4.7 Article

Melt-Mixed 3D Hierarchical Graphene/Polypropylene Nanocomposites with Low Electrical Percolation Threshold

Journal

NANOMATERIALS
Volume 9, Issue 12, Pages -

Publisher

MDPI
DOI: 10.3390/nano9121766

Keywords

graphene; polypropylene; electrical percolation; electrical conductivity; nanocomposites; melt mixing

Funding

  1. Swedish Foundation for Strategic Research (SSF) [SM15-0054, FFL15-0147]
  2. SIO Grafen Grants [2018-01475, 2018-03311, 2017-016774]
  3. Chalmers Area of Advance-Production
  4. Chalmers Area of Advance-Materials Science
  5. Borealis AB
  6. Swedish Foundation for Strategic Research (SSF) [SM15-0054, FFL15-0147] Funding Source: Swedish Foundation for Strategic Research (SSF)

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Graphene-based materials are a family of carbonaceous structures that can be produced using a variety of processes either from graphite or other precursors. These materials are typically a few layered sheets of graphene in the form of platelets and maintain some of the properties of pristine graphene (such as two-dimensional platelet shape, aspect ratio, and graphitic bonding). In this work we present melt mixed graphene-based polypropylene systems with significantly reduced percolation threshold. Traditionally melt-mixed systems suffer from poor dispersion that leads to high electrical percolation values. In contrast in our work, graphene was added into an isotactic polypropylene matrix, achieving an electrical percolation threshold of similar to 1 wt.%. This indicates that the filler dispersion process has been highly efficient, something that leads to the suppression of the beta phase that have a strong influence on the crystallization behavior and subsequent thermal and mechanical performance. The electrical percolation values obtained are comparable with reported solution mixed systems, despite the use of simple melt mixing protocols and the lack of any pre or post-treatment of the final compositions. The latter is of particular importance as the preparation method used in this work is industrially relevant and is readily scalable.

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