In this study we using zirconium sulfate, Punica granatum plant extract, and an alkaline medium, to created ZrO2 nanoparticles. They were then characterized using a variety of techniques, including FT-IR, UV-visible, atomic force microscopy, X-ray diffraction, transmission electron microscopy, scanning electron microscopy, and energy-dispersive X-ray spectroscopy. The Debye-Scherrer equation was used to calculate the crystal size in X-ray diffraction and found to be 27.82 nm. The particle size of ZrO2 nanoparticles was determined using atomic force microscopy, scanning electron microscopes, and transmission electron microscopy. Utilizing ZrO2 NPs, the metal ions M (II) = Co, Ni, and Cu were successfully adsorbed, proving that the three metal ions could be removed from the water at the same time. Over the time frame and under the circumstances, Ni(II) has the highest rate of adsorption. Co, Ni, and Cu ions had removal efficiencies of 32.79%, 75.00%, and 30.20%, respectively. Three concentrations of the ZrO2 nanoparticles were tested against two types of bacteria, Escherichia coli and staphylococcus, and one type of fungus, Candida, in various concentrations of (25, 50, and 75) mg/L. The outcomes were contrasted with those attained using the medications Amoxicillin and Metronidazole.
APDBN Rashid, Review of International Geographical Education Online (RIGEO), 2021
The research involves using phenol – formaldehyde (Novolak) resin as matrix for making composite material, while glass fiber type (E) was used as reinforcing materials. The specimen of the composite material is reinforced with (60%) ratio of glass fiber.
The impregnation method is used in test sample preparation, using molding by pressure presses.
All samples were exposure to (Co60) gamma rays of an average energy (2.5)Mev. The total doses were (208, 312 and 728) KGy.
The mechanical tests (bending, bending strength, shear force, impact strength and surface indentation) were performed on un irradiated and irrad
... Show MoreThe current issues in spam email detection systems are directly related to spam email classification's low accuracy and feature selection's high dimensionality. However, in machine learning (ML), feature selection (FS) as a global optimization strategy reduces data redundancy and produces a collection of precise and acceptable outcomes. A black hole algorithm-based FS algorithm is suggested in this paper for reducing the dimensionality of features and improving the accuracy of spam email classification. Each star's features are represented in binary form, with the features being transformed to binary using a sigmoid function. The proposed Binary Black Hole Algorithm (BBH) searches the feature space for the best feature subsets,
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