Investigating the Electromechanical Properties of Carbon Black-Based Conductive Polymer Composites via Stochastic Modeling

Albright, Tyler; Hobeck, Jared D.

doi:10.3390/nano13101641

Cited by 2 publications

(4 citation statements)

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“…For example, a simulation model was recently put forth by Albright and Hobeck (AH) based on a stochastic fractal aggregate placement algorithm (Figure 9). [78] In this model, a statistical analysis was performed and compared to 2D image statistics of stochastically RVE with comparable volume properties to extract information for quantifying dispersion quality of CB filled PNC. One key advantage of this approach was that microscopy images revealed similar statistical distributions of CB agglomerates.…”

Section: Conclusion and Some Future Directionsmentioning

confidence: 99%

“…For example, a simulation model was recently put forth by Albright and Hobeck (AH) based on a stochastic fractal aggregate placement algorithm ( Figure ). [ 78 ]…”

Section: Conclusion and Some Future Directionsmentioning

confidence: 99%

“…In this model, the resistance of electrically conductive pathways consists of intrinsic resistance between fused primary particles in aggregates and tunnelling resistance between non‐fused primary particles separated by a small distance by the polymer matrix. [ 78 ] Reproduced under the terms of the CC‐BY license. 2023, Albright and Hobeck, Published by MDPI.…”

Section: Conclusion and Some Future Directionsmentioning

confidence: 99%

“…3D CB microstructures simulated by a stochastic fractal aggregate placement algorithm: a) primary CB particle, b) aggregate of fused CB particles,and c) corresponding RVE. In this model, the resistance of electrically conductive pathways consists of intrinsic resistance between fused primary particles in aggregates and tunnelling resistance between non-fused primary particles separated by a small distance by the polymer matrix [78]. Reproduced under the terms of the CC-BY license.…”

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confidence: 99%

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Modeling the Interface Between Phases in Dense Polymer‐Carbon Black Nanoparticle Composites by Dielectric Spectroscopy: Where Are We Now and What are the Opportunities?

Brosseau

2024

Macro Theory & Simulations

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The macroscopic properties of polymer nanocomposites (PNC) rely largely on the interphase between the polymer chains and the filler particles. One significant difficulty to solve this issue is to quantitatively model the structure‐property correlations due to the interfacial region in these complex materials. While dielectric spectroscopy (DS) measurements are routinely used to characterize the effective permittivity of filled polymers, fitting standard effective medium models and mixing equations to these data remains notoriously difficult to interpret. This is due to the absence of explicit reference to internal length scales characterizing the interfaces in the PNC. As an illustrative example, a two‐level homogenization framework is proposed which enables the extraction of useful information on the impact of a thin interphase confined on a nanometer length scale based on broadband DS data. This model leads to new ways of tuning the interphase so as to optimize the material's response to electric field, a situation relevant for electromagnetic shielding. This approach provides guidance on how to observe directly and experimentally the actual properties of the interface between the phases (as opposed to model‐based inference). Aside from its secure physical foundation in the theory of effective medium, a significant advantage of this approach is that a genetic algorithm (GA) technique applied to this physics‐based model enables the uniqueness of the fit parameters to be considered, as the GA method is robust in terms of finding globally optimum solutions, therefore placing confidence in non‐universal values of the percolation exponents. Recent work in physics‐informed machine learning indicates that the effective dielectric properties of PNC with many degrees of freedom due to their complex morphology can be described by considering only a few degrees of freedom describing the interface features between the phases in these composites.

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Section: Conclusion and Some Future Directionsmentioning

confidence: 99%

“…For example, a simulation model was recently put forth by Albright and Hobeck (AH) based on a stochastic fractal aggregate placement algorithm ( Figure ). [ 78 ]…”

Section: Conclusion and Some Future Directionsmentioning

confidence: 99%

Section: Conclusion and Some Future Directionsmentioning

confidence: 99%

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confidence: 99%

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