Quasi-1D Conductive Network Composites for Ultra-Sensitive Strain Sensing.

ABSTRACT

Highly performance flexible strain sensor is a crucial component for wearable devices, human-machine interfaces, and e-skins. However, the sensitivity of the strain sensor is highly limited by the strain range for large destruction of the conductive network. Here the quasi-1D conductive network (QCN) is proposed for the design of an ultra-sensitive strain sensor. The orientation of the conductive particles can effectively reduce the number of redundant percolative pathways in the conductive composites. The maximum sensitivity will reach the upper limit when the whole composite remains only "one" percolation pathway. Besides, the QCN structure can also confine the tunnel electron spread through the rigid inclusions which significantly enlarges the strain-resistance effect along the tensile direction. The strain sensor exhibits state-of-art performance including large gauge factor (862227), fast response time (24 ms), good durability (cycled 1000 times), and multi-mechanical sensing ability (compression, bending, shearing, air flow vibration, etc.). Finally, the QCN sensor can be exploited to realize the human-machine interface (HMI) application of acoustic signal recognition (instrument calibration) and spectrum restoration (voice parsing).