Identification and quantification of components in ternary vapor mixtures using a microcantilever sensor array and a neural network

Journal Article · Tue Jan 01 04:00:00 EST 2008 · Journal of Applied Physics

DOI:https://doi.org/10.1063/1.2921866· OSTI ID:966723

Zhao, W ^[1]; Pinnaduwage, Lal A ^[2]; Leis, J. W. ^[3]; Gehl, Anthony C ^[2]; Allman, Steve L ^[2]; Shepp, A. ^[1]; Mahmud, K. ^[1]

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1. Triton Systems, Inc.
2. ORNL
3. University of Southern Queensland

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 recently been presented [L. A. Pinnaduwage et al., Appl. Phys. Lett. 91, 044105 (2007)]. Our microelectromechanical-system-based electronic nose is composed of a microcantilever sensor array with seven individual sensors used for vapor detection and an artificial neural network for 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 of generating different response patterns to mixtures with different component proportions. Therefore, once the electronic nose was trained by 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 at ppb concentrations and water and ethanol at 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.

• Research Organization: Oak Ridge National Laboratory (ORNL)
• Sponsoring Organization: ORNL work for others
• DOE Contract Number: AC05-00OR22725
• OSTI ID: 966723
• Journal Information: Journal of Applied Physics, Journal Name: Journal of Applied Physics Journal Issue: 10 Vol. 103; ISSN JAPIAU; ISSN 0021-8979
• Country of Publication: United States
• Language: English

Related Subjects

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEFENSE
ACETONE
BIOLOGICAL WARFARE AGENTS
ETHANOL
GAS ANALYSIS
MIXTURES
NEURAL NETWORKS
PATTERN RECOGNITION
PERFORMANCE TESTING
PHOSPHONATES
SENSORS