The widespread use of biological tissue adhesives for tissue repair is limited by their weak adhesion in a wet environment. Herein, we report the wet adhesion mechanism of a dry granular natural bioadhesive from Andrias davidianus skin secretion (ADS). Once contacting water, ADS granules self-assemble to form a hydrophobic hydrogel strongly bonding to wet substrates in seconds. ADS showed higher shear adhesion than current commercial tissue adhesives and an impressive 72-h underwater adhesion strength of ∼47kPa on porcine skin tissue. The assembled hydrogel in water maintained a dissipation energy of ∼8 kJ/m 3 , comparable to the work density of muscle, exhibiting its robustness. Unlike catechol adhesion mechanism, ADS wet adhesion mechanism is attributed to water absorption by granules, and the unique equilibrium of protein hydrophobicity, hydrogen bonding, and ionic complexation. The in vivo adhesion study demonstrated its excellent wet adhesion and hemostasis performance in a rat hepatic and cardiac hemorrhage model.
Hydrogel-based flexible artificial tactility is equipped to intelligent robots to mimic human mechanosensory perception. However, it remains a great challenge for hydrogel sensors to maintain flexibility and sensory performances during cyclic loadings at high or low temperatures due to water loss or freezing. Here, a flexible robot tactility is developed with high robustness based on organohydrogel sensor arrays with negligent hysteresis and temperature tolerance. Conductive polyaniline chains are interpenetrated through a poly(acrylamide- co -acrylic acid) network with glycerin/water mixture with interchain electrostatic interactions and hydrogen bonds, yielding a high dissipated energy of 1.58 MJ m −3 , and ultralow hysteresis during 1000 cyclic loadings. Moreover, the binary solvent provides the gels with outstanding tolerance from −100 to 60 °C and the organohydrogel sensors remain flexible, fatigue resistant, conductive (0.27 S m −1 ), highly strain sensitive (GF of 3.88) and pressure sensitive (35.8 MPa −1 ). The organohydrogel sensor arrays are equipped on manipulator finger dorsa and pads to simultaneously monitor the finger motions and detect the pressure distribution exerted by grasped objects. A machine learning model is used to train the system to recognize the shape of grasped objects with 100% accuracy. The flexible robot tactility based on organohydrogels is promising for novel intelligent robots.
危险品化学品经营许可证（带存储）