journal article·2024-04

Quantifying phase mixing and separation behaviors across length and time scales

M. Skolnick, S. Torquato

Acta Materialia· Volume 268
Publisher page

Abstract

Phase mixing and separation phenomena abound in the formation of natural and synthetic material systems, including alloys, composites, granular media, geological media, complex fluids, and biological media. While characterizing phase mixing is critical to understanding material microstructure formation and physical properties, previous metrics that measure the degree of mixing in condensed phases often rely on empirical observation, are applicable only to certain microstructures, and/or fail to detect nontrivial changes in mixing with respect to length scale. To overcome these shortcomings, we introduce and study a mixing metric that is applicable to a broad spectrum of complex microstructures and leverages the hyperuniformity concept to provide a unifying framework to rank and classify mixing in materials across both length and time scales. Specifically, we employ the local phase volume-fraction/field-intensity variance σ V / F 2 ( R , t ) associated with a spherical sampling window of radius R at time t as our metric. We demonstrate that σ V / F 2 ( R , t ) provides a sensitive measure of mixing across both length and time scales of a structurally diverse set of real and simulated material microstructures: exotic disordered multihyperuniform avian retina photoreceptor cell patterns, time-evolving spinodal decomposition patterns, and a model material mixed via the baker’s transformation. We also describe how this metric can be used to guide future work on fabricating materials whose bulk properties can be controlled by tuning their mixing characteristics across length and time scales.
Source: expertdata · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Theoretical and Computational Physics · Material Dynamics and Properties · Geological formations and processes

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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