Quantitative evaluation of morphological characteristics of self-assembled aggregates using multifractal analysis

Sato, Yoshihito; Mizukami, Yuka; Takeda, Mariko; Okubo, Kazuaki; Kobayashi, Ryota; Munakata, Fumio

doi:10.1016/j.physa.2021.126219

Cited by 6 publications

(1 citation statement)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is because when estimating the interaction energy (E int. ) between particle groups using multifractal analysis, [7,10,26,27] E int. between the SN particle groups [10] tends to increase with the addition amount; however, E int.…”

Section: Resultsmentioning

confidence: 99%

Thermal conductivities and complex network properties of fractal self-assembled/self-organized texture in binary composite materials

Munakata,

Ogiya,

Sato

2024

Appl. Phys. A

View full text Add to dashboard Cite

To investigate the relationship between histological properties and thermal conductivities of binary self-assembled/self-organized fractal materials fabricated by the mixed diffusion of filler particles, the particle group distribution of the filler particles was analyzed. Unlike conventional research on particle size distribution, we performed an analysis that focuses on interactions and connectivity between particles from the perspective of the Barabasi-Albert model. In three complex network systems of β-Si3N4 (SN)/austenitic stainless steel (SUS), SN/piezoelectric polyvinylidene fluoride (PVDF), and BaTiO3 (BT)/PVDF, the size distribution of secondary particles was a power-law distribution until the particle group grew relatively large. In the SN/SUS and SN/PVDF systems, a crossover phenomenon was observed in which the order distribution exhibited a power-law in the region where the particle group area was small, and as the particle group became larger, the order distribution became exponential distribution. On the other hand, the BT/PVDF system deviated from the power law distribution as the particle group area increased but did not exhibit an exponential distribution. The characteristics of the particle group distribution of these filler particles were closely related to thermal conductivity, indicating that the connection of particle groups was important. Furthermore, the relationship between the multifractal capacitance dimension, which is strongly correlated with the distance between the particle groups, and the total number of filler particle groups suggests that the texture of these composite material systems may have both small-world and scale-free properties. We also focused on the relationship between the multifractal and complex network properties of the three particle groups. As a result, a correlation was found between the slope γ, which reflects the characteristics of the power distribution, and the thermodynamic parameter α (corresponding to internal energy change) obtained from multifractals. In particular, the smaller the change in γ relative to the change in α, the more pronounced the bonding of particles and particle groups, while the larger change in γ suggested the dispersion of particle groups.

show abstract

Section: Resultsmentioning

confidence: 99%

Thermal conductivities and complex network properties of fractal self-assembled/self-organized texture in binary composite materials

Munakata,

Ogiya,

Sato

2024

Appl. Phys. A

View full text Add to dashboard Cite

show abstract

Preparation and performance optimization in high‐voltage cable semiconductive shielding layer for polyolefin composite based on multifractal analysis

Zhang,

Liu

et al. 2023

Polymer Composites

View full text Add to dashboard Cite

In this study, the semiconductive shielding composite was prepared successfully using the melt mixing method. The effects of combining multiwall carbon nanotubes (MWCNT) and carbon black (CB) into polyolefin plasticizers/ethylene vinyl acetate/linear low density polyethylene matrix on the mechanical performances, electrical conductivity, and processing rheological properties of the samples were investigated. The research results indicate that adding MWCNT to the prepared semiconductive shielding composite can form a complete conductive network, and its synergistic effect with CB jointly improves the conductivity of the composite material. Compared with the absence of MWCNT, its bulk resistivity decreased from 1.61 W • cm to 0.89 W • cm, a decrease of 44.7%. At the same time, the prepared semiconductive shielding composite has excellent mechanical and processing performances. The elongation at break, maximum tensile strength, and elastic modulus of the composites were increased by 30.9%, 17.9%, and 4.9%, respectively. Based on multifractal analysis, by adjusting the content of MWCNT in composite and optimized processing paths, the distribution and microstructure of MWCNT in composite can be optimized to obtain adjustable mechanical and processing rheological properties. This study presents a guiding approach for designing and developing functional composites requiring adjustable mechanical performances.Highlights The semiconductive shielding composite was prepared successfully. The prepared composite has excellent mechanical and processing performances. Multiwall carbon nanotubes with carbon black can form conductive network. The microstructure can be optimized by quantitative characterization of the morphology.

show abstract

Sandbox edge-based algorithm for multifractal analysis of complex networks

Zhao

Liu

Zhou

2023

Chaos, Solitons & Fractals

View full text Add to dashboard Cite

Quantitative evaluation of morphological characteristics of self-assembled aggregates using multifractal analysis

Cited by 6 publications

References 15 publications

Thermal conductivities and complex network properties of fractal self-assembled/self-organized texture in binary composite materials

Thermal conductivities and complex network properties of fractal self-assembled/self-organized texture in binary composite materials

Preparation and performance optimization in high‐voltage cable semiconductive shielding layer for polyolefin composite based on multifractal analysis

Sandbox edge-based algorithm for multifractal analysis of complex networks

Contact Info

Product

Resources

About