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

Wagner B. Lenz; Usman Yaqoob; Rodrigo T. Rocha; Mohammad I. Younis

doi:10.1109/SENSORS52175.2022.9967178

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

Wagner B. Lenz, Usman Yaqoob, Rodrigo T. Rocha, Mohammad I. Younis

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

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.

Original language	English (US)
Title of host publication	2022 IEEE Sensors, SENSORS 2022 - Conference Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665484640
DOIs	https://doi.org/10.1109/SENSORS52175.2022.9967178
State	Published - 2022
Event	2022 IEEE Sensors Conference, SENSORS 2022 - Dallas, United States Duration: Oct 30 2022 → Nov 2 2022

Publication series

Name	Proceedings of IEEE Sensors
Volume	2022-October
ISSN (Print)	1930-0395
ISSN (Electronic)	2168-9229

Conference

Conference	2022 IEEE Sensors Conference, SENSORS 2022
Country/Territory	United States
City	Dallas
Period	10/30/22 → 11/2/22

Bibliographical note

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

Publisher Copyright:
© 2022 IEEE.

Keywords

Classification
Data processing
Machine Learning
Smart sensing

ASJC Scopus subject areas

Electrical and Electronic Engineering

Access to Document

10.1109/SENSORS52175.2022.9967178

Cite this

@inproceedings{8ea1e1276a6042fea2263585a31b58c4,

title = "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.",

keywords = "Classification, Data processing, Machine Learning, Smart sensing",

author = "Lenz, {Wagner B.} and Usman Yaqoob and Rocha, {Rodrigo T.} and Younis, {Mohammad I.}",

note = "Funding Information: This publication is based upon work supported by King Abdullah University of Science and Technology (KAUST). Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE Sensors Conference, SENSORS 2022 ; Conference date: 30-10-2022 Through 02-11-2022",

year = "2022",

doi = "10.1109/SENSORS52175.2022.9967178",

language = "English (US)",

series = "Proceedings of IEEE Sensors",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2022 IEEE Sensors, SENSORS 2022 - Conference Proceedings",

address = "United States",

}

Lenz, WB, Yaqoob, U, Rocha, RT & Younis, MI 2022, Selective gas detection using conductivity-based MEMS resonator and machine learning. in 2022 IEEE Sensors, SENSORS 2022 - Conference Proceedings. Proceedings of IEEE Sensors, vol. 2022-October, Institute of Electrical and Electronics Engineers Inc., 2022 IEEE Sensors Conference, SENSORS 2022, Dallas, United States, 10/30/22. https://doi.org/10.1109/SENSORS52175.2022.9967178

Selective gas detection using conductivity-based MEMS resonator and machine learning. / Lenz, Wagner B.; Yaqoob, Usman; Rocha, Rodrigo T. et al.
2022 IEEE Sensors, SENSORS 2022 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc., 2022. (Proceedings of IEEE Sensors; Vol. 2022-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Selective gas detection using conductivity-based MEMS resonator and machine learning

AU - Lenz, Wagner B.

AU - Yaqoob, Usman

AU - Rocha, Rodrigo T.

AU - Younis, Mohammad I.

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

KW - Classification

KW - Data processing

KW - Machine Learning

KW - Smart sensing

UR - http://www.scopus.com/inward/record.url?scp=85144045879&partnerID=8YFLogxK

U2 - 10.1109/SENSORS52175.2022.9967178

DO - 10.1109/SENSORS52175.2022.9967178

M3 - Conference contribution

AN - SCOPUS:85144045879

T3 - Proceedings of IEEE Sensors

BT - 2022 IEEE Sensors, SENSORS 2022 - Conference Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2022 IEEE Sensors Conference, SENSORS 2022

Y2 - 30 October 2022 through 2 November 2022

ER -

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

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this