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

Article


Zhao, Weichang, Pinnaduwage, Lal A., Leis, John W., Gehl, Anthony C., Allman, Steve L., 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). https://doi.org/10.1063/1.2921866
Article Title

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

ERA Journal ID994
Article CategoryArticle
AuthorsZhao, Weichang (Author), Pinnaduwage, Lal A. (Author), Leis, John W. (Author), Gehl, Anthony C. (Author), Allman, Steve L. (Author), Shepp, Allan (Author) and Mahmud, Ken K. (Author)
Journal TitleJournal of Applied Physics
Journal Citation103 (10)
Article Number104902
Number of Pages11
Year2008
PublisherAIP Publishing
Place of PublicationUnited States
ISSN0021-8979
1089-7550
Digital Object Identifier (DOI)https://doi.org/10.1063/1.2921866
Web Address (URL)http://link.aip.org/link/doi/10.1063/1.2921866
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.

Keywordsquantitative analysis; electronic nose; ternary vapor mixtures; ternary vapour mixtures
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020409902. Engineering instrumentation
340108. Sensor technology (incl. chemical aspects)
469999. Other information and computing sciences not elsewhere classified
Public Notes

This article may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing. This article appeared in J. Appl. Phys. 103, 104902 (2008) and may be found at https://doi.org/10.1063/1.2921866.

Byline AffiliationsTriton Systems, United States
University of Tennessee, United States
Department of Electrical, Electronic and Computer Engineering
Oak Ridge National Laboratory, United States
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