Identification and quantification of components in ternary vapor mixtures using a microelectromechanical-system-based electronic nose
Article
Article Title | Identification and quantification of components in ternary vapor mixtures using a microelectromechanical-system-based electronic nose |
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ERA Journal ID | 994 |
Article Category | Article |
Authors | Zhao, 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 Title | Journal of Applied Physics |
Journal Citation | 103 (10) |
Article Number | 104902 |
Number of Pages | 11 |
Year | 2008 |
Publisher | AIP Publishing |
Place of Publication | United States |
ISSN | 0021-8979 |
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. |
Keywords | quantitative analysis; electronic nose; ternary vapor mixtures; ternary vapour mixtures |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 409902. 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 Affiliations | Triton Systems, United States |
University of Tennessee, United States | |
Department of Electrical, Electronic and Computer Engineering | |
Oak Ridge National Laboratory, United States |
https://research.usq.edu.au/item/9yv2q/identification-and-quantification-of-components-in-ternary-vapor-mixtures-using-a-microelectromechanical-system-based-electronic-nose
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