Characterization of Carbon-Black-Based Nanocomposite Mixtures of Varying Dispersion for Improving Stochastic Model Fidelity

Abstract

Carbon black nanocomposites are complex systems that show potential for engineering applications. Understanding the influence of preparation methods on the engineering properties of these materials is critical for widespread deployment. In this study, the fidelity of a stochastic fractal aggregate placement algorithm is explored. A high-speed spin-coater is deployed for the creation of nanocomposite thin films of varying dispersion characteristics, which are imaged via light microscopy. Statistical analysis is performed and compared to 2D image statistics of stochastically generated RVEs with comparable volumetric properties. Correlations between simulation variables and image statistics are examined. Future and current works are discussed.

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

“…Mixtures were produced with ultrasonic homogenization energies 50 kJ and 100 kJ. These mixtures were spin coated and imaged via light microscopy to investigate dispersion characteristics via the methods described in the authors’ previous research [ 68 ]. Images from the spin-coated films are shown in Figure 3 a,b.…”

“…Stochastic CPC RVEs were generated with the aforementioned fractal aggregate-based stochastic modeling program. A set of CPC simulation parameters were maintained for the generation of high-fidelity RVEs, defined in accordance with the results of the model fidelity validation and tuning research presented in a previous article [ 68 ]. Relevant simulation parameters are as follows: Number of primary particles per fractal aggregate = 100, fractal dimension and prefactor of fractal aggregates = 2.7 and 0.7, diameter of primary particles = 30 nm, weight percentage of CB in CPC = 3.15%, percentage of aggregate particles placed at random = 0.028%, percentage of particles placed via aggregate forced agglomeration = 20%, maximum allowable degree of interparticle penetration = 5 nm, maximum tunneling distance considered numerically relevant = 3 nm.…”

“…The original derivation of the model by Zhang assumes the spherical conductive filler in the CPC is arranged in a cubic lattice for simplification of the mathematical calculation of the average interparticle tunneling distance ( ) [ 52 ]. The research previously presented by the authors provides evidence that this assumption is not appropriate for real mixtures due to their stochastic particle dispersions [ 68 ]. Instead, the cubic lattice assumption serves as a theoretical upper bound for such CPC systems with maximally ordered particle distributions of isotropic interparticle spacing ( ).…”

“…A stochastic modeling program was developed for purposes of studying the electromechanical properties of CB-based CPC materials [ 67 ]. Experimental results in the authors’ previous research garnered confidence in the ability of the model to accurately represent physical CPC specimens through optical microscopy-based model parameter tuning methods [ 68 ].…”

“…In this research, the stochastic modeling program developed by the authors in previous works [ 68 ] is used to generate predictions of CPC electromechanical properties. A simplified formulation of Simmons’ theory of tunneling conductivity is deployed to study the conductivity and piezoresistivity of CB-based CPC specimens that are compared directly to physical CPC specimens of whom the stochastically generated volumes are modeled.…”