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A zinc oxide (ZnO) hybrid structure was successfully fabricated on a glass substrate by metal organic chemical vapor deposition (MOCVD). In-situ growth of a multi-dimensional ZnO hybrid structure was achieved by adjusting the growth temperature to determine the morphologies of either film or nanorods without any catalysts such as Au, Cu, Co, or Sn. The ZnO hybrid structure was composed of one-dimensional (1D) nanorods grown continuously on the two-dimensional (2D) ZnO film. The ZnO film of 2D mode was grown at a relatively low temperature, whereas the ZnO nanorods of 1D mode were grown at a higher temperature. The change of the morphologies of these materials led to improvements of the electrical and optical properties. The ZnO hybrid structure was characterized using various analytical tools. Scanning electron microscopy (SEM) was used to determine the surface morphology of the nanorods, which had grown well on the thin film. The structural characteristics of the polycrystalline ZnO hybrid grown on amorphous glass substrate were investigated by X-ray diffraction (XRD). Hall-effect measurement and a four-point probe were used to characterize the electrical properties. The hybrid structure was shown to be very effective at improving the electrical and the optical properties, decreasing the sheet resistance and the reflectance, and increasing the transmittance via refractive index (RI) engineering. The ZnO hybrid structure grown by MOCVD is very promising for opto-electronic devices as Photoconductive UV Detectors, anti-reflection coatings (ARC), and transparent conductive oxides (TCO).
Photovoltaic power generation is currently being recognized as one of the most popular sources for renewable resources over the country. Although it is also being adapted to rural area for may reasons, it is important to estimate the magnitudes of power outputs with reliable statistical methodologies, while applying historical daily solar energy data, for correct feasibility analysis. In this study, one of the well-known statistical methodologies is employed to define the appropriate probability distributions for monthly power outputs for the selected rural area, county of Seo-san, province of Chungnam. The results imply that the assumption of normal distributions for several months may lead to incorrect decision-making and therefore lead to the unreliable feasibility analysis. Generalized beta and triangular distributions were found to be superior to normal distribution, when describing monthly probability distributions for daily photovoltaic power. Based on the appropriate distributions resulted from this study, Monte Carlo simulation technique was also applied to provide additional flexible information for the relevant decision makers. This study found out new finding that the probability distributions should be considered to make planning of the photovoltaic power system in rural village unit, in order to give reasonable economic analysis to the decision makers.
This study aims to estimate the optimal probability distribution of daily electricity generation by wind power, in order to contribute in rural green-village planning. Wind power generation is now being recognized as one of the most popular sources for renewable resources over the country. Although it is also being adapted to rural area for may reasons, it is important to estimate the magnitudes of power outputs with reliable statistical methodologies while applying historical daily wind data, for correct feasibility analysis. In this study, one of the well-known statistical methodology is employed to define the appropriate statistical distributions for monthly power outputs for specific rural areas. The results imply that the assumption of normal distributions for many cases may lead to incorrect decision-making and therefore lead to the unreliable feasibility analysis. Subjective methodology for testing goodness of fit for normal distributions on all the cases in this study, provides possibilities to consider the other various types of statistical distributions for more precise feasibility analysis.