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 Ti3C2T
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.