تم في هذا البحث استخدام المحفز الجديد المصنع من تحميل دقائق البلاتين النانوية على سطح الصفائح النانوية للكرافين كمحفز ضوئي واختباره لدراسة التجزئة الضوئية لملوثات المياه وازالتها بشكل نهائي من مصادر المياه لما لها من تأثير سلبي على البيئة. حيث تم استخدام صبغة البروموفينول الأزرق كمثال على أحد الملوثات. في البدء تم التأكد من تحضير المحفز بالطريقة المستخدمة في طريقة العمل من خلال تشخيصه باستخدام عدد من التقنيات ومنها تقنية المجهر الالكتروني النافذ عالي الدقة، تقنية طاقة تشتت الاشعة السينية الطيفي عن طريق قياس الامتزاز/ الامتزاز باستخدام غاز النتروجين. كذلك تم قياس المساحة السطحية للمحفز المصنع، بالإضافة الى فحص التركيب الكريستال للمحفز باستخدام تقنية حيود الاشعة السينية. وبعد ان تم التأكد من التركيب النهائي للمحفز الضوئي تضمن الجزء الثاني من العمل دراسة قدرة المحفز المصنع على استخدامه في التجزئة الضوئية لصبغة البروموفينول الأزرق تحت الاشعة فوق البنفسجية حيث تم تحضير عدة تراكيز من صبغة البروموفينول الأزرق، تم تشعيع الصبغة بدون وجود المحفز ووجد بان التجزئة الضوئية لم تكن فعالة وبعد ذلك تم استخدام المحفز مع المحلول المائي للصبغة وبتركيز 15 جزء من المليون وأجريت التجارب باستخدام عدة اوزان من المحفز لتحديد افضل وزن يمكن استخدامه من المحفز في كمية محددة من محلول الصبغة ووجد ان 0.01 غرام من الصبغة لكل 250 ملليتر من المحلول المائي للصبغة هو افضل نسبة يمكن الحصول عليها. كما تم اختبار الوسط للتفاعل في الوسطين الحامضي والقاعدي ووجد ان تفكك الصبغة يزداد بشكل ملحوظ في الوسط القاعدي. تم اقتراح ميكانيكية التفاعل التي بينت ان تكون الجذور الحرة لها دور كبير في مهاجمة الاواصر المزدوجة في الصبغة.
In this paper we shall prepare an sacrificial solution for fuzzy differential algebraic equations of fractional order (FFDAEs) based on the Adomian decomposition method (ADM) which is proposed to solve (FFDAEs) . The blurriness will appear in the boundary conditions, to be fuzzy numbers. The solution of the proposed pattern of equations is studied in the form of a convergent series with readily computable components. Several examples are resolved as clarifications, the numerical outcomes are obvious that the followed approach is simple to perform and precise when utilized to (FFDAEs).
Trip generation is the first phase in the travel forecasting process. It involves the estimation of the
total number of trips entering or leaving a parcel of land per time period (usually on a daily basis);
as a function of the socioeconomic, locational, and land-use characteristics of the parcel.
The objective of this study is to develop statistical models to predict trips production volumes for a
proper target year. Non-motorized trips are considered in the modeling process. Traditional method
to forecast the trip generation volume according to trip rate, based on family type is proposed in
this study. Families are classified by three characteristics of population social class, income, and
number of vehicle ownersh
The present work aims to improve the flux of forward osmosis with the use of Thin Film Composite membrane by reducing the effect of polarization on draw solution (brine solution) side.This study was conducted in two parts. The first is under the effect of polarization in which the flux and the water permeability coefficient (A) were calculated. In the second part of the study the experiments were repeated using a circulating pump at various speeds to make turbulence and reduce the effect of polarization on the brine solution side.
A model capable of predicting water permeability coefficient has been derived, and this is given by the following equations:
Z=Z0 +C.R.T/9.8(d2/D2+1) [Exp. [-9.8(d
Extended calculations for sputtering yield through bombed Iron – target by ( H,D ,T ,He ) ions plasma are accomplished .The calculations include changing the input parameters : the energy of ( H,D ,T ,He ) ions plasma, the hit target angle of Iron, change atomic mass of incident ion. The program TRIM is used to accomplish these calculations. The results show that sputtering yield is directly dependent on these parameters. It can change the incident angle of ( H,D ,T ,He ) ions and energy&n
... Show MoreA modified chemical method was used to prepare titanium dioxide nanoparticles (TiO2 NPs), which were diagnosed by several techniques: X-ray diffraction, Fourier transform infrared, field emission scaning electron microscopy, energy disperse X-ray, and UV-visible spectroscopy, which proved the success of the preparation process at the nanoscale level. Where the titanium oxide particles have an average particle size equal to 6.8 nm, titanium dioxide particles were used in the process of adsorption of Congo red dye from its aqueous solutions using a batch system. The titanium oxide particles gave an adsorption efficiency of Congo red dye up to more than 79 %. The experimental data of the adsorption process were analyzed with kinetic models and
... Show MoreThis Action research aimed at Assisting Students of Faculty of Educational Sciences at Al-Quds Open University to design computerized lessons using the Power Point software and according to ADDIE model. The study sample consisted of 40 students who were taking a course titled Technology of Education during the second semester of the 2014-2015 academic year and three academic instructors . To collect the required date , the researchers used focus group technique and structured interviews to get information from the 40 students and the three academic instructors involved in the course Technology of Education in QOU /Nablus Branch. In addition to these methods, a workshop with a guiding checklist was employed t
... Show MoreTransmission lines are generally subjected to faults, so it is advantageous to determine these faults as quickly as possible. This study uses an Artificial Neural Network technique to locate a fault as soon as it happens on the Doukan-Erbil of 132kv double Transmission lines network. CYME 7.1-Programming/Simulink utilized simulation to model the suggested network. A multilayer perceptron feed-forward artificial neural network with a back propagation learning algorithm is used for the intelligence locator's training, testing, assessment, and validation. Voltages and currents were applied as inputs during the neural network's training. The pre-fault and post-fault values determined the scaled values. The neural network's p
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