regions that are treated at different levels of theory. The key question then becomes how to evaluate the interactions between particles on opposite sides of the boundary. One approach is to place the boundary in such a way that particles near the boundary on both sides are of the same type, thus simplifying the evaluation of the interactions. If mobile particles are present, such as solvent molecules, and particles are allowed to cross the boundary, the conservation of energy and atomic forces is problematic unless the computational effort is increased significantly. By preventing particles from crossing the boundary but allowing the boundary to be flexible, an accurate estimate of average thermodynamic properties is obtained in principle as illustrated by the flexible inner region ensemble separator (FIRES) method [C. Rowley and B. Roux, J. Chem. Theory Comput. 2012, 8, 3526]. In FIRES, a harmonic restraint is applied to particles near the boundary. Therefore, it can occur that particle cross the boundary to some extent resulting in anomalies in the particle density. Here, a constraint approach is presented where particles instantaneously scatter from the boundary. This scattering-adapted FIRES (SAFIRES) implementation makes use of a variable-time-step propagation algorithm where the time step is scaled automatically to identify the moment a collision should occur. If the length of the time step is kept constant, this propagator reduces to a regular Langevin dynamics algorithm, and to the velocity Verlet algorithm for conservative dynamics if the friction coefficient is set to zero. Correct average ensemble statistics are obtained as demonstrated in simulations where, for testing purposes, the particles in the two regions are treated at the same level of theory, namely, a homogeneous Lennard-Jones (LJ) liquid and liquid water based on the TIP4P potential function. In order to illustrate this approach in solid-liquid interface simulations, a LJ liquid in contact with the surface of a crystal is also simulated. The simulations using SAFIRES are shown to reproduce the unconstrained reference simulations without significant deviations in the particle density and the dynamics are shown to conserve energy when coupling to the heat bath is turned off.

Simulations of the Oxidation and Degradation of Platinum Electrocatalysts.

Improved understanding of the fundamental processes leading to degradation of platinum nanoparticle electrocatalysts is essential to the continued advancement of their catalytic activity and stability. To this end, the oxidation of platinum nanoparticles is simulated using a ReaxFF reactive force field within a grand-canonical Monte Carlo scheme. 2-4 nm cuboctahedral particles serve as model systems, for which electrochemical potential-dependent phase diagrams are constructed from the thermodynamically most stable oxide structures, including solvation and thermochemical contributions. Calculations in this study suggest that surface oxide structures should become thermodynamically stable at voltages around 0.80-0.85 V versus standard hydrogen electrode, which corresponds to typical fuel cell operating conditions. The potential presence of a surface oxide during catalysis is usually not accounted for in theoretical studies of Pt electrocatalysts. Beyond 1.1 V, fragmentation of the catalyst particles into [Pt6 O8 ]4- clusters is observed. Density functional theory calculations confirm that [Pt6 O8 ]4- is indeed stable and hydrophilic. These results suggest that the formation of [Pt6 O8 ]4- may play an important role in platinum catalyst degradation as well as the electromotoric transport of Pt2+/4+ ions in fuel cells.

Water-Soluble Polymeric Carbon Nitride Colloidal Nanoparticles for Highly Selective Quasi-Homogeneous Photocatalysis.

Heptazine-based polymeric carbon nitrides (PCN) are promising photocatalysts for light-driven redox transformations. However, their activity is hampered by low surface area resulting in low concentration of accessible active sites. Herein, we report a bottom-up preparation of PCN nanoparticles with a narrow size distribution (ca. 10±3 nm), which are fully soluble in water showing no gelation or precipitation over several months. They allow photocatalysis to be carried out under quasi-homogeneous conditions. The superior performance of water-soluble PCN, compared to conventional solid PCN, is shown in photocatalytic H2 O2 production via reduction of oxygen accompanied by highly selective photooxidation of 4-methoxybenzyl alcohol and benzyl alcohol or lignocellulose-derived feedstock (ethanol, glycerol, glucose). The dissolved photocatalyst can be easily recovered and re-dissolved by simple modulation of the ionic strength of the medium, without any loss of activity and selectivity.

Molybdenum Doping Augments Platinum-Copper Oxygen Reduction Electrocatalyst.

Improving the efficiency of Pt-based oxygen reduction reaction (ORR) catalysts while also reducing costs remains an important challenge in energy research. To this end, we synthesized highly stable and active carbon-supported Mo-doped PtCu (Mo-PtCu/C) nanoparticles (NPs) from readily available precursors in a facile one-pot reaction. Mo-PtCu/C displays two-to-fourfold-higher ORR half-cell kinetics than reference PtCu/C and Pt/C materials, a trend that was confirmed in proof-of-concept experiments by using a H2 /O2 microlaminar fuel cell. This Mo-induced activity increase mirrors observations for Mo-PtNi/C NPs and possibly suggests an emerging trend. Electrochemical-accelerated stability tests revealed that dealloying was greatly reduced in Mo-PtCu/C in contrast to the binary alloys PtCu/C and PtMo/C. Supporting DFT studies suggested that the exceptional stability of Mo-PtCu could be attributed to oxidative resistance of the Mo-doped atoms. Furthermore, our calculations revealed that oxygen could induce segregation of Mo to the catalytic surface, at which it effected beneficial changes to the surface oxygen adsorption energetics in the context of the Sabatier principle.