Waste-derived materials obtained from the recovery and recycling of electronic waste (e-waste) such as batteries and printed circuit boards have attracted enormous attention from academia and industry in recent years, especially due to their eco-friendly nature and the massive increment in e-waste due to technological development. Several investigations in the literature have covered the advances achieved so far. Meanwhile, photocatalytic applications are especially of interest since they maintain mutual benefits and can be used for H₂ production from solar water splitting based on semiconductor processing as a proper environmentally friendly technique for solar energy conversion. In addition, they can be utilized to degrade a variety of organic and non-organic contaminations. Nonetheless, to the best of the authors’ knowledge, there has not been any comprehensive review that has specifically been focused on e-waste-derived photocatalytic materials. In this regard, the present work is dedicated to thoroughly discussing the related mechanisms, strategies, and methods, as well as the various possible photocatalysts synthesized from e-wastes with some critiques in this field. This brief overview can introduce modern technologies and promising possibilities for e-waste valorization, photocatalytic processes, and new photocatalytic degradation methods of eco-friendly nature. This paper discusses various e-waste-obtained photocatalytic materials, synthesis procedures, and applications, as well as several types of e-waste, derived materials such as TiO₂, ZnO, indium tin oxide, and a variety of sulfide- and ferrite-based photocatalytic materials.

In this study, an intelligent control scheme is developed for induction motors (IMs). The dynamics of IMs are unknown and are perturbed by the variation of rotor resistance and load changes. The control system has two stages. In the identification stage, the group method of data-handling (GMDH) neural network (NN) was designed for online modeling of the IM. In the control stage, the GMDH-NN was applied to compensate for the impacts of disturbances and uncertainties. The stability is shown by the Lyapunov approach. Simulations demonstrated the good accuracy of the suggested new control approach under disturbances and unknown dynamics.