Water pollution is considered a serious threat to human life. An advanced oxidation process in the presence of semiconductor photocatalysts is a popular method for the effective decomposition of organic pollutants from wastewater. TiO₂ nanoparticles are widely used as photocatalysts due to their low cost, chemical stability, environmental compatibility and significant efficiency. The aim of this study is to review the photocatalytic processes and their mechanism, reaction kinetics, optical and electrical properties of semiconductors and unique characteristics of titanium as the most widely used photocatalyst; and to compare the photocatalytic activity between different titania phases (anatase, rutile, and brookite) and between colorful and white TiO₂ nanoparticles. Photocatalytic processes are based on the creation of electron–hole pairs. Therefore, increasing stability and separation of charge carriers could improve the photocatalytic activity. The synthesis method has a significant effect on the intensity of photocatalytic activity. The increase in the density of surface hydroxyls as well as the significant mobility of the electron–hole pairs in the anatase phase increases its photocatalytic activity compared to other phases. Electronic and structural changes lead to the synthesis of colored titania with different photocatalytic properties. Among colored titania materials, black TiO₂ showed promising photocatalytic activity due to the formation of surface defects including oxygen vacancies, increasing the interaction with the light irradiation and the lifetime of photogenerated electron–hole pairs. Among non-metal elements, nitrogen doping could be effectively used to drive visible light-activated TiO₂.

In this study graphene oxide (GO) is applied as a substrate for the synthesis of the magnetic ZnO nanocomposite (GO-Fe₃O₄-ZnO). The prepared samples are characterised using XRD and VSM. The photocatalytic performance of the synthesised samples is evaluated according to the photodegradation of methyl orange (MO) as an organic dye pollutant model. The XRD results show that the prepared nanocomposites contain the modified GO with the cubic structure of Fe₃O₄ and hexagonal wurtzite structure of ZnO nanoparticles. Base on the VSM results, it can be confirmed that the synthesised Fe₃O₄-ZnO, GO-Fe₃O₄ and GO-Fe₃O₄-ZnO have superparamagnetic properties. Meanwhile, the saturation magnetisation (Ms) of GO-Fe₃O₄ (45.788 emu/g) is higher than that of Fe₃O₄-ZnO and GO-Fe₃O₄-ZnO. The UV–vis spectrophotometer results reveal that all of the prepared samples can adsorb the UV irradiation and decompose the MO. The removal efficiency of MO using GO-Fe₃O₄-ZnO at each concentration and irradiation time is more than that of Fe₃O₄-ZnO. The analysis of variance (ANOVA) results confirm that main factors (weight fraction and irradiation time) and their interactions have a significant effect on the removal efficiency of MO.

Pure anatase TiO₂ nanoparticles and two kinds of TiO₂–SnO₂ nanocomposite with different amounts of SnO₂ nanoparticles (A-S (1-1) and A-S (1-2)) are synthesised. The X-ray diffraction (XRD) pattern is used for the characterisation of the synthesised samples. The XRD results confirmed that synthesised TiO₂ nanoparticles have an anatase structure and SnO₂ nanoparticles possess a tetragonal structure. The results of TEM reveal that the particle size of the TiO₂ nanoparticles is less than 30 nm. Meanwhile, the SnO₂ nanoparticle is successfully introduced onto the outer surface of TiO₂ nanoparticles. The variation in the removal efficiency of methyl orange as an organic pollutant with UV irradiation time, weight fraction of synthesised photocatalysts and pH is investigated. The obtained results confirm that the removal efficiency of methyl orange increases in respect of the UV irradiation time and weight fraction. In addition, it is observed that the highest and lowest removal efficiencies occur at pH = 3 and pH = 11, respectively. Meanwhile, the results reveal that the removal efficiency of methyl orange is enhanced in respect of the SnO₂ nanoparticles content in the synthesised nanocomposite. The analysis of variance investigation reveals that all the main factors have a significant effect on the removal efficiency of pollutant. Meanwhile, the proposed models can accurately predict the variation of the removal efficiency with studied parameters.