All-printed nanomembrane wireless bioelectronics using a biocompatible solderable graphene for multimodal human-machine interfaces

被引:0
作者
Young-Tae Kwon
Yun-Soung Kim
Shinjae Kwon
Musa Mahmood
Hyo-Ryoung Lim
Si-Woo Park
Sung-Oong Kang
Jeongmoon J. Choi
Robert Herbert
Young C. Jang
Yong-Ho Choa
Woon-Hong Yeo
机构
[1] George W. Woodruff School of Mechanical Engineering,Department of Materials Science and Chemical Engineering
[2] Institute for Electronics and Nanotechnology,undefined
[3] Georgia Institute of Technology,undefined
[4] Hanyang University,undefined
[5] School of Biological Sciences,undefined
[6] Georgia Institute of Technology,undefined
[7] Wallace H. Coulter Department of Biomedical Engineering,undefined
[8] Parker H. Petit Institute for Bioengineering and Biosciences,undefined
[9] Georgia Institute of Technology,undefined
[10] Neural Engineering Center,undefined
[11] Flexible and Wearable Electronics Advanced Research,undefined
[12] Institute for Materials,undefined
[13] Institute for Robotics and Intelligent Machines,undefined
[14] Georgia Institute of Technology,undefined
来源
Nature Communications | / 11卷
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摘要
Recent advances in nanomaterials and nano-microfabrication have enabled the development of flexible wearable electronics. However, existing manufacturing methods still rely on a multi-step, error-prone complex process that requires a costly cleanroom facility. Here, we report a new class of additive nanomanufacturing of functional materials that enables a wireless, multilayered, seamlessly interconnected, and flexible hybrid electronic system. All-printed electronics, incorporating machine learning, offers multi-class and versatile human-machine interfaces. One of the key technological advancements is the use of a functionalized conductive graphene with enhanced biocompatibility, anti-oxidation, and solderability, which allows a wireless flexible circuit. The high-aspect ratio graphene offers gel-free, high-fidelity recording of muscle activities. The performance of the printed electronics is demonstrated by using real-time control of external systems via electromyograms. Anatomical study with deep learning-embedded electrophysiology mapping allows for an optimal selection of three channels to capture all finger motions with an accuracy of about 99% for seven classes.
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