Issue

Vol. 17 (2025)

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

https://doi.org/10.1007/s40820-025-01780-7
Kai Zheng
Key Lab of Brain Health Intelligent Evaluation and Intervention, Beijing Institute of Technology, Beijing 100081, People’s Republic of China
Chengcheng Zheng
Key Lab of Brain Health Intelligent Evaluation and Intervention, Beijing Institute of Technology, Beijing 100081, People’s Republic of China
Lixian Zhu
Key Lab of Brain Health Intelligent Evaluation and Intervention, Beijing Institute of Technology, Beijing 100081, People’s Republic of China
Bihai Yang
Key Lab of Brain Health Intelligent Evaluation and Intervention, Beijing Institute of Technology, Beijing 100081, People’s Republic of China
Xiaokun Jin
Key Lab of Brain Health Intelligent Evaluation and Intervention, Beijing Institute of Technology, Beijing 100081, People’s Republic of China
Su Wang
Key Lab of Brain Health Intelligent Evaluation and Intervention, Beijing Institute of Technology, Beijing 100081, People’s Republic of China
Zikai Song
Key Lab of Brain Health Intelligent Evaluation and Intervention, Beijing Institute of Technology, Beijing 100081, People’s Republic of China
Jingyu Liu
Key Lab of Brain Health Intelligent Evaluation and Intervention, Beijing Institute of Technology, Beijing 100081, People’s Republic of China
Yan Xiong
Analysis & Testing Center of Fangshan District, Beijing Institute of Technology, Beijing 100081, People’s Republic of China
Fuze Tian
School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, People’s Republic of China
Ran Cai
Key Lab of Brain Health Intelligent Evaluation and Intervention, Beijing Institute of Technology, Beijing 100081, People’s Republic of China
Bin Hu
Key Lab of Brain Health Intelligent Evaluation and Intervention, Beijing Institute of Technology, Beijing 100081, People’s Republic of China

Corresponding Author: Bin Hu

bh@bit.edu.cn

Nano-Micro Letters, Vol. 17 (2025), Article Number: 281

Abstract

Due to their high mechanical compliance and excellent biocompatibility, conductive hydrogels exhibit significant potential for applications in flexible electronics. However, as the demand for high sensitivity, superior mechanical properties, and strong adhesion performance continues to grow, many conventional fabrication methods remain complex and costly. Herein, we propose a simple and efficient strategy to construct an entangled network hydrogel through a liquid–metal-induced cross-linking reaction, hydrogel demonstrates outstanding properties, including exceptional stretchability (1643%), high tensile strength (366.54 kPa), toughness (350.2 kJ m−3), and relatively low mechanical hysteresis. The hydrogel exhibits long-term stable reusable adhesion (104 kPa), enabling conformal and stable adhesion to human skin. This capability allows it to effectively capture high-quality epidermal electrophysiological signals with high signal-to-noise ratio (25.2 dB) and low impedance (310 ohms). Furthermore, by integrating advanced machine learning algorithms, achieving an attention classification accuracy of 91.38%, which will significantly impact fields like education, healthcare, and artificial intelligence.


Highlights:
1 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.
2 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.
3 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.

Keywords

Entangled network Reusable adhesion Epidermal sensor Machine learning Attention assessment
Zheng, Kai, Chengcheng Zheng, Lixian Zhu, Bihai Yang, Xiaokun Jin, Su Wang, Zikai Song, Jingyu Liu, Yan Xiong, Fuze Tian, Ran Cai, and Bin Hu. 2025. “Machine Learning Enabled Reusable Adhesion, Entangled Network-Based Hydrogel for Long-Term, High-Fidelity EEG Recording and Attention Assessment”. Nano-Micro Letters 17 (May):281. https://doi.org/10.1007/s40820-025-01780-7.
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