3D Waterproof MXene‐Based Textile Electronics for Physiology and Motion Signals Monitoring. Issue 14 (25th March 2022)

• Record Type: Journal Article
• Authors: Fan, Wenjing; Li, Cheng; Li, Wei; Yang, Jin
• Is Part Of: Advanced materials interfaces. Volume 9:Issue 14(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-03-25
• Subjects: fast response time of textile pressure sensors -- MXene -- support vector machine -- textile electronics -- waterproof; Materials science -- Periodicals; 620.11
• DOI: 10.1002/admi.202102553
• Languages: English
• ISSNs: 2196-7350

Abstract

Abstract: MXene‐based textile electronics are great promising wearable devices due to their potential applications in human healthcare and artificial intelligence but are generally hindered by the long response time and absence of physiological signal details. Herein, a novel MXene‐based textile pressure sensor fabricated by assembled MXene onto the scuba knit with a hollow structure is constructed. Benefiting from the high electrical conductivity, specific capacitance, and outstanding mechanical properties of MXene, the as‐designed textile sensor exhibits multiple splendid sensing properties, such as high sensitivity (GF = 23.7 kPa −1 ), fast response time (71.5 ms), great stability (over 20 000 cycles), and excellent waterproof capability (up to 6 h immersion in water). Based on these splendid properties, this sensor can not only perceive pulse signals with feature points and respiration signals with relatively low frequency but also identify joint activity and different words when attached to the subject's knee and throat. Furthermore, a personalized intelligent health monitoring system integrated with the MXene‐based textile sensor is developed to collect the physiological signals from different people and the support vector machine algorithm applied to identify and distinguish them, proposing a great advance in wearable health monitoring. Abstract : A soft, waterproof, and multifunctional MXene‐based textile pressure sensor with excellent sensing performance is constructed by integrating MXene onto the scuba knit to monitor precise physiological signals and identify general motion. In addition, a support vector machine algorithm is used to distinguish the physiological signals of different people obtained from long‐term monitoring.