Functionally graded materials (FGMs), with ceramic –ceramic constituents are fabricated using powder technology techniques. In this work three different sets of FGMs samples were designed in to 3 layers, 5 layers and 7 layers. The ceramic constituents were represented by hard ferrite (Barium ferrite) and soft ferrite (lithium ferrite). All samples sintered at constant temperature at 1100^oC for 2 hrs. and characterized by FESEM. Some physical properties were measured for fabricated FGMs include apparent density, bulk density, porosity, shrinkage and hardness. The results indicated that the density increase with the increase the number of layer. Lateral shrinkage is one of the important parameter f

         In this study miconazole nitrate was formulated as topically applied emulgel; different formulas were prepared using sodium carboxymethylcellulose (SCMC) and carboxypolymethylene (carbomer 941) as gelling agents. The influence of type of gelling agent and concentration of both oil phase and emulsifying agent on drug release was studied and compared with commercially available miconazole nitrate cream (Mecozalen^®). The results of in vitro release showed that SCMC emulgel bases gave better release than carbomer 941 bases and the release of drug increase from both bases as a function of increasing the concentration of emulisifying agent. The oil phase had retardation effect when

Unsaturated polyester was used as a matrix which was filled with different percentages of cobalt ferrite using hand lay-up method. Cobalt ferrite was synthesized using solid state ceramic method with reagent of CoO and Fe2O3. Mechanical properties such tensile strength, Young's modulus and shore D hardness of the composite have been studied. All these properties have increased by 10% with increasing cobalt ferrite contents. Also the thermal properties such thermal conductivity and specific heat capacity are highly increased as the ferrite content increased, while the thermal diffusivity increased by 22 %. On the other hand dielectric strength of composite has been measured which increased by 50% by increasing the cobalt ferrite content.&

Refractories are mineral and chemical-, based, materials with excellent heat resistance, making them ideal for use in the construction of ovens, furnace walls industries. According to this our research is concerned to study the effect of addition of (4% CaO) and (5% graphite) on the silica brick properties. Different amounts of CaO and Graphite were included in the white sand (raw ingredients) of silica bricks as a binder to prepare the composition then the composition were sintered using Different sintering temperatures ranging from (1000–1400)𝛐C under static air. Density, thermal conductivity, porosity, and water absorption Compression there was power tested after sintering. XRD analysis was used to identify raw materials’

Engineered geopolymer composite (EGC) is a high-performance material with enhanced mechanical and durability capabilities. Ground granulated blast furnace slag (GGBFS) and silica fume (SF) are common binder materials in producing EGC. However, due to the scarcity and high cost of these materials in some countries, sustainable alternatives are needed. This research focused on producing eco-friendly EGC made of cheaper and more common pozzolanic waste materials that are rich in aluminum and silicon. Rice husk ash (RHA), granite waste powder (GWP), and volcanic pumice powder (VPP) were used as partial substitutions (10–50%) of GGBFS in EGC. The effects of these wastes on workability, unit weight, compressive strength, tensile strengt

A pioneering idea for increasing the thermal performance of heat transfer fluids was to use ultrafine solid particles suspended in the base fluid. Nanofluids, synthesized by mixing solid nanometer sized particles at low concentrations with the base fluid, were used as a new heat transfer fluid and developed a remarkable effect on the thermophysical properties and heat transfer coefficient. For any nanofluid to be usable in heat transfer applications, the main concern is its long-term stability. The aim of this research is to investigate the effect of using four different surfactants (sodium dodecyl benzene sulfonate (SDBS), sodium dodecyl sulfate (SDS), cetyl trimethylammonium bromide (CTAB), and gum Arabic (GA)), each with three different

Increase in unconventional resources of calcium (Ca⁺²) for fowls, aquaculture and native animals was improved. This work was planned to define the most polymorph of calcium carbonate (CaCO₃) that take place in the two types of chicken eggshells (local and imported type). In this research, the comparative analysis of calcium carbonate (CaCO₃) content was approved for nominated eggshells of native strain and imported chicken via Field Emission Scanning Electron Microscope (FESEM), Transmission Electron Microscope (TEM), Fourier-Transform Infrared Spectroscopy (FTIR) and Powder X-Ray Diffraction (PXRD) analysis. The results demonstrate that native and imported chicken eggshells comprise calcite morph that ha

Optimum perforation location selection is an important study to improve well production and hence in the reservoir development process, especially for unconventional high-pressure formations such as the formations under study. Reservoir geomechanics is one of the key factors to find optimal perforation location. This study aims to detect optimum perforation location by investigating the changes in geomechanical properties and wellbore stress for high-pressure formations and studying the difference in different stress type behaviors between normal and abnormal formations. The calculations are achieved by building one-dimensional mechanical earth model using the data of four deep abnormal wells located in Southern Iraqi oil fields. The magni

The method of predicting the electricity load of a home using deep learning techniques is called intelligent home load prediction based on deep convolutional neural networks. This method uses convolutional neural networks to analyze data from various sources such as weather, time of day, and other factors to accurately predict the electricity load of a home. The purpose of this method is to help optimize energy usage and reduce energy costs. The article proposes a deep learning-based approach for nonpermanent residential electrical ener-gy load forecasting that employs temporal convolutional networks (TCN) to model historic load collection with timeseries traits and to study notably dynamic patterns of variants amongst attribute par