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April 27, 2026Advanced Functional Materials0 citations

Dual‐Anchored Interfacial Adhesion‐Enhanced Strain‐Insensitive Hydrogels Electronic Skin for Consciousness‐Driven Brain‐Computer Interface

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DLDeliang LiHZHongxing ZhouLLLe Liu

Key Points

  • This research aims to develop a hydrogel that enhances adhesion and electrical performance for brain-computer interfaces.
  • Developed a 3D-printable AgNSs/AA-DMAPS hydrogel with dynamic bonding capabilities.
  • Evaluated electrical conductivity and strain insensitivity through various tests.
  • Implemented a CNN-LSTM model for consciousness classification using EEG signals.
  • Hydrogel showed strain insensitivity within 500% elongation and high conductivity (over 16 × 10^4 S/m).
  • Achieved 90.8% accuracy in consciousness classification tasks across six categories.
  • Enabled real-time EEG-controlled robotic hand movement through a BCI e-skin.

Abstract

ABSTRACT Hydrogel‐based flexible circuits demand robust rigid‐soft interfacial adhesion, strain‐insensitive electrical performance, and reliable digital‐analog signal transmission capabilities to enable high‐performance electronic skin (e‐skin) applications. Here, we developed a 3D‐printable AgNSs/AA‐DMAPS hydrogel with tunable viscosity via a dual‐anchoring strategy, where silver nanosheets (AgNSs) create nano‐adhesion by forming dynamic crosslinked hydrogel networks. The resulting hydrogel exhibits strain insensitivity within 500% elongation, high electrical conductivity (>16 × 10 4 S/m), and superior multi‐interfacial adhesion properties. Notably, the dual‐anchoring design enables dynamic bonding with metal surfaces (∼450 kPa), allowing self‐welding to hardware without treatment. Flexible circuits printed with AgNSs/AA‐DMAPS hydrogel demonstrate near‐field communication (NFC) functionality and wireless charging, with flexible printed circuit (FPC) capable of transmitting high‐speed signals up to 8 MHz while maintaining effective image signal transmission under 50% strain. Our printed brain‐computer interface (BCI) e‐skin based on this hydrogel achieves EEG signal monitoring. In consciousness classification tasks using the BCI e‐skin, our proposed CNN‐LSTM model attained 90.8% accuracy across six categories. The classification system was deployed on a local server to drive robotic manipulation, enabling real‐time EEG‐controlled robotic hand movement through locally models. The AgNSs/AA‐DMAPS hydrogel‐based BCI e‐skin opens new possibilities for daily brain monitoring applications and continuous exploration of neuropsychiatric disease progression patterns.

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Cite This Study

Li et al. (2026) studied this question.

synapsesocial.com/papers/69eefde9fede9185760d4bc3https://doi.org/10.1002/adfm.202532170
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