An unprecedented strategy with electric double layer and adaptive ionic liquid in fully ionogel fiber-based TENG for enhanced output and dynamic stability

The triboelectric nanogenerator (TENG) has attracted considerable research interest in wearable electronics due to its self-powered functionality. However, developing a TENG simultaneously combining lightweight, flexibility, stability and high output performance remains challenging. Herein, a fully ionogel fiber-based single-electrode TENG (IL-TENG), integrating both the friction layer and electrodes, was fabricated for the first time by using the electrospinning technology. Firstly, the ionogels are composed of a polyurethane network crosslinked by hyperbranched polymers and an ionic liquid. The polyurethane network contains polycarbonate diol (PCDL) segments acting as low-solvent regions in the ionic liquid, and polyethylene glycol (PEG) segments acting as high-solvent regions. The combination of phase separation and flexible crosslinkers ingeniously resolves the contradiction between the mechanical strength and high electrical conductivity of the ionogels, and simultaneously enhances their compatibility and stability. Secondly, the incorporation of a moderately conductive ionogel fiber membrane as an intermediate layer, functioning as an ionic electric double layer (iEDL), effectively reduces charge dissipation during friction. This innovation enhances the output performance of IL-TENG by 4 times. Furthermore, the integration of highly conductive ionogel fiber membrane electrodes, combined with the tunable mechanical properties of the ionogel, significantly improves the tensile performance and operational stability of IL-TENG under dynamic conditions. The IL-TENG demonstrates an impressive output power density of 27 mW/m² and maintains outstanding cyclic stability across 5000 cycles. As a wearable, self-powered sensor, the IL-TENG offers significant potential for advanced applications, including human motion detection, handwriting recognition, real-time medical feedback and smart carpet for monitoring home intrusion. This research lays a foundation for next-generation health monitoring and human-computer interaction.