Computational studies of metallic nanoparticles applicable to heterogeneous catalysis

Understanding the factors controlling a catalyst activity, selectivity, and stability is a complicated task with potential applications for a large number of technologies with high impact for our society. For example, optimised catalysts can have a central role to improve the durability, efficiency and decrease the cost of technologies such as fuel cells, which are devices able to convert the chemical energy from molecules into electricity by performing chemical reactions that are specific to the used fuel and the type of fuel cell. Fuel cell catalysts are commonly made from metallic nanoparticles on different types of supports, meaning that characteristics such as the nanoparticle composition, size, shape, composition of the support, electrolyte and many others can be simultaneously controlled to tune the catalyst towards a specific goal. The complexity involved in the catalyst optimisation makes the problem extremely exciting and challenging, requiring significant effort from many different research areas ranging from synthetic chemistry and electrochemistry to computational chemistry.This thesis describes computational studies on metallic nanoparticles, focusing on nanoparticle size effects and its interplay with other variables that are important in the context of fuel cell catalysts such as the presence of support and adsorbate coverage effects. We also present a framework in ONETEP’s linear-scaling DFT formalism for the implementation of local and angular momentum projected density of states (l-p-DOS), which is an important tool to study metallic nanoparticles used as catalysts. The four different bases used to project the density of states are tested, and its results are compared against other DFT code, helping to validate our l-p-DOS implementation. The results obtained for metallic nanoparticles show similar trends with all the implemented options, demonstrating the reliability of the method for such studies. The ONETEP code with the implemented l-p-DOS functionality is used to perform a set of large-scale DFT calculations to study Pt nanoparticles isolated and supported on pristine graphene. The results show a weak metal-support interaction, with the adhesion energy per Pt atom decreasing with the nanoparticle size and being dominated by dispersion interactions for larger nanoparticles. The interaction with the support induces geometric and electronic changes in the nanoparticle, with the inter-atomic distances expanding (contracting) for Pt facets close (far) to the support and with charge redistribution happening at the interface between Pt clusters and graphene. The changes in the geometric and electronic properties induced by the interaction with the support are size dependent, correlate with the interaction strength between cluster and support, and generate shifts in the d-band centres of the Pt nanoparticles. The isolated and supported nanoparticles were used to study how the nanoparticle size and presence of support alters the interaction of the nanoparticles with atomic oxygen, carbon monoxide, and ethanol. We show that nanoparticle size decreases strengthen the adsorption energies of O and CO, which can hinder the activity of these nanoparticles towards important reactions in the context of fuel cells. The size effect is observed to control the adsorption energies for O and CO to a larger extent than for ethanol. We also note that the presence of graphene weakens the adsorption energies for all adsorbates, with this effect being more significant for smaller nanoparticles. Finally, we study how the adsorbate coverage changes the adsorption of atomic oxygen on Pt nanoparticles and how coverage effects vary with the nanoparticle size. We observe that the increase in O coverage weakens the adsorption energies per O atom, with this effect being more significant for the larger simulated nanoparticles. These studies can help to understand different influences that the nanoparticle size can have by simulating non-trivial combinations of size and support effects and size and coverage effects which could have implications to designing more efficient catalysts for fuel cells and other applications