ACS Nano · 2017 · 913 citations · 47 references
EngineeringMaterials EngineeringMaterials ScienceNanomaterialsNanotechnologySensorsCarbon NanotubesNanocompositesNanotubesSensor MaterialsSoft SensorsStrain SensorsCarbon Nanotube NetworksCarbon Nanotube SensorsCarbon Nanotube Strain SensorsNanomaterial-based Strain SensorsPercolationStrain Sensor MaterialsStrain-sensing MaterialsStretchable Strain Sensors
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.
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.
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