Identification and quantification of components in ternary vapor mixtures using a microelectromechanical-system-based electronic nose

Zhao, Weichang and Pinnaduwage, Lal A. and Leis, John W. and Gehl, Anthony C. and Allman, Steve L. and Shepp, Allan and Mahmud, Ken K. (2008) Identification and quantification of components in ternary vapor mixtures using a microelectromechanical-system-based electronic nose. Journal of Applied Physics, 103 (10). pp. 1-11. ISSN 0021-8979

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Abstract

We report the experimental details on the successful application of the electronic nose approach to identify and quantify components in ternary vapor mixtures. Preliminary results have been presented recently (L. A. Pinnaduwage et al., Appl. Phys. Lett. 91, 044105 (2007)). Our MEMS-based electronic nose is composed of a microcantilever sensor array with seven individual sensors used for vapor detection and an artificial neural network (ANN) for the pattern recognition. A set of custom vapor generators generated reproducible vapor mixtures in different compositions for training and testing of the neural network. The sensor array was selected to be capable to generating different response patterns to mixtures with different component proportions. Therefore, once the electronic nose was trained using the response patterns to various compositions of the mixture, it was able to predict the composition of “unknown” mixtures. We have studied two vapor systems: one included the nerve gas simulant dimethylmethyl phosphonate (DMMP) at parts-per-billion (ppb) concentrations and water and ethanol at parts-per-million (ppm) concentrations; the other system included acetone, water and ethanol all of which were at ppm concentrations. In both systems, individual, binary and ternary mixtures were analyzed with good reproducibility.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Deposited in accordance with the copyright policy of the publisher.
Depositing User: Assoc Prof John Leis
Faculty / Department / School: Historic - Faculty of Engineering and Surveying - Department of Electrical, Electronic and Computer Engineering
Date Deposited: 11 Jun 2008 05:00
Last Modified: 02 Jul 2013 23:03
Uncontrolled Keywords: quantitative analysis; electronic nose; ternary vapor mixtures; ternary vapour mixtures
Fields of Research (FOR2008): 09 Engineering > 0999 Other Engineering > 099902 Engineering Instrumentation
03 Chemical Sciences > 0301 Analytical Chemistry > 030107 Sensor Technology (Chemical aspects)
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080108 Neural, Evolutionary and Fuzzy Computation
Socio-Economic Objective (SEO2008): E Expanding Knowledge > 97 Expanding Knowledge > 970109 Expanding Knowledge in Engineering
Identification Number or DOI: doi: 10.1063/1.2921866
URI: http://eprints.usq.edu.au/id/eprint/4191

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