Hybrid nanocomposites reinforced with a mixture of graphene nanoplatelets (GNPs) and carbon nanotubes (CNTs) have shown improvement in filler dispersion while providing a cost-effective alternative to CNT monofiller composites. Depending on their composition, hybrid composites can exhibit electrical performance superior to either of the constituent monofiller composites due to synergistic effects. In this work, we develop a three-dimensional tunneling-based continuum percolation model for hybrid nanocomposites filled with hardcore particles of elliptical GNPs and cylindrical CNTs. Using Monte Carlo simulations, parametric studies of the filler content, composition and morphology are carried out to analyze the conditions required for synergy in percolation onset and electrical conductivity. Our results suggest that for hybrid systems with well-dispersed fillers, the electrical performance is linked to the number of tunneling junctions per filler inside the percolated network of the nanocomposites. More importantly, hybrid composites filled with specific morphology of GNP and CNT, exhibit synergy in their electrical performance when the monofiller composites of each of those exact fillers have similar percolation onset values. The simulations results are in agreement with relevant experimental data on hybrid nanocomposites.
The sensitivity of the electronic properties of carbon nanotubes to gases, chemicals, temperature, and mechanical strain enables their use as fillers in nanocomposites for sensing applications. In this paper, the authors develop a low-cost and scalable process based on inkjet printing technology to fabricate printed flexible sensors used for strain and damage detection. A well-dispersed conductive water-based ink is fabricated with functionalized multiwall carbon nanotubes (MWCNT) and deposited onto paper and Kapton substrates to obtain a sheet resistance as low as 500 Ω sq −1 with about 30 printed layers. The number of printed layers, the direction of the electrical resistance measurement, and the type of substrate have clear effects on the sensor's electrical performances related to the detection of mechanical strain and impact damage. This work demonstrates the effectiveness of the printed sensors for micrometeoroid and orbital debris (MMOD) impact damage detection through hypervelocity testing.
Hybrid nanocomposites with multiple fillers like carbon nanotubes (CNT) and graphene nanoplatelets (GNP) are known to exhibit improved electrical and electromechanical performance when compared to monofiller composites. We developed a two-dimensional Monte Carlo percolation network model for hybrid nanocomposite with CNT and GNP fillers and utilized it to study the electrical conductivity and piezoresistivity as a function of nanocomposite microstructural variations. The filler intersections are modeled considering electron tunneling as the mechanism for electrical percolation. Network modification after elastic deformation is utilized to model the nanocomposite piezoresistive behavior. Systematic improvement in electrical conductivity and piezoresistivity was observed in the hybrid nanocomposites, compared to monofiller CNT nanocomposites. Parametric studies have been performed to show the effect of GNP content, size, aspect ratio, and alignment on the percolation threshold, the conductivity, and piezoresistivity of hybrid CNT–GNP polymer composites.
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