In this work we used the environmentally friendly method to prepared ZrO2 nanoparticles utilizing the extract of Thyms plant In basic medium and at pH 12, the ZrO2 NPs was characterized by different techniques such as FTIR, ultraviolet visible, Atomic force microscope, Scanning Electron Microscopy, X-ray diffraction and Energy dispersive X-ray. The average crystalline size was calculated using the Debye Scherres equation in value 7.65 nm. Atomic force microscope results showed the size values for ZrO2 NPs were 45.11nm, and there are several distortions due to the presence of some large sizes. Atomic force microscope results showed the typical size values for ZrO2 NPs were 45.11 nm, and there are several distortions due to the presence of so

Magnetic nanoparticles (MNPs) of iron oxide (Fe₃O₄) represent the most promising materials in many applications. MNPs have been synthesized by co-precipitation of ferric and ferrous ions in alkaline solution. Two methods of synthesis were conducted with different parameters, such as temperature (25 and 80 ̊C), adding a base to the reactants and the opposite process, and using nitrogen as an inert gas. The product of the first method (MNPs-1) and the second method (MNPs-2) were characterized by x-ray diffractometer (XRD), Zeta Potential, atomic force microscope (AFM) and scanning electron microscope (SEM). AFM results showed convergent particle size of (MNPs-1) and (MNPs-2) with (86.01) and (74.14)

The study of biopolymers and their derivative materials had received a considerable degree of attention from researchers in the preparation of novel material. Biopolymers and their derivatives have a wide range of applications as a result of their bio-compatibility, bio-degradability and non-toxicity. In this paper, chitosan reacted with different aldehydes(2,4 –dichloro- benzaldehyde or 2-methyl benzaldehyde), different ketones (4-bromoacetophenone or 3-aminoacetophenone) to produce chitosan schiff base (1-4) . Chitosan schiff base (1-4) reacted with glutaric acid or adipic acid in acidic media in distilled water according to the steps of Fischer and Speier to produce compounds (5-12)

This study includes the preparation of the ferrite nanoparticles Cu_xCe_0.3-XNi_0.7Fe₂O₄ (where: x = 0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3) using the sol-gel (auto combustion) method, and citric acid was used as a fuel for combustion. The results of the tests conducted by X-ray diffraction (XRD), emitting-field scanning electron microscopy (FE-SEM), energy-dispersive X-ray analyzer (EDX), and Vibration Sample Magnetic Device (VSM) showed that the compound has a face-centered cubic structure, and the lattice constant is increased with increasing Cu ion. On the other hand, the compound has apparent porosity and spherical particles, and t

Ursolic acid (UA, 3 ?-hydroxy-urs-12-en-28-oic acid) are isomeric triterpenic acids. The high quantities of pentacyclic triterpenoids in Scabiosa species seems to be obvious and there is an evidence that most of pentacyclic triterpenoids that have been isolated are saponins. This is one of the most important characteristic of the genus Scabiosa, the main aglycones are ursolic acid and oleanolic acid. In the current study, isolation from the aerial part and roots of Scabiosa palaestina L. was performed using Preparative HPLC. Furthermore, detection and quantitation of ursolic acid was performed by high performance thin layer chromatography (HPTLC). The identification of isolated triterpenoid involves two methods including FT-IR coupl

During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve

During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve