An all-optical multidirectional mechano-sensor inspired by biologically mechano-sensitive hair sensilla

Yuxiang Li, Zhihe Guo, Xuyang Zhao, Sheng Liu, Zhenmin Chen, Wen-Fei Dong, Shixiang Wang (), Yun-Lu Sun () and Xiang Wu ()
Additional contact information
Yuxiang Li: Fudan University
Zhihe Guo: Fudan University
Xuyang Zhao: Fudan University
Sheng Liu: Fudan University
Zhenmin Chen: Peng Cheng Laboratory (PCL)
Wen-Fei Dong: Chinese Academy of Sciences
Shixiang Wang: Fudan University
Yun-Lu Sun: Fudan University
Xiang Wu: Fudan University

Nature Communications, 2024, vol. 15, issue 1, 1-11

Abstract: Abstract Mechano-sensitive hair-like sensilla (MSHS) have an ingenious and compact three-dimensional structure and have evolved widely in living organisms to perceive multidirectional mechanical signals. Nearly all MSHS are iontronic or electronic, including their biomimetic counterparts. Here, an all-optical mechano-sensor mimicking MSHS is prototyped and integrated based on a thin-walled glass microbubble as a flexible whispering-gallery-mode resonator. The minimalist integrated device has a good directionality of 32.31 dB in the radial plane of the micro-hair and can detect multidirectional displacements and forces as small as 70 nm and 0.9 μN, respectively. The device can also detect displacements and forces in the axial direction of the micro-hair as small as 2.29 nm and 3.65 μN, respectively, and perceive different vibrations. This mechano-sensor works well as a real-time, directional mechano-sensory whisker in a quadruped cat-type robot, showing its potential for innovative mechano-transduction, artificial perception, and robotics applications.

Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-024-47299-0 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-47299-0

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-024-47299-0

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().