Ag-doped NiO thin films were fabricated by dip coating and subsequently annealed at 373, 473, 573, and 673 K to elucidate the influence of annealing temperature on their structural, morphological, optical, and NO2-sensing characteristics. X-ray diffraction confirmed the formation of polycrystalline Ag/NiO films, with progressively improved crystallinity and larger crystallite size at elevated annealing temperatures. Atomic force microscopy revealed a homogeneous surface morphology with grain growth, with the average grain diameter increasing from 26.40 to 34.2 nm. Optical analysis showed high visible-region transmittance (65–70%) and a gradual reduction in the optical band gap from 2.75 to 2.07 eV with increasing annealing temperature. Ga

The current study used extracts from the aloe vera (AV) plant and the hibiscus sabdariffa flower to make Ag-ZnO nanoparticles (NPs) and Ag-ZnO nanocomposites (NCs). Ag/ZnO NCs were compared to Ag NPs and ZnO NPs. They exhibited unique properties against bacteria and fungi that aren't present in either of the individual parts. The Ag-ZnO NCs from AV showed the best performance against E. coli, with an inhibition zone of up to 27 mm, compared to the other samples. The maximum absorbance peaks were observed at 431 nm and 410 nm for Ag NPs, at 374 nm and 377 nm for ZnO NPs and at 384 nm and 391 nm for Ag-ZnO NCs using AV leaf extract and hibiscus sabdariffa flower extract, respectively. Using field emission-scanning electron microscopes (FE-

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Background: Diabetic mellitus type ? is a metabolic disorder of diverse etiological factors, characterized by hyperglycemia resulting from an absolute deficiency of insulin affected childhood and adolescent. Some of these patients seek an orthodontic care .The orthodontist who is treating these medically compromised patients should have a working knowledge of the multitude of medically complex problems. This information will support and enable for delivery of high standards of dental care in general and orthodontic care in particular. The aim of this study was to analyze serum IgG levels and salivary secretory IgA (sIgA) levels in human dentine extract (HDE) before (T0) and 6 months after (T6) orthodontic treatment and to correlate anti-HDE

Evaluation of trace elements in Iraqi chewing gums are unavailable, particularly pollution of toxic elements, materials which change the values of PH in the Oral. Atomic Absorption Spectroscopy (AAS) were successfully employed to determine the concentration of 7 trace elements (essentially toxic and nonessential) and the PH, in thirteen different brands of chewing gum generally consumed in Iraq. Combined wet and dry digestion procedures were applied. Two types of heated graphite tubes were used, coated and uncoated tubes treated with tungsten solution. Result showed that Cu, Al and Zn were at very high levels in almost all brands whereas Mn was found to be high in brands A and O only.

Background: Oral Lichen Planus (OLP) is a chronic inflammatory mucosal disease, presenting in various clinical forms WHO had regarded OLP as a precancerous conditions in 1978 because of its potential with cancer. Both antigen-specific and nonspecific mechanisms involved in the pathogenesis of OLP. Oral Squamous Cell Carcinoma (OSCC) is the most common malignant neoplasm of the oral cavity representing more than 94% of oral cancer. It occurs in different sites and has many etiological factors. Cyclin Dl is a proto-oncogene which consider as the key protein in the regulation of cell proliferation and its overexpression led to the occurrence and progression of malignant tumors.NF-KB p65 is a member ofNF-kB family of transcription factors that

Empirical and statistical methodologies have been established to acquire accurate permeability identification and reservoir characterization, based on the rock type and reservoir performance. The identification of rock facies is usually done by either using core analysis to visually interpret lithofacies or indirectly based on well-log data. The use of well-log data for traditional facies prediction is characterized by uncertainties and can be time-consuming, particularly when working with large datasets. Thus, Machine Learning can be used to predict patterns more efficiently when applied to large data. Taking into account the electrofacies distribution, this work was conducted to predict permeability for the four wells, FH1, FH2, F