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Machine Learning Enabled Reusable Adhesion, Entangled Network-Based Hydrogel for Long-Term, High-Fidelity EEG Recording and Attention Assessment

Authors: Kai Zheng; Chengcheng Zheng; Lixian Zhu; Bihai Yang; Xiaokun Jin; Su Wang; Zikai Song; Jingyu Liu; Yan Xiong; Fuze Tian; Ran Cai; Bin Hu

DOI: 10.1007/s40820-025-01780-7Status: Verified Translated Edition
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Key Findings in This Report

• A dual-network hydrogel (PGEH) cross-linked via liquid metal induction was developed exhibiting remarkable mechanical properties and skin-temperature-triggered on-demand adhesion capabilities. • The PGEH capacitive sensor demonstrates exceptional sensitivity (1.25 kPa), rapid dynamic response (30 ms), and long-term cycling stability (20,000 cycles), enabling precise monitoring of human motion and reliable signal transmission. • Low-impedance electrophysiological sensor (310 ohms) maintains 14-day signal fidelity (25.2 dB), paired with machine learning-based attention monitoring (91.38% of accuracy) for real-time cognitive feedback in focus-demanding scenarios. • The hydrogel exhibits outstanding stretchability (1643%), high tensile strength (366.54 kPa), and toughness (350.2 kJ m−3), with reusable adhesion (104 kPa) for conformal skin contact.