Bio-Inspired HydrogenBonding Cross-Linking Strategyfor DIW-Printed Carbon-Based Conductive Hydrogels in Wearable Self-PoweredSensing Systems

With the rapid development of health and human–computer interaction technologies, strain sensing systems for human movement and health detection have become essential components in smart health. Most existing wearable strain sensors rely on external power sources or achieve self-powered transient sensing, significantly limiting their utility for real-time data monitoring in wearable applications. In this study, we are inspired by natural biological protein materials, and, using tannic acid (TA) as a molecularly coupled bridge between cellulose nanocrystals (CNCs), poly(vinyl alcohol) (PVA) chains, and carboxylated multiwalled carbon nanotubes (MWCNT-COOH), we construct a multiple hydrogen bonding system. The dynamic breaking of hydrogen bonds within the multiple hydrogen bonding system and the formation of a dense conductive network impart the hydrogels with superior properties. This approach produces conductive hydrogels with rich internal microstructures, excellent electrical conductivity (0.47 S/m), tensile strength (600%), mechanical properties (1.76 MPa), and self-recovery (97%) for cross-cutting applications in multiple fields. The unmodified precursor solution of TA exhibits excellent rheological properties, enabling high-precision printing of conductive hydrogel electrodes for mass production and flexible customization of application requirements. The synergy of these process and material advantages allows triboelectric nanogenerators (TENG) to harvest motion energy and use it for human motion detection with strain sensors. Additionally, integrating this sensing system with Internet of Things (IoT) technology and utilizing 5G signals facilitates the remote transmission of data, enabling real-time motion monitoring over long distances. This comprehensive approach addresses the limitations of existing wearable sensors, providing a robust solution for continuous health monitoring and human motion detection in various practical scenarios.