Probing deformation substructure by synchrotron X-ray diffraction and dislocation dynamics modelling.

Materials characterization at the nano-scale is motivated by the desire to resolve the structural aspects and deformation behavior at length scales relevant to those mechanisms that define the novel and unusual properties of nano-structured materials. A range of novel techniques has recently become accessible with the help of synchrotron X-ray beams that can be focused down to spot sizes of less than a few microns on the sample. The unique combination of tunability (energy selection), parallelism and brightness of synchrotron X-ray beams allows their use for high resolution diffraction (determination of crystal structure and transformations, analysis of dislocation sub-structures, orientation and texture analysis, strain mapping); small angle X-ray scattering (analysis of nano-scale voids and defects; orientation analysis) and imaging (radiography and tomography). After a brief review of the state-of-the-art capabilities for monochromatic and white beam synchrotron diffraction, we consider the usefulness of these techniques for the task of bridging the gap between experiment and modeling. Namely, we discuss how the experiments can be configured to provide information relevant to the validation and improvement of modeling approaches, and also how the results of various simulations can be post-processed to improve the possibility of (more or less) direct comparison with experiments. Using the example of some recent experiments carried out on beamline 116 at Diamond Light Source near Oxford, we discuss how such experimental results can be interpreted in view and in conjunction with numerical deformation models, particularly those incorporating dislocation effects, e.g., finite-element based pseudo-continuum strain gradient formulations, and discrete dislocation simulations. Post-processing of FE and discrete dislocation simulations is described, illustrating the kind of information that can be extracted from comparisons between modeling and experimental data