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Machine learning enabled reusable adhesion, entangled network‑based hydrogel for long‑term, high‑fidelity EEG recording and attention assessment

09.07.25 | Shanghai Jiao Tong University Journal Center

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

Flexible electronics just took a quantum leap forward. A joint team from Beijing Institute of Technology and Lanzhou University, led by Prof. Bin Hu and Prof. Ran Cai, published in Nano-Micro Letters , has unveiled a next-generation hydrogel sensor that combines liquid-metal conductivity, reusable skin adhesion, and machine-learning-powered attention decoding—all in one ultrathin patch.

Why the PGEH Patch Is a Breakthrough

From Lab to Life

With its rare blend of mechanical resilience, biocompatibility, and AI integration, the PGEH platform is poised to redefine wearable neuromonitoring—turning everyday accessories into intelligent health guardians.

Nano-Micro Letters

10.1007/s40820-025-01780-7

Experimental study

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

29-May-2025

Keywords

Hydrogels

Article Information

Journal

Nano-Micro Letters

DOI

10.1007/s40820-025-01780-7

Method of Research

Experimental study

Article Publication Date

2025-05-29

Article Title

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

Contact Information

Bowen Li

Shanghai Jiao Tong University Journal Center

qkzx@sjtu.edu.cn

Source

Original Source

https://link.springer.com/article/10.1007/s40820-025-01780-7

How to Cite This Article

APA:

Shanghai Jiao Tong University Journal Center. (2025, September 7). Machine learning enabled reusable adhesion, entangled network‑based hydrogel for long‑term, high‑fidelity EEG recording and attention assessment. Brightsurf News. https://www.brightsurf.com/news/LKNW0KWL/machine-learning-enabled-reusable-adhesion-entangled-networkbased-hydrogel-for-longterm-highfidelity-eeg-recording-and-attention-assessment.html

MLA:

"Machine learning enabled reusable adhesion, entangled network‑based hydrogel for long‑term, high‑fidelity EEG recording and attention assessment." Brightsurf News, Sep. 7 2025, https://www.brightsurf.com/news/LKNW0KWL/machine-learning-enabled-reusable-adhesion-entangled-networkbased-hydrogel-for-longterm-highfidelity-eeg-recording-and-attention-assessment.html.