Purpose: To use the L25 Taguchi orthogonal array for optimizing the three main solvothermal parameters that affect the synthesis of metal-organic frameworks-5 (MOF-5). Methods: The L25 Taguchi methodology was used to study various parameters that affect the degree of crystallinity (DOC) of MOF-5. The parameters comprised temperature of synthesis, duration of synthesis, and ratio of the solvent, N,N-dimethyl formamide (DMF) to reactants. For each parameter, the volume of DMF was varied while keeping the weight of reactants constant. The weights of 1,4-benzodicarboxylate (BDC) and Zn(NO3)2.6H2O used were 0.390 g and 2.166 g, respectively. For each parameter investigated, five different levels were used. The MOF-5 samples were synthesi

This study examines the removal of ciprofloxacin in an aqueous solution using green tea silver nanoparticles (Ag-NPs). The synthesized Ag-NPs have been classified by the different techniques of SEM, AFM, BET, FTIR, and Zeta potential. Spherical nanoparticles with average sizes of 32 nm and a surface area of 1.2387m2/g are found to be silver nanoparticles. The results showed that the ciprofloxacin removal efficiency depends on the initial pH (2.5-10), CIP (2-15 mg/L), temperature (20-50°C), time (0-180 min), and Ag-NPs dosage (0.1-1g/L). Batch experiments revealed that the removal rate with ratio (1:1) (w/w) were 52%, and 79.8% of the 10 mg/L of CIP at 60, and 180 minutes, respectively with optimal pH=4. Kinetic models for adsorpti

Some coordination complexes of Co(??), Ni(??), Cu(??), Cd(??) and Hg(??) are reacted in ethanol with Schiff base ligand derived from of 2,4,6- trihydroxybenzophenone and 3-aminophenol using microwave irradiation and then reacted with metal salts in ethanol as a solvent in 1:2 ratio (metal: ligand). The ligand [H4L] is characterized by FTIR, UV-Vis, C.H.N, 1H-NMR,13C-NMR, and mass spectra. The metal complexes are characterized by atomic absorption, infrared spectra, electronic spectra, molar conductance, (C.H.N for Ni(??) complex) and magnetic moment measurements. These measurements indicate that the ligand coordinates with metal (??) ion in a tridentate manner through the nitrogen and oxygen atoms of the ligand, octahedral st

Some coordination complexes of Co(??), Ni(??), Cu(??), Cd(??) and Hg(??) are reacted in ethanol with Schiff base ligand derived from of 2,4,6- trihydroxybenzophenone and 3-aminophenol using microwave irradiation and then reacted with metal salts in ethanol as a solvent in 1:2 ratio (metal: ligand). The ligand [H4L] is characterized by FTIR, UV-Vis, C.H.N, 1H-NMR,13C-NMR, and mass spectra. The metal complexes are characterized by atomic absorption, infrared spectra, electronic spectra, molar conductance, (C.H.N for Ni(??) complex) and magnetic moment measurements. These measurements indicate that the ligand coordinates with metal (??) ion in a tridentate manner through the nitrogen and oxygen atoms of the ligand, octahedral structures

Some coordination complexes of Co(ІІ), Ni(ІІ), Cu(ІІ), Cd(ІІ) and Hg(ІІ) are reacted in ethanol with Schiff base ligand derived from of 2,4,6- trihydroxybenzophenone and 3-aminophenol using microwave irradiation and then reacted with metal salts in ethanol as a solvent in 1:2 ratio (metal: ligand). The ligand [H4L] is characterized by FTIR, UV-Vis, C.H.N, 1H-NMR,13C-NMR, and mass spectra. The metal complexes are characterized by atomic absorption, infrared spectra, electronic spectra, molar conductance, (C.H.N for Ni(ІІ) complex) and magnetic moment measurements. These measurements indicate that the ligand coordinates with metal (ІІ) ion in a tridentate manner through the nitrogen and oxygen atoms of the ligand, octahed

Many academics have concentrated on applying machine learning to retrieve information from databases to enable researchers to perform better. A difficult issue in prediction models is the selection of practical strategies that yield satisfactory forecast accuracy. Traditional software testing techniques have been extended to testing machine learning systems; however, they are insufficient for the latter because of the diversity of problems that machine learning systems create. Hence, the proposed methodologies were used to predict flight prices. A variety of artificial intelligence algorithms are used to attain the required, such as Bayesian modeling techniques such as Stochastic Gradient Descent (SGD), Adaptive boosting (ADA), Deci