In this research, titanium dioxide nanoparticles (TiO2 NPs) were prepared through the sol-gel process at an acidic medium (pH3).TiO2 nanoparticles were prepared from titanium trichloride (TiCl3) as a precursor with Ammonium hydroxide (NH4OH) with 1:3 ratio at 50 °C. The resulting gel was dried at 70 °C to obtain the Nanocrystalline powder. The powder from the drying process was treated thermally at temperatures 500 °C and 700 °C. The crystalline structure, surface morphology, and particle size were studied by using X-ray diffraction (XRD), Atomic Force Microscopy (AFM), and Scanning Electron Microscope (SEM). The results showed (anatase) phase of titanium dioxide with the average grain size of 110 nm at 500 °C calcination temperature, and (anatase- rutile) mixed phase of titanium dioxide with the average particle size of 118.1 nm at 700 °C calcination temperature. The anti-bacterial activity of the synthesis specimens was recorded through the Kirby-Bauer disc method (disc devotion method). The results displayed a pretty excellent antibacterial activity of TiO2 NPs to bacteria strains: Gram positive staphylococcus aureus, gram negative pseudomonas aeruginosa, and "gram negative escherichia coli. The sensitivity of the tested bacteria to TiO2 NPs depends on the oxidation state of the TiO2 NPs, particle size, volume, and the density of the unit cell. The small- average particle size of titanium dioxide particles showed high antibacterial activity against bacteria, while the larger- average particle size of titanium dioxide particles showed less antibacterial activity. The novelty of this production is the manufacturing of a novel kind of TiO2 NPs and achievement its best antibacterial activity.
This paper aims to propose a hybrid approach of two powerful methods, namely the differential transform and finite difference methods, to obtain the solution of the coupled Whitham-Broer-Kaup-Like equations which arises in shallow-water wave theory. The capability of the method to such problems is verified by taking different parameters and initial conditions. The numerical simulations are depicted in 2D and 3D graphs. It is shown that the used approach returns accurate solutions for this type of problems in comparison with the analytic ones.
in this paper copper oxide (cuO thin films were prepared by the method of vacum thermal evaporation a pressure.
In this work, metal oxides nanostructures, mainly, copper oxide (CuO), nickel oxide (NiO), titanium dioxide (TiO2), and multilayer structure were synthesized by dc reactive magnetron sputtering technique. The structural purity and nanoparticle size of the prepared nanostructures were determined. The individual metal oxide samples (CuO, NiO and TiO2) showed high structural purity and minimum particle sizes of 34, 44, 61 nm, respectively. As well, the multilayer structure showed high structural purity as no elements or compounds other than the three oxides were founds in the final sample while the minimum particle size was 18 nm. This reduction in nanoparticle size can be considered as an advantage for the dc reactive magnetron sputtering tec
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In this work, the modified Lyapunov-Schmidt reduction is used to find a nonlinear Ritz approximation of Fredholm functional defined by the nonhomogeneous Camassa-Holm equation and Benjamin-Bona-Mahony. We introduced the modified Lyapunov-Schmidt reduction for nonhomogeneous problems when the dimension of the null space is equal to two. The nonlinear Ritz approximation for the nonhomogeneous Camassa-Holm equation has been found as a function of codimension twenty-four.
A modification to cascaded single-stage distributed amplifier (CSSDA) design by using active inductor is proposed. This modification is shown to render the amplifier suitable for high gain operation in small on-chip area. Microwave office program simulation of the Novel design approach shows that it has performance compatible with the conventional distributed amplifiers but with smaller area. The CSSDA is suitable for optical and satellite communication systems.
In this research the a-As flims have been prepared by thermal evaporation with thickness 250 nm and rata of deposition (1.04nm/sec) as function to annealing temperature (373 and 373K), from XRD analysis we can see that the degree of crystalline increase with , and I-V characteristic for dark and illumination shows that forward bias current varieties approximately exponentially with voltage bias. Also we found that the quality factor and saturation current dependence on annealing temperatures.
In this research the a-As flims have been prepared by thermal evaporation with thickness 250 nm and rata of deposition r_d(1.04nm/sec) as function to annealing temperature (373 and 473K), from XRD analysis we can see that the degree of crystalline increase with T_a, and I-V characteristic for dark and illumination shows that forward bias current varieties approximately exponentially with voltage bias. Also we found that the quality factor and saturation current dependence on annealing temperatures.
This paper proposes a new encryption method. It combines two cipher algorithms, i.e., DES and AES, to generate hybrid keys. This combination strengthens the proposed W-method by generating high randomized keys. Two points can represent the reliability of any encryption technique. Firstly, is the key generation; therefore, our approach merges 64 bits of DES with 64 bits of AES to produce 128 bits as a root key for all remaining keys that are 15. This complexity increases the level of the ciphering process. Moreover, it shifts the operation one bit only to the right. Secondly is the nature of the encryption process. It includes two keys and mixes one round of DES with one round of AES to reduce the performance time. The W-method deals with
... Show MoreSupport vector machines (SVMs) are supervised learning models that analyze data for classification or regression. For classification, SVM is widely used by selecting an optimal hyperplane that separates two classes. SVM has very good accuracy and extremally robust comparing with some other classification methods such as logistics linear regression, random forest, k-nearest neighbor and naïve model. However, working with large datasets can cause many problems such as time-consuming and inefficient results. In this paper, the SVM has been modified by using a stochastic Gradient descent process. The modified method, stochastic gradient descent SVM (SGD-SVM), checked by using two simulation datasets. Since the classification of different ca
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