Mimicking efferent nerves using a graphdiyne-based artificial synapse with multiple ion diffusion dynamics

Huanhuan Wei; Rongchao Shi; Lin Sun; Haiyang Yu; Jiangdong Gong; Chao Liu; Zhipeng Xu; Yao Ni; Jialiang Xu; Wentao Xu

doi:10.1038/s41467-021-21319-9

Nature Communications (Feb 2021)

Mimicking efferent nerves using a graphdiyne-based artificial synapse with multiple ion diffusion dynamics

Huanhuan Wei,
Rongchao Shi,
Lin Sun,
Haiyang Yu,
Jiangdong Gong,
Chao Liu,
Zhipeng Xu,
Yao Ni,
Jialiang Xu,
Wentao Xu

Affiliations

Huanhuan Wei: Institute of Photoelectronic Thin Film Devices and Technology, Key Laboratory of Optoelectronic Thin Film Devices and Technology of Tianjin, College of Electrical Information and Optical Engineering, School of Materials Science and Engineering, National Institute for Advanced Materials, Nankai University
Rongchao Shi: Institute of Photoelectronic Thin Film Devices and Technology, Key Laboratory of Optoelectronic Thin Film Devices and Technology of Tianjin, College of Electrical Information and Optical Engineering, School of Materials Science and Engineering, National Institute for Advanced Materials, Nankai University
Lin Sun: Institute of Photoelectronic Thin Film Devices and Technology, Key Laboratory of Optoelectronic Thin Film Devices and Technology of Tianjin, College of Electrical Information and Optical Engineering, School of Materials Science and Engineering, National Institute for Advanced Materials, Nankai University
Haiyang Yu: Institute of Photoelectronic Thin Film Devices and Technology, Key Laboratory of Optoelectronic Thin Film Devices and Technology of Tianjin, College of Electrical Information and Optical Engineering, School of Materials Science and Engineering, National Institute for Advanced Materials, Nankai University
Jiangdong Gong: Institute of Photoelectronic Thin Film Devices and Technology, Key Laboratory of Optoelectronic Thin Film Devices and Technology of Tianjin, College of Electrical Information and Optical Engineering, School of Materials Science and Engineering, National Institute for Advanced Materials, Nankai University
Chao Liu: Institute of Photoelectronic Thin Film Devices and Technology, Key Laboratory of Optoelectronic Thin Film Devices and Technology of Tianjin, College of Electrical Information and Optical Engineering, School of Materials Science and Engineering, National Institute for Advanced Materials, Nankai University
Zhipeng Xu: Institute of Photoelectronic Thin Film Devices and Technology, Key Laboratory of Optoelectronic Thin Film Devices and Technology of Tianjin, College of Electrical Information and Optical Engineering, School of Materials Science and Engineering, National Institute for Advanced Materials, Nankai University
Yao Ni: Institute of Photoelectronic Thin Film Devices and Technology, Key Laboratory of Optoelectronic Thin Film Devices and Technology of Tianjin, College of Electrical Information and Optical Engineering, School of Materials Science and Engineering, National Institute for Advanced Materials, Nankai University
Jialiang Xu: Institute of Photoelectronic Thin Film Devices and Technology, Key Laboratory of Optoelectronic Thin Film Devices and Technology of Tianjin, College of Electrical Information and Optical Engineering, School of Materials Science and Engineering, National Institute for Advanced Materials, Nankai University
Wentao Xu: Institute of Photoelectronic Thin Film Devices and Technology, Key Laboratory of Optoelectronic Thin Film Devices and Technology of Tianjin, College of Electrical Information and Optical Engineering, School of Materials Science and Engineering, National Institute for Advanced Materials, Nankai University

DOI: https://doi.org/10.1038/s41467-021-21319-9
Journal volume & issue: Vol. 12, no. 1
pp. 1 – 10

Abstract

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Constructing artificial sensorimotor systems for robotic applications calls for development of synaptic connections for complicated information processing. Wei et al. propose a graphdiyne-based artificial synapse capable of parallel processing signals and utilize it in an artificial mechanoreceptor system.

Published in Nature Communications

ISSN: 2041-1723 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science
Website: https://www.nature.com/ncomms/

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