Chemical Engineering & Technology最新文献

This study examines the impact of electromagnetic Lorentz force and thermal convective boundaries on second-order nanofluid flow over a Riga plate, considering heat sources varying with both location and temperature. A Lorentz force acts along the plate, and the Grinberg-related component is included in the governing equations. A no-mass-transfer condition on the solid Riga boundary suppresses nanoparticle concentration. Using modern heat and mass transfer principles, the Cattaneo–Christov thermal flux model and extended Fick's law are applied. The governing equations are transformed into ordinary differential form through similarity transformations and solved using the Runge–Kutta–Fehlberg method. Results show enhanced nanoparticle temperature with higher radiation parameters and Biot numbers, whereas concentration decreases under constructive and increases under destructive chemical reactions.

A series of CuO@CdO/MgO ternary nanocomposites with variable CuO and CdO concentrations (10%, 20%, and 30%) in an MgO matrix were synthesized using a one-step coprecipitation-hydrothermal technique. The aim of this research is to create and test multifunctional CuO@CdO/MgO nanocomposites with varying structural, optical, electrochemical, and biological properties. By altering the CuO and CdO ratios, the composites improve their photocatalytic potential, energy storage capability, and biocompatibility. The effects of composition on crystallinity, morphology, band gap, photoluminescence, redox behavior, and cytocompatibility were thoroughly investigated. Cyclical voltammetry demonstrated increased capacitance and redox activity, particularly in the 30% sample. MTT experiments demonstrated concentration-dependent biocompatibility, with no significant cytotoxicity found for 10% and 20% samples at 50 µg/mL after 24–48 h of incubation.

To examine hydrogen explosion characteristics and propagation in confined spaces, providing a theoretical basis for prevention and mitigation, experiments were conducted in a 20-L spherical vessel at various hydrogen concentrations. Computational fluid dynamics simulations analyzed explosions under different initial conditions (concentration, ignition source temperature, initial temperature, and initial pressure). Results show that higher hydrogen concentration reduces the induction period, increasing pressure rise rate and peak values of pressure and temperature. Greater ignition source temperature and pressure also boost the pressure rise rate, slightly raise peak pressure, and increase maximum temperature. Elevated initial temperatures speed up the pressure rise but decrease peak pressure. Qualitative and quantitative analyses of explosion parameters like explosion impulse, explosive energy, and detonation index were performed to explain hydrogen explosion mechanisms.

This study examines the antimicrobial properties and drying kinetics of chitosan–silver nanoparticle membranes. Chitosan exhibits limited antimicrobial properties, whereas its incorporation with silver nanoparticles (37.08 nm) enhances its efficacy by mixing chitosan and silver nitrate, followed by microwave heating at 300 W for 1 min. The solution was then microwave-dried at 100–450 W, with the highest drying rate observed at 450 W. Effective diffusivities increased with microwave power, and activation energy for chitosan–silver nanoparticle membranes was determined at 49.28 W g⁻¹, respectively. Fourier transform infrared (FTIR) spectra showed reduced peak steepness at lower microwave power. Field emission scanning electron microscopy (FESEM) images revealed nanoparticle agglomeration and surface rupture at 300 W. Studies showed that chitosan–silver nanoparticle membranes exhibited stronger antimicrobial properties, highlighting microwave drying's potential in antimicrobial wound-healing materials.

Magnetic Fe₃O₄ nanoparticles (Fe₃O₄ NPs) were synthesized via a green method using Camellia sinensis leaf extract as a reducing and stabilizing agent. The obtained superparamagnetic nanoparticles exhibited a saturation magnetization of ∼50 emu/g and an average crystallite size of ∼20 nm, formed at 180°C over 8 h. These nanoparticles were applied as adsorbents for polyacrylamide (PAM) removal from aqueous solutions. Adsorption followed a pseudo-second-order kinetic model and fitted the Langmuir isotherm, with optimal conditions at pH 8, 50 min contact time, and 1.5 g/L dosage, achieving 99% removal efficiency. The Fe₃O₄ NPs were easily recovered using an external magnetic field, highlighting their potential as low-cost, eco-friendly, and reusable adsorbents for water treatment applications.

Traditional data-driven optimization methods using case-based reasoning (CBR) rely on heuristic similarity matching and lack probabilistic rigor, especially in complex processes like fluid catalytic cracking (FCC) with high dimensionality and uncertainty. To address these challenges, a novel data-driven framework that integrates compact posterior estimation with CBR is proposed. The method first identifies key variables affecting product yields through information-theoretic dimensionality reduction. Optimal operating parameters are then inferred using a combination of K-nearest neighbors for similarity matching and Markov Chain Monte Carlo sampling for probabilistic estimation. Industrial validation showed gasoline and total liquid yields increased by 7.31% and 6.94%, respectively, with coke yield reduced by 5.83%. This approach successfully improves computational efficiency and optimization accuracy in practical applications.