Preparation and Application of Surface Acoustic Wave Sensor for Inorganic Gases

碩士 === 國立臺灣師範大學 === 化學系 === 93 === A multichannel surface acoustic wave (SAW) gas sensor system was prepared to detect NO2 and CO in the air. The coated Ru3+/ cryptand[2,2] and Zn2+/ cryptand[2,2] SAW crystals were applied to recognize NO2 and CO, respectively. The physical adsorption was found for...

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Bibliographic Details
Main Authors: Hsien-Jen Tsai, 蔡顯仁
Other Authors: Jeng-Shong Shih
Format: Others
Language:zh-TW
Published: 2005
Online Access:http://ndltd.ncl.edu.tw/handle/65858618073233508584
Description
Summary:碩士 === 國立臺灣師範大學 === 化學系 === 93 === A multichannel surface acoustic wave (SAW) gas sensor system was prepared to detect NO2 and CO in the air. The coated Ru3+/ cryptand[2,2] and Zn2+/ cryptand[2,2] SAW crystals were applied to recognize NO2 and CO, respectively. The physical adsorption was found for the adsorption of these inorganic gases onto respective coating materials. The SAW sensor also showed good reproducibility and good enough lifetime of ≧ 30 days for detection of NO2 and CO. The detection limits of this SAW sensor with Ru3+/ cryptand[2,2] and Zn2+/ cryptand[2,2] coatings for NO2 and CO were 0.172 and 0.699 ppm respectively, which were lower than occapational exposure limits for both gases and implied that the developed SAW sensor in this study could be employed for environmental analysis for both gases. The concentration effect of NO2 and CO on the frequency responses of the SAW sensor was studied and showed good linear responses with the concentrations of NO2 and CO, respectively. Effects of temperature and humidity on the SAW sensor were also investigated and discussed. Furthermore, the interference of some organic vapors to the detection of NO2 and CO with the SAW sensor was also studied and discussed. The principal component analysis (PCA) was also applied in this study to confirm that appropriate coating materials for NO2 and CO were selected. Two dimension PCA scores plot showed good separation between NO2 and CO which implied that NO2 and CO can be distinguished clearly by the two-channel SAW sensor. In addition, an artificial neural network, using back propagation network (BPN), was also used to recognize NO2 and CO gases and it shows the distinction of these inorganic gases qualitatively by the two-channel SAW sensor with Ru3+/ crypand[2,2] and Zn2+/ crypatnd[2,2] coatings. The quantitative analysis for NO2 and CO were also studied by the multivariate multiple regression analysis.