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Solution combustion method was adopted for the preparation of ultrafine cerium oxide nanoparticles, which act as excellent photocatalysts for the degradation of dye from aqueous solution.

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Cerium oxide (CeO2) nanoparticles were successfully synthesized by solution combustion method using cerium nitrate and urea. The prepared sample was characterized by X-Ray Diffraction (XRD), Fourier Transform Infra-red Spectroscopy (FTIR), Ultraviolet (UV) and Photoluminescence (PL), Field Emission Scanning Electron Microscopy (FESEM), High Resolution Transmission Electron Microscope (HRTEM), Thermogravimetric-Differential Thermal Analysis (TG-DTA) and photocatalytic measurements. The XRD results confirmed that the synthesized nanocrystalline CeO2 samples have cubic structure with average grain size about 12, 18 and 26 nm (as prepared at 120 °C, sintered at 300 °C and sintered at 600 °C respectively). The HRTEM results confirmed that the synthesized nanoparticles have good polycrystalline nature. In the PL spectra, a strong and broad emission band was observed at 425 nm due to the presence of blue shift in the visible region. The TEM results revealed that the synthesized CeO2 nanoparticles were sphere shaped particles of size in the range 10–15 nm. The degradation of methylene blue catalysed by CeO2 nanoparticles was studied under visible light irradiation. The maximum degradation was obtained with 1.0 g/L CeO2 at pH 11 within 5 hours.

Reliability improvement of distribution system components is gaining more attention due to the structural changes of a power system which provides multiple choices to the consumers for selecting the utility. The optimal enhancement strategy is based on the trade-off between the investment required for improving the reliability parameters of the system components from the present level and the reduction in interruption power which is in terms of cost. These costs are modeled in terms average failure rate and average interruption duration of system sections. Adequacy of the power supply at loads points is ensured by imposing the constraints on upper bounds on load points and sections reliability parameters of the system. Consumers concern on supply reliability is fulfilled by imposing bounds on customer oriented reliability indices. This makes the optimal design problem as optimization problem of non-linear objective with linear and non-linear constraints. This paper applies the population based natural computational algorithms such as genetic algorithm, particle swarm optimization, differential evolution and firefly algorithm for solving the optimal reliability enhancement model of the sample test system. Results obtained are compared with the results of existing literature which uses polynomial time algorithm.