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

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 and Woon-Hong Yeo ()
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Young-Tae Kwon: George W. Woodruff School of Mechanical Engineering, Institute for Electronics and Nanotechnology, Georgia Institute of Technology
Yun-Soung Kim: George W. Woodruff School of Mechanical Engineering, Institute for Electronics and Nanotechnology, Georgia Institute of Technology
Shinjae Kwon: George W. Woodruff School of Mechanical Engineering, Institute for Electronics and Nanotechnology, Georgia Institute of Technology
Musa Mahmood: George W. Woodruff School of Mechanical Engineering, Institute for Electronics and Nanotechnology, Georgia Institute of Technology
Hyo-Ryoung Lim: George W. Woodruff School of Mechanical Engineering, Institute for Electronics and Nanotechnology, Georgia Institute of Technology
Si-Woo Park: Hanyang University
Sung-Oong Kang: Hanyang University
Jeongmoon J. Choi: School of Biological Sciences, Georgia Institute of Technology
Robert Herbert: George W. Woodruff School of Mechanical Engineering, Institute for Electronics and Nanotechnology, Georgia Institute of Technology
Young C. Jang: School of Biological Sciences, Georgia Institute of Technology
Yong-Ho Choa: Hanyang University
Woon-Hong Yeo: George W. Woodruff School of Mechanical Engineering, Institute for Electronics and Nanotechnology, Georgia Institute of Technology

Nature Communications, 2020, vol. 11, issue 1, 1-11

Abstract: Abstract 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.

Date: 2020
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DOI: 10.1038/s41467-020-17288-0

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