A Model-free Method for Profiling Size Dispersity in Soft Condensed Matter Using Small-angle Scattering

ORAL

Abstract

In this work, we introduce a model-free approach for determining the size polydispersity of soft condensed matter through small-angle scattering techniques. We outline a strategy that utilizes the method of central moment expansion to extract the mean and fluctuation of particle size and skewness of the size distribution function (SDF) from spectral analysis in a bias-free manner. With the polydispersity being moderate, we can reconstruct the SDF using the maximum entropy principle. We demonstrate the validity of our analytical approach by numerically benchmarking a model study over a wide range of size non-uniformity. Our results show that this method is effective for quantifying the size distribution of general soft matter systems.

* This research used resources at the Spallation Neutron Source and Center for Nanophase Materials Sciences, two DOE Office of Science User Facilities operated by the Oak Ridge National Laboratory. G.R.H. is supported by the National Science and Technology Council (NSTC) in Taiwan with grant no. NSTC 111-2112-M-110-021-MY3. Y.W. was supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Early Career Research Program Award KC0402010, under contract no. DE-AC05-00OR22725. Y.S. was supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Materials and Science and Engineering Division. The helpful discussion with Christoph U. Wildgruber is gratefully acknowledged. We gratefully appreciate the D22 SANS beamtime from ILL. This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE).

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Publication: Macromolecules 56(16), 6436–6443 (2023).

Presenters

  • Guan-Rong Huang

    National Sun Yat-sen University

Authors

  • Guan-Rong Huang

    National Sun Yat-sen University

  • Chi-Huan Tung

    Natl Tsing Hua Univ

  • Lionel Porcar

    Institut Laue-Langevin

  • Yangyang Wang

    Oak Ridge National Lab, Oak Ridge National Laboratory

  • Yuya Shinohara

    Oak Ridge National Lab

  • Changwoo Do

    Oak Ridge National Lab

  • Wei-Ren Chen

    Oak Ridge National Lab