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Bioinspired Cold‐Laminated Ultrathin Hydrogels as a Broadly Adaptive Platform for Physiological Monitoring

Hui Chen, Jian Zhou*, Jianfei Xie*, Lin Shi, Lu Wang, Yuanfan Yang, Yihao Guo, Jianhui Cao, Qingyun Fu, Yongqing Fu, Huigao Duan*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

Soft bioelectronics provide a powerful platform for physiological monitoring, yet conventional hydrogel-based devices are constrained by inherent trade-offs between thickness, robustness, and multifunctionality, restricting their adaptability across diverse epidermal and implantable applications. Here, a novel cold-lamination strategy is developed to mechanically interlock a TPU nanomesh within a temperature-responsive hydrogel network, mimicking the structure of the extracellular matrix. This method enables the fabrication of devices with precise control of thickness (17–90 µm), tunable Young's modulus (36.5–761.1 kPa), excellent breathability, and large-area scalability (> 225 cm2). Furthermore, the resulting bioelectronics exhibit reversible, on-demand adhesion enabled by switchable hydrogen-bond interactions. The ultrathin hydrogels exhibit exceptional conformability and durability, with a 17 µm film achieving tensile stress of 835.23 kPa and toughness of 1.57 MJ m−3, thereby overcoming the fragility of conventional ultrathin films. By forming stable tissue interfaces, they enabled reliable epidermal electrocardiogram monitoring under daily and clinical conditions and served as implantable cardiac patches for arrhythmia detection in mice. When integrated with a dual-branch deep learning network, the intelligent platform achieved 99% in vivo classification accuracy of ventricular tachycardia, ventricular fibrillation, and other arrhythmias. Together, these results establish a broadly adaptable hydrogel system, offering a universal and scalable strategy for next-generation bioelectronics tailored to diverse organ-specific demands.
Original languageEnglish
Article numbere24317
Number of pages12
JournalAdvanced Science
Volume13
Issue number22
Early online date15 Feb 2026
DOIs
Publication statusPublished - 17 Apr 2026

Keywords

  • AI
  • cardiac arrhythmia diagnosis
  • ECG
  • ECM
  • hydrogel

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