Activation-Free and Strain-Resilient Island-Bridge Liquid Metal Inks for Machine-Learning-Enabled Epidermal Electromyography.

Authors

Yidong Peng, Yanqing Gu, Haoxuan Zhu, Lingqi Liu, Haoran Liu, Haitao Huang, Biao Zhou, Jiancheng Dong, Yunpeng Huang

Published in

Advanced materials (Deerfield Beach, Fla.). Oct 26, 2026. Epub Oct 26, 2026.

Abstract

Liquid metal particle-based inks offer a scalable route toward stretchable electronics, yet their practical implementation is hindered by activation-dependent conductivity and poor electrical stability under deformation. Herein, we report an intrinsically conductive and strain-resilient liquid metal ink with a rationally engineered island-bridge micro-architecture, in which Cu-decorated carbon nanotubes (CuCNTs) bridge adjacent liquid metal particles (LMPs) through initial hydrogen-bonding/coordination-assisted attachment to the native gallium-oxide shell, followed by local Cu-Ga alloying at the CuCNTs/LMPs interface. Enabled by solvent-mediated dispersion and interfacial hybridization, the strain-resilient composite ink supports high-resolution (100 µm) screen printing and deformable circuit integration. The dynamically reconfigurable island-bridge network retains a sheet resistance of 1.3 × 10^-1 Ω sq^-1 at 300% strain, together with stable resistance during prolonged cyclic stretching and bending. When printed into a compact 12-channel array on breathable fibrous substrates, the resulting electrode array exhibits low skin-electrode impedance and superior high-density surface electromyography (HD-sEMG) performance compared with commercial gel electrodes. Combined with machine-learning-assisted analysis, the electromyography arrays enable training-state recognition, individualized load assessment, and continuous monitoring of fatigue-associated exercise states during dynamic exercise. This work provides a scalable strategy for intrinsically conductive liquid metal electronics and advances their application in intelligent wearable biointerfaces and soft electronic systems.

PMID:
42935125
Bibliographic data and abstract were imported from PubMed on 27 Oct 2026.

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