Minia University

About: Minia University is a education organization based out in Minya, Egypt. It is known for research contribution in the topics: Population & Medicine. The organization has 4967 authors who have published 8986 publications receiving 108384 citations.

Electrospun Cu-doped titania nanofibers for photocatalytic hydrolysis of ammonia borane

Abstract: Among reported hydrogen storage materials, ammonia borane is a promising candidate to be utilized in many industrial applications. The high chemical resistance of the ceramic catalysts makes them one of the most stable classes of catalytic materials. In this study, CuO nanoparticles (NPs) -doped TiO2 nanofibers (NFs) are introduced as a highly effective and reusable catalyst for ammonia borane complex hydrolysis. The incorporation of CuO NPs inside the TiO2 NFs provided distinct advantages for the introduced catalyst; the aggregation problem of the CuO NPs was overcome and a synergistic effect was created as the synthesized CuO NPs-doped TiO2 revealed higher activity compared to the individual components. Typically, after 10 min, the obtained hydrogen equivalent was 2.7, 0.9 and 0.95 when CuO NPs-doped TiO2 nanofibers, CuO NPs and pristine TiO2 nanofibers were used as the catalyst, respectively. The catalytic activity of the introduced nanofibers did not change after being used for three successive cycles. Moreover, the catalytic performance was strongly modified when the hydrolysis process was performed under sunlight irradiation because of the photocatalytic activity of the TiO2 and CuO. The introduced nanofibers were prepared by the simple, effective, low cost and high yielding technique of electrospinning. The present study introduces TiO2 nanofibers as a promising catalyst for the ammonia borane complex, as well as an interesting support used for functional materials.

Enhanced Elephant Herding Optimization for Global Optimization

Abstract: The elephant herding optimization (EHO) algorithm is a relatively novel population-based optimization technique, which mimics herding behavior and can be modeled into two operators: clan updating operators and separating operators. Also, in the literature, EHO has received a great deal of attention from researchers since it was proposed applied to many application fields for its advantages of excellent global optimization ability and ease of implementation. However, there is still an insufficiency in the EHO algorithm regarding its lack of exploitation, which leads to slow convergence. In this paper, we propose three enhanced versions of EHO based on the $\gamma $ value termed EEHO15, EEHO20, and EEHO25 to overcome the problems of fast unjustified convergence toward the origin of the basic EHO. The exploration/exploitation abilities of the EEHO algorithms are achieved by the updating of the two operators (clan and separation operator). To tackle this drawback, a constant function is used as a benchmark for inspecting the biased convergence of evolutionary algorithms in general. Moreover, we utilize CEC’17 test suite benchmark functions to test the performance of the proposed three versions of EEHO against EHO, particle swarm optimization (PSO), bird swarm algorithm (BSA), and ant lion optimizer (ALO) algorithms. Eventually, the experimental results revealed that the proposed EEHO algorithms extremely obtained better results compared with other competitive algorithms.