Publication | Closed Access
Correlations between Percolation Threshold, Dispersion State, and Aspect Ratio of Carbon Nanotubes
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Citations
20
References
2007
Year
EngineeringMechanical EngineeringNanostructured PolymerPolymer NanocompositesCarbon-based MaterialPolymer Nanostructured MaterialsNanoscale ModelingDispersion StatePolymer CompositesCarbon NanotubesMaterials ScienceNanoscale SystemPhysicsNanotechnologyPolymer Nanostructured CompositesOne-dimensional MaterialPercolation ThresholdDispersion ParametersNanomaterialsPolymer ScienceApplied PhysicsAspect RatioNanocompositesNanocomposite
The study investigates critical factors that determine the percolation threshold of carbon nanotube‑reinforced polymer nanocomposites. An analytical model incorporating interparticle distance and two dispersion parameters was developed and applied to CNT–epoxy nanocomposites fabricated under varied processing conditions to capture entangled bundle and well‑dispersed states. Percolation thresholds ranged from 0.1 to over 1.0 wt %, with dispersion parameters and CNT aspect ratios accounting for the variation, and key controlling factors were identified through model–experiment comparison.
Abstract Critical factors that determine the percolation threshold of carbon nanotube (CNT)‐reinforced polymer nanocomposites are studied. An improved analytical model is developed based on an interparticle distance concept. Two dispersion parameters are introduced in the model to correctly reflect the different dispersion states of CNTs in the matrix—entangled bundles and well‐dispersed individual CNTs. CNT–epoxy nanocomposites with different dispersion states are fabricated from the same constituent materials by employing different processing conditions. The corresponding percolation thresholds of the nanocomposites vary over a wide range, from 0.1 to greater than 1.0 wt %, and these variations are explained in terms of dispersion parameters and aspect ratios of CNTs. Important factors that control the percolation threshold of nanocomposites are identified based on the comparison between modeling data and experimental results.
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