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Paintable on-skin dry electrodes with robust skin and device connection for wireless sensing and human-machine interfaces
Journal article   Open access   Peer reviewed

Paintable on-skin dry electrodes with robust skin and device connection for wireless sensing and human-machine interfaces

Wanqing Zhang, Xin Xin, Yuqi Wang, Xianzhe Zhang, Senhao Zhang, Yangbo Yuan, Shihao Xia, Abu Musa Abdullah, Fatema Tuz Zohra, Bowen Li, …
Proceedings of the National Academy of Sciences - PNAS, v 123(29), e2615835123
21 Jul 2026
DOI: https://doi.org/10.1073/pnas.2615835123
PMID: 42441849
Featured in Collection :   Drexel's Newest Publications
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https://doi.org/10.1073/pnas.2615835123View
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Abstract

Electric Impedance Electrodes Equipment Design Humans Skin Wearable Electronic Devices Wireless Technology - instrumentation
Reliable and continuous electrophysiological recording is important for health monitoring and human-machine interactions. However, most existing epidermal electrodes suffer from either limited skin-electrode contact during skin deformation and sweating, or unstable connections between soft electrodes and relatively rigid data acquisition systems due to the inherent mechanical mismatch. Besides, their lack of personalization further discourages long-term use, particularly among children, adolescents, and individuals sensitive to stigma. Here, this work presents a paintable, drawn-on-skin dry electrode that forms an ultraconformal interface on hierarchically textured skin topographies with a thickness gradient to minimize interfacial stress, achieving low contact impedance (10.8 kΩ cm ) and high adhesion (~963 kPa) on skin. The resulting electrodes are customizable in shape and color, transforming them from "medical devices" into playful wearable accessories, thus enhancing user compliance and long-term wearability. Moreover, the in situ paintability enables seamless integration with porous silver textile connectors, yielding an interlocked junction with a built-in modulus gradient for stable signal transmission. The versatility of this platform is demonstrated through diverse use cases, including wireless electrocardiogram monitoring during long-term complex daily activities, machine learning-enabled electromyogram for gesture recognition and robotic hand control, and through-hair electroencephalogram detection for neural response analysis. In addition, the absence of image artifact highlights its potential for multimodal MRI imaging and electrophysiology. Overall, this strategy establishes a personalized, scalable platform for next-generation electronic tattoos toward continuous healthcare and interactive bioelectronics.

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