Study and optimization of the ultrasound-enhanced cleaning of an ultrafiltration ceramic membrane through a combined experimental–statistical approach

Membrane fouling is one of the main drawbacks of ultrafiltration technology during the treatment of dye-containing effluents. Therefore, the optimization of the membrane cleaning procedure is essential to improve the overall efficiency. In this work, a study of the factors affecting the ultrasound-assisted cleaning of an ultrafiltration ceramic membrane fouled by dye particles was carried out. The effect of transmembrane pressure (0.5, 1.5, 2.5 bar), cross-flow velocity (1, 2, 3 m s⁻¹), ultrasound power level (40%, 70%, 100%) and ultrasound frequency mode (37, 80 kHz and mixed wave) on the cleaning efficiency was evaluated. The lowest frequency showed better results, although the best cleaning performance was obtained using the mixed wave mode.A Box–Behnken Design was used to find the optimal conditions for the cleaning procedure through a response surface study. The optimal operating conditions leading to the maximum cleaning efficiency predicted (32.19%) were found to be 1.1 bar, 3 m s⁻¹ and 100% of power level.Finally, the optimized response was compared to the efficiency of a chemical cleaning with NaOH solution, with and without the use of ultrasound. By using NaOH, cleaning efficiency nearly triples, and it improves up to 25% by adding ultrasound.     

A statistical model for global optimization by means of select and clone

A statistical model tor giobai optimization is constructed generalizing some properties ofthe Wiener process to the multidimensional case. A new approach, which is similar to the Branch and Bound approach, is proposed to the construction of algorithms based on statistical models. A two dimensional version of the algorithm is implemented, and test results are presented     

Optimization of energy extraction in transverse galloping

A numerical method to analyse the stability of transverse galloping based on experimental measurements, as an alternative method to polynomial fitting of the transverse force coefficient C_z, is proposed in this paper. The Glauert–Den Hartog criterion is used to determine the region of angles of attack (pitch angles) prone to present galloping. An analytic solution (based on a polynomial curve of C_z) is used to validate the method and to evaluate the discretization errors. Several bodies (of biconvex, D-shape and rhomboidal cross sections) have been tested in a wind tunnel and the stability of the galloping region has been analysed with the new method. An algorithm to determine the pitch angle of the body that allows the maximum value of the kinetic energy of the flow to be extracted is presented.     

Sulfur-Containing Vinyl Monomers. XV. Kinetic Study of Radical Polymerization of Vinyl Mercaptobenzothiazole and Its Copolymerization

A kinetic study of radical polymerization of vinyl mercaptobenzothiazole (VMBT) with α,α′-azobisisobutyonitrile (AIBN) at 60°C was carried out. The rate of polymerization (R_p) was found to be expressed by the rate equation: R_p = k[AIBN]^0.5 [VMBT]^1.0, indicating that the polymerization of this monomer proceeds via an ordinary radical mechanism. The apparent activation energy for overall polymerization was calculated to be 20.9 kcal/mole. Moreover, this monomer was copolymerized with methyl methacrylate, acrylonitrile, vinyl acetate, phenyl vinyl sulfide, maleic anhydride, and fumaronitrile at 60°C. From the results obtained, the copolymerization parameters were determined and discussed.     

Development and optimization of the determination of pharmaceuticals in water samples by SPE and HPLC with diode‐array detection

This paper describes the development, optimization, and validation of a method for the determination of five pharmaceuticals from different therapeutic classes (antibiotics, anthelmintics, glucocorticoides) in water samples. Water samples were prepared using SPE and extracts were analyzed by HPLC with diode‐array detection. The efficiency of 11 different SPE cartridges to extract the investigated compounds from water was tested in preliminary experiments. Then, the pH of the water sample, elution solvent, and sorbent mass were optimized. Except for optimization of the SPE procedure, selection of the optimal HPLC column with different stationary phases from different manufacturers has been performed. The developed method was validated using spring water samples spiked with appropriate concentrations of pharmaceuticals. Good linearity was obtained in the range of 2.4–200 μg/L, depending on the pharmaceutical with the correlation coefficients >0.9930 in all cases, except for ciprofloxacin (0.9866). Also, the method has revealed that low LODs (0.7–3.9 μg/L), good precision (intra‐ and interday) with RSD below 17% and recoveries above 98% for all pharmaceuticals. The method has been successfully applied to the analysis of production wastewater samples from the pharmaceutical industry.     

Alginate‐Based Microcapsules with a Molecule Recognition Linker and Photosensitizer for the Combined Cancer Treatment

A reactive template method was used to fabricate alginate‐based hydrogel microcapsules. The uniform and well‐dispersed hydrogel capsules have a high drug loading capacity. After they are coated by a folate‐linked lipid mixture on the surface, the capsules possess higher cell uptake efficiency by the molecule recognition between folate and the folate‐receptor overexpressed by the cancer cells. Moreover, in this bioconjugate, the lipid could remarkably reduce the release rate of hydrophilic doxorubicin from the hydrogel microcapsules and encapsulate the hydrophobic photosensitizer hypocrellin B. The biointerfaced capsules could be used as drug carriers for combined treatment against cancer cell proliferation in vitro; this was much more effective than chemotherapy or photodynamic therapy alone.     

Flow injection analysis simulations and diffusion coefficient determination by stochastic and deterministic optimization methods

Stochastic and deterministic simulations of dispersion in cylindrical channels on the Poiseuille flow have been presented. The random walk (stochastic) and the uniform dispersion (deterministic) models have been used for computations of flow injection analysis responses. These methods coupled with the genetic algorithm and the Levenberg–Marquardt optimization methods, respectively, have been applied for determination of diffusion coefficients. The diffusion coefficients of fluorescein sodium, potassium hexacyanoferrate and potassium dichromate have been determined by means of the presented methods and FIA responses that are available in literature. The best-fit results agree with each other and with experimental data thus validating both presented approaches.     

钠离子电池用高性能锑基负极材料的调控策略研究进展

锑(Sb)具有高的理论比容量、较小的电极极化、合适的Na⁺脱嵌电位、价格低廉以及环境友好的优势,而成为一种具有较大应用前景的钠离子电池负极材料。但是,Sb基负极材料的一个重要挑战是在循环过程中高比容量伴随着大的体积变化,进而导致活性材料粉化,并从集流体上脱落,这大大限制了其在钠离子电池领域的大规模应用。因此,如何解决Sb基负极材料充放电过程中体积膨胀问题对于高性能的钠离子电池设计是至关重要的。本文详细综述和讨论了Sb基材料的结构-性能关系及其在钠离子电池中的应用,详细介绍了钠离子电池Sb基负极材料在氧化还原反应机理、形貌设计、结构-性能关系等方面的最新研究进展。本综述的主要目的是探讨影响Sb基负极材料性能的决定因素,从而提出有前途的改性策略,以提高其可逆容量和循环稳定性。最后,对Sb基钠离子电池负极材料的未来发展、面临的挑战和前景进行了展望。本文可为Sb负极材料的构建和优化提供具体的观点,阐明了Sb基负极材料未来的发展方向,从而促进钠离子电池的快速发展和实际应用。     

Optimization of Fenoprofen solubility within green solvent through developing a novel and accurate GSO-GPR predictive model

Indisputable importance of drug solubility in various industrial perspectives has motivated the scientists to evaluate different techniques to improve it. Fenoprofen is a significant nonsteroidal anti-inflammatory drug (NSAID), that is the orally administered to relieve mild to moderate pain and the unfavorable symptoms of osteoarthritis and rheumatoid arthritis (i.e., inflammation and stiffness). Supercritical fluids (SCFs) belong to a certain type of fluids, in which their temperature and pressure are higher than the critical point. This property allows the CO₂SCF to simultaneously possess the characteristics of both a liquid and a gas. The prominent target of this paper is to mathematically develop three predictive models via machine learning (ML) technique to optimize the solubility of Fenoprofen in CO₂SCF. In this study, we have 32 data vectors in each dataset, including two input features of pressure and temperature. The output target is solubility, which we are going to model and analyze. Models are constructed through the use of Modular ANN (MANN), Gaussian processes regression (GPR), and the K-Nearest Neighbor technique (KNN) in this body of work. The glowworm swarm optimization (GSO) swarm-based method is utilized in order to carry out the process of model optimization. The root mean squared error (RMSE) rates for GSO-KNN, GSO-MANN, and GSO-GPR are respectively 5.25E-04, 5.46E-04, and 3.01E-05. The aforementioned models were also judged according to a number of other criteria, and since the GSO-GPR model was found to be the most effective according to all of these standards, it is being treated as the conclusive model of this investigation. In addition, the maximum error has been brought down to 5.02E-05 with the help of this model, which has an R2-score of 0.999.