In the present work, a first-row divalent d-transition metal obtained from curcumin(Curc) and L-3,4-dihydroxyphenylalanin(L-dopa)have been synthesized which their complexes and characterized by C.H.N, conductance, spectral methods: FT-IR, Ultra–Visible. Magneto-chemical measurements, molar conductance ΛM (1×10−3 mol/L in DMSO):36- 0.84 ohm-1.cm2.mol-1 (non-electrolyte). The data shows that the complexes have the structure [M((II))-(Curc)-(L-dopa)] system. Electronic and magnetic data suggest an octahedral geometry for all complexes in which the (L-dopa) and curcumin act as bidentate ligands. Curcumin coordinated to the metal ions M (II) through the lone pair of electrons of oxygen in 2(C=O) groups. The (L-dopa) coordinated to M (II) a

The synthesis of the bisaldehyde ligand 2-(1,1-dimethyl-1,3-dihydro-2H-benzo[e]indol-2-ylidene)malonaldehyde (B) and its coordinated compounds with Cr(III), Mn(II), Fe(II), Co(II), Ni(II) and Cu(II) ions are reported. The synthetic route of B was completed by adopting the Vilsmeier-Haack reaction. This was based on the mixing of 1,1,2-trimethyl-1H-benzo[e]indole with phosphoryl trichloride and N, N-dimethylformamide (anhydrous) that gave the aminomethylenemalondialdehyde. The use of POCl3 and DMF was aimed to give the Vilsmeier-Haack intermediate, which was kept at 5°C and then heated with stirring at 85°C. The addition of an aqueous NaOH solution (35%) to the reaction mixture resulted in the isolation of B. The monomeric coordinated comp

Titanium oxide nanoparticles-modified smectite (SMC-nTiO2) as a low-cost adsorbent was investigated for the removal of Rhodamine B (RhB) from aqueous solutions. The adsorbents (SMC and SMC-nTiO2) were characterized by scanning electron microscopy, Fourier transforms infrared spectroscopy, and energy-dispersive X-ray spectroscopy. The effects of various parameters like contact time, adsorbent weight, pH, and temperatures were examined. Three kinetic equations (pseudo-first-order (PFO), pseudo-second-order (PSO), and intra-particle diffusion) were used to evaluate the experimental kinetic of the data and the results showed that the adsorption process is in line with the PSO kinetic model. Adsorption equilibrium isotherms were modeled using La

The investigation of determining solutions for the Diophantine equation  over the Gaussian integer ring for the specific case of  is discussed. The discussion includes various preliminary results later used to build the resolvent theory of the Diophantine equation studied. Our findings show the existence of infinitely many solutions. Since the analytical method used here is based on simple algebraic properties, it can be easily generalized to study the behavior and the conditions for the existence of solutions to other Diophantine equations, allowing a deeper understanding, even when no general solution is known.

This study was done to find a cheap, available and ecofriendly materials that can remove eosin y dye from aqueous solutions by adsorption in this study, two adsorbent materials were used, the shells of fresh water clam (Cabicula fluminea) and walnut shells. To make a comparison between the two adsorbents, five experiments were conducted. First, the effects of the contact time, here the nut shell removed the dye quickly, while the C. flumina need more contact time to remove the dye. Second, the effects of adsorbent weight were examined. The nut shell was very promising and for all used adsorbent weight, the R% ranged from 94.87 to 99.29. However C. fluminea was less effective in removing the dye with R% ranged from 47.59 to 55.39. The thi

Face Recognition Systems (FRS) are increasingly targeted by morphing attacks, where facial features of multiple individuals are blended into a synthetic image to deceive biometric verification. This paper proposes an enhanced Siamese Neural Network (SNN)-based system for robust morph detection. The methodology involves four stages. First, a dataset of real and morphed images is generated using StyleGAN, producing high-quality facial images. Second, facial regions are extracted using Faster Region-based Convolutional Neural Networks (R-CNN) to isolate relevant features and eliminate background noise. Third, a Local Binary Pattern-Convolutional Neural Network (LBP-CNN) is used to build a baseline FRS and assess its susceptibility to d