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

n this study, Cr−Mo−N thin films with different Mo contents were synthesised via closed field unbalanced magnetron sputtering ion plating. The effects of Mo content on the microstructure, chemical bonding state, and optical properties of the prepared films were investigated by X-ray diffraction spectroscopy (XRD), X-ray photoelectron spectroscopy (XPS), field emission scanning electron microscopy, and ultraviolet-visible spectrophotometry. XRD results determined the face centered cubic (fcc) structure of pure CrN film. The incorporation of molybdenum (Mo) in the CrN matrix was confirmed by both XRD and XPS analyses. The CrMoN coatings demonstrate various polycrystalline phases including CrN, γ-Mo2N, Cr with oxides layers of MoO3, CrO3,

Nitinol (NiTi) is used in many medical applications, including hard tissue replacements, because of its suitable characteristics, including a close elastic modulus to that of bones. Due to the great importance of the mechanical properties of this material in tissue replacements, this work aims to study the hysteresis response in an attempt to explore the ability of the material to remember its previous mechanical state in addition to its ability to withstand stress and to obtain the optimal dimensions and specifications for the manufacturer of NiTi actuators. Stress-strain examination is done in a computational way using a mutable Lagoudas MATLAB code for various coil radii, environment temperatures, and coil lengths. The computational m

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’

         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

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

The preparation of a new Azo compounds of highly conjugated dimeric and polymeric liquid crystal to achieve the crystalline characteristics Which have structures assigned based on elemental analysis, IR 1HNMR and CHNS-O while mesogenic properties have been set for DSC and hot-stage polarizing optical microscopy. The compounds show enantiotropicnematic phase being displayed. The compounds show photoluminescence properties in the organic solution at room temperature, with the fluorescence band centered around 400 nm.

Motivated by the vital role played by transition metal nitride (TMN) composites in various industrial applications, the current study reports electronic properties, thermodynamic stability phase diagram, and vacancy formation energies of the plausible surfaces of NiAs and WC-type structures of δ3-MoN and δ-WN hexagonal phases, respectively. Low miller indices of various surface terminations of δ3-MoN and δ-WN namely, (100), (110), (111), and (001) have been considered. Initial cleaving of δ3-MoN bulk unit cell offers separate Mo and N terminations signified as δ3-MoN (100): Mo, δ3-MoN(100):N, δ3-MoN(111):Mo, δ3-MoN(111):Mo, and δ3-MoN(001):Mo. However, the (110) plane reveals mix-truncated with both molybdenum and nitrogen atoms i

Precise forecasting of pore pressures is crucial for efficiently planning and drilling oil and gas wells. It reduces expenses and saves time while preventing drilling complications. Since direct measurement of pore pressure in wellbores is costly and time-intensive, the ability to estimate it using empirical or machine learning models is beneficial. The present study aims to predict pore pressure using artificial neural network. The building and testing of artificial neural network are based on the data from five oil fields and several formations. The artificial neural network model is built using a measured dataset consisting of 77 data points of Pore pressure obtained from the modular formation dynamics tester. The input variables