MSc (Physics) / Department of Physics / Titanium dioxide (TiO2) is considered to be an ideal semiconductor for photocatalysis because of its high stability, low cost and safety towards both humans and the environment. Doping TiO2 with different elements has attracted much attention as the most important way of enhancing the visible light absorption, in order to improve the efficiency of the dye sensitized solar cells (DSSCs). In this study, first principle density functional theory was used to investigate electronic and optical properties of bulk anatase TiO2, undoped, and ruthenium (Ru) and strontium (Sr) doped anatase TiO2 (1 0 0) surface. Two different doping approaches i.e., substitutional and adsorption mechanisms were considered in this study. The results showed that absorption band edges of Ru and Sr-doped anatase TiO2 (1 0 0) surface shift to the long wavelength region compared to the bulk anatase TiO2 and undoped anatase TiO2 (1 0 0) surface. Also, the results revealed that the band gap values and the carrier mobility in the valence band, conduction band and impurity energy levels have a synergetic influence on the visible-light absorption and photocatalytic activity of the doped anatase TiO2 (1 0 0) surface. Furthermore, according to the calculated results, we propose the optical transition mechanisms of Ru and Sr-doped anatase TiO2 (1 0 0) surface. Thus, we conclude that the visible light response of TiO2 can be modulated by doping with both Ru and Sr. However, Sr-doped system shows higher photocatalytic activity than the Ru-doped system. The study has successfully probed the interesting optical response mechanism of TiO2 (1 0 0) surface. / NRF

MSc (Statistics) / Department of Statistics / This study discusses an application of partially linear additive quantile regression models in predicting medium-term global solar irradiance using data from Tellerie radiometric station in South Africa for the period August 2009 to April 2010. Variables are selected using a least absolute shrinkage and selection operator (Lasso) via hierarchical interactions and the parameters of the developed models are estimated using the Barrodale and Roberts's algorithm. The best models are selected based on the Akaike information criterion (AIC), Bayesian information criterion (BIC), adjusted R squared (AdjR2) and generalised cross validation (GCV). The accuracy of the forecasts is evaluated using mean absolute error (MAE) and root mean square errors (RMSE). To improve the accuracy of forecasts, a convex forecast combination algorithm where the average loss su ered by the models is based on the pinball loss function is used. A second forecast combination method which is quantile regression averaging (QRA) is also used. The best set of forecasts is selected based on the prediction interval coverage probability (PICP), prediction interval normalised average width (PINAW) and prediction interval normalised average deviation (PINAD). The results show that QRA is the best model since it produces robust prediction intervals than other models. The percentage improvement is calculated and the results demonstrate that QRA model over GAM with interactions yields a small improvement whereas QRA over a convex forecast combination model yields a higher percentage improvement. A major contribution of this dissertation is the inclusion of a non-linear trend variable and the extension of forecast combination models to include the QRA. / NRF