Instant self-healing adhesive hydrogel sensors: dual-network design for real-time human motion tracking

Abstract

Hydrogels are extensively utilized as strain sensors in the domain of wearable devices. However, balancing mechanical properties, adhesion, and self-healing capabilities in hydrogel sensors has consistently been a research challenge. Here, we report a facile one-pot synthesis of a double-network hydrogel integrating poly(2-(methacryloyloxy)ethyl)(dimethyl-(3-sulfopropyl)ammonium-co-acrylic acid) with tannic acid/polyethylenimine (as SATP), demonstrating exceptional mechanical robustness (tensile strength ∼0.31 MPa), high adhesion strength to diverse substrates (122.1 kPa on porcine skin), rapid self-healing (80% recovery within 1 h) and favorable conductivity (0.45 S m−1), which is attributed to the dynamically reversible hydrogen bonds and electrostatic interactions. When configured as a strain sensor, the material achieves desirable sensitivity (GF = 1.27), fast response (0.84 s), and stable signal output over 500 cycles (0–300% strain). Practical applications include real-time monitoring of physiological motions from subtle facial expressions to joint movements. Notably, the hydrogel sensor can achieve information transmission through resistance modulation related to strain rate, and successfully decode the Morse code signal (“SOS”) by bending the finger. This work provides a method for designing multifunctional hydrogels that simultaneously address the critical challenges of mechanical durability, environmental adaptability, and self-repair in wearable electronics, with applications in telemedicine and human-machine interfaces.

Graphical abstract: Instant self-healing adhesive hydrogel sensors: dual-network design for real-time human motion tracking

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Article information

Article type
Paper
Submitted
05 Jun 2025
Accepted
28 Jul 2025
First published
05 Aug 2025
This article is Open Access
Creative Commons BY-NC license

J. Mater. Chem. A, 2025, Advance Article

Instant self-healing adhesive hydrogel sensors: dual-network design for real-time human motion tracking

Y. Li, H. Liu, R. Ma, Q. Liu, S. Ma, N. Han and L. Xing, J. Mater. Chem. A, 2025, Advance Article , DOI: 10.1039/D5TA04564A

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