期刊
ACS APPLIED MATERIALS & INTERFACES
卷 12, 期 8, 页码 9682-9692出版社
AMER CHEMICAL SOC
DOI: 10.1021/acsami.9b22787
关键词
graphene-polymer nanocomposites; segregated network; liquid-phase self-assembly; strain sensor
资金
- Key Program for International Science and Technology Innovation Cooperation between China and Italy Government [2016YFE0104000]
A three-dimensional graphene (GE) segregated network structure is of significance for improving the conductivity of composites. However, constructing such a GE network structure in composites still remains a challenge. Here, we demonstrate a facile process, that is, liquid-phase redispersion and self-assembly (LRS) to prepare polymer nanocomposites with graphene segregated networks. High shear liquid-phase mixing accompanied by the diffusion of dissolved polymer chains into the interstices and voids of the loose graphene powders can lead to redispersion of GE in polymer solution. Once the stirring is stopped, the self-assembly and segregation of redispersed GE occurs in a poor solvent driven by pi-pi interaction. After solvent evaporation, the GE assembly structures are retained as networks in the GE/polymer composite prepared by hot pressing. The graphene/(isobutylene-isoprene rubber) nanocomposite (GE/IIR) was investigated as a demonstration for the advantages of the LSR method. The morphologies of GE assemblies in the liquid phase and GE networks in the solid composite were observed. Due to the existence of the homogeneously distributed graphene segregated networks, the tensile strength and elongation at break for GE/IIR nanocomposites increase by similar to 410 and similar to 126%, respectively, and the electrical conductivity reaches similar to 10(0) S m(-1) at a GE content of 3.76 vol %. The LRS method was also successfully tried for systems with different polymer matrixes and different solvents, suggesting the robustness of the proposed method. The prepared flexible GE/IIR nanocomposites with GE networks are sensitive to tiny strain and can be applied in wearable sensors for the detection of human physiological signals.
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