Stretchable Ti<sub>3</sub>C<sub>2</sub>T<sub><i>x</i></sub> MXene/Carbon Nanotube Composite Based Strain Sensor with Ultrahigh Sensitivity and Tunable Sensing Range

Yichen Cai, Jie Shen, Gang Ge, Yizhou Zhang, Wanqin Jin, Wei Huang, Jinjun Shao, Jian Yang, Xiaochen Dong

ACS Nano · 2017 · 913 citations · 47 references

Concepts

TL;DR

Strain‑sensing materials that combine ultra‑high sensitivity, low detection limits, high stretchability, tunable range, and thin form factors remain difficult to fabricate. The authors engineered a percolation network of Ti₃C₂Tₓ MXene nanostacks woven with conductive, stretchable carbon nanotubes, creating a versatile strain‑sensor architecture. The resulting sensor detects deformations from 0.1 % to 130 % strain with a gauge factor of 772.6, stretchability up to 130 %, thickness below 2 µm, and retains performance over 5 000 cycles, making it suitable for wearable AI physiological monitoring.

Abstract

It remains challenging to fabricate strain-sensing materials and exquisite geometric constructions for integrating extraordinary sensitivity, low strain detectability, high stretchability, tunable sensing range, and thin device dimensions into a single type of strain sensor. A percolation network based on Ti3C2Tx MXene/carbon nanotube (CNT) composites was rationally designed and fabricated into versatile strain sensors. This weaving architecture with excellent electric properties combined the sensitive two-dimensional (2D) Ti3C2Tx MXene nanostacks with conductive and stretchable one-dimensional (1D) CNT crossing. The resulting strain sensor can be used to detect both tiny and large deformations with an ultralow detection limit of 0.1% strain, high stretchability (up to 130%), high sensitivity (gauge factor up to 772.6), tunable sensing range (30% to 130% strain), thin device dimensions (<2 μm), and excellent reliability and stability (>5000 cycles). The versatile and scalable Ti3C2Tx MXene/CNT strain sensors provide a promising route to future wearable artificial intelligence with comprehensive tracking ability of real-time and in situ physiological signals for health and sporting applications.

References

47