Journal of Materials Science

, Volume 52, Issue 5, pp 2560–2580 | Cite as

The effect of geometric factor of carbon nanofillers on the electrical conductivity and electromagnetic interference shielding properties of poly(trimethylene terephthalate) composites: a comparative study

  • Chien-Lin Huang
  • Yu-Jyun Wang
  • Yang-Chun Fan
  • Chia-Lin Hung
  • Yu-Chia Liu
Original Paper


In this study, the effects of filler geometry on the electrical conductivity and electromagnetic interference (EMI) shielding properties of poly(trimethylene terephthalate) (PTT) composites filled with graphene nanosheets (GNSs), carbon nanotubes (CNTs), and GNS–CNT hybrid nanofillers have been investigated. The GNSs, CNTs, and hybrid GNS–CNT were well dispersed in the PTT matrix using a simple coagulation process. GNSs were prepared from graphene oxide (GO) through hydrazine reduction, and thermal reduction of GO at two different temperatures of 1050 and 1500 °C. PTT filled with different aspect ratios and oxygen functional groups of GNS were also prepared in order to compare the electrical conductivity and EMI shielding properties. The aspect ratios of GNSs and CNTs were estimated by using an ellipsoid model. Percolation scaling laws were applied to the magnitudes of conductivity to reveal the percolation network and filler dispersion. The percolation exponent of the PTT/GNS composites was larger than that of the PTT/CNT composites. The percolated filler–filler network at which the percolation exponent changed was correlated with the filler geometric structure. GNS–CNT hybrid nanofillers formed a complex double brush structure in the PTT/GNS–CNT composites. The geometric structure, aspect ratio, and intrinsic conductivity of carbon nanofillers affected the electrical percolation threshold and EMI shielding efficiency of the composites.


Graphene Oxide Oxygen Functional Group Trimethylene Terephthalate Carbon Nanofillers Electrical Percolation Threshold 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors would like to thank the National Science Council of Taiwan (ROC) for the research Grant (NSC 101-2218-E-035-006-) that supported this work. The authors also appreciate the Precision Instrument Support Center of Feng Chia University and Prof. C. Wang in NCKU for providing the fabrication and measurement facilities.

Supplementary material

10853_2016_549_MOESM1_ESM.doc (6.8 mb)
Supplementary material 1 (DOC 6957 kb)


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Chien-Lin Huang
    • 1
  • Yu-Jyun Wang
    • 1
  • Yang-Chun Fan
    • 1
  • Chia-Lin Hung
    • 1
  • Yu-Chia Liu
    • 2
  1. 1.Department of Fiber and Composite MaterialsFeng Chia UniversityTaichungTaiwan
  2. 2.Department of Chemical EngineeringNational Cheng Kung UniversityTainanTaiwan

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