Abstract

We developed a simple, low-cost process to fabricate a flexible pressure sensor with linear sensitivity by using a porous carbon nanotube (CNT)/polydimethylsiloxane (PDMS) composite structure (CPCS). The working principle of this pressure sensor is based on the change in electrical resistance caused by the contact/non-contact of the CNT tip on the surface of the pores under pressure. The mechanical and electrical properties of the CPCSs could be quantitatively controlled by adjusting the concentration of CNTs. The fabricated flexible pressure sensor showed linear sensitivity and excellent performance with regard to repeatability, hysteresis, and reliability. Furthermore, we showed that the sensor could be applied for human motion detection, even when attached to curved surfaces.

Highlights

  • The development of flexible pressure sensors is gaining attention because of the requirements of the generation of wearable systems

  • We propose a facile yet effective method to improve the sensor performance, in·terms of sensitivity and linearity, of flexible pressure sensors based on a porous carbon nanotube (CNT)—polydimethylsiloxane (PDMS) composite structure (CPCS)

  • After filling the empty the sugar cube with the PDMS precursor, the PDMS precursor was cured in an oven

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Summary

Introduction

The development of flexible pressure sensors is gaining attention because of the requirements of the generation of wearable systems. To use flexible sensors for wearable systems, they should be flexible and elastic so that they can be used on curved surfaces. To fulfill these requirements, polymer-based flexible sensors have been investigated. Previous studies have reported polymer-based flexible sensors utilizing piezoresistive [9,10,11,12,13], capacitive [14,15,16,17], piezoelectric [18,19], and triboelectric sensing technologies [20,21]. Piezoresistive devices, which transduce pressure into a resistance signal, have been widely used because of their cost-effective fabrication, simple working principle, and easy signal processing

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