Detecting Orientational Order in Model Systems by X-ray Cross Correlation Methods

Abstract

We present the results of a computational X-ray cross correlation analysis (XCCA) study on two dimensional polygonal model structures. We show how to detect and identify the orientational order of such systems, demonstrate how to eliminate the influence of the "computational box" on the XCCA results and develop new correlation functions that reflect the sample's orientational order only. For this purpose, we study the dependence of the correlation functions on the number of polygonal clusters and wave vector transfer $q$ for various types of polygons including mixtures of polygons and randomly placed particles. We define an order parameter that describes the orientational order within the sample. Finally, we determine the influence of detector noise and non-planar wavefronts on the XCCA data which both appear to affect the results significantly and have thus to be considered in real experiments.

References in corpus (4)

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• X-ray cross-correlation analysis applied to disordered two-dimensional systems
• X-ray cross-correlation analysis of liquid crystal membranes in the vicinity of hexatic-smectic phase transition
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Cited by in corpus (4)

• Crystal nucleation as the ordering of multiple order parameters
• The role of the coherence in the cross-correlation analysis of diffraction patterns from two-dimensional dense mono-disperse systems
• Dynamics of Local Symmetry Correlators for Interacting Many-Particle Systems
• Quantifying multi-point ordering in alloys