Previous studies on the synthesis and characterization of metal chelates with uracil by elemental analysis, conductivity, IR, UV-Vis, NMR spectroscopy, and thermal analysis were covered in this review article. Reviewing these studies, we found that uracil can be coordinated through the electron pair on the N1, N3, O2, or O4 atoms. If the uracil was a mono-dentate ligand, it will be coordinated by one of the following atoms: N1, N3 or O2. But if the uracil was bi-dentate ligand, it will be coordinated by atoms N1 and O2, N3 and O2 or N3 and O4. However, when uracil forms complexes in the form of polymers, coordination occurs through the following atoms: N1 and N3 or N1 and O4.

The MTX was converted to MTX nanoparticles by the modified method based on changing the pH gradually with exposure to ultrasound and shaking , changing the pH with exposure to ultrasound plays an significant role in the formation of nanoparticles, and this is shown in some previous studies. As the change in pH affects the nature of bonding between molecules, as well as the strength of bonding that depends on the change of electrical charges The exposure to ultrasound waves will greatly affect the breakdown of large particles into small particles that reach the level of nanoparticles. The MTX NPs formation was characterized by UV-Vis spectra analysis , Atomic force microscopy (AFM) analysis, Scanning electron microscope (SEM) and Fou

Well dispersed Cu2FeSnSe4 (CFTSe) nanofilms were synthesized by hot-injection method. The structural and morphological measurements were characterized using XRD (X-ray diffraction), Raman spectroscopy, SEM (scanning electron microscopy), and TEM (transmission electron microscopy). Chemical composition and optical properties of as-synthesized CFTSe nanoparticles were characterized using EDS (energy dispersive spectroscopy) and UV-Vis spectrophotometry. The average particle size of the nanoparticles was about 7-10 nm. The UV-Vis absorption spectra showed that the synthesized CFTS nanofilms have a band gap (Eg) of about 1.16 eV. Photo-electrochemical characteristics of CFTSe nanoparticles were studied and indicated their potential application

New series of imidazole[1,2-a]pyridine-sulfonamides was designed and synthesized from 2-aminopyridine, which was reacted with p-bromo phenacyl bromide in the present of MgO to produce the corresponding imidazole[1,2-a]pyridine, which was then reacted with chlorosulfonic acid to produce 2-(4-bromophenyl)imidazole[1,2-a]pyridine-3-sulfonyl chloride [2]. Following that, treatment of (2) with different amines using the grand method to generate imidazole [1,2-a] pyridine sulfonamides. All the synthesized compounds have been characterized by FTIR, 1HNMR and 13CNMR and C.H.N analysis. The DFT, POM analysis and molecular docking were carried out on for all final compounds to investigate drug like attributes, and the results revealed showed that the

This study uses an environmentally friendly and low-cost synthesis method to manufacture zinc oxide nanoparticles (ZnO NPs) by using zinc sulfate. Eucalyptus leaf extract is an effective chelating and capping agent for synthesizing ZnO NPs. The structure, morphology, thermal behavior, chemical composition, and optical properties of ZnO nanoparticles were studied utilizing FT-IR, FE-SEM, EDAX, AFM, and Zeta potential analysis. The FE-SEM pictures confirmed that the ZnO NPs with a size range of (22-37) nm were crystalline and spherical. Two methods were used to prepare ZnO NPs. The first method involved calcining the resulting ZnO NPs, while the second method did not. The prepared ZnO NPs were used as adsorbents for removing acid black 210

In this study, multi-objective optimization of nanofluid aluminum oxide in a mixture of water and ethylene glycol (40:60) is studied. In order to reduce viscosity and increase thermal conductivity of nanofluids, NSGA-II algorithm is used to alter the temperature and volume fraction of nanoparticles. Neural network modeling of experimental data is used to obtain the values of viscosity and thermal conductivity on temperature and volume fraction of nanoparticles. In order to evaluate the optimization objective functions, neural network optimization is connected to NSGA-II algorithm and at any time assessment of the fitness function, the neural network model is called. Finally, Pareto Front and the corresponding optimum points are provided and