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The synthesis of the Fe2O3–CuO–CuFe2O4 nanocomposite was effectively achieved through the sol–gel technique, utilizing ethanol as a reactive fuel. Investigation of the nanocomposite's structure via X-ray Diffraction confirmed the coexistence of Fe2O3, CuO, and CuFe2O4 phases within the material. The Scherrer equation was applied to determine an average crystallite size ranging from 60 to 95 nm. UV–visible spectroscopy studies suggested the material possesses an approximate energy bandgap of 4 eV. Scanning Electron Microscopy provided insights into the nanocomposite's surface morphology, which exhibited a porous and heterogeneous aggregation of particles in various sizes and shapes. When tested for antibacterial efficacy, the nanocomposite exhibited activity against gram-positive S. aureus with a maximum zone of inhibition (ZOI) measuring 9 mm at the highest concentration, whereas no inhibitory effect was detected against gram-negative E.coli. [ABSTRACT FROM AUTHOR]

In this paper, tri-phase Fe2O3–MgO–CuO nanocomposites (NCs) and pure CuO, Fe2O3 and MgO nanoparticles (NPs) were prepared using sol–gel technique. The physical properties of the prepared products were examined using SEM, XRD, and UV–visible. The XRD data indicated the formation of pure CuO, Fe2O3 and MgO NPs, as well as nanocomposite formation with Fe2O3 (cubic), MgO (cubic), and CuO (monoclinic). The crystallite size of all the prepared samples was calculated via Scherrer's formula. The energy bandgap of CuO, Fe2O3 and MgO and Fe2O3–MgO–CuO NCs were computed from UV–visible spectroscopy as following 2.13, 2.29, 5.43 and 2.96 eV, respectively. The results showed that Fe2O3–MgO–CuO NCs is an alternative material for a wide range of applications as optoelectronics devices due to their outstanding properties. [ABSTRACT FROM AUTHOR]

In this paper, Energy Valley Optimizer (EVO) is proposed as a novel metaheuristic algorithm inspired by advanced physics principles regarding stability and different modes of particle decay. Twenty unconstrained mathematical test functions are utilized in different dimensions to evaluate the proposed algorithm's performance. For statistical purposes, 100 independent optimization runs are conducted to determine the statistical measurements, including the mean, standard deviation, and the required number of objective function evaluations, by considering a predefined stopping criterion. Some well-known statistical analyses are also used for comparative purposes, including the Kolmogorov–Smirnov, Wilcoxon, and Kruskal–Wallis analysis. Besides, the latest Competitions on Evolutionary Computation (CEC), regarding real-world optimization, are also considered for comparing the results of the EVO to the most successful state-of-the-art algorithms. The results demonstrate that the proposed algorithm can provide competitive and outstanding results in dealing with complex benchmarks and real-world problems. [ABSTRACT FROM AUTHOR]