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Title: Generation and Structural Characterization of Non-Hyperuniform and Hyperuniform Disordered Systems
Authors: Ma, Zheng
Advisors: Torquato, Salvatore
Contributors: Physics Department
Keywords: Hyperuniformity
Soft Matter
Subjects: Condensed matter physics
Statistical physics
Issue Date: 2020
Publisher: Princeton, NJ : Princeton University
Abstract: This dissertation focuses on the generation of certain special disordered systems, as well as general tools that have been developed to characterize their microstructures. Particular attention is devoted to disordered hyperuniform systems, which are exotic amorphous states of matter that lie between crystals and liquids. In the first part of the dissertation (Chapters 2-4), we report progress on novel methods developed for constructing disordered hyperuniform systems. Importantly, many of these methods are experimentally realizable. In Chapter 2, we generalize the hyperuniformity concept to characterize scalar fields and explicitly construct hyperuniform scalar fields from spatial patterns generated from Gaussian random fields, the Cahn-Hilliard equation, and the Swift-Hohenberg equation. In Chapter 3, we study emergent hyperuniformity in the random organization model, which is a nonequilibrium particle system. We generalize the model to particles with a size distribution and/or nonspherical shapes and find that their critical states are hyperuniform as two-phase media. In Chapter 4, we propose a feasible equilibrium protocol to fabricate hyperuniform materials using binary paramagnetic colloidal particles confined in a 2D plane. Specifically, we numerically find a family of optimal size ratios that makes the two-phase system effectively hyperuniform. In the second part of the dissertation (Chapters 5-7), we present computational tools developed for characterizing microstructures of general disordered systems and their applications. In Chapter 5, we devise algorithms to compute surface correlation functions. Our approach overcomes the current technical difficulty involved in sampling these functions, which has been a stumbling block in their widespread use. In Chapter 6, we apply the algorithms developed in Chapter 5 as well as other popular microstructural descriptors (e.g., lineal-path function) to characterize Debye random media. Importantly, we also devise accurate semi-analytic and empirical formulas for these descriptors. In Chapter 7, we show that these microstructural descriptors can be fed into a statistical learning pipeline to predict permeabilities of porous media.
Alternate format: The Mudd Manuscript Library retains one bound copy of each dissertation. Search for these copies in the library's main catalog:
Type of Material: Academic dissertations (Ph.D.)
Language: en
Appears in Collections:Physics

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