Morphological characteristics of self-assembled aggregate textures using multifractal analysis: Interpretation of Multifractal τ(q) Using Simulations

Yoshihiro Sato and Fumio Munakata

Physica A: Statistical Mechanics and its Applications, 2022, vol. 603, issue C

Abstract: In recent years, multifractal analysis has been used in various fields, such as medicine and urban development. In particular, multifractal analysis is expected to offer an understanding of the distribution of the morphology, arrangement, and dispersibility of dispersed particles due to the process of particle self-assembly. However, the numerical values obtained by multifractal analysis have not been quantitatively mapped to the filler arrangement, dispersion, and morphology. In this study, we generate simulated images that mimic self-assembled aggregates and clarify the interpretation of local (micro) and global (macro) changes in the system using multifractal analysis results τ(q). The results show that q < 0 in the τ(q)−q graph can be quantitatively evaluated as macro characteristics (aggregate network structure), whereas q > 0 can be quantitatively evaluated as micro characteristics (aggregate morphology and arrangement). Additionally, the occurrence and signs of percolation can be confirmed using macro- and micro-level internal energies. The same trend is observed from a comparison of a multifractal analysis of actual composite materials with the simulation results. The results of this study can also serve as an indicator in various fields where multifractal analysis is performed.

Keywords: Multifractal; Micro and macrotextures; Material texture; Aggregate; Particle distribution; Image analysis (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:603:y:2022:i:c:s0378437122005118

DOI: 10.1016/j.physa.2022.127771

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