University of Oulu

Jarkko Tolvanen et al 2021 Flex. Print. Electron. 6 034005

Kirigami-inspired dual-parameter tactile sensor with ultrahigh sensitivity, multimodal and strain-insensitive features

Saved in:
Author: Tolvanen, Jarkko1; Hannu, Jari1; Jantunen, Heli1
Organizations: 1Microelectronics Research Unit, Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.8 MB)
Persistent link:
Language: English
Published: IOP Publishing, 2021
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.

see all

Series: Flexible and printed electronics
ISSN: 2058-8585
ISSN-E: 2058-8585
ISSN-L: 2058-8585
Volume: 6
Article number: 034005
DOI: 10.1088/2058-8585/ac20e1
Type of Publication: A1 Journal article – refereed
Field of Science: 216 Materials engineering
Funding: 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
Detailed Information: 320017 (Academy of Finland Funding decision)
Copyright information: © 2021 IOP Publishing Ltd. As the Version of Record of this article is going to be/has been published on a subscription basis, this Accepted Manuscript will be available for reuse under a CC BY-NC-ND 3.0 licence after a 12 month embargo period.