A Schiff base ligand (L) was synthesized via condensation of N-( 1-naphthyl) ethylenediamine dihydrochloride with phthalaldehyde. The ligand was characterized by FT-IR, UV–Vis, 1H NMR, mass spectrometry, and elemental analysis (C, H, N). Five metal complexes (Co(II), Ni(II), Cu(II), Zn(II), and Cd(II)) were prepared with the ligand in a 1:1 (M:L) ratio using an aqueous ethanol solution. The complexes were characterized by FT-IR, UV–Vis, mass spectrometry, and elemental analysis (C, H, N). Additionally, 1H NMR spectroscopy was employed for Cd(II) complex. Antimicrobial activity of the ligand and its metal complexes against pathogenic bacteria (K. pneumoniae, E. coli, S. aureus, and S. epidermidis) and fungus (C. albicans) were evaluated

Transition metal complexes of Co(II), Ni(II), Cu(II), and Zn(II) with 2-(4-antipyrine azo)-4-nitroaniline derived from 4-aminoantipyrine and 4-nitroaniline were synthesized. Characterization of these compounds has been done on the basis of elemental analysis, electronic data, FT-IR, UV-Vis and 1HNMR, 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). High molar absorbtivity of the complex solutions were observed. From the analytical data, the stoichiometry of the complexes has been found to be 1:2 (metal:ligand). On the basis of physicochemical data octa

The present study envisaged utilizing 4-aminoantipyrine as key intermediate for the synthesis of some new derivatives bearing anti-bacterial and anti-cancer activities moieties viz., antipyrine diazenyl benzaldehydes 2(ad) which were obtained by coupling of diazotized 4-aminoantipyrine (1) with substituted benzaldehydes at 0◦C (iced) temperature. The other antipyrine derivatives where containing bis heterocycles like bis thiazolidinone-antipyrine (4), bis imidazolidinone -antipyrine (5) and bis azetidinone -antipyrine (6).These compounds were prepared through the reaction between 4- aminoantipyrine and terephthaldicarboxaldehyde to get (3) which were reacted with mercaptoacetic acid , glycine or chloroacetyl chloride separately to get com

Structural and optical properties of CdO and CdO0.99Cu0.01 thin
films were prepared in this work. Cadmium Oxide (CdO) and
CdO0.99Cu0.01semiconducting films are deposited on glass substrates
by using pulsed laser deposition method (PLD) using SHG with Qswitched
Nd:YAG pulsed laser operation at 1064nm in 6x10-2 mbar
vacuum condition and frequency 6 Hz. CdO and CdO0.99Cu0.01 thin
films annealed at 550 C̊ for 12 min. The crystalline structure was
studied by X-ray diffraction (XRD) method and atomic force
microscope (AFM). It shows that the films are polycrystalline.
Optical properties of thin films were analyzed. The direct band gap
energy of CdO and CdO0.99Cu0.01 thin films were determined from
(αhυ)1/2 v

We present a simple model of charge transfer current through sensitizer N3 molecule contact to TiO2 and ZnO semiconductors to calculate the charge transfer current. The model underlying depends on the fundamental parameters of the charge transfer reaction and it is based on the quantum transition theory approach. A transition energy, driving energy and potential barrier have been taken into account charge transfer current at N3 / TiO2 and N3 / ZnO devices with wide polarity solvents Acetic acid, 2-Methoxyethanol, 1-Butanol, Methyl alcohol, chloroform, N,N-Dimethylacetamide and Ethyl alcohol via the quantum donor-acceptor system.The effects of the transition energy and potential barrier are computed and discussion on charge transfer current.

The dynamic development of computer and software technology in recent years was accompanied by the expansion and widespread implementation of artificial intelligence (AI) based methods in many aspects of human life. A prominent field where rapid progress was observed are high‐throughput methods in biology that generate big amounts of data that need to be processed and analyzed. Therefore, AI methods are more and more applied in the biomedical field, among others for RNA‐protein binding sites prediction, DNA sequence function prediction, protein‐protein interaction prediction, or biomedical image classification. Stem cells are widely used in biomedical research, e.g., leukemia or other disease studies. Our proposed approach of