Liu, Z, Li, Z, Yi, Y et al. (9 more authors) (2022) Flexible strain sensing percolation networks towards complicated wearable microclimate and multi-direction mechanical inputs. Nano Energy, 99. 107444. ISSN 2211-2855
Abstract
A dramatic proliferation of research is placed on wearable and skin-mountable sensing devices because of the prominent serviceability in motion and health recognition, man-machine interaction, as well as artificial intelligence. State-of-the-art wearable sensors, however, lack sensing reliability towards either fickle wearable microclimate or multi-direction mechanical inputs, which leads to a suboptimal sensing accuracy throughout the implementation. In this work, we propose an assembly-flexible strain sensing network based on a carbon nanotube percolated configuration. The sensor possesses high reliability upon microenvironment change of wearable interfaces by taking advantage of the sensing stability in various temperatures, humidity, aqueous acid, and alkaline solutions. The response to bending, twisting, and pressuring is also marginal, guaranteeing sensing dependability against multi-direction mechanical inputs in practical wearable scenarios. By being integrated with deep learning and control systems, the high-performance and biocompatible strain gauges can precisely identify hand gestures and manipulate the upwards/downwards bending of a robot wrist. It demonstrates huge potential in motion identification and man-machine interaction.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | Crown Copyright © 2022 Published by Elsevier Ltd. All rights reserved. This is an author produced version of an article, published in Nano Energy. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Strain sensor; Carbon nanotube; Wearable interface; Sensing reliability; Wearable microclimate |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Robotics, Autonomous Systems & Sensing (Leeds) |
Funding Information: | Funder Grant number Royal Society IEC\NSFC\191095 EPSRC (Engineering and Physical Sciences Research Council) EP/S019219/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 17 Jun 2022 10:44 |
Last Modified: | 01 Jun 2023 00:13 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.nanoen.2022.107444 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:188130 |