Shape memory-based tunable resistivity of polymer composites

Abstract

A conductive composite in bi-layer structure was fabricated by embedding hybrid nanofillers, namely carbon nanotubes (CNTs) and silver nanoparticles (AgNPs), into a shape memory polyurethane (SMPU). The CNT/AgNP-SMPU composites exhibited a novel tunable conductivity which could be facially tailored in wide range via the compositions or a specifically designed thermo-mechanical shape memory programming. The morphologies of the conductive fillers and the composites were investigated by scanning electron microscope (SEM). The mechanical and thermal measurements were performed by tensile tests and differential scanning calorimetry (DSC). By virtue of a specifically explored shape memory programming, the composites were stretched and fixed into different temporary states. The electrical resistivity (Rs) varied accordingly, which was able to be stabilized along with the shape fixing. Theoretical prediction based upon the tunneling model was performed. The Rs–strain curves of the composites with different compositions were well fitted. Furthermore, the relative resistivity and the Gauge factor along with the elongation were calculated. The influence of the compositions on the strain-dependent Rs was disclosed. The findings provided a new avenue to tailor the conductivity of the polymeric nano-composites by combining the composition method and a thermo-mechanical programming, which may greatly benefit the application of intelligent polymers in flexible electronics and sensors fields.