his study aimed to investigate the usability of Recycled Concrete Aggregate (RCA) in warm mix asphalt (WMA) as the implementation of sustainable construction technology. Five replacement rates (0%, 25%, 50%, 75%, and 100%) were tested for the coarse fraction of virgin aggregate (VA) with 3 types of RCA: untreated RCA, HL-treated RCA, and HCL-treated RCA. Scanning electron microscopy (SEM) analyses were performed to investigate the surface morphology for both treated and untreated RCA. The optimum asphalt cement content for every substitution rate was determined using Marshall mix design method. Thereafter, asphalt concrete specimens were prepared using the optimum asphalt cement content, followed by the evaluation of their performance properties, including the moisture damage, resilient modulus, and permanent deformation characteristics. These properties were assessed using indirect tensile strength and uniaxial repeated loading tests, respectively. It was found that, for mixes with RCA content of 100%, the untreated RCA mixes possess higher OAC than that of treated RCA mixes by 0.26%. The moisture susceptibility was improved in case of HL and HCL treated RCA by 9.09% and 10.34% as compared to untreated RCA. However, the resilient modulus and resistance to permanent deformation for the mixes with 100% RCA were lower than those prepared with VA; the decrement in Mr values are 39.17%, 28.56% and 23.09% for the untreated RCA, HL-treated RCA and HCL-treated RCA, respectively. Finally, the cost, material, and energy-saving implications of RCA were discussed.
المواقف افي الدول العربية قراءة تحليلية في مواقف لدولية من التغيير (الولايات المتحدة - الاتحاد الاوروبي - روسيا)
حزب العمال الكردستاني ودوره في تطوير القضية الكردية في تركيا من 1991-2013
In this paper we proposed the method of X-ray fluorescence (XRF) determination of some essential trace elements in medicinal herbs and vitamin-mineral complexes at the level of 100-101 mg/ml. To increase sensitivity and selectivity of the determination we simple and effective approach based on the extraction of metal ions from aqueous solutions with chemically modified polyurethane foam sorbents followed by direct XRF analysis. The conditions of sorption preconcentration of Co(II), Ni(II) and Zn(II) ions with modified sorbents were optimized. The proposed approach is used for the determination of trace elements in several kinds of medicinal herbs (coltsfoot leaves, nettle leaves and yarrow herb) and vitamin-mineral
... Show MoreThe Ligand 6,6--(1,2-benzenediazo) bis (3-aminobenzoicacid) derived from o-phenylenediamine and 3-aminobenzoicacid was synthesized. The prepared ligand was identified by Microelemental Analysis, 1HNMR, FT-IR and UV-Vis spectroscopic techniques. Treatment of the ligand with the following metal ions (CoII, NiII, CuII and ZnII ) in aqueous ethanol with a 1:1 M:L ratio and at optimum pH. Characterization of these compounds has been done on the basis of elemental analysis, electronic data, FT-IR and UV-Vis, as well as magnetic susceptibility and conductivity measurements. The nature of the complexes formed were studied following the mole ratio and continuous variation methods, Beer's law obeyed over a concentration range (1×10-4 - 3×10-4 M). H
... Show MoreAPDBN Rashid, Review of International Geographical Education Online (RIGEO), 2021
In the current study, a direct method was used to create a new series of charge-transfer complexes of chemicals. In a good yield, new charge-transfer complexes were produced when different quinones reacted with acetonitrile as solvent in a 1:1 mole ratio with N-phenyl-3,4-selenadiazo benzophenone imine. By using analysis techniques like UV, IR, and 1H, 13C-NMR, every substance was recognized. The analysis's results matched the chemical structures proposed for the synthesized substances. Functional theory of density (DFT)
has been used to analyze the molecular structure of the produced Charge-Transfer Complexes, and the energy gap, HOMO surfaces, and LUMO surfaces have all been created throughout the geometry optimization process ut
Empirical and statistical methodologies have been established to acquire accurate permeability identification and reservoir characterization, based on the rock type and reservoir performance. The identification of rock facies is usually done by either using core analysis to visually interpret lithofacies or indirectly based on well-log data. The use of well-log data for traditional facies prediction is characterized by uncertainties and can be time-consuming, particularly when working with large datasets. Thus, Machine Learning can be used to predict patterns more efficiently when applied to large data. Taking into account the electrofacies distribution, this work was conducted to predict permeability for the four wells, FH1, FH2, F
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