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

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...

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Detalles Bibliográficos
Autores principales: Kwon, Young-Tae, Kim, Yun-Soung, Kwon, Shinjae, Mahmood, Musa, Lim, Hyo-Ryoung, Park, Si-Woo, Kang, Sung-Oong, Choi, Jeongmoon J., Herbert, Robert, Jang, Young C., Choa, Yong-Ho, Yeo, Woon-Hong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7351733/
https://www.ncbi.nlm.nih.gov/pubmed/32651424
http://dx.doi.org/10.1038/s41467-020-17288-0
Descripción
Sumario: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.