Computational studies of mono- and bimetallic nanoclusters for potential polymer electrolyte fuel cell applications

摘要

A problem with the Polymer Electrolyte Fuel Cell (PEFC) is the expensive platinum (Pt) electrocatalyst. This thesis aims to investigate alloying of Pt with cheaper metals that not only reduce the overall cost but also alter the electronic properties to improve reaction kinetics. A Genetic Algorithm (GA) coupled with Density Functional Theory (DFT) approach has been used to perform structural searches on small Pt clusters doped with early transition metals (M). It is found that varying spin can have significant effects on the minimum energy structures of pure Pt clusters, while doping with early transition metals leads to spin quenching.ududDFT studies have been performed to predict potential Pt-based alloy nanoparticles that will result in weaker Pt–O interactions. This is achieved by investigating nanoalloys that lead to filling of the Pt d-band. Early transition metals are found to be promising, where donation of electron density from M to Pt results in additional filling of the Pt d-band. The surfaces of pure Pt clusters are found to distort, facilitating fast oxygen dissociation. It is found that the strong Pt-M interactions, which lead to filling of the d-band, can lead to Pt clusters becoming more structurally rigid, which inhibits oxygen dissociation. A search has been performed to find the best compromise for a system that retains flexibility of the Pt surface, to allow fast dissociation while also allowing M to Pt electron donation, leading to filling of the Pt d-band.