2026/02/01 by Boyu Du, Jiajun Zhang, Yuxin Yang +6 · 1 voice
Chemistry · Engineering · Materials Science · #Advanced Sensor and Energy Harvesting Materials #Conducting polymers and applications #Polydiacetylene-based materials and applications
paper · pdf · doi:10.1002/agt2.70291
openalex publication_date 2026/02/01 · openalex created_date 2026/02/14 · openalex updated_date 2026/08/01
ABSTRACT With the continuous advancement of social technology and the increasing awareness of health management, biomass‐based triboelectric nanogenerator (TENG) displayed significant potential as flexible wearable electronics for continuous foot gait monitoring. Nevertheless, existing biomass‐based TENG often faces challenges of insufficient mechanical robustness and durability in practical applications, where they are prone to surface abrasion and structural fracture under continuous compression and friction, severely limiting their long‐term performances. In order to address these challenges, this work proposed a multiscale crosslinking strategy, which strengthened the noncovalent interactions within the polymer by constructing multiple reinforcement networks, successfully fabricating a dual‐network C─lignin‐based triboelectric material (CLTM) with excellent durability and crack resistance. Among them, the optimal CLTM (PSGCL‐0.2) exhibited high mechanical strength (strain 445%, tensile strength 41.56 MPa, Young's modulus 41.25 MPa, toughness 159.67 MJ/m 3 ) and excellent cyclic stability (300 cycles) with versatile functionalities, including antibacterial, antioxidant, and UV‐shielding properties, water stabilization (255.51 g/m 2 /d), efficient photothermal conversion, and full recyclability. Furthermore, biomass‐based TENG device assembled from PSGCL‐0.2 achieved stable triboelectric output properties (102.5 V, 2.9 µA, and 61.3 nC), and sustainable for 2000 cycles, fast response time (68 ms), and excellent power density (325.9 mW/m 2 ), effectively converting mechanical energy into electrical energy. Especially, PSGCL‐0.2 was also integrated into the wireless self‐powered smart insole, successfully enabling real‐time visual monitoring of plantar pressure distribution and dynamic gait. Meanwhile, combined with the machine learning algorithm, the self‐powered smart insole achieved precise recognition and classification of eight different motion states with an accuracy of 98%. This study provides the feasible strategy for developing extremely stable and durable biomass‐based TENG, aimed at advancing sustainable intelligent healthcare systems.