Influence of precursor concentration on structural, morphological and electrical properties of spray deposited ZnO thin films
ABSTRACT Nanostructured ZnO thin films were coated on glass substrate by spray pyrolysis using Zinc acetate dihydrate as precursor. Effect of precursor concentration on structural, morphological, optical and electrical properties of the films was investigated. The crystal structure and orientation of the ZnO thin films prepared with four different precursor solution concentrations were studied and it was observed that, the prepared films are polycrystalline in nature with hexagonal wurzite structure. The peaks are indexed to (100), (002), (101), (102) and (110) planes. Grain size and texture coefficient (TC) were calculated and the grain size found to increase with an increase in precursor concentration. Presence of Zn and O elements was confirmed with EDAX spectra. Optical absorption measurements were carried out in the wavelength region of 380 to 800 nm and the band gap decreases as precursor concentration increases. The current-voltage characteristics were observed at room temperature and in dark. It was found that for the films deposited at four different precursor
concentrations, the conductivity improves as precursor concentration increases. As trimethylamine (TMA) is a good marker for food quality discrimination, sensing behavior of the films at an optimized operating temperature of 373 K, towards various concentrations of (TMA) was observed and reported.
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ABSTRACT: This paper investigates a rapid and accurate detection system for spoilage in meat. We use unsupervised feature learning techniques (stacked restricted Boltzmann machines and auto-encoders) that consider only the transient response from undoped zinc oxide, manganese-doped zinc oxide, and fluorine-doped zinc oxide in order to classify three categories: the type of thin film that is used, the type of gas, and the approximate ppm-level of the gas. These models mainly offer the advantage that features are learned from data instead of being hand-designed. We compare our results to a feature-based approach using samples with various ppm level of ethanol and trimethylamine (TMA) that are good markers for meat spoilage. The result is that deep networks give a better and faster classification than the feature-based approach, and we thus conclude that the fine-tuning of our deep models are more efficient for this kind of multi-label classification task.Sensors 01/2013; 13(2):1578-92. · 1.95 Impact Factor
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ABSTRACT: Nanostructured ZnO thin films were deposited on glass substrates at 503 K using spray pyrolysis tech-nique and the films were post annealed at 673 K in air atmosphere for 3 h. The structural, morphological and optical properties of as-deposited and annealed samples were investigated. The annealed films showed uniform spherical morphology in contrast with as-deposited films. XRD patterns of both the annealed and as-deposited films confirmed the polycrystalline nature of the films with hexagonal wurtzite structure. Room temperature ammonia sensing characteristics of annealed films were studied for various concentration levels of ammonia at dry air and humidity conditions. A highest room temper-ature response of 233 was achieved at 25 ppm of NH 3 with a response and recovery times of 20 and 25 s respectively. The response of the sensor to other gases such as methanol, ethanol, 2-propanol, benzyl alcohol and acetone indicated a high selectivity towards ammonia gas. The room temperature (303 K) operation, with high selectivity, repeatability and fast transition times of the sensor together with the low deposition cost suggests suitability for developing a low power cost-effective ammonia sensor.Sensors and Actuators B Chemical 04/2013; 183:459-466. · 3.54 Impact Factor