An Antifreeze Gel as Strain Sensors and Machine Learning Assisted Intelligent Motion Monitoring of Triboelectric Nanogenerators in Extreme Environments

Abstract

Traditional hydrogels tend to freeze and lose performance at low temperatures, limiting their applications. Additionally, hydrogels need to exhibit low hysteresis, excellent cycling stability, and self-adhesion to ensure high-quality signal acquisition in complex environments. To address this challenge, this study designed a dual-network gel in a glycerol (Gly)/H₂O solvent system. Due to the combination of chemical and physical crosslinking (hydrogen bonding and electrostatic interactions), the resulting gel exhibits skin-adaptive modulus, high cycling stability, anti-freezing ability, body temperature-induced adhesion, and excellent electrical performance, making it suitable for wearable sensors at low temperatures. Based on this gel, a single-electrode triboelectric nanogenerator (gel-TENG) is developed, achieving efficient conversion of mechanical energy into electrical energy. Further applied to a smart insole, it successfully enabled real-time visualization of plantar pressure distribution and skiing motion recognition. Using a random forest machine learning algorithm, the system accurately classified 11 basic skiing motions, achieving a classification accuracy of 97.1%. This study advances flexible sensors and self-powered systems, supporting intelligent materials research in extreme environments.