Arignar Anna Government Arts College, Attur

Piperazine analogs play a crucial role in many drugs due to their diverse structures and biological importance. In the present research, piperazine carboxamide substituted vanillin derivatives (PCSVs) were synthesized. Seven different chemical entities with different substitutions like N-Boc piperazine, simple piperazine, benzamide, thiophene-2-carboxamide, furan-2-carboxamide, 4-methoxy benzamide and Boc-glycinamide are synthesized. The synthesized PCSVs compounds are thoroughly characterized using spectroscopic techniques like Nuclear magnetic resonances (1H NMR & 13C NMR), Fourier transform infrared (FT-IR), Ultraviolet (UV-Visible) and Electrospray ionization Mass spectroscopy (ESI-Mass). All synthesized PCSVs were tested for in vitro alpha-amylase inhibition and protein anti-denaturation activity. The synthesized PCSVs showed superior antidiabetic activities as well as good to moderate anti-inflammatory activities. The in silico molecular docking studies were also performed with alpha-amylase (1HNY.pdp), cyclooxygenase-1 (1PGG.pdp) and cyclooxygenase-2 (4COX.pdp) enzymes to compare the experimental biological results. The frontier molecular orbitals, molecular electrostatic potential and mulliken population analysis were performed by density functional theory calculations using Gaussian 09W software. Additionally, ADMET, drug likeness properties and toxicity studies are performed with the use of the Swiss ADMET and Protox-II web server.

Predicting student performance is vital to educational data mining. The design and development of rapid, efficient, and accurate methods for predicting student achievement are the keystones of this field. Predicting student performance is an essential indicator of students’ understanding of course material and instructors’ teaching effectiveness. Accurate prediction of student performance not only helps provide timely feedback to students and educators but also assists university administrators in assessing course quality. This article presents a hybridization approach to predicting student achievement, combining the Weighted Extreme Learning Machine with the Coati Optimization Algorithm (COA). The robustness of the prediction model is established and formulated using an Extreme Learning Machine (ELM) and a Weighted Extreme Learning Machine (WELM), with a thorough investigation of the interplay between courses and student attributes. The biases and weights in the ELM and WELM architectures are randomly initialized, which may lead to inconsistent results, resulting in training errors and a decline in prediction accuracy. Hence, to solve this issue, the COA is combined with WELM to get optimal input weights and hidden biases, thereby enabling WELM to achieve greater accuracy. The predictive capabilities of the implemented models are evaluated using two standard student datasets, and their effectiveness is compared with that of the Spike Neural Network (SNN) algorithm. The experimental results indicated that WELM-COA outperformed SNN.

Two novel (3-naphthol-based derivatives, BNMMA and CNMMA, were synthesized through a one-pot, solvent-free multicomponent reaction catalyzed by ZnO nanoparticles. This eco-friendly method enabled the efficient formation of the target N-methylacetamide derivatives through condensation of (3-naphthol, halogenated benzaldehydes, and N-methylacetamide. Structural characterization was carried out using FT-IR, 1H NMR, UV-Vis, and HRMS spectroscopy. The experimental data showed excellent agreement with theoretical predictions obtained from DFT and TD-DFT (B3LYP/6-311G(d,p)) calculations, confirming molecular geometries and electronic structures. CNMMA exhibited superior optical properties, including a red-shifted absorption (lambda max = 330 nm), higher oscillator strength, and greater light-harvesting efficiency (t)lambda = 0.522) compared to BNMMA. It also exhibited a higher dipole moment (5.13 D) and significantly enhanced first-order hyperpolarizability ((3 = 714.6 a.u.), indicating strong nonlinear optical (NLO) potential. Complementary topological analyses (NBO, QTAIM, NCI-RDG, ELF, LOL, and Hirshfeld surface) revealed efficient intramolecular charge transfer, pronounced it-electron delocalization, and stable noncovalent interactions. These findings collectively confirm that CNMMA, with its donor-it-acceptor architecture and halogen-mediated electronic tuning, is a promising candidate for highperformance photonic, electro-optic, and NLO applications. Molecular docking against COX-2 (PDB: 1PVG) revealed that CNMMA exhibits stronger binding affinity than BNMMA, despite forming fewer hydrogen bonds. Its enhanced interaction is attributed to dominant it-it stacking and hydrophobic contacts with aromatic residues (TRP340, PHE259). This suggests that halogen substitution in CNMMA plays a crucial role in improving both optical and biological properties, highlighting its dual potential as both a photonic material and COX-2 inhibitory agent.

BiFeO3-Graphene-LiNbO3 ternary nanocomposites has been synthesized using wet chemical method for the first time. The existence of ternary entities chosen for the preparation of composite matrix has been ascertained from the conventional analytical tools like XRD, SEM & TEM, XPS and Raman measurements. Addition of ABO3 type dielectric materials namely BiFeO3 and LiNbO3 over graphene matrix found have improved the photocatalytic behaviour of synthesized ternary composites, especially in degrading methylene blue effluent within 120 min for the graphene loaded sample with a concentration of 0.025 and exhibits maximum degradation efficiency of 88 % over the other graphitic concentrations studied. Hence, this study indicates that the optimum concentration of graphene required for enhancing methylene blue dye degradation in similar intervals of time stands at 0.025 for BiFeO3-Graphene-LiNbO3 photocatalyst. The other studies substantiating the present conclusive evidence is also appended in this work.

This study investigates the synthesis of pure LaFeO3 and Al-doped LaFeO3 nanoparticles using a solid-state reaction method, with the goal of analyzing the physicochemical properties of the synthesized nanoparticles. The powder X-ray diffraction (XRD) analysis of pure and Al-doped LaFeO3 system confirms an orthorhombic crystalline structure. Detailed discussions were provided on the diverse IR and Raman vibrational bands observed in the synthesized nanomaterials. Optical bandgap energy values were determined by Tauc’s plots, yielding values of 2.07 eV and 1.99 eV, respectively. The Scanning Electron Microscopy (SEM) analysis confirms the polycrystalline structure and surface features of the pure LaFeO3 and Al-doped LaFeO3 nanoparticles. Electrochemical performance of the synthesized materials was evaluated using cyclic voltammetry (CV) within a potential window of 0.46 V. From the electrochemical analysis, the specific capacitance value of the pure LaFeO3 nanoparticles was measured to be 189 F/g at a current density of 1 A/g, while the Al-doped LaFeO3 nanoparticles affected the specific capacitance of 80 F/g at the same current density.