Machine Learning-Assisted Multifunctional Environmental Sensing Based on a Piezoelectric Cantilever

Li, Dongsheng, Liu, Weiting, Zhu, Boyi, Qu, Mengjiao, Zhang, Qian, Fu, Yong Qing and Xie, Jin (2022) Machine Learning-Assisted Multifunctional Environmental Sensing Based on a Piezoelectric Cantilever. ACS Sensors, 7 (9). pp. 2767-2777. ISSN 2379-3694

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Multifunctional environmental sensing is crucial for various applications in agriculture, pollution monitoring, and disease diagnosis. However, most of these sensing systems consist of multiple sensors, leading to significantly increased dimensions, energy consumption, and structural complexity. They also often suffer from signal interferences among multiple sensing elements. Herein, we report a multifunctional environmental sensor based on one single sensing element. A MoS2 film was deposited on the surface of a piezoelectric microcantilever (300 × 1000 μm2) and used as both a sensing layer and top electrode to make full use of the changes in multiple properties of MoS2 after its exposure to various environments. The proposed sensor has been demonstrated for humidity detection and achieved high resolution (0.3% RH), low hysteresis (5.6%), and fast response (1 s) and recovery (2.8 s). Based on the analysis of the magnitude spectra for transmission using machine learning algorithms, the sensor accurately quantifies temperatures and CO2 concentrations in the interference of humidity with accuracies of 91.9 and 92.1%, respectively. Furthermore, the sensor has been successfully demonstrated for real-time detection of humidity and temperature or CO2 concentrations for various applications, revealing its great potential in human–machine interactions and health monitoring of plants and human beings.

Item Type: Article
Additional Information: Funding information: This work is supported by the “Zhejiang Provincial Natural Science Foundation of China (LZ19E050002)”, and the “National Natural Science Foundation of China (NSFC 51875521, 52175552)”. the UK Engineering and Physical Sciences Research Council (EPSRC) under grant EP/P018998/1, International Exchange Grant (IEC/NSFC/201078) through Royal Society UK and the NSFC.
Uncontrolled Keywords: AlN piezoelectric, cantilever environmental sensor, human−machine interaction machine learning, MoS2, multifunctional sensor
Subjects: G600 Software Engineering
H600 Electronic and Electrical Engineering
Department: Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
Depositing User: Rachel Branson
Date Deposited: 08 Sep 2022 08:16
Last Modified: 15 Sep 2023 03:30

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