The use of hydrogen is increasingly seen as a key component to meeting our goals on net zero carbon energy sources. Hydrogen fuel cells are used to convert hydrogen to electricity, but are limited by the sluggish oxygen reduction reaction at the fuel cell cathode. Catalysts based on platinum are used to increase this reaction rate, but Pt is an expensive metal limiting the economic viability of hydrogen-based energy. Alloying Pt with cheaper base metals reduces the mass of Pt required, and surprisingly can enhance activity beyond that of Pt. The origins of the enhanced activity are not fully understood and may be associated with compositional clustering of species, the chemical effects of mixing metals or the effect of lattice strain if the composition is inhomogeneous. The challenge is that measuring either strain or composition within a nanoparticle is right at the limits of current experimental capabilities, especially as we need methods that can examine many particles to understand the ensemble properties. This project will make use of state-of-the-art electron microscope technologies for imaging and spectroscopy to determine composition including degree of oxidation and the resulting strain. Machine learning may play a role in allowing larger numbers of particles to be analysed. Density functional theory modelling may be used to understand the link between structure and activity. The project would suit candidates interested in experiments, data processing and/or modelling working right at the limits of what can be achieved. The successful candidate will have either a chemistry, physics or materials science background and be comfortable with practical experimentation and advanced data processing, and/or materials modelling.