Ultra-Elastic, Transparent, and Conductive Gelatin/Alginate-Based Bioadhesive Hydrogel for Enhanced Human–Machine Interactive Applications
Shiqiang Zhang, Jingjing Xia, Jiyu Chen, Lei Chen, Jingjiang Qiu, Zhongwei Guo, Ronghan Wei, Wei Sun
Abstract
Growing interest has focused on next-generation flexible adhesive sensors (FAS) integrated with deep learning for intelligent electronics. Existing conductive hydrogels, however, fail to concurrently achieve high stretchability, transparency, robust adhesion, and interactive precision. Here, a novel class of high-performance ionic hydrogels (AGG-M n+ hydrogels, with M n+ being Cu 2+, Zn 2+, Fe 3+, and Zr 4+ ) that synergistically combines the above features is developed. The hydrogel is fabricated by incorporating aldehyde-modified sodium alginate (Ald-alginate), gelatin methacrylate (GelMA), and glycerol into the poly(acrylic acid) network through free-radical polymerization plus an ionic coordination strategy. This endows the hydrogel with remarkable properties required for FAS applications, including excellent stretchability (1038%), optimal ionic conductivity (3.25 S/m), and high sensitivity (gauge factor = 1.932, 0%–600% strain range), accompanied by reliable long-term stability over 300 cycles. We also demonstrated its efficiency through the real-time monitoring of diverse physiological signals. Furthermore, the hydrogel was integrated with a deep learning algorithm into a wearable electronic control system, which can instantaneously recognize a diverse array of complex gestures with up to 99.4% accuracy. This work provides new insights for the future development of multifunctional hydrogels, wearable electronics, and human–machine interactions.