Multifunctional Nano-Conductive Hydrogels With High Mechanical Strength, Toughness and Fatigue Resistance as Self-Powered Wearable Sensors and Deep Learning-Assisted Recognition System

Abstract

High mechanical strength, toughness, and fatigue resistance are essential to improve the reliability of conductive hydrogels for self-powered sensing. However, achieving mutually exclusive properties simultaneously remains challenging. Hence, a novel directed interlocking strategy based on topological network structure and mechanical training is proposed to construct tough hydrogels by optimizing the network structure and modulating the orientation of molecular chains. Combining Zn²⁺ crosslinked cellulose nanofibers (CNFs) and a polyacrylamide-poly(vinyl alcohol) double-network, the unique interlocked-network structure exhibits an enhanced toughening effect due to hydrogen bonding and metal-ligand interactions. The aligned nanocrystalline domains achieved by training further contribute to an increase in the toughness and fatigue thresholds. This innovative approach synergistically enhances the mechanical properties of the nano-conductive hydrogel, achieving a maximum tensile strength of 4.98 MPa and a toughness of 48 MJ m⁻³. Notably, the CNFs template with anchored polyaniline, when oriented through mechanical training, forms a unique directional conductive pathway, which significantly enhances the power output performance. Besides, a motion recognition system based on a self-powered sensing device is designed with the assistance of deep learning techniques to accurately identify human motion behaviors. This work showcases a potentially transformative flexible electronic material for self-powered sensing systems and intelligent recognition systems.