Numerical Simulation of Mixing in a Micro-well Scale Bioreactor by Computational Fluid Dynamics

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Characterization of the Engineering Flow in a Miniaturised Bioreactor by Computational Fluid Dynamics

Three approaches based on computational fluid dynamics(CFD) techniques have been assessed for their ability to describe the engineering flow environment in a miniaturized mechanically agitated bioreactor. The three approaches tested were the source-sink(SS), the multiple reference frames (MRF) and the sliding grids(SG). In all the cases, the predictions of the velocity components agree with reported experimental data. However, the analysis of the results of the turbulent intensities predicted by the three approaches indicates the MRF and the SG techniques under predicted turbulent intensities are comparable to both experimental measurements and the SS method. The predicted power number and pumping number based on the SS ap-proach are closer to typical reported experimental values compared to those obtained from the MRF and SG methods.     

Polyvinylimidazole/sol–gel composite as a novel solid‐phase microextraction coating for the determination of halogenated benzenes from aqueous solutions

A polyvinylimidazole/sol–gel composite is proposed as a novel solid‐phase microextraction fiber to extract five halobenzenes from the headspace of aqueous solutions in combination with gas chromatography with mass spectrometry. The prepared fiber was characterized by scanning electron microscopy and Fourier transform infrared spectroscopy. The obtained results showed that porous polyvinylimidazole/sol–gel composite was chemically deposited on fused silica fiber. The effect of important extraction parameters including extraction temperature, extraction time, and salt content were investigated. The optimum conditions were as follows: extraction temperature 25°C, extraction time 20 min, and salt concentration 30 w/v%. Detection limits and relative standard deviations of the developed method for halogenated benzenes were below 0.1 pg/mL and 15%, respectively. Repeatability of the proposed method, explained by relative standard deviation, varied between 5.48 and 9.15% (n = 5). The limits of detection (S/N = 3) ranged between 0.01 and 0.10 ng/L using gas chromatography with mass spectrometry with selected ion monitoring mode. For real sample analysis, three types of water samples with different matrices (ground, surface, and tap water) were studied. The optimized procedure was applied to extraction and method validation of halogenated benzenes in spiked water samples.     

Synthesis of Arylidene – Isoquinolinones bearing Combretastatin Skeleton by Cyclocarbopalladation/cross coupling Tandem Heck-Suzuki Miaura Reactions using nano catalyst Pd@Py-IL-SPION

Cyclocarbopalladation/cross-coupling cascade intramolecular Heck–Suzuki–Miyaura reactions is applied for the first time by palladium immobilized on pyridine-imidazolium ionic liquid supported magnetic iron oxide nanoparticle catalyst (denoted Pd@Py-IL-SPION) for the last step to synthesize trisubstituted arylidene–isoquinolinones derivatives having Combretastatin skeleton. The reaction is performed via propargylamide intermediates prepared by Ugi 4-CR reactions, which undergoes intramolecular Heck–Suzuki–Miyaura domino reaction to produce the desired trisubstituted arylidene-isoquinolinones. The method shows full regio- and stereoselectivity derives from the particular Pd-catalyzed syn-insertion of triple bond.     

ZnO/Fe3O4 nanoparticles promoted green synthesis of pyrazolo pyrimidinones: Study of antioxidant activity

The preparation of pyrazolo pyrimidinone derivatives was performed by using five component reactions of phthalaldehyde, cyanomethylamine, electron deficient acetylenic compounds, isocyanate, hydrazine, and catalytic amounts of ZnO/Fe₃O₄-magnetic nanoparticles as a high performance catalyst under ultrasonic conditions at ambient temperature in aquause media at room temperature. It should be mentioned that this catalyst was prepared using Spinacia oleracea water extract. In addition, for investigation of antioxidant ability, radical trapping by DPPH and reducing power of ferric ion experiments was performed. As a result, synthesized compounds show excellent radical trapping by DPPH and good reducing ability of ferric ion. The current procedure has the benefits for instance excellent yield of reaction, green media, and easy separation of product and catalyst.     

Monitoring real-time curing of epoxies in situ using single-sided NMR

Adhesively bonded joints using epoxy are widely used in aircraft and aerospace structures. Quality control and defect detection during epoxy curing in such applications is critical. We used single-sided nuclear magnetic resonance (NMR) to nondestructively probe and spatially resolve the change in the characteristic NMR relaxation time (T₂) of epoxies during curing on a substrate. Time-dependent T₂ values were fit to a Weibull function to model temporal changes in the NMR measurables. Our results demonstrate that the reduction in molecular mobility of various epoxy/curing agent mixtures occurs more rapidly at the interface than in the bulk. Further use of single-sided NMR to acquire spatially resolved T₂ data will provide a route for elucidatory epoxy curing studies. © 2020 Wiley Periodicals, Inc. J. Polym. Sci. 2020 , 58, 616–623     

A Non-Linear Procurement Model with Quantity Discounts

This paper presents a procurement model for a multinational oil company with affiliate plants located worldwide. The proposed model uses a quantity discount relationship to reduce the total procurement cost. We use a non-linear programming method to solve the model and make comparisons with the current approach used by the oil company. The proposed approach has several distinct advantages: assured minimum cost, ease of data management, flexibility in design, and accuracy in results.     

Improving Prediction of Neural Networks: A Study of Two Financial Prediction Tasks

Neural networks are excellent mapping tools for complex financial data. Their mapping capabilities however do not always result in good generalizability for financial prediction models. Increasing the number of nodes and hidden layers in a neural network model produces better mapping of the data since the number of parameters available to the model increases. This is detrimental to generalizability of the model since the model memorizes idiosyncratic patterns in the data. A neural network model can be expected to be more generalizable if the model architecture is made less complex by using fewer input nodes. In this study we simplify the neural network by eliminating input nodes that have the least contribution to the prediction of a desired outcome. We also provide a theoretical relationship of the sensitivity of output variables to the input variables under certain conditions. This research initiates an effort in identifying methods that would improve the generalizability of neural networks in financial prediction tasks by using mergers and bankruptcy models. The result indicates that incorporating more variables that appear relevant in a model does not necessarily improve prediction performance.     

Synthesis, characterization and catalytic oxidation of cyclohexene with molecular oxygen over host (zeolite-Y)/guest (nickel(II) complexes of R2[12]1,3-dieneN2O2 and R2[13]1,4-dieneN2O2; R = H, Me, Ph) nanocatalyst (HGN)

12- and 13-Membered diaza dioxa Schiff-base nickel(II) complexes were successfully prepared in a nanoscale microreactor by the template condensation of (1,8-diamino-3,6-dioxaoctane)nickel(II) complex with bifunctional diketones within the nanodimensional pores of zeolite Y. The host–guest nanocatalyst (HGN); ([Ni((R₂[12]1,3-dieneN₂O₂)]²⁺-NaY, [Ni(R₂[13]1,4-dieneN₂O₂)]²⁺-NaY; R = H, Me and Ph) is catalytically very efficient as compared to other neat complexes for oxidation of cyclohexene with molecular oxygen as oxidant in the absence of solvent at 70 °C, affording 2-cyclohexene-1-ol and 2-cyclohexene-1-one.