Cited 35 time in webofscience Cited 34 time in scopus

Self-Powered Pressure- and Vibration-Sensitive Tactile Sensors for Learning Technique-Based Neural Finger Skin

Title
Self-Powered Pressure- and Vibration-Sensitive Tactile Sensors for Learning Technique-Based Neural Finger Skin
Authors
Chun, SungwooSon, WonkyeongKim, HaeyeonLim, Sang KyooPang, ChanghyunChoi, Changsoon
DGIST Authors
Lim, Sang Kyoo; Choi, Changsoon
Issue Date
2019-05
Citation
Nano Letters, 19(5), 3305-3312
Type
Article
Article Type
Article
Author Keywords
Self-powermechanoreceptorsskin electronicssensorstriboelectric nanogeneratorfinger skin
Keywords
GRAPHENENANOGENERATORINFORMATIONTEXTURE
ISSN
1530-6984
Abstract
Finger skin electronics are essential for realizing humanoid soft robots and/or medical applications that are very similar to human appendages. A selective sensitivity to pressure and vibration that are indispensable for tactile sensing is highly desirable for mimicking sensory mechanoreceptors in skin. Additionally, for a human-machine interaction, output signals of a skin sensor should be highly correlated to human neural spike signals. As a demonstration of fully mimicking the skin of a human finger, we propose a self-powered flexible neural tactile sensor (NTS) that mimics all the functions of human finger skin and that is selectively and sensitively activated by either pressure or vibration stimuli with laminated independent sensor elements. A sensor array of ultrahigh-density pressure (20 × 20 pixels on 4 cm 2 ) of interlocked percolative graphene films is fabricated to detect pressure and its distribution by mimicking slow adaptive (SA) mechanoreceptors in human skin. A triboelectric nanogenerator (TENG) was laminated on the sensor array to detect high-frequency vibrations like fast adaptive (FA) mechanoreceptors, as well as produce electric power by itself. Importantly, each output signal for the SA- and FA-mimicking sensors was very similar to real neural spike signals produced by SA and FA mechanoreceptors in human skin, thus making it easy to convert the sensor signals into neural signals that can be perceived by humans. By introducing microline patterns on the top surface of the NTS to mimic structural and functional properties of a human fingerprint, the integrated NTS device was capable of classifying 12 fabrics possessing complex patterns with 99.1% classification accuracy. © 2019 American Chemical Society.
URI
http://hdl.handle.net/20.500.11750/9897
DOI
10.1021/acs.nanolett.9b00922
Publisher
American Chemical Society
Related Researcher
Files:
There are no files associated with this item.
Collection:
Smart Textile Convergence Research Group1. Journal Articles


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