Platinum-based catalysts for hydrogen fuel-cell applications have progressed greatly with the addition of a second element in either a mixed-alloy or core-shell structure. Not only do they contain a reduced amount of the more expensive platinum metal but they have been shown to demonstrate a significant improvement in catalytic activity. Further improvement of these systems can only be made by careful investigation of such catalyst panoparticles on an atomic scale. These nanoparticles provide a significant characterisation challenge due to their minute size and beam sensitivity. A new method of quantifying the annular dark-field (ADF) scanning transmission electron microscope (STEM) signal on an absolute scale has been developed to address this problem. Experimental images are scaled to a fraction of the incident beam intensity from a detector map. The integrated intensity of each individual atomic column is multiplied by the pixel area to yield a more robust imaging parameter: a scattering cross section, σ. Using this cross section approach and simulated reference data, I show it is possible to count the number of atoms in individual columns. With some prior knowledge of the sample, this makes it possible to reconstruct the 3-dimensional structures of pure platinum nanoparticles. Such an approach has subsequently been extended to bimetallic particles here the elements are close in atomic number, using the platinum-iridium system as an example. In the same way that the cross section can be calculated from ADF image intensity, it is possible to calculate an energy dispersive x-ray (EDX) partial scattering cross section, beneficial especially because of the simplicity of its implementation. In sufficiently thin samples such that the number of x-ray counts is linearly proportional to sample thickness, we can determine element-specific atom counts. Finally, it is possible to combine EDX and ADF cross sections to provide us with quantitative structural and compositional information.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:667032 |
| Date | January 2015 |
| Creators | MacArthur, Katherine E. |
| Contributors | Nellist, Peter D.; Lozano-Perez, Sergio; Ozkaya, Dogan |
| Publisher | University of Oxford |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://ora.ox.ac.uk/objects/uuid:6ce29b0c-1e7e-4604-ba5b-22f2ebd03d4e |
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