AFM-based model of percolation in graphene-based polymer nanocomposites

Julia Syurik, Natalya Alyabyeva, Alexander Alekseev, Oleg A. Ageev

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


Here, we show that a prediction of conductivity in composites can be improved by replacing fitting parameters of the percolation models by information on composite's microstructure. The methodology was demonstrated on the modified McCullough's structure-oriented model combined with current maps obtained by Conductive Atomic Force Microscopy (CA-AFM). The approach was tested on nanocomposites with graphene nanoplatelets (GNPs/PS) and proved to be coherent with experimental conductivity measurements and able to predict a percolation threshold. For the composite GNPs/PS both experimental and calculated percolation thresholds are approximately equal to 0.9. wt.% of GNPs. The model can be used for a prediction of conductivity of different kinds of conductive-dielectric composites.

Original languageEnglish
Pages (from-to)38-43
Number of pages6
JournalComposites Science and Technology
Publication statusPublished - May 1 2014


  • Atomic force microscopy (AFM)
  • Electrical properties
  • Modeling
  • Nanocomposites
  • Scanning electron microscopy (SEM)

ASJC Scopus subject areas

  • Ceramics and Composites
  • Engineering(all)

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