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Artificial tactile perception enabled by triboelectric effect

Authors: Hao Lei; Yihan Wei; Jiayi Wang; Chun Zhao; Zhen Wen; Xuhui Sun;

Artificial tactile perception enabled by triboelectric effect

Abstract

Artificial tactile receptors based on triboelectric nanogenerators (TENGs) hold great promise due to their high sensitivity and active pressure sensing. In this Perspective, we summarize the working mechanisms of triboelectric tactile sensors, highlighting their applications in pressure and sliding perception. Additionally, tribotronic transistors based on TENGs and synaptic transistors have attracted attention due to their neuromorphic computing capabilities for tactile information. The working mechanisms of tribotronic transistors are divided into the triboelectric potential model and the triboelectric electron transfer model. The physical mechanisms by which pressure and sliding forces change the channel conductance states of tribotronic transistors are analyzed in detail. Applications of tactile information perception and modal identification based on tribotronic transistors are presented to show their potential in near-sensor computing. Finally, the challenges faced by TENGs in large-scale tactile information perception and recognition are further discussed.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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Average
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