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In this study, the effect of thermal annealing on copper oxide thin films was investigated. The surface properties of copper thin films were examined with the goal of employing them as active layers in optoelectronic devices such as solar cells and optical detectors. Thin layers of copper oxide were deposited on glass substrates by spin coating method. The samples were annealed in air at atmospheric pressure and at different temperatures from 200°C to 600°C. The microstructural, morphological, and optical properties of the surface of thin films were studied by diagnostic techniques such as X-ray diffraction, Raman spectroscopy, ultraviolet-visible absorption spectroscopy, scanning electron microscopy, and X-ray photoelectron spectroscopy. The thickness of copper oxide layers was about 760 nm. The results indicated that the degree of crystallinity improves with increasing annealing temperature, while the values of the strain and dislocation density decrease. XPS analysis showed that deposited copper oxide films have Cu1+ (Cu2O) and Cu2+ (CuO) compounds, and CuO concentration increases with increasing the annealing temperature. Ultraviolet-visible absorption spectroscopy showed that the thin films are absorbent in the visible region and transparent in the nearinfrared region. As the annealing temperature increases above 400 °C, the reflection spectrum changes due to the increase in scattering caused by the growth of crystallite size and surface roughness. [ABSTRACT FROM AUTHOR]

To solve challenges occurred in the existence of large sets of data, recent improvements of machine learning furnish promising results. Here to propose a tool for predicting lesser liquid credit default swap (CDS) rates in the presence of CDS spreads over a large period of time, we investigate different machine learning techniques and employ several measures such as the root mean square relative error to derive the best technique, which is useful for this type of prediction in finance. It is shown that the nearest neighbor is not only efficient in terms of accuracy but also desirable with respect to the elapsed time for running and deploying on unseen data. [ABSTRACT FROM AUTHOR]