University Yahia Fares of Medea

An integrated computational study combining density functional theory and molecular dynamics simulation were performed to elucidate the adsorption mechanism of acetaminophen on Copper Oxide (CuO) and Zinc Oxide (ZnO) nanoparticles in aqueous media. The global electronic descriptors of the optimized adsorption complexes show clear differences. CuO/acetaminophen exhibits E HOMO = −7.33, E LUMO = −6.87 (ΔEgap = 0.461 eV), low chemical hardness (η = 0.23), high softness (S = 4.33) and very large electrophilicity (ω = 109.35). In contrast, ZnO/acetaminophen has E HOMO = −5.76, E LUMO = −0.92 (ΔE gap = 4.84 eV), η = 2.42, S = 0.41 and ω = 2.30, indicating CuO as a much stronger electron acceptor. Fukui and MEP maps identify the carbonyl (O11) and phenolic (O7) oxygens as primary donors (O11 f − = 0.1398, f + = 0.0866, f° = 0.2264). NBO analysis reveals larger donor → acceptor stabilization for CuO (E 2 = 37.7 kcal/mol) than ZnO (E 2 = 8.5 kcal/mol), consistent with significant LP(O/N) → metal charge-transfer on CuO. MD simulations corroborate stronger binding on CuO (E ads = −276 to −277.6 kcal/mol) than ZnO (−219 to −230 kcal/mol) over 290-318 K. Energy decomposition shows that (E deformation = −182 to −186 kcal/mol) dominates stabilization in both systems, while CuO displays a much larger E rigid (−93 to −95 kcal/mol) than ZnO (−36 to −45 kcal/mol). RDFs and non-bonded decomposition indicate short range, electrostatic coordination bonding on CuO (1.9–2.0 Å), and longer-range, dispersion H-bonding dominated contacts on ZnO (> 3.5 Å). These descriptors support a mechanism in which CuO favors charge transfer coordination (chemisorption), while ZnO favors relaxation assisted physisorption. This could guide the development of practical adsorbents for water treatment. CuO for strong sequestration and ZnO for reversible capture. • Mechanistic Contrast. • Electronic Structure Drivers. • Primary Adsorption Sites. • Stronger Binding to CuO. • Charge Transfer Evidence. • Practical Implications for Water Treatment.

Mineral scale deposition remains a major flow-assurance constraint in oil and gas operations, especially in water-flooding and produced-water reinjection, where mixing between incompatible brines promotes super-saturation and precipitation of poorly soluble salts. This work introduces a novel extension of traditional methods used for modeling chemical inhibition and the predictive evaluation of oilfield scale-inhibitor molecules. A systematically optimized Two-Dimensional Quantitative Structure–Activity Relationship Model based on the k-Nearest Neighbors algorithm 2D-QSAR-KNN model was developed to quantitatively link molecular constitution of phosphonate inhibitors, brine chemistry, and operating factors with inhibition efficiency IE %. The optimized model achieved strong accuracy and generalization R2train = 0.9182, R2test = 0.9306, and R2global = 0.9208 with low prediction errors RMSEtrain = 4.7888%, RMSEtest = 4.5485%, and RMSEglobal = 4.7421%. Median absolute errors remained minimal for the train set = 0.80%, and test set = 1.63%, and model stability was confirmed by high correlation with experimental IE % r = 0.94 and R2train/R2test ≈ 0.99, showing no sign of overfitting. Additionally, an inverse-2D-QSAR framework was applied to identify the optimal molecular descriptor profile expected to maximize inhibitory performance within normalized bounds, providing rational rules for next-generation inhibitor design. The findings highlight the practical value of QSAR-inspired AI modeling to accelerate molecule screening and dosage exploration prior to laboratory validation, supporting more cost-effective, interpretable, and environmentally aware sulfate-scale inhibition strategies under high-salinity reservoir conditions.

The present research introduces an electrochemical detection system constructed from chitosan-functionalized Ag–Cu-ferrite nanoparticles (Ag0.02Cu0.98Fe2O4@chitosan). This nanocomposite-based sensor provides an efficient approach for the targeted measurement of ascorbic acid (AA). Characterization using XRD and FTIR techniques confirmed the effective incorporation of chitosan into the Ag0.02Cu0.98Fe2O4 spinel framework. Using CV (cyclic voltammetry), the Ag0.02Cu0.98Fe2O4@chitosan-supported CPE electrode exhibited an oxidation peak at +0.28 V compared to +0.55 V for bare CPE in phosphate buffer (pH = 7), highlighting its superior electrocatalytic achievement. The current response exhibited a linear dependence with the square root of the scan rate (R2 = 0.9895), suggesting that the process is governed by diffusion. Sensitivities of 411.59 µA mM−1 cm2 and 327.31 µA mM−1 cm2 were achieved by the sensor over two distinct linear ranges, 100–300 µM and 300 µM–13 mM. With a high degree of certainty, the limit of detection (LOD) was recorded at 89 µM. The fabricated electrode displayed high selectivity within the presence of commonly used interferents (glucose, fructose, sucrose, NaCl, and KCl), as well as impressive reproducibility (RSD = 1.96%) and repeatability (RSD = 1.15%).It also achieved a superior AA recovery rate of up to 98% in a real sample, demonstrating its consistency and reliability. DFT computations confirmed AA's chemical reactivity through a moderate energy gap (ΔE = 5.42 eV) and identified reactive sites on oxygen atoms using molecular electrostatic potential (MESP) and Fukui function analysis (FIs). Additionally, Monte Carlo simulations (MCS) revealed a highly favorable adsorption energy of −26.65 kcal mol−1 for AA on the Ag0.02Cu0.98Fe2O4@chitosan-CPE surface, validating the strong affinity of the target analyte toward the modified electrode. Overall, the integration of chitosan into the Ag0.02Cu0.98Fe2O4 nanostructure significantly improved the electrocatalytic efficiency, selectivity, and stability of the sensor, making it an excellent option for practical applications in vitamin C detection.

This study presents the structural analysis and optimization of an external hydraulic gear pump with the aim of improving mechanical performance while reducing material usage and overall mass. The work addresses two main aspects: optimization of the pump casing and optimization of the internal gears. The geometric model was constructed in SolidWorks with fully parameterized dimensions to enable an efficient optimization workflow. Finite Element Analysis (FEA) in ANSYS Workbench was used to evaluate stress and deformation under realistic operating conditions. For the pump casing, simulation results were integrated into a Response Surface Methodology (RSM) framework to assess the influence of geometric parameters on structural behavior. The optimized casing achieved a 9.5% weight reduction while ensuring structural integrity, with a maximum deformation of 0.031 mm, a maximum stress of 127 MPa, and a safety factor of 3. The efficiency-to-mass ratio improved from 7.9 to 8.8. In parallel, optimization of the gear pair focused on minimizing stress concentrations and improving stiffness. Using ANSYS simulations combined with RSM, the optimized gear design reduced von Mises stress from 239.4 MPa to 224 MPa, reflecting better load distribution and enhanced durability. Overall, the results demonstrate the effectiveness of integrating finite element simulation with response surface optimization to achieve structural and performance improvements in hydraulic gear pumps.

Pharmaceutical contamination of aquatic systems poses an increasing environmental concern due to the persistence, bioactivity, and incomplete removal of these compounds by conventional wastewater treatment processes. This study investigates the adsorptive removal of three structurally distinct pharmaceuticals: ketotifen fumarate (KF), doxycycline hyclate (DXC), and nystatin (Nyst), using raw bentonite (RB). By combining batch experiments with an interpretable machine learning (ML) framework, adsorption kinetics, equilibrium, and thermodynamics were evaluated. Additionally, four Ant Lion Optimizer (ALO)-optimized models: Artificial Neural Network (ANN), Support Vector Regression (SVR), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost), were employed to predict adsorption capacity under diverse conditions. RB exhibited high adsorption capacities: 178.86 ± 1.26 mg/g for KF, 222.91 ± 2.02 mg/g for DXC, and 190.25 ± 2.86 mg/g for Nyst. Adsorption equilibrium was best described by the Freundlich isotherm, indicating multilayer adsorption on a heterogeneous surface. Thermodynamic and spectroscopic analyses revealed a dual mechanism involving electrostatic attraction, hydrogen bonding, cation exchange, and van der Waals interactions, with DXC and Nyst adsorption being endothermic and KF adsorption exothermic. SHAP (SHapley Additive exPlanations) analysis identified adsorbent dosage, initial concentration, and pH as dominant operational factors, while the molecular descriptor nC (number of carbon atoms) emerged as key to differentiating pharmaceuticals, linking larger molecular size to stronger adsorption. The XGBoost model achieved the highest accuracy (R2 = 0.972, RMSE = 0.1225), demonstrating robust generalizability. These findings highlight RB as a low-cost, scalable adsorbent and establish an interpretable ML approach capable of linking molecular structure to adsorption behavior.

Wind energy systems using doubly fed induction generators (DFIGs) rely on vector-oriented control (VOC) to achieve decoupled regulation of active and reactive power. Conventional VOC strategies typically employ proportional-integral (PI) controllers for rotor current control; however, these controllers struggle under nonlinear and variable operating conditions, such as wind speed fluctuations, leading to degraded dynamic performance and power quality. To overcome these limitations, this paper proposes an intelligent VOC approach based on the M5-Pruned model tree (M5P) algorithm, complemented by a comparative study with fuzzy logic controllers (FLC). The M5P-based controller introduces a data-driven mechanism that adapts to system dynamics, ensuring improved accuracy and robustness compared to traditional PI and FLC methods. A detailed MATLAB/Simulink simulation of a grid-connected wind turbine with DFIG evaluates the controllers under varying wind profiles. Results demonstrate that the proposed M5P strategy significantly reduces overshoot and settling time, enhances power regulation, and improves overall system stability. These findings highlight the advantages of machine learning-based control in addressing the shortcomings of conventional VOC techniques for renewable energy applications.