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High-Performance Flexible Strain Sensor Based on Thermoplastic Polyurethane Melt-Blown Nonwoven with Molybdenum Disulfide for Human Motion Monitoring

热塑性聚氨酯 材料科学 聚氨酯 二硫化钼 热塑性塑料 复合材料 二硫键 弹性体 化学 冶金 生物化学
作者
Hui Sun,Youxiu Xie,Xinyu Liu,G.L. Chen,Fengchun Li,Lei Xu,Bin Yu
出处
期刊:ACS applied electronic materials [American Chemical Society]
卷期号:6 (10): 7551-7562 被引量:10
标识
DOI:10.1021/acsaelm.4c01385
摘要

Flexible wearable strain sensors have received great attention due to the wide applications in human motion monitoring, human–machine interfaces, and artificial intelligence robots. Thermoplastic elastic polymer films and fabrics are often used as their substrates. Thermoplastic polyurethane melt-blown nonwoven (TPU MB) can be considered as a substrate because of some advantages over these materials in terms of simple fabrication process, low price, and good breathability. Molybdenum disulfide (MoS2), a member of transition metal dichalcogenides, is a promising candidate for next-generation flexible sensing devices due to its unique semiconductor essence and outstanding mechanical strength. Hence, we develop a high-performance flexible wearable strain sensor based on TPU MB with a microcrack structure consisting of two-dimensional (2D) MoS2 nanosheets bridged by one-dimensional (1D) multiwalled carbon nanotubes (MWCNTs). MoS2 and MWCNTs are anchored on the modified surface of TPU MB by polydopamine (PDA) with the assistance of simple ultrasound to obtain MoS2/MWCNTs@TPU MB flexible strain sensors. The influence of the mass ratio of MoS2 to MWCNTs on the sensing performances of the sensors is discussed. Due to the synergistic effect of MoS2 with high electron mobility and MWCNTs with good conductivity, when the mass ratio of MoS2 to MWCNTs is 1:0.9, the MoS2/MWCNTs0.9@TPU MB flexible strain sensor exhibits a wide sensing range from 0.5% to 300%, remarkable sensitivity (GF = 4271.9), fast response time (330 ms), and excellent durability (2600 tensile cycles). Benefiting from these superior sensing performances, this sensor can be successfully applied in monitoring large human motion (squatting, walking, and finger, elbow, and wrist bending) and subtle facial expression change (smiling, opening mouth, frowning, and raising eyebrows) as well as recognizing various vocal cord vibration modes (swallowing and pronunciation of various English words), which displays a great potential in intelligent wearable devices and soft robots.
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