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Machine Learning-Assisted Multifunctional Environmental Sensing Based on a Piezoelectric Cantilever.
Li, Dongsheng; Liu, Weiting; Zhu, Boyi; Qu, Mengjiao; Zhang, Qian; Fu, YongQing; Xie, Jin.
Afiliación
  • Li D; State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, Zhejiang 310027, P. R. China.
  • Liu W; State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, Zhejiang 310027, P. R. China.
  • Zhu B; State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, Zhejiang 310027, P. R. China.
  • Qu M; State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, Zhejiang 310027, P. R. China.
  • Zhang Q; State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, Zhejiang 310027, P. R. China.
  • Fu Y; Faculty of Engineering and Environment, University of Northumbria, Newcastle upon Tyne NE1 8ST, U.K.
  • Xie J; State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, Zhejiang 310027, P. R. China.
ACS Sens ; 7(9): 2767-2777, 2022 09 23.
Article en En | MEDLINE | ID: mdl-36106454
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Dióxido de Carbono / Molibdeno Límite: Humans Idioma: En Revista: ACS Sens Año: 2022 Tipo del documento: Article Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Dióxido de Carbono / Molibdeno Límite: Humans Idioma: En Revista: ACS Sens Año: 2022 Tipo del documento: Article Pais de publicación: Estados Unidos