文献类型：期刊文献

中文题名：碳纳米管修饰三维纤维网非织造布传感器的制备及其性能

英文题名：Preparation and properties of carbon nanotube modified three-dimensional fiber-mesh nonwoven sensors

作者：张蕊[1,2];应迪[2];陈冰冰[2];田欣[2];郑莹莹[2];王建[1,2];邹专勇[1,2]

机构：[1]绍兴文理学院浙江省清洁染整技术研究重点实验室,浙江绍兴312000;[2]绍兴文理学院绍兴市高性能纤维及制品重点实验室,浙江绍兴312000

年份：2024

卷号：45

期号：11

起止页码：46

中文期刊名：纺织学报

外文期刊名：Journal of Textile Research

收录：北大核心2023、CSTPCD、、EI(收录号：20250217650178)、CSCD2023_2024、北大核心、CSCD

基金：浙江省大学生科技创新活动计划(新苗人才计划)项目(2023R465032);国家级大学生创新创业训练计划项目(202310349047);浙江省教育厅一般科研项目(Y202351466)。

语种：中文

中文关键词：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

外文关键词：polyethylene/polypropylene hot melt fiber;three-dimensional fiber mesh nonwoven;carbon nanotube;piezoresistive sensor;sensing property;health monitoring

中文摘要：为改善柔性可穿戴压力传感器在使用时灵敏度低、耐久性差、柔韧舒适性不足等问题,提出了一种基于预针刺-热加固技术制备的三维聚乙烯/聚丙烯热熔纤维与涤纶非织造布为基材的高灵敏、较耐磨的压阻传感器。使用扫描电子显微镜、数显推拉力计和数字万用表等仪器表征了碳纳米管修饰三维纤维网非织造布前后的微观形貌、力电学性能和传感性能。结果表明:柔性纺织传感器在低压力范围(0~0.17 kPa)内的灵敏度高达0.91 kPa^(-1);能在73 ms内实现对压力的快速响应;具有0~166 kPa较宽的感测范围,在超过2000次施压循环后仍然保持较稳定的相对电阻变化,表现出较优异的耐久性。此外,该传感器可应用于信息加密,监测人体微弱活动(眨眼、吞咽)和大形变运动(关节活动等)。在健康监测、人机交互、语音识别和手写字识别等领域具有广阔的应用前景。

外文摘要：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 CNN 5,CNN 10,CNN 20 and CNN 25,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 CNN 5 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(>2000 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.