3D printable dual-sensing hydrogels with exceptional anti-swelling and toughness
Source abstract
Abstract
Abstract Three-dimensional (3D) printing has been serving the demand for high-precision, personalized, and integrated fabrication of sensory hydrogels for signal monitoring, soft robotics, and other applications. However, the 3D printed sensory hydrogels still face problems such as structural instability and insufficient mechanical properties. In this work, an anti-swelling dual-sensing hydrogel system for digital light processing (DLP) 3D printing technology is introduced and successfully applied to the preparation of a stable self-sensing soft gripper underwater. The anti-swelling dual-sensing hydrogel system is composed of a copolymer of N-acryloylsemicarbazide and acrylic acid and the piezoelectric filler poly(vinylidene fluoride-trifluoroethylene). During the solvent exchange of organic hydrogels into hydrogels, abundant physical cross-linking and the formation of hydrophobic structures inhibit the swelling behavior of hydrogels in water. The resulting hydrogel exhibits high toughness (15.2 MJ·m −3 ), high strain level (1 093.3%), high ultimate stress (2.8 MPa), and swelling-resistant behavior (equilibrium swelling ratio of 1.9% in water for 100 days). Meanwhile, calcium ions enter the hydrogel to improve the ionic conductivity of the hydrogel, and the presence of piezoelectric fillers endows the hydrogel with dual-sensing properties. The use of DLP printed hydrogel allows for personalized and customized structures, and an underwater soft gripper based on anti-swelling dual-sensing hydrogel is being further developed for the initial determination of the size and shape of the object and for guiding further underwater work, demonstrating the potential for visualizing underwater invisible motions.
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Research context
OpenAlex topics: Advanced Sensor and Energy Harvesting Materials · Advanced Materials and Mechanics · Hydrogels: synthesis, properties, applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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