关键词: 聚乙烯/聚丙烯热熔纤维, 三维纤维网非织造布, 碳纳米管, 压阻式传感器, 传感性能, 健康监测

Abstract:

Objective Flexible sensors, as core components of flexible smart wearable devices, have a promising future in many fields. However, a common problem is that lower sensitivity and poor durability affect the performance of flexible sensors. In order to improve the problems of low sensitivity, poor durability, and lack of flexibility and comfort in the use of flexible wearable pressure sensors, a highly sensitive and more wear-resistant piezoresistive sensor based on a three-dimensional (3-D) fiber mesh nonwovens prepared from polyethylene/polypropylene hot-melt fibers with polyester fibers was proposed.

Method Firstly, three-dimensional fiber mesh nonwovens were prepared by blending polyethylene/polypropylene hot-melt fibers with polyester fibers, which were pre-strengthened and heated to shape. Then, using piezoresistive sensing as its basic principle, carbon nanotube/nonwoven (CNN) sensors were prepared by immersing 3-D fiber mesh nonwovens into CNT suspension for surface treatment through ultrasonic-assisted modification and impregnation-drying method. Scanning electron microscopy, DM6500 series digital multimeter, and homemade tensile tester were used to characterise and analyse the CNT-modified CNN sensors.

Results Nonwovens with four different hot-melt fiber proportions (5%, 10%, 20% and 25% by mass), denoted as CNN₅, CNN₁₀, CNN₂₀ and CNN₂₅, were prepared, and four different proportions of CNT-modified nonwovens sensors were compared for sensitivity and sensing performance. The results showed that the sensitivity would decrease with increasing hot-melt fiber proportion and pressure, attributing to the increase in fiber density leading to higher compression modulus. The polyester hot-melt nonwoven fabric with a base of CNN₅ has the highest sensitivity up to 0.91 kPa^-1 in the range of 0-0.17 kPa, 3.5×10^-3 kPa^-1 in the range of 0.17-53.65 kPa and 4.8×10^-4 kPa^-1 in the range of 53.65-166 kPa. Sensing performance studies of the CNN sensors showed that the sensor exhibited a stable dynamic signal response when pressure was continuously applied and released using weights with different forces, demonstrating that the sensor is able to accurately discriminate between different pressures and has a fast response and recovery time (73/122 ms). In addition to high durability (>2 000 cycles), the CNN sensors can also be applied to information encryption, monitoring of human physiological signals, speech monitoring and handwriting monitoring, and multi-site sensing arrays.

Conclusion The above characterization shows that the sensing performance of CNN sensors prepared from 3-D fiber mesh nonwovens modified by CNT is significantly improved. The experimental results show that the CNN sensors have higher sensitivity, faster response time and more stable durability due to the unique 3-D structure of the fiber mesh nonwovens. It can be used to monitor human physiological signals, voice signals as well as handwriting signals. In the future, by collecting a large number of data signals and using machine learning to train and predict their signals, it will pave the way for health monitoring, speech recognition, handwriting recognition and other fields.

Key words: polyethylene/polypropylene hot melt fiber, three-dimensional fiber mesh nonwoven, carbon nanotube, piezoresistive sensor, sensing property, health monitoring