Jarkko Tolvanen et al 2021 Flex. Print. Electron. 6 034005 https://doi.org/10.1088/2058-8585/ac20e1
Kirigami-inspired dual-parameter tactile sensor with ultrahigh sensitivity, multimodal and strain-insensitive features
|Author:||Tolvanen, Jarkko1; Hannu, Jari1; Jantunen, Heli1|
1Microelectronics Research Unit, Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland
|Online Access:||PDF Full Text (PDF, 0.8 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2021083044590
|Publish Date:|| 2022-08-25
Soft sensors with strain-insensitive and multimodal features are intriguing due to their high practical relevance. However, incorporating these functionalities into sensors made of soft materials has been challenging. Herein, a Kirigami-inspired dual-parameter tactile sensor was developed with strain-insensitive and multimodal features. The tactile sensor uses piezoresistive and capacitive transduction modes allowing simultaneous detection of dynamic and static tensile strains, proximity and normal pressures. The convenient structural design enables ultrahigh piezoresistive sensitivity ~23 000 kPa-1 in its resistivity-switching threshold region (in high pressure regimes > 50 kPa). It achieves a linear capacitive gauge factor of ~14.48 for uniaxial elongation up to 80% strain and can accurately measure proximity (≥ 0.01 pF/mm) of objects within distances up to 100 mm. The ultrahigh sensitivity in high pressure regimes allows force adjustable lower limit of detection and sensitivity of the sensor by pre-stress enabling real-time measurement of arterial pulsation. The findings of this work support the design of soft sensors for touch recognition applications in the automotive industry, soft robots or self-adjusting grippers requiring a sense of touch and multimodal and strain-insensitive features.
Flexible and printed electronics
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
216 Materials engineering
The research was financially supported by the ENTITY project (Infotech Oulu, University of Oulu) and the Printed Intelligence Infrastructure (Academy Finland, grant no. 320017).
|Academy of Finland Grant Number:||
320017 (Academy of Finland Funding decision)
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