The 17 α-ethinylestradiol (EE2) adsorption from aqueous solution was examined using a novel adsorbent made from rice husk powder coated with CuO nanoparticles (CRH). Advanced analyses of FTIR, XRD, SEM, and EDSwere used to identify the classification parameters of a CRH-like surface morphology, configuration, and functional groups. The rice husk was coated with CuO nanoparticles, allowing it to create large surface area materials with significantly improved textural qualities with regard to functional use and adsorption performance, according to a detailed characterization of the synthesized materials. The adsorption process was applied successfully with elimination effectiveness of 100% which can be kept up to 61.3%. The parameters of adsorption were affecting the adsorption process significantly. Thermodynamic data stated that the process of adsorption was endothermic, spontaneous, chemisorption and the molecules of EE2 show affinity with the CRH. It was discovered that the adsorption process controlled by a pseudo-second–order kinetic model demonstrates that the chemisorption process was controlling EE2 removal. The Sips model is regarded as optimal for representing this practice, exhibiting a significantly high determination coefficient of 0.948. This coefficient implies that the adsorption mechanism indicates the occurrence of both heterogeneous and homogeneous adsorption. According to the findings, biomass can serve as a cheap, operative sorbent to remove estrogen from liquified solutions.
Objectives To tailor composites of polyethylene–hydroxyapatite to function as a new intracanal post for the restoration of endodontically treated teeth (ETT). Methods Silanated hydroxyapatite (HA) and zirconium dioxide (ZrO2) filled low-density polyethylene (LDPE) composites were fabricated by a melt extrusion process and characterised using infrared spectroscopy (FTIR), differential scanning calorimetry (DSC) and dynamic mechanical analysis (DMA). The flexural strength and modulus were determined in dry state and post ageing in simulated body fluid and fractured surfaces analysed by SEM. The water uptake and radiographic appearance of the experimental composites were also measured and compared with a commercially known endodontic fibre
... Show MoreCarbon nanoparticles are prepared by sonication using carbon black powder. The surface morphology of carbon black (CB) and carbon nanoparticles (CNPs) is investigated using scanning electron microscopy (SEM). The particles size ranges from 100 nm to 400 nm for CB and from 10 nm to 100 nm for CNPs. CNPs and CB are mixed with silicon glue of different ratios of 0.025, 0.2, 0.05, and 0.1 to synthesis films. The optical properties of the prepared films are investigated through reflectance and absorbance analyses. The ratio of 0.05 for CNPs and CB is the best for solar paint because of its higher solar water heater efficiency and is then added to the silicon glue . Temperature of cold water and temperature of hot water in storage tank were ta
... Show MoreThis research aims at measuring the relationship between moral intelligence and academic adjustment for sixth year primary School Pupils.
The research is assigned to sixth year primary school pupils- Baghdad –the 2nd karkh of the both genders .The total sample includes (500) pupils .The researchers has built two scales one for Moral Intelligence and another for Academic Adjustment and applied them on the total sample of the research .The researchers treated data by appropriate statistical means .The research has reached the following results:
- The pupils of sixth year primary school characterized by Moral Intelligence.
- The
In this paper, we derived an estimators and parameters of Reliability and Hazard function of new mix distribution ( Rayleigh- Logarithmic) with two parameters and increasing failure rate using Bayes Method with Square Error Loss function and Jeffery and conditional probability random variable of observation. The main objective of this study is to find the efficiency of the derived of Bayesian estimator compared to the to the Maximum Likelihood of this function using Simulation technique by Monte Carlo method under different Rayleigh- Logarithmic parameter and sample sizes. The consequences have shown that Bayes estimator has been more efficient than the maximum likelihood estimator in all sample sizes with application
Prediction of penetration rate (ROP) is important process in optimization of drilling due to its crucial role in lowering drilling operation costs. This process has complex nature due to too many interrelated factors that affected the rate of penetration, which make difficult predicting process. This paper shows a new technique of rate of penetration prediction by using artificial neural network technique. A three layers model composed of two hidden layers and output layer has built by using drilling parameters data extracted from mud logging and wire line log for Alhalfaya oil field. These drilling parameters includes mechanical (WOB, RPM), hydraulic (HIS), and travel transit time (DT). Five data set represented five formations gathered
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