Electrically conductive strain sensing polyurethane nanocomposites with synergistic carbon nanotubes and graphene bifillers

TLDR

The study develops thermoplastic polyurethane conductive composites with a reduced percolation threshold and tunable resistance–strain sensing by incorporating synergistic carbon nanotube and graphene bifillers. Synergistic CNT–graphene networks, modeled by excluded volume theory, use graphene as spacers and CNTs as bridges to disperse fillers and form efficient conductive paths, while prestraining further tunes the network under larger strain. The composites exhibit a graphene percolation threshold of 0.006 vol % with 0.255 vol % CNT, single‑peak strain responses, excellent tunability, reversibility, and reproducibility under cyclic loading, demonstrating their promise as strain sensors.

Abstract

Thermoplastic polyurethane (TPU) based conductive polymer composites (CPCs) with a reduced percolation threshold and tunable resistance–strain sensing behavior were obtained through the addition of synergistic carbon nanotubes (CNT) and graphene bifillers. The percolation threshold of graphene was about 0.006 vol% when the CNT content was fixed at 0.255 vol% that is below the percolation threshold of CNT/TPU nanocomposites. The synergistic effect between graphene and CNT was identified using the excluded volume theory. Graphene acted as a 'spacer' to separate the entangled CNTs from each other and the CNT bridged the broad gap between individual graphene sheets, which was beneficial for the dispersion of CNT and formation of effective conductive paths, leading to better electrical conductivity at a lower conductive filler content. Compared with the dual-peak response pattern of the CNT/TPU based strain sensors, the CPCs with hybrid conductive fillers displayed single-peak response patterns under small strain, indicating good tunability with the synergistic effect of CNT and graphene. Under larger strain, prestraining was adopted to regulate the conductive network, and better tunable single-peak response patterns were also obtained. The CPCs also showed good reversibility and reproductivity under cyclic extension. This study paves the way for the fabrication of CPC based strain sensors with good tunability.