Quantitative compositional and structural characterisation of bimetallic nanoparticles using HAADF STEM

Proton Exchange Membrane fuel cells (PEMFCs) represent one of the most readily viable electrical power solutions for the future particularly because they are zero-emission. PEMFCs rely on platinum nanoparticles to catalyse the reaction particularly at the site for oxygen reduction. The high cost of platinum is a key limiting factor in preventing PEMFCs from becoming commercially viable. A great deal of research is aimed at alloying platinum with other elements; aiming to reduce the cost and increase the catalytic activity. In order to understand and further improve these bimetallic nanoparticles it is necessary to characterise them down to the atomic scale, with particular focus on where the platinum is at the surface.
This project is focused on using z-contrast nature of High Angle Annular Dark Field (HAADF) Scanning Transmission Electron Microscopy (STEM) images. It is possible to quantify the number of electrons scattered out to the detector and use this to estimate the number of atoms within an atomic column. For single element nanoparticles this technique makes it possible to reconstruct a full 3-dimensional structure from one image.
From a 3-dimensional structure it is then possible to extract information about surface structure and the degree of coordination for the surface atoms. It would then be possible to relate back theoretical modelling and EXAFS in terms of surface ratios and which surface types are more catalytically active.
Determining the structure of bimetallic particles requires additional compositional information from high resolution energy dispersive x-ray (EDX) or electron energy loss spectroscopy (EELS). These compositional techniques come with their own difficulties because getting accurate counting statistics is difficult from such a small mass of sample, leading to problems with drift and sample damage.
The fundamental aim of this research is on automated analysis that can be repeated easily for a large selection of particles in order to start getting an idea of statistical distributions which are really representative of a catalyst sample.