Abstract
Conductive polymer composites (CPCs) are suitable as piezoresistive-sensing materials. When using CPCs for strain sensing, it is still a big challenge to simultaneously improve the piezoresistive sensitivity and linearity along with the electrical conductivity and mechanical properties. Here, highly tunable piezoresistive behavior is reported for multiwalled carbon nanotube (CNT)-filled CPCs based on blends of two semicrystalline polymers poly(vinylidene fluoride) (PVDF) and poly(butylene succinate) (PBS), which are miscible in the melt. When cooling the homogeneous mixture of the blend components, successive crystallization of PVDF and PBS occurs, creating complex crystalline structures in a mixed amorphous phase. The morphology of the blend matrix, the crystallinity of the blend components, and the dispersion and location of the CNTs in the blend depend on the CNT content and the blend composition. Compared with PVDF/CNT composites, the substitution of 10 to 50 wt % PVDF by PBS in the composites shifts the electrical percolation concentration Φc from 0.79 wt % to filler contents as low as 0.50 wt % while improving the stretchability. The piezoresistive behavior is highly tunable by changing the PVDF/PBS ratio. The ternary composites with matrix compositions of PVDF (90 wt %)/PBS (10 wt %) and PVDF (50 wt %)/PBS (50 wt %) show either higher piezoresistive sensitivity or linearity, respectively, caused by the differences in the microstructure of the CPCs. For example, the crystallinity of PBS in the ternary composites increased from 19.8% to 52.0% as the PBS content increased from 10 wt % to 50 wt %, which is connected with altered CNT distribution and conductive network structure and substantial improvement of the linearity of the electrical response to strains up to >20%. Our findings highly contribute to the understanding of the piezoresistive properties of CPCs based on two semicrystalline polymers and are important for future studies to tune the piezoresistive behavior to achieve simultaneously improved sensitivity and linearity.
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