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Percolation mechanism and effective conductivity of mechanically deformed 3-dimensional composite networks: Computational modeling and experimental verification

Composites Part B: Engineering, ISSN: 1359-8368, Vol: 207, Page: 108552
2021
  • 56
    Citations
  • 0
    Usage
  • 46
    Captures
  • 0
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    56
    • Citation Indexes
      56
  • Captures
    46

Article Description

In this work, the structural evolution of conductive polymer composites (CPCs) in response to mechanical deformation (uniaxial and biaxial compressive and tensile strains) is theoretically modeled and experimentally verified. The structural responses in mechanically deformed CPCs were simulated by incorporating the corresponding topological changes in representative volume element (RVE) and embedded filler networks. The percolating filler networks were then modeled as an equivalent electrical circuit consisting of tunneling and intrinsic resistances to examine the effect of deformation on the percolation threshold and the effective electrical conductivity. The results revealed that the filler alignment caused by strain changed both the vertical and lateral percolation thresholds, albeit with different trends. With an increase of uniaxial tensile (or equivalently, biaxial compressive) strain, applied on the vertical direction, the vertical percolation threshold initially reached a minimum value before rising, while the lateral percolation threshold monotonically increased. On the other hand, following incremental uniaxial compression (or equivalently, biaxial tension), the lateral percolation threshold reached a minimum value before increasing, while the vertical percolation threshold monotonically increased. The same relationship was observed in CPCs containing 1D fillers with different aspect ratios. The validity of the theoretical models was verified by comparing the predicted electrical conductivity values with the experimentally observed data obtained from polypropylene - multiwalled carbon nanotube nanocomposites.

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