Ultrastretchable High-Conductivity MXene-Based Organohydrogels for Human Health Monitoring and Machine-Learning-Assisted Recognition
Qingqing Li, Xinrong Zhi, Yifan Xia, Shilei Han, Wenyu Guo, Mingyuan Li, Xin Wang
Abstract
Conductive hydrogels as promising candidates of wearable electronics have attracted considerable interest in health monitoring, multifunctional electronic skins, and human–machine interfaces. However, to simultaneously achieve excellent electrical properties, superior stretchability, and a low detection threshold of conductive hydrogels remains an extreme challenge. Herein, an ultrastretchable high-conductivity MXene-based organohydrogel (M-OH) is developed for human health monitoring and machine-learning-assisted object recognition, which is fabricated based on a Ti 3 C 2 T x MXene/lithium salt (LS)/poly(acrylamide) (PAM)/poly(vinyl alcohol) (PVA) hydrogel through a facile immersion strategy in a glycerol/water binary solvent. The fabricated M-OH demonstrates remarkable stretchability (2000%) and high conductivity (4.5 S/m) due to the strong interaction between MXene and the dual-network PVA/PAM hydrogel matrix and the incorporation between MXene and LS, respectively. Meanwhile, M-OH as a wearable sensor enables human health monitoring with high sensitivity and a low detection limit (12 Pa). Furthermore, based on pressure mapping image recognition technology, an 8 × 8 pixelated M-OH-based sensing array can accurately identify different objects with a high accuracy of 97.54% under the assistance of a deep learning neural network (DNN). This work demonstrates excellent comprehensive performances of the ultrastretchable high-conductive M-OH in health monitoring and object recognition, which would further explore extensive potential application prospects in personal healthcare, human–machine interfaces, and artificial intelligence.