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Nano Research 2023, 16(2): 3180-3187 https://doi.org/10.1007/s12274-022-4973-3
Research Article | Issue | Published: 17 September 2022

An intelligent MXene/MoS2 acoustic sensor with high accuracy for mechano-acoustic recognition

Show Author's Information Jingwen Chen1,2,3,  Linlin Li2,3,  Wenhao Ran2,3,  Di Chen1( ),  Lili Wang2,3( ),  Guozhen Shen4( )
School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China
State Key Laboratory for Superlattices and Microstructures, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China
Center of Materials Science and Optoelectronic Engineering, University of Chinese Academy of Sciences, Beijing 100083, China
School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing 100081, China
Keywords:
machine learning, MXene/MoS2, intelligent acoustic sensors, high accuracy, mechano-acoustic recognition ABSTRACT
Topics:
Nanosensors
Cite this article:
Chen J, Li L, Ran W, et al. An intelligent MXene/MoS2 acoustic sensor with high accuracy for mechano-acoustic recognition. Nano Research, 2023, 16(2): 3180-3187. https://doi.org/10.1007/s12274-022-4973-3
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Abstract Full text Electronic supplementary material About this article

Auditory systems are the most efficient and direct strategy for communication between human beings and robots. In this domain, flexible acoustic sensors with magnetic, electric, mechanical, and optic foundations have attracted significant attention as key parts of future voice user interfaces (VUIs) for intuitive human–machine interaction. This study investigated a novel machine learning-based voice recognition platform using an MXene/MoS2 flexible vibration sensor (FVS) with high sensitivity for acoustic recognition. The performance of the MXene/MoS2 FVS was systematically investigated both theoretically and experimentally, and the MXene/MoS2 FVS exhibited high sensitivity (25.8 mV/dB). An MXene/MoS2 FVS with a broadband response of 40–3,000 Hz was developed by designing a periodically ordered architecture featuring systematic optimization. This study also investigated a machine learning-based speaker recognition process, for which a machine-learning-based artificial neural network was designed and trained. The developed neural network achieved high speaker recognition accuracy (99.1%).


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About this article

An intelligent MXene/MoS2 acoustic sensor with high accuracy for mechano-acoustic recognition

Show Author's information Jingwen Chen1,2,3, Linlin Li2,3, Wenhao Ran2,3, Di Chen1( ), Lili Wang2,3( ), Guozhen Shen4( )
School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China
State Key Laboratory for Superlattices and Microstructures, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China
Center of Materials Science and Optoelectronic Engineering, University of Chinese Academy of Sciences, Beijing 100083, China
School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing 100081, China

Abstract

Auditory systems are the most efficient and direct strategy for communication between human beings and robots. In this domain, flexible acoustic sensors with magnetic, electric, mechanical, and optic foundations have attracted significant attention as key parts of future voice user interfaces (VUIs) for intuitive human–machine interaction. This study investigated a novel machine learning-based voice recognition platform using an MXene/MoS2 flexible vibration sensor (FVS) with high sensitivity for acoustic recognition. The performance of the MXene/MoS2 FVS was systematically investigated both theoretically and experimentally, and the MXene/MoS2 FVS exhibited high sensitivity (25.8 mV/dB). An MXene/MoS2 FVS with a broadband response of 40–3,000 Hz was developed by designing a periodically ordered architecture featuring systematic optimization. This study also investigated a machine learning-based speaker recognition process, for which a machine-learning-based artificial neural network was designed and trained. The developed neural network achieved high speaker recognition accuracy (99.1%).

Keywords: machine learning, MXene/MoS2, intelligent acoustic sensors, high accuracy, mechano-acoustic recognition ABSTRACT

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Publication history
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Acknowledgements

Publication history

Received: 13 April 2022
Revised: 27 August 2022
Accepted: 28 August 2022
Published: 17 September 2022
Issue date: February 2023

Copyright

© Tsinghua University Press 2022

Acknowledgements

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Nos. 51972025, 61888102, and 62174152), the Young Elite Scientists Sponsorship Program by the China Association for Science and Technology (CAST) (No. 2018QNRC001), the Strategic Priority Program of the Chinese Academy of Sciences (No. XDA16021100), and the Science and Technology Development Plan of Jilin Province (No. 20210101168JC).

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