# A Flexible Piezoresistive Sensor Based on ZnO/MWCNTs/PDMS Composite Foam with Overall Performance Trade-Offs

## TL;DR

A new flexible sensor made from a composite foam can detect strain effectively and is suitable for wearable technology applications.

## Contribution

A dual-filler strategy with ZnO and MWCNTs in PDMS foam improves sensor performance for wearable strain detection.

## Key findings

- The sensor has a wide linear detection range (0–80% strain) and high sensitivity of 9.02 kPa−1.
- It shows rapid response/recovery times of 50/70 ms and stability after 5000 compression cycles.
- The sensor can detect human motion and vibrations for applications like gait analysis and vocal fold recognition.

## Abstract

The flexible foam piezoresistive sensor demonstrates significant potential for wearable strain-sensing applications due to its substantial deformation capacity, excellent flexibility, and cost effectiveness. However, conventional flexible foam piezoresistive sensors often struggle to simultaneously achieve high sensitivity, a wide pressure detection range, fast response and long-term stability. This paper employed a glucose-based sugar-templating method to fabricate a fine-pore (50 μm) foam structure complemented by a dual-filler strategy to enhance overall performance. A robust porous conductive network was constructed by embedding zinc oxide (ZnO) and multi-walled carbon nanotubes (MWCNTs) into a polydimethylsiloxane (PDMS) matrix. The resulting sensor exhibits outstanding piezoresistive properties, featuring a wide linear detection range (0–80% strain) and a high sensitivity of 9.02 kPa−1 within the 0–10 kPa pressure range. It demonstrates rapid response/recovery times of 50/70 ms and maintains stable output performance even after 5000 compression cycles at 300 kPa. The sensor also exhibits negligible environmental interference and excellent long-term stability. When attached to finger joints, feet soles, or the throat, the sensor enables functions such as finger bending recognition, race-walking violation discrimination, gait analysis, and vocal fold vibration recognition, thereby demonstrating its considerable potential for application in human–computer interaction and human motion detection.