• 文献标题:   Rheological and conductivity percolations of syndiotactic polystyrene composites filled with graphene nanosheets and carbon nanotubes: A comparative study
  • 文献类型:   Article
  • 作  者:   CHIU YC, HUANG CL, WANG C
  • 作者关键词:   syndiotactic polystyrene, graphene nanosheet, carbon nanotube, rheological propertie, electrical conductivity, percolation
  • 出版物名称:   COMPOSITES SCIENCE TECHNOLOGY
  • ISSN:   0266-3538 EI 1879-1050
  • 通讯作者地址:   Natl Cheng Kung Univ
  • 被引频次:   18
  • DOI:   10.1016/j.compscitech.2016.08.016
  • 出版年:   2016

▎ 摘  要

Two-dimensional graphene nanosheets (GNSs) and one-dimensional carbon nanotubes (CNTs) possess large aspect ratios but different geometries. In this study, syndiotactic polystyrene (sPS) composites reinforced with different contents of GNSs were prepared via a solution coagulation method. The dispersion and microstructure of GNSs in sPS were confirmed via transmission electron microscopy and atomic force microscopy. Rheological and electrical properties of sPS/GNS composites were investigated to reveal the effect of filler concentration, and the results were compared with those of sPS/CNT composites. Percolation scaling laws were applied to the magnitudes of storage modulus and electrical conductivity to determine the threshold concentration and corresponding exponent The sPS/GNS and sPS/CNT composites possess a similar percolation threshold for melt elasticity and solid conductivity. However, the rheological threshold (similar to 0.1 vol%) is distinctly lower than the conductivity threshold (similar to 0.5 vol%) because of the formation of polymer nanofiller hybrid network. The exponents for rheological and electrical percolations of sPS/GNS composites are determined as 4.12 and 4.71, respectively, which are higher than the corresponding values of 2.64 and 2.87 for sPS/CNT composites. The derived exponents indicate that the 2D GNS-related network is more effective than the 1D CNT-related network for enhancing the melt elasticity and solid conductivity of sPS matrix. (C) 2016 Elsevier Ltd. All rights reserved.