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  5. Cutaneous Ionogel Mechanoreceptors for Soft Machines, Physiological Sensing, and Amputee Prostheses

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Article
English
2021

Cutaneous Ionogel Mechanoreceptors for Soft Machines, Physiological Sensing, and Amputee Prostheses

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English
2021
Advanced Materials
Vol 33 (38)
DOI: 10.1002/adma.202102069

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Guoying Gu
Guoying Gu

Shanghai Jiao Tong University

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Zequn Shen
Xiangyang Zhu
Carmel Majidi
+1 more

Abstract

Touch sensing has a central role in robotic grasping and emerging human–machine interfaces for robot‐assisted prosthetics. Although advancements in soft conductive polymers have promoted the creation of diverse pressure sensors, these sensors are difficult to be employed as touch skins for robotics and prostheses due to their limited sensitivity, narrow pressure range, and complex structure and fabrication process. Here, a highly sensitive and robust soft touch skin is presented with ultracapacitive sensing that combines ionic hydrogels with commercially available conductive fabrics. Prototypical designs of the capacitive sensors through facile manufacturing methods are introduced and a high sensitivity up to 1.5 kPa −1 (≈44 times higher than conventional parallel‐plate capacitive counterparts), a broad pressure detection range of over four orders of magnitudes (≈35 Pa to 330 kPa), ultrahigh baseline of capacitance, fast response time (≈18 ms), and good repeatability are demonstrated. Ionogel skins composed of an array of cutaneous mechanoreceptors capable of monitoring various physiological signals and shape detection are further developed. The touch skin can be integrated within a soft bionic hand and provide an industrial robot and an amputee with robust tactile feedback when handling delicate objects, illustrating its potential applications in next‐generation human‐in‐the‐loop robotic systems with tactile sensing.

How to cite this publication

Zequn Shen, Xiangyang Zhu, Carmel Majidi, Guoying Gu (2021). Cutaneous Ionogel Mechanoreceptors for Soft Machines, Physiological Sensing, and Amputee Prostheses. Advanced Materials, 33(38), DOI: 10.1002/adma.202102069.

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Publication Details

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Article

Year

2021

Authors

4

Datasets

0

Total Files

0

Language

English

Journal

Advanced Materials

DOI

10.1002/adma.202102069

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