Selective gas detection using conductivity-based MEMS resonator and machine learning

Abstract
This work demonstrates multiple gases identification using a heated MEMS resonator and machine learning. The working principle of the gas sensor is based on the cooling/heating effect of the injected gases on the electrothermally actuated micro beam. As a case study, we demonstrate the concept using two analytes: Acetone and Helium. Machine learning algorithms and Principal Component Analysis are employed to classify each gas with its specific concentration level. The results show that a 100% accuracy rate is achieved for the identification of the different analytes with their concentration levels.

Citation
Lenz, W. B., Yaqoob, U., Rocha, R. T., & Younis, M. I. (2022). Selective gas detection using conductivity-based MEMS resonator and machine learning. 2022 IEEE Sensors. https://doi.org/10.1109/sensors52175.2022.9967178

Acknowledgements
This publication is based upon work supported by King Abdullah University of Science and Technology (KAUST).

Publisher
IEEE

Conference/Event Name
2022 IEEE Sensors

DOI
10.1109/sensors52175.2022.9967178

Additional Links
https://ieeexplore.ieee.org/document/9967178/

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