Ionic hydrogels-based electronic skins for electrophysiological monitoring
Source abstract
Abstract
Ionic hydrogels possess great advantages over traditional electronic devices due to their notable properties such as excellent flexibility, high conductivity, and tunable gel structure, making them widely employed in electronic skins for electrophysiological monitoring. However, conventional electronic skins are usually easy to detach from the skin and suffer from stress fatigue due to frequent body movements, impeding user experience in health monitoring. Therefore, the fabrication of ionic hydrogels with certain unique properties could address these issues, offering broad prospects for ionic hydrogels. This review first introduces the fabrication materials for ionic hydrogels. Then the unique properties of ionic hydrogels in adhesiveness, self-healing, and recyclability are discussed. Subsequently, the applications of ionic hydrogels in electrophysiological monitoring are summarized. Finally, we give an outlook on the current status and future prospects of ionic hydrogel-based electronic skins. Hopefully this work will contribute insights into the advance of ionic hydrogel-based electronic skins.Graphical abstractThis review introduces the fabrication materials for ionic hydrogels. The unique properties of ionic hydrogels in adhesiveness, self-healing, and recyclability are discussed. Subsequently, the applications of ionic hydrogels in electrophysiological monitoring are summarized. Finally, we give an outlook on the current status and future prospects of ionic hydrogel-based electronic skins. It is expected that continued efforts in material design, device integration will shape the future of ionic hydrogel advancements, enabling new possibilities in healthcare monitoring.
Source: expertdata · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Advanced Sensor and Energy Harvesting Materials · Conducting polymers and applications · Tactile and Sensory Interactions
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (100+)
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