Information thermodynamics of ternary fractal material texture: Application to BaTiO3/β-Si3N4/polyvinylidene fluoride composites

The thermodynamic information characteristics of fractal material structures, fabricated via a self-organization process, were analyzed using a ternary BaTiO3 (BT)/β-Si3N4 (SN)/polyvinylidene fluoride (PVDF) composite, notable for its combined thermal conductivity and dielectric properties. BT/SN/PVDF composites were prepared using (a) lamination, where prefabricated BT/PVDF and SN/PVDF melt sheets were alternately folded, and (b) simple mixing and kneading. To investigate the relationship between the materialographic characteristics and the material properties (dielectric properties and thermal conductivity) of self-assembled/self-organized fractal structures formed through the mixed diffusion of filler particles, the distribution of filler particle populations was analyzed via multifractal analysis. The resulting composite film texture was found to be process-independent, demonstrating a distinct microstructure where SN and BT formed separate aggregates. Notably, the mutual information, I, calculated using the information dimension D±1, revealed a strong correlation between the two filler particle groups. This indicates that like particles were attracted, while unlike particles were repelled, suggesting that the two fillers exist separately. These results suggest SN aggregates form a thermal conductive network, and BT aggregates contribute to high dielectric properties, each enhancing specific material properties. This study proposes a pathway to construct materials with independently controlled properties, offering a new design approach for multifunctional materials.

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Publication Details

Journal
Journal of Applied Physics
Published
2025-03-17
DOI
https://doi.org/10.1063/5.0248706
Primary Topic
Machine Learning in Materials Science
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article
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article

Information thermodynamics of ternary fractal material texture: Application to BaTiO3/β-Si3N4/polyvinylidene fluoride composites

Yoshihiro Sato, Fumio Munakata, Hitoshi Kawaji, Suguru Kitani et al.
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Journal of Applied Physics
Machine Learning in Materials Science
article

Information thermodynamics of ternary fractal material texture: Application to BaTiO3/β-Si3N4/polyvinylidene fluoride composites

Yoshihiro Sato, Fumio Munakata, Hitoshi Kawaji, Suguru Kitani, Taito Ogiya
article en

Abstract

The thermodynamic information characteristics of fractal material structures, fabricated via a self-organization process, were analyzed using a ternary BaTiO3 (BT)/β-Si3N4 (SN)/polyvinylidene fluoride (PVDF) composite, notable for its combined thermal conductivity and dielectric properties. BT/SN/PVDF composites were prepared using (a) lamination, where prefabricated BT/PVDF and SN/PVDF melt sheets were alternately folded, and (b) simple mixing and kneading. To investigate the relationship between the materialographic characteristics and the material properties (dielectric properties and thermal conductivity) of self-assembled/self-organized fractal structures formed through the mixed diffusion of filler particles, the distribution of filler particle populations was analyzed via multifractal analysis. The resulting composite film texture was found to be process-independent, demonstrating a distinct microstructure where SN and BT formed separate aggregates. Notably, the mutual information, I, calculated using the information dimension D±1, revealed a strong correlation between the two filler particle groups. This indicates that like particles were attracted, while unlike particles were repelled, suggesting that the two fillers exist separately. These results suggest SN aggregates form a thermal conductive network, and BT aggregates contribute to high dielectric properties, each enhancing specific material properties. This study proposes a pathway to construct materials with independently controlled properties, offering a new design approach for multifunctional materials.

Journal of Applied PhysicsVol. 137(11)
Tokyo City University (JP), Institute of Materials Science (VN), Institute of Science Tokyo (JP)
Openalex Percentile: Top 97%
Machine Learning in Materials Science
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