With the continuous advancement of smart technology, the requirements for the smart fluorescent materials that can sense and respond to different external stimuli are getting higher and higher. It is...

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.

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Electronic skin (E‐skin) with multimodal sensing ability demonstrates huge prospects in object classification by intelligent robots. However, realizing the object classification capability of E‐skin faces severe challenges in multiple types of output signals. Herein, a hierarchical pressure–temperature bimodal sensing E‐skin based on all resistive output signals is developed for accurate object classification, which consists of laser‐induced graphene/silicone rubber (LIG/SR) pressure sensing layer and NiO temperature sensing layer. The highly conductive LIG is employed as pressure‐sensitive material as well as the interdigital electrode. Benefiting from high conductivity of LIG, pressure perception exhibits an excellent sensitivity of −34.15 kPa−1. Meanwhile, a high temperature coefficient of resistance of −3.84%°C−1 is obtained in the range of 24–40 °C. More importantly, based on only electrical resistance as the output signal, the bimodal sensing E‐skin with negligible crosstalk can simultaneously achieve pressure and temperature perception. Furthermore, a smart glove based on this E‐skin enables classifying various objects with different shapes, sizes, and surface temperatures, which achieves over 92% accuracy under assistance of deep learning. Consequently, the hierarchical pressure–temperature bimodal sensing E‐skin demonstrates potential application in human‐machine interfaces, intelligent robots, and smart prosthetics.