Strain effects in core-shell PtCo nanoparticles: a comparison of experimental observations and computational modelling.

Strain in Pt nanoalloys induced by the secondary metal has long been suggested as a major contributor to the modification of catalytic properties. Here, we investigate strain in PtCo nanoparticles using a combination of computational modelling and microscopy experiments. We have used a combination of molecular dynamics (MD) and large-scale density functional theory (DFT) for our models, alongside experimental work using annular dark field scanning transmission electron microscopy (ADF-STEM). We have performed extensive validation of the interatomic potential against DFT using a Pt568Co18 nanoparticle. Modelling gives access to 3 dimensional structures that can be compared to the 2D ADF-STEM images, which we use to build an understanding of nanoparticle structure and composition. Strain has been measured for PtCo and pure Pt nanoparticles, with MD annealed models compared to ADF-STEM images. Our analysis was performed on a layer by layer basis, where distinct trends between the Pt and PtCo alloy nanoparticles are observed. To our knowledge, we show for the first time a way in which detailed atomistic simulations can be used to augment and help interpret the results of ADF-STEM strain mapping experiments, which will enhance their use in characterisation towards the development of improved catalysts.

Exploring fuel cell cathode materials using ab initio high throughput calculations and validation using carbon supported Pt alloy catalysts.

We employ a combined density functional theory (DFT) and experimental approach to screen different elements (M) and Pt3M alloys (M = Sc, Y, V, Nb, Ta, Ti, Zr, Hf, Cr, Fe, Ru, Os, Co, Rh, Ir, Ni, Pd, Cu, Ag, Au and Al) for oxygen reduction reaction (ORR) activity and stability. The results of the calculations are validated using a series of carbon supported alloy nanoparticles measured within membrane electrode assembly (MEA) environments. We assess the reliability of descriptors such as surface d-band centre and O adsorption energy as computed from DFT calculations. We also assess the stability of the alloy surfaces under different adsorbate environments as encountered under ORR conditions. Our calculations predict that under an oxygen atmosphere segregation of M to the surface is likely to occur. The calculated segregation energies correlate reasonably well with the amount of base metal leached in the carbon-supported catalysts and good correlation of computed O adsorption energies with ORR activity is also shown.

Modification of O and CO binding on Pt nanoparticles due to electronic and structural effects of titania supports.

Metal oxide supports often play an active part in heterogeneous catalysis by moderating both the structure and the electronic properties of the metallic catalyst particle. In order to provide some fundamental understanding on these effects, we present here a density functional theory (DFT) investigation of the binding of O and CO on Pt nanoparticles supported on titania (anatase) surfaces. These systems are complex, and in order to develop realistic models, here, we needed to perform DFT calculations with up to ∼1000 atoms. By performing full geometry relaxations at each stage, we avoid any effects of "frozen geometry" approximations. In terms of the interaction of the Pt nanoparticles with the support, we find that the surface deformation of the anatase support contributes greatly to the adsorption of each nanoparticle, especially for the anatase (001) facet. We attempt to separate geometric and electronic effects and find a larger contribution to ligand binding energy arising from the former. Overall, we show an average weakening (compared to the isolated nanoparticle) of ∼0.1 eV across atop, bridge and hollow binding sites on supported Pt55 for O and CO, and a preservation of site preference. Stronger effects are seen for O on Pt13, which is heavily deformed by anatase supports. In order to rationalize our results and examine methods for faster characterization of metal catalysts, we make use of electronic descriptors, including the d-band center and an electronic density based descriptor. We expect that the approach followed in this study could be applied to study other supported metal catalysts.

Predicting the Oxygen-Binding Properties of Platinum Nanoparticle Ensembles by Combining High-Precision Electron Microscopy and Density Functional Theory.

Many studies of heterogeneous catalysis, both experimental and computational, make use of idealized structures such as extended surfaces or regular polyhedral nanoparticles. This simplification neglects the morphological diversity in real commercial oxygen reduction reaction (ORR) catalysts used in fuel-cell cathodes. Here we introduce an approach that combines 3D nanoparticle structures obtained from high-throughput high-precision electron microscopy with density functional theory. Discrepancies between experimental observations and cuboctahedral/truncated-octahedral particles are revealed and discussed using a range of widely used descriptors, such as electron-density, d-band centers, and generalized coordination numbers. We use this new approach to determine the optimum particle size for which both detrimental surface roughness and particle shape effects are minimized.

Ammonia mobility in chabazite: insight into the diffusion component of the NH3-SCR process.

The diffusion of ammonia in commercial NH3-SCR catalyst Cu-CHA was measured and compared with H-CHA using quasielastic neutron scattering (QENS) and molecular dynamics (MD) simulations to assess the effect of counterion presence on NH3 mobility in automotive emission control relevant zeolite catalysts. QENS experiments observed jump diffusion with a jump distance of 3 Å, giving similar self-diffusion coefficient measurements for both Cu- and H-CHA samples, in the range of ca. 5-10 × 10(-10) m(2) s(-1) over the measured temperature range. Self-diffusivities calculated by MD were within a factor of 6 of those measured experimentally at each temperature. The activation energies of diffusion were also similar for both studied systems: 3.7 and 4.4 kJ mol(-1) for the H- and Cu-chabazite respectively, suggesting that counterion presence has little impact on ammonia diffusivity on the timescale of the QENS experiment. An explanation is given by the MD simulations, which showed the strong coordination of NH3 with Cu(2+) counterions in the centre of the chabazite cage, shielding other molecules from interaction with the ion, and allowing for intercage diffusion through the 8-ring windows (consistent with the experimentally observed jump length) to carry on unhindered.

Perspective: Methods for large-scale density functional calculations on metallic systems.

Current research challenges in areas such as energy and bioscience have created a strong need for Density Functional Theory (DFT) calculations on metallic nanostructures of hundreds to thousands of atoms to provide understanding at the atomic level in technologically important processes such as catalysis and magnetic materials. Linear-scaling DFT methods for calculations with thousands of atoms on insulators are now reaching a level of maturity. However such methods are not applicable to metals, where the continuum of states through the chemical potential and their partial occupancies provide significant hurdles which have yet to be fully overcome. Within this perspective we outline the theory of DFT calculations on metallic systems with a focus on methods for large-scale calculations, as required for the study of metallic nanoparticles. We present early approaches for electronic energy minimization in metallic systems as well as approaches which can impose partial state occupancies from a thermal distribution without access to the electronic Hamiltonian eigenvalues, such as the classes of Fermi operator expansions and integral expansions. We then focus on the significant progress which has been made in the last decade with developments which promise to better tackle the length-scale problem in metals. We discuss the challenges presented by each method, the likely future directions that could be followed and whether an accurate linear-scaling DFT method for metals is in sight.

Hydration Structures on γ-Alumina Surfaces With and Without Electrolytes Probed by Atomistic Molecular Dynamics Simulations.

A wide range of systems, both engineered and natural, feature aqueous electrolyte solutions at interfaces. In this study, the structure and dynamics of water at the two prevalent crystallographic terminations of gamma-alumina, [110] and [100], and the influence of salts─sodium chloride, ammonium acetate, barium acetate, and barium nitrate on such properties─were investigated using equilibrium molecular dynamics simulations. The resulting interfacial phenomena were quantified from simulation trajectories via atomic density profiles, angle probability distributions, residence times, 2-D density distributions within the hydration layers, and hydrogen bond density profiles. Analysis and interpretation of the results are supported by simulation snapshots. Taken together, our results show stronger interaction and closer association of water with the [110] surface, compared to [100], while ion-induced disruption of interfacial water structure was more prevalent at the [100] surface. For the latter, a stronger association of cations is observed, namely sodium and ammonium, and ion adsorption appears determined by their size. The differences in surface-water interactions between the two terminations are linked to their respective surface features and distributions of surface groups, with atomistic-scale roughness of the [110] surface promoting closer association of interfacial water. The results highlight the fundamental role of surface characteristics in determining surface-water interactions, and the resulting effects on ion-surface and ion-water interactions. Since the two terminations of gamma-alumina considered represent interfaces of significance to numerous industrial applications, the results provide insights relevant for catalyst preparation and adsorption-based water treatment, among other applications.

Machine learning accelerated high-throughput screening of zeolites for the selective adsorption of xylene isomers.

The production of widely used polymers such as polyester currently relies upon the chemical separation of and transformation of xylene isomers. The least valuable but most prevalent isomer is meta-xylene which can be selectively transformed into the more useful and expensive para-xylene isomer using a zeolite catalyst but at a high energy cost. In this work, high-throughput screening of existing and hypothetical zeolite databases containing more than two million structures was performed, using a combination of classical simulation and deep neural network methods to identify promising materials for selective adsorption of meta-xylene. Novel anomaly detection techniques were applied to the heavily biased classification task of identifying structures with a selectivity greater than that of the best performing existing zeolite, ZSM-5 (MFI topology). Eight hypothetical zeolite topologies are found to be several orders of magnitude more selective towards meta-xylene than ZSM-5 which may provide an impetus for synthetic efforts to realise these promising materials. Moreover, the leading hypothetical frameworks identified from the screening procedure require a markedly lower operating temperature to achieve the diffusion seen in existing materials, suggesting significant energetic savings if the frameworks can be realised.

Ideal versus real: simulated annealing of experimentally derived and geometric platinum nanoparticles.

Platinum nanoparticles find significant use as catalysts in industrial applications such as fuel cells. Research into their design has focussed heavily on nanoparticle size and shape as they greatly influence activity. Using high throughput, high precision electron microscopy, the structures of commercially available Pt catalysts have been determined, and we have used classical and quantum atomistic simulations to examine and compare them with geometric cuboctahedral and truncated octahedral structures. A simulated annealing procedure was used both to explore the potential energy surface at different temperatures, and also to assess the effect on catalytic activity that annealing would have on nanoparticles with different geometries and sizes. The differences in response to annealing between the real and geometric nanoparticles are discussed in terms of thermal stability, coordination number and the proportion of optimal binding sites on the surface of the nanoparticles. We find that annealing both experimental and geometric nanoparticles results in structures that appear similar in shape and predicted activity, using oxygen adsorption as a measure. Annealing is predicted to increase the catalytic activity in all cases except the truncated octahedra, where it has the opposite effect. As our simulations have been performed with a classical force field, we also assess its suitability to describe the potential energy of such nanoparticles by comparing with large scale density functional theory calculations.

Can Palladium Acetate Lose Its "Saltiness"? Catalytic Activities of the Impurities in Palladium Acetate.

Commercially available palladium acetate often contains two major impurities, whose presence can impact the overall catalytic efficacy. This systematic study provides a comparison of the differences in catalytic activity of pure palladium acetate, Pd3(OAc)6, with the two impurities: Pd3(OAc)5(NO2) and polymeric [Pd(OAc)2]n in a variety of cross-coupling reactions. The solid state (13)C NMR spectra of all three compounds in conjunction with DFT calculations confirm their reported geometries.