3D-Printed Conductive Aerogel Humidity Sensor for Advanced Wearable Sleep and Health Monitoring

材料科学 可穿戴计算机 气凝胶 睡眠(系统调用) 湿度 导电体 可穿戴技术 光电子学 纳米技术 电气工程 汽车工程 导电的
作者
Xiaojun Chen,Xitong Lin,Yuanyu Huang,Yihuan Li,Haishan Lian,Zhihao Zhao,Jiangtao Zhou
出处
期刊:ACS Applied Materials & Interfaces [American Chemical Society]
卷期号:18 (22): 31753-31766
标识
DOI:10.1021/acsami.6c01464
摘要

Recent advances in personalized sleep medicine and home-based health monitoring have grown rapidly, yet progress remains limited by the lack of comfortable, reliable, and durable sensors for long-term respiration tracking. Here, we present a flexible humidity sensor fabricated through a freeze-drying-assisted direct-ink-writing (DIW) 3D printing strategy. This sensor is constructed from a poly(vinyl alcohol)/nanocellulose/graphene/multiwalled carbon nanotubes (PVA/CNF/Gr/MWCNTs, PCGM) composite aerogel, with a hierarchically porous conductive architecture. This aerogel was stabilized by a biocompatible PVA matrix reinforced by nanocellulose, while graphene ensures high conductivity, and it was further enhanced by carbon nanotubes in the 3D structure. Moreover, the considerable hydrogen bonding and π-π conjugation within this hybrid material contribute to exceptional interfacial stability, water-adsorption capacity, and electrical conductivity, resulting in a remarkable humidity-sensing performance with high sensitivity, fast response/recovery times, and excellent stability across a broad humidity range. Remarkably, the sensor can accurately differentiate diverse sleep postures and respiratory patterns, including normal, snoring, and coughing. Furthermore, we further enabled a 600-sample-trained deep convolutional neural network and achieved a high-precision pattern recognition, with 100% accuracy in respiratory state classification and 97% accuracy for spoken-word recognition classification. Our work provides an integrated strategy for next-generation high-performance wearable health monitoring and human-machine interaction systems.
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