High-fidelity stochastic modeling of carbon black-based conductive polymer composites for strain and fatigue sensing

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

“…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 convergence of the model’s conductivity predictions with respect to RVE cubic length is shown in Figure 5 c. This trend is consistent with previously demonstrated simulation convergence behaviors observed with RVEs of perfect dispersion [ 67 ]. In contrast to the perfect dispersion convergence threshold, which was estimated at a RVE cubic length ≈ 6200 nm, convergence was estimated for this set of simulation parameters at a RVE cubic length ≈ 12,000 nm.…”

“…The convergence of the model's conductivity predictions with respect to RVE cubic length is shown in Fig- ure 5c. This trend is consistent with previously demonstrated simulation convergence behaviors observed with RVEs of perfect dispersion [67]. In contrast to the perfect dispersion convergence threshold, which was estimated at a RVE cubic length ≈ 6200 nm, Table 1.…”

“…These methods utilize the capabilities of high-performance computing systems to create representative volume elements (RVEs) composed of conductive particles suspended in a polymer that is not discretely modeled. 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 ].…”

“…Local strain values are then calculated and applied to the interparticle distance calculations. New interparticle relationships are defined, and a modified resistive network is formed where the resulting RVE resistance is calculated via the methods described in the authors’ previous research [ 6 , 67 ].…”

Enhancing Part-to-Part Repeatability of Force-Sensing Resistors Using a Lean Six Sigma Approach

“…However, one of the most important drawbacks of FSRs, which has not been yet addressed by specific literature, is the inability to know sensor sensitivity a priori. Given the random dispersion of conductive nanoparticles along the insulating polymer matrix [33], it is not possible to determine the resulting sensitivity of a given nanocomposite, i.e., every specimen has a different sensitivity. This characteristic limits the extensive usage of FSRs since individual sensor calibration is required before use.…”