Abstract

In situ scanning electron microscope (SEM) characterization have enabled the stretching, compression, and bending of micro/nanomaterials and have greatly expanded our understanding of small-scale phenomena. However, as one of the fundamental approaches for material analytics, torsion tests at a small scale remain a major challenge due to the lack of an ultrahigh precise torque sensor and the delicate sample assembly strategy. Herein, we present a microelectromechanical resonant torque sensor with an ultrahigh resolution of up to 4.78 fN∙m within an ultrawide dynamic range of 123 dB. Moreover, we propose a nanorobotic system to realize the precise assembly of microscale specimens with nanoscale positioning accuracy and to conduct repeatable in situ pure torsion tests for the first time. As a demonstration, we characterized the mechanical properties of Si microbeams through torsion tests and found that these microbeams were five-fold stronger than their bulk counterparts. The proposed torsion characterization system pushes the limit of mechanical torsion tests, overcomes the deficiencies in current in situ characterization techniques, and expands our knowledge regarding the behavior of micro/nanomaterials at various loads, which is expected to have significant implications for the eventual development and implementation of materials science.

Sensors: measuring torque

A microelectromechanical resonant sensor is proposed for small-scale torsion tests and demonstrated for the testing of silicon microbeams. It is important to understand the mechanical properties of small-scale materials used in MEMS and NEMS. Various systems have been reported for this under a range of loading conditions, but the application of torque to small-scale samples is particularly hard to measure. Here, a joint team led by Xueyong Wei from Xi’an Jiaotong University and Yajing Shen from City University of Hong Kong reports a MEMS-based resonator sensor that can measure bending and torsion over a wide dynamic range with a high force resolution. They demonstrate their system for the testing of silicon microbeams, which are found to be five times stronger than bulk silicon under pure torsion.

Details

Title
Robot-aided fN∙m torque sensing within an ultrawide dynamic range
Author
Wang, Shudong 1   VIAFID ORCID Logo  ; Wei Xueyong 2   VIAFID ORCID Logo  ; Lu Haojian 3   VIAFID ORCID Logo  ; Ren Ziming 2 ; Jiang Zhuangde 2 ; Ren, Juan 4 ; Yang, Zhan 5 ; Sun Lining 5 ; Shang Wanfeng 6   VIAFID ORCID Logo  ; Wu, Xinyu 6 ; Shen Yajing 7 

 Xi’an Jiaotong University, State Key Laboratory for Manufacturing Systems Engineering, Xi’an, China (GRID:grid.43169.39) (ISNI:0000 0001 0599 1243); City University of Hong Kong, Mechanical and Biomedical Engineering Department, Hong Kong, China (GRID:grid.35030.35) (ISNI:0000 0004 1792 6846) 
 Xi’an Jiaotong University, State Key Laboratory for Manufacturing Systems Engineering, Xi’an, China (GRID:grid.43169.39) (ISNI:0000 0001 0599 1243) 
 City University of Hong Kong, Mechanical and Biomedical Engineering Department, Hong Kong, China (GRID:grid.35030.35) (ISNI:0000 0004 1792 6846) 
 Xi’an Jiaotong University, State Key Laboratory for Manufacturing Systems Engineering, Xi’an, China (GRID:grid.43169.39) (ISNI:0000 0001 0599 1243); Chang’an University, Department of Mechatronics, Xi’an, China (GRID:grid.440661.1) (ISNI:0000 0000 9225 5078) 
 Soochow University, Robotics and Microsystems Center, Suzhou, China (GRID:grid.263761.7) (ISNI:0000 0001 0198 0694) 
 Chinese Academy of Sciences, Shenzhen Institutes of Advanced Technology, Shenzhen, China (GRID:grid.9227.e) (ISNI:0000000119573309) 
 City University of Hong Kong, Mechanical and Biomedical Engineering Department, Hong Kong, China (GRID:grid.35030.35) (ISNI:0000 0004 1792 6846); City University of Hong Kong, Shenzhen Research Institute, Shenzhen, China (GRID:grid.35030.35) (ISNI:0000 0004 1792 6846) 
Publication year
2021
Publication date
2021
Publisher
Springer Nature B.V.
ISSN
20961030
e-ISSN
20557434
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2474754321
Copyright
© The Author(s) 2021. This work is published under https://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.