A superbranched multi-armed crosslinking strategy for robust conducting polymer hydrogels toward wearable sensing and encrypted communication
Year: 2026
Type: article
Abstract: Conducting polymer hydrogels have attracted extensive interest in flexible electronics and wearable sensors owing to their intrinsic softness, stretchability, and biocompatibility. However, conventional hydrogel systems typically rely on linear cross-linking strategies that generate discrete junctions and topologically simple networks, limiting their mechanical robustness and functional tunability. Herein, we propose a superbranched multi-armed crosslinking strategy to construct a robust and branched architecture (RBA) conducting polymer hydrogel by integrating an amino-terminated hyperbranched polymer network (HBPN) into poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS)-based conducting polymer hydrogels. The abundant terminal amino groups in HBPN form multidirectional noncovalent interactions with hydroxyl and carboxyl groups, yielding a densely entangled and hierarchical network. The RBA conducting polymer hydrogel exhibits high stretchability (>300%) and outstanding toughness (342.76 kJ m –3 ), more than twice of its linear-cross-linked counterpart. RBA-based strain sensors deliver rapid, reversible, and stable responses with a gauge factor of 3.73, enabling precise detection of both subtle and large-scale human motions. Moreover, Morse code-based encoding system coupled with a lightweight machine-learning classifier achieves 96.26% decoding accuracy, enabling real-time assistive communication. This work offers a versatile design paradigm for conductive hydrogels toward next-generation wearable electronics and intelligent human-machine interfaces. • A superbranched crosslinking strategy was developed for robust PEDOT:PSS hydrogels. • The hydrogels exhibited excellent mechanical and electromechanical properties. • Real-time wearable sensing and encrypted communication were achieved.
Source: Wearable electronics.
Authors: Jiayi Li, Juan Teng, Xiaokai Jia, Yu Lin, Liuyu Zhang, Hai Li, Sooman Lim, Baoyang Lu
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid:
Awards: G1508152134, G2427898174
OpenAlex ID: W7147418007
