4.7 Article

Rational Design of Graphene Surface Chemistry for High-Performance Rubber/Graphene Composites

Journal

MACROMOLECULES
Volume 47, Issue 24, Pages 8663-8673

Publisher

AMER CHEMICAL SOC
DOI: 10.1021/ma502201e

Keywords

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Funding

  1. National Basic Research Program of China [2015CB654703]
  2. National Natural Science Foundation of China [51222301, 51221002, 51320105012]
  3. Fundamental Research Project for the Central Universities [2014ZG0001]

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In a rubber/filler composite, the surface chemistry of the filler is a critical factor in determining the properties of the composite because it affects the dispersion of the filler and the interfacial adhesion between the filler and rubber. In this study, we primarily focus on how graphene surface chemistry affects the dispersion of graphene and interfacial adhesion in butadienestyrene rubber (SBR)/graphene composites and on the resultant properties of the composites. Composites that contain graphene with tailored surface chemistry are prepared via the chemical reduction of graphene oxide (GO) in situ. Subsequently, the dispersion of the graphene and interfacial adhesion are fully investigated in relation to the graphene surface chemistry. As revealed by dielectric relaxation spectroscopy, the bulk segmental relaxation is independent of the graphene surface chemistry, whereas the interfacial relaxation mode is retarded in the composite with stronger graphenerubber affinity. The contribution of the graphene surface chemistry to the dispersion of the graphene and interfacial adhesion is quantified by calculating the surface energies. The results indicate that, when the COx fraction in the graphene is greater than 0.2, it exerts an increasingly strong effect on the dispersion of the graphene; in contrast, when the COx fraction is less than 0.2, it exerts a significant and positive effect on the interfacial interaction. In particular, on the basis of the surface energy analysis, quantitative predictors for the dispersion of graphene and interfacial adhesion are presented; these predictors can be used for the virtual design of graphene surface chemistry to optimize the properties of composites.

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