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Research Article

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

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
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Graphical Abstract

A machine learning-based voice recognition platform was established using the MXene/MoS2 flexible vibration sensor (FVS) with a high sensitivity for acoustic recognition. The performance of MXene/MoS2 FVS was systematically studied theoretically as well as experimentally. The MXene/MoS2 FVS showed high sensitivity (25.8 mV/dB) and a broadband response. Moreover, an artificial neural network (ANN)-based speaker recognition algorithm was adopted in voice data training.

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

MXene/MoS2intelligent acoustic sensorsmachine learninghigh accuracymechano-acoustic recognition ABSTRACT

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References

[1]

Sun, J. Z.; Du, H.; Chen, Z. J.; Wang, L. L.; Shen, G. Z. MXene quantum dot within natural 3D watermelon peel matrix for biocompatible flexible sensing platform. Nano Res. 2022, 15, 3653–3659.
Crossref Google Scholar
[2]

Li, H. B.; Lv, S. Y.; Fang, Y. Bio-inspired micro/nanostructures for flexible and stretchable electronics. Nano Res. 2020, 13, 1244–1252.
Crossref Google Scholar
[3]

Blossey, R. Self-cleaning surfaces—Virtual realities. Nat. Mater. 2003, 2, 301–306.
Crossref Google Scholar
[4]

Ward, D. J.; MacKay, D. J. C. Artificial intelligence: Fast hands-free writing by gaze direction. Nature 2002, 418, 838.
Crossref Google Scholar
[5]

Li, Y. J.; Tang, J. S.; Gao, B.; Li, X. Y.; Xi, Y.; Zhang, W. R.; Qian, H.; Wu, H. Q. Oscillation neuron based on a low-variability threshold switching device for high-performance neuromorphic computing. J. Semicond. 2021, 42, 064101.
Crossref Google Scholar
[6]

Hirtz, T.; Huurman S.; Tian, H.; Yang, Y.; Ren, T. L. Framework for TCAD augmented machine learning on multi- I–V characteristics using convolutional neural network and multiprocessing. J. Semicond. 2021, 42, 124101.
Crossref Google Scholar
[7]
In-situ/operando characterization techniques for organic semiconductors and devicesJ. Semicond.20224304110110.1088/1674-4926/43/4/041101

Jiang, S.; Dai, Q. Y.; Guo J. H.; Li, Y. In-situ/operando characterization techniques for organic semiconductors and devices. J. Semicond. 2022, 43, 041101.
Crossref Google Scholar
[8]

Perrachione, T. K.; Del Tufo, S. N.; Gabrieli, J. D. E. Human voice recognition depends on language ability. Science 2011, 333, 595.
Crossref Google Scholar
[9]

Guo, H. Y.; Pu, X. J.; Chen, J.; Meng, Y.; Yeh, M. H.; Liu, G. L.; Tang, Q.; Chen, B. D.; Liu, D.; Qi, S. et al. A highly sensitive, self-powered triboelectric auditory sensor for social robotics and hearing aids. Sci. Robot. 2018, 3, eaat2516.
Crossref Google Scholar
[10]

Pu, X. J.; Guo, H. Y.; Chen, J.; Wang, X.; Xi, Y.; Hu, C. G.; Wang, Z. L. Eye motion triggered self-powered mechnosensational communication system using triboelectric nanogenerator. Sci. Adv. 2017, 3, e1700694.
Crossref Google Scholar
[11]

Mohamad, N.; Iovenitti, P.; Vinay, T. Modelling and optimisation of a spring-supported diaphragm capacitive MEMS microphone. Engineering 2010, 2, 762–770.
Crossref Google Scholar
[12]
Weigold, J. W.; Brosnihan, T. J.; Bergeron, J.; Zhang, X. A MEMS condenser microphone for consumer applications. In 19th IEEE International Conference on Micro Electro Mechanical Systems, Istanbul, Turkey, 2006, pp 86–89.
[13]

Mamun, M. A. A.; Yuce, M. R. Recent progress in nanomaterial enabled chemical sensors for wearable environmental monitoring applications. Adv. Funct. Mater. 2020, 30, 2005703.
Crossref Google Scholar
[14]

Li, L. L.; Wang, D. P.; Zhang, D.; Ran, W. H.; Yan, Y. X.; Li, Z. X.; Wang, L. L.; Shen, G. Z. Near-infrared light triggered self-powered mechano-optical communication system using wearable photodetector textile. Adv. Funct. Mater. 2021, 31, 2104782.
Crossref Google Scholar
[15]

Wang, L. L.; Chen, S.; Li, W.; Wang, K.; Lou, Z.; Shen, G. Z. Grain-boundary-induced drastic sensing performance enhancement of polycrystalline-microwire printed gas sensors. Adv. Mater. 2019, 31, 1804583.
Crossref Google Scholar
[16]

Ma, X. H.; Jiang, Z. F.; Lin, Y. J. Flexible energy storage devices for wearable bioelectronics. J. Semicond. 2021, 42, 101602.
Crossref Google Scholar
[17]

Tang, G. Q.; Yan, F. Flexible perovskite solar cells: Materials and devices. J. Semicond. 2021, 42, 101606.
Crossref Google Scholar
[18]

Ma, Z.; Kong, D. S.; Pan, L. J.; Bao, Z. N. Skin-inspired electronics: Emerging semiconductor devices and systems. J. Semicond. 2020, 41, 041601.
Crossref Google Scholar
[19]

Wang, D. Y.; Wang, L. L.; Shen, G. Z. Nanofiber/nanowires-based flexible and stretchable sensors. J. Semicond. 2020, 41, 041605.
Crossref Google Scholar
[20]

Jin, X. J.; Li, L. L.; Zhao, S. F.; Li, X. H.; Jiang, K.; Wang, L. L.; Shen, G. Z. Assessment of occlusal force and local gas release using degradable bacterial cellulose/Ti3C2Tx MXene bioaerogel for oral healthcare. ACS Nano 2021, 15, 18385–18393.
Crossref Google Scholar
[21]

Zhong, B. W.; Jiang, K.; Wang, L. L.; Shen, G. Z. Wearable sweat loss measuring devices: From the role of sweat loss to advanced mechanisms and designs. Adv. Sci. 2022, 9, 2103257.
Crossref Google Scholar
[22]

Wang, L. L.; Chai, R. Q.; Lou, Z.; Shen, G. Z. Highly sensitive hybrid nanofiber-based room-temperature CO sensors: Experiments and density functional theory simulations. Nano Res. 2018, 11, 1029–1037.
Crossref Google Scholar
[23]

Yang, J.; Chen, J.; Liu, Y.; Yang, W. Q.; Su, Y. J.; Wang, Z. L. Triboelectrification-based organic film nanogenerator for acoustic energy harvesting and self-powered active acoustic sensing. ACS Nano 2014, 8, 2649–2657.
Crossref Google Scholar
[24]

Han, J. H.; Kwak, J. H.; Joe, D. J.; Hong, S. K.; Wang, H. S.; Park, J. H.; Hur, S.; Lee, K. J. Basilar membrane-inspired self-powered acoustic sensor enabled by highly sensitive multi tunable frequency band. Nano Energy 2018, 53, 198–205.
Crossref Google Scholar
[25]

Lee, H. S.; Chung, J.; Hwang, G. T.; Jeong, C. K.; Jung, Y.; Kwak, J. H.; Kang, H. M.; Byun, M.; Kim, W. D.; Hur, S. et al. Flexible inorganic piezoelectric acoustic nanosensors for biomimetic artificial hair cells. Adv. Funct. Mater. 2014, 24, 6914–6921.
Crossref Google Scholar
[26]

Lang, C. H.; Fang, J.; Shao, H.; Ding, X.; Lin, T. High-sensitivity acoustic sensors from nanofibre webs. Nat. Commun. 2016, 7, 11108.
Crossref Google Scholar
[27]

Allen, J. Short term spectral analysis, synthesis, and modification by discrete Fourier transform. IEEE Trans. Acoust. Speech Signal Process. 1977, 25, 235–238.
Crossref Google Scholar
[28]

Chen, X. R.; Huang, X. W.; Jie, D.; Zheng, C. F.; Wang, X. L.; Zhang, B. W.; Shao, W. H.; Wang, G. L.; Zhang, W. D. Combining genetic risk score with artificial neural network to predict the efficacy of folic acid therapy to hyperhomocysteinemia. Sci. Rep. 2021, 11, 21430.
Crossref Google Scholar
[29]

Pergialiotis, V.; Pouliakis, A.; Parthenis, C.; Damaskou, V.; Chrelias, C.; Papantoniou, N.; Panayiotides, I. The utility of artificial neural networks and classification and regression trees for the prediction of endometrial cancer in postmenopausal women. Public Health 2018, 164, 1–6.
Crossref Google Scholar
[30]

Pomeroy, B.; Grilc, M.; Likozar, B. Artificial neural networks for bio-based chemical production or biorefining: A review. Renew. Sustain. Energy Rev. 2022, 153, 111748.
Crossref Google Scholar
[31]

Ran, W. H.; Ren, Z. H.; Wang, P.; Yan, Y. X.; Zhao, K.; Li, L. L.; Li, Z. X.; Wang, L. L.; Yang, J. H.; Wei, Z. M. et al. Integrated polarization-sensitive amplification system for digital information transmission. Nat. Commun. 2021, 12, 6476.
Crossref Google Scholar
[32]

Li, B.; Lee, Y.; Yao, W.; Lu, Y.; Fan, X. J. Development and application of ANN model for property prediction of supercritical kerosene. Comput. Fluids 2020, 209, 104665.
Crossref Google Scholar
[33]

Chen, J.; Li, J. C.; Li, Y.; Miao, X. S. Multiply accumulate operations in memristor crossbar arrays for analog computing. J. Semicond. 2021, 42, 013104.
Crossref Google Scholar
[34]

Wei, S. J. Reconfigurable computing: A promising microchip architecture for artificial intelligence. J. Semicond. 2020, 41, 020301.
Crossref Google Scholar
[35]

Song, J.; Wang, X. M.; Zhao, Z. P.; Li, W.; Zhi, T. A survey of neural network accelerator with software development environments. J. Semicond. 2020, 41, 021403.
Crossref Google Scholar
[36]

Meng, J. L.; Wang, T. Y.; He, Z. Y.; Li, Q. X.; Zhu, H.; Ji L.; Chen, L.; Sun, Q. Q.; Zhang, D. W. A high-speed 2D optoelectronic in-memory computing device with 6-bit storage and pattern recognition capabilities. Nano Res. 2022, 15, 2472–2478.
Crossref Google Scholar
[37]

Ji, Z. H.; Zhang, L. L.; Tang, D. M.; Chen, C. M.; Nordling, T. E. M.; Zhang, Z. D.; Ren, C. L.; Da, B.; Li, X.; Guo, S. Y. et al. High-throughput screening and machine learning for the efficient growth of high-quality single-wall carbon nanotubes. Nano Res. 2021, 14, 4610–4615.
Crossref Google Scholar
Nano Research
Volume 16 Issue 2,
February 2023
Pages 3180-3187
DOI: 10.1007/s12274-022-4973-3
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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Received: 13 April 2022
Revised: 27 August 2022
Accepted: 28 August 2022
Published: 17 September 2022
© Tsinghua University Press 2022
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