Multifractal Analysis of Particle Dispersion and Interphase Percolation in Nanocomposites

Karasinski, Eduardo do Nascimento; Sasse, F. D.; Coelho, Luci Boa Nova

doi:10.1590/1980-5373-mr-2018-0265

Cited by 7 publications

(9 citation statements)

References 27 publications

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“…One may define the interphase as a zone of transition between the nanoparticle interface and the polymer bulk, which can lead to a change of morphological, chemical, mechanical, and physical properties of the final material, differing from the bulk or the isolated nanoparticle, which, may appear as an unexpected or unwanted characteristic in nanocomposites [2][3][4][5] .…”

Section: Introductionmentioning

confidence: 99%

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Percolation Threshold and Depression in Properties of Polymer Nanocomposites

et al. 2022

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Percolation threshold is an important phenomenon to be addressed when producing nanocomposites, especially because the literature suggests a depression of properties near this region. In this study, epoxy matrix nanocomposites were produced with different volume fractions of multi-walled carbon nanotubes and were characterized according to their electrical, thermal, and mechanical properties. In addition, digital image correlation (DIC) was used to measure the strain of nanocomposites and to show how it behaves in different percolation states. Electrical conductivity indicated a percolation threshold near 0.22% v/v of nanoparticles. Differential scanning calorimetry analysis showed a depression followed by an increase in glass transition temperature near the percolation threshold. Tensile strength tests presented a depression followed by an increasing near percolation threshold. DIC images showed that nanocomposites present a different behavior when near the percolation threshold, with a more distributed strain over the surface of the sample under stress and fracture toughness decreased near the percolation threshold.

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Section: Introductionmentioning

confidence: 99%

“…Thus, nanocomposites may present distinct properties before, after, or near the percolation threshold 8 . Hence, the percolation threshold influences directly the final properties of nanocomposites 4,5,9 .…”

Section: Introductionmentioning

confidence: 99%

Percolation Threshold and Depression in Properties of Polymer Nanocomposites

et al. 2022

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“…Different applications of multifractal analysis in process engineering have been reported 26–29. Karasinski et al.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Different applications of multifractal analysis in process engineering have been reported [26][27][28][29]. Karasinski et al [27] applied multifractal analysis to binary images to quantify the degree of particle dispersion in nanocomposites. The authors were also able to predict the properties of nanocomposites due to temperature and concentration modifications.…”

Section: Introductionmentioning

confidence: 99%

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Diagnosis of the Cane Sugar Crystallization Process by Multifractal Analysis of Temperature Time Series

et al. 2021

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The monitoring and diagnosis of crystallization processes are difficult due to the interaction of nucleation and crystal growth phenomena. In recent years, image and time series analysis using fractal methodologies showed potential as an alternative for monitoring crystal growth, although the available results are scarce. In this work, the multifractal detrended fluctuation analysis (MF‐DFA) was applied to temperature time series obtained from a laboratory‐scale cane sugar crystallization operated at different operating conditions. MF‐DFA reflects that the crystallization process exhibits multifractal properties associated with the dynamic behavior of the underlying phenomena. Thus, multifractal analysis can identify how operational changes influence the crystal growth and formed crystal mass.

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Automated damage detection and characterization of polymer composite images using Tsallis‐particle swarm optimization‐based multilevel threshold and multifractals

Ronickom

Sobhana²,

Robert

et al. 2020

Polymer Composites

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Automatic detection and quantification of damage in the composite structure are a vital requirement in the assessment of the overall structural integrity of modern aerospace systems. In this work, the indentation‐induced damage in the glass fiber‐reinforced polymer composite (GFRP) laminates is investigated using multilevel threshold‐based particle swarm optimization (PSO) and multifractals. Initially, the digital images are acquired after the composite laminates are subjected to 5 mm, 6 mm, and 7 mm indentation displacements. The indentation‐induced damage images are filtered using anisotropic diffusion filter and damage regions are segmented using Tsallis‐PSO method. The magnitude of the damage is analyzed using multifractal spectrum features. Results show that Tsallis‐PSO with the four‐level thresholds is optimal for the segmentation of the damage. Tsallis‐PSO is also able to segment the boundaries of the damage precisely. The damage due to different levels of indentation depth is well differentiated by the multifractal spectrum features. Multifractal spectrum constructed using the scale, ranges from q = −1 to +1, discriminates the damage with a high statistical significance of P < .001. The damage dimension linearly progresses (R = .993) with increasing the level of indentation depth. Even though, the proposed process pipeline is an initial step of investigation of damages in GFRP, this framework can be used in the industries for the quantification of damage in GFRP composite laminates.

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Multifractal Analysis of Particle Dispersion and Interphase Percolation in Nanocomposites

Cited by 7 publications

References 27 publications

Percolation Threshold and Depression in Properties of Polymer Nanocomposites

Percolation Threshold and Depression in Properties of Polymer Nanocomposites

Diagnosis of the Cane Sugar Crystallization Process by Multifractal Analysis of Temperature Time Series

Automated damage detection and characterization of polymer composite images using Tsallis‐particle swarm optimization‐based multilevel threshold and multifractals

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