Abstract

Electronic skins (e-skins) aim to replicate the capabilities of human skin by integrating electronic components and advanced materials into a flexible, thin, and stretchable substrate. Electrical impedance tomography (EIT) has recently been adopted in the area of e-skin thanks to its robustness and simplicity of fabrication compared to previous methods. However, the most common EIT configurations have limitations in terms of low sensitivities in areas far from the electrodes. Here we combine two piezoresistive materials with different conductivities and charge carriers, creating anisotropy in the sensitive part of the e-skin. The bottom layer consists of an ionically conducting hydrogel, while the top layer is a self-healing composite that conducts electrons through a percolating carbon black network. By changing the pattern of the top layer, the resulting distribution of currents in the e-skin can be tuned to locally adapt the sensitivity. This approach can be used to biomimetically adjust the sensitivities of different regions of the skin. It was demonstrated how the sensitivity increased by 500% and the localization error reduced by 40% compared to the homogeneous case, eliminating the lower sensitivity regions. This principle enables integrating the various sensing capabilities of our skins into complex 3D geometries. In addition, both layers of the developed e-skin have self-healing capabilities, showing no statistically significant difference in localization performance before the damage and after healing. The self-healing bilayer e-skin could recover full sensing capabilities after healing of severe damage.

Details

Title
Variable sensitivity multimaterial robotic e-skin combining electronic and ionic conductivity using electrical impedance tomography
Author
Costa Cornellà, Aleix 1 ; Hardman, David 2 ; Costi, Leone 2 ; Brancart, Joost 3 ; Van Assche, Guy 3 ; Iida, Fumiya 2 

 Vrije Universiteit Brussel, Physical Chemistry and Polymer Science, Brussels, Belgium (GRID:grid.8767.e) (ISNI:0000 0001 2290 8069); University of Cambridge, Bio-Inspired Robotics Lab, Cambridge, UK (GRID:grid.5335.0) (ISNI:0000 0001 2188 5934) 
 University of Cambridge, Bio-Inspired Robotics Lab, Cambridge, UK (GRID:grid.5335.0) (ISNI:0000 0001 2188 5934) 
 Vrije Universiteit Brussel, Physical Chemistry and Polymer Science, Brussels, Belgium (GRID:grid.8767.e) (ISNI:0000 0001 2290 8069) 
Pages
20004
Publication year
2023
Publication date
2023
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2890174169
Copyright
© The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.