Abstract
Detection of volatile organic compounds (VOCs) using non-selective sensor requires an array of multiplexed sensors followed by pattern recognition approach. Based on this concept, we compare three different approaches for selective detection of ethanol, ammonia, toluene, acetone and chloroform at different concentrations using non-selective sensors which are: (a) an array of sensors operated at a fixed temperature (hybrid class sensors), (b) operating one sensor at different temperatures (mono-class sensors), and (c) operating all sensors in an array at different temperatures (hybrid and mono-class sensors). Contrary to common practice of using sensors with partially overlapping response patterns (hybrid class sensors) in an array, we demonstrate that even one type of sensors (mono-class sensors) operated at different temperatures can be used for the selective detection of VOCs. It is further shown that an array consisting of hybrid and mono-class sensors each operated at different temperatures not only results in approaching 100% classification but also the quantified samples fall within 10% of error, which is an encouraging result.
Original language | English (US) |
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Pages (from-to) | 244-252 |
Number of pages | 9 |
Journal | Sensors and Actuators, B: Chemical |
Volume | 117 |
Issue number | 1 |
DOIs | |
State | Published - Sep 12 2006 |
Funding
This work was supported primarily by the Nanoscale Science and Engineering Initiative of the National Science Foundation under NSF Award Number EEC-0118025.
Keywords
- Gas sensors
- Hybrid class sensors
- Mono-class sensors
- Neural networks (NN)
- Principal component analysis (PCA)
- Volatile organic compounds (VOCs)
ASJC Scopus subject areas
- Electronic, Optical and Magnetic Materials
- Instrumentation
- Condensed Matter Physics
- Surfaces, Coatings and Films
- Metals and Alloys
- Electrical and Electronic Engineering
- Materials Chemistry