The analysis of local structural effects in alloys using total scattering and reverse Monte Carlo techniques
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Over the years `short-range order' (SRO), whereby the local atomic arrangement differs from that of a random distribution, has been used to explain physical phenomena such as thermodynamic discontinuities, increased strength, anomalous electrical resistivity and magnetic variations in a host of alloys. However, due mainly to experimental difficulties and the complexity of the calculations required for the analysis of diffuse scattering, such work has been largely abandoned and hence quantification and assessment of SRO is notably sparse in the literature. The recent development of reverse Monte-Carlo (RMC) methods for the analysis of total scattering data has opened a promising route for the assessment of a material's local environment and has already provided important insights into a host of complex chemical systems, including liquids, network glasses, nano-materials, functional oxides and metal organic frameworks. The work presented in this thesis focuses on the development of a new methodology for the analysis of local structural effects in metallic systems using total scattering, and the first systematic application to the study of alloys. The simulation of total scattering data from a range of model structures is used to show that the information content of total scattering functions, in particular the pair distribution function (PDF), is sufficiently high to allow the assessment of different types and degrees of short-range order. This is supported by a demonstration of how such orders can be quantified from large box models, produced by fitting total scattering data using the RMC algorithm, with the mathematical analyses outlined. This culminates in a proposed methodology for the analysis of SRO in alloys. Having developed this analytical methodology it is subsequently applied to a number of interesting alloy systems. To demonstrate the efficacy of this methodology it was first applied to the study of a sample of Cu
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Playford, Helen Y.
Tucker, Matthew G.