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- W2765291279 abstract "This paper successfully achieved the recognition and quantitative prediction of different amine vapors in high humid atmosphere through the combination of PPy-metal oxide semiconductor (MOS)-based sensor array and multiple linear regression (MLR)-based signal processing technique. PPy-MOS hybrid nanocomposite films with various metal oxide semiconductor materials, including WO <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>3</sub> , NiO, ZnO-NiO, and SnO <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2</sub> -NiO were successfully prepared and deposited on interdigitated electrodes by electrospinning technique. The synthesized metal oxide particles and PPy-MOS hybrid nanocomposites were characterized in terms of their crystalline structures, morphologies, and compositional features by XRD, FE-SEM, and EDAX methods. The sensing performance of the designed array exposed to various alkyl amines, including methyl-, dimethyl-, ethyl- and propylamine, ethanol, acetone, and water vapors have been studied. The principal component analysis was utilized to test the ability of the proposed sensor array to recognize different gas classes. MLR technique was used for simultaneous determination of amine vapors in humid atmosphere. The results showed the capability of developed fabricated sensor array for identification and quantification of different amine vapors in humid atmosphere at low temperature." @default.
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- W2765291279 date "2017-12-15" @default.
- W2765291279 modified "2023-10-18" @default.
- W2765291279 title "PPy-Metal Oxide Hybrid Nanocomposite Sensor Array for Simultaneous Determination of Volatile Organic Amines in High Humid Atmosphere" @default.
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- W2765291279 doi "https://doi.org/10.1109/jsen.2017.2766122" @default.
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