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

Engineering and Technology

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

Y. Kwon, Y. Kim, et al.

Discover the groundbreaking p-NHE, an all-printed, nanomembrane hybrid electronic system that revolutionizes human-machine interfaces! Harnessing biocompatible, solderable functionalized conductive graphene for flexible circuits and precise EMG recordings, this research by Young-Tae Kwon and team promises real-time control through innovative electrode optimization and deep learning techniques.... show more
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.
Publisher
Nature Communications
Published On
Jul 10, 2020
Authors
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
Tags
hybrid electronic systems
human-machine interfaces
biocompatible graphene
electromyogram recording
deep learning
flexible circuits
real-time control
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