Transport Coefficient Approach for Characterizing Non-Equilibrium Dynamics in Soft Matter with X-ray Photon Correlation Spectroscopy

ORAL

Abstract

Since the response of individual particles under external drives governs the rheological and mechanical properties of the entire system, a comprehensive understanding of the particle dynamics is crucial for improving the manufacturability and applications of many soft matter systems. By capturing dynamics with X-ray photon correlation spectroscopy (XPCS), intricate dynamical phenomena within these systems, such as aging, yielding, dynamical heterogeneity, and avalanches, are revealed, providing captivating insight with exceptional spatiotemporal resolution. Nevertheless, the approaches employed to study these dynamical processes still remain primitive, overlooking the intricate details in underlying these non-equilibrium phenomena. Here, we develop an innovative method to integrate the collective influence of internal and external forces acting on a particle within the framework of Markov chain and introduce a universal parameter, transport coefficient, to characterize dynamics over time. This method is verified by molecular dynamics (MD) simulated colloidal system subjected to temperature change and a soft matter system under experimental conditions reported in the literature known for their complex non-equilibrium characteristics. The results reveal detailed dynamical information in non-equilibrium states and align with previous observation while providing enhanced vision of the dynamical processes. This approach represents an advancement in the dynamical analysis of soft matter systems, addressing the growing demand to extract intricate non-equilibrium dynamics.

* The research presented in this paper was supported by Basic Energy Sciences (BES) of Department of Energy and the Laboratory Directed Research and Development (LDRD) program of Argonne National Laboratory.

Presenters

  • Hongrui He

    Argonne National Laboratory

Authors

  • Hongrui He

    Argonne National Laboratory

  • Heyi Liang

    University of Chicago

  • Miaoqi Chu

    Argonne National Laboratory

  • Zhang Jiang

    Argonne National Laboratory

  • Juan J De Pablo

    University of Chicago

  • Matthew V Tirrell

    University of Chicago

  • Suresh Narayanan

    Argonne National laboratory

  • Wei Chen

    Argonne National Laboratory