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     

Determination of the formation constant for the inclusion complex between Lanthanide ions and Dansyl chloride derivative by fluorescence spectroscopy: Theoretical and experimental investigation

In this paper, a sensitive, easy, efficient, and suitable method for the calculation of K_f values of complexation between one derivative of Dansyl chloride [5-(dimethylamino) naphthalene-1-sulfonyl 4-phenylsemicarbazide] (DMNP) and Lanthanide(III) (Ln) ions is proposed, using both spectrofluorometric and spectrophotometric methods. Determination of K_f showed that DMNP was mostly selective towards the erbium (III) ion. The validity of the method was also confirmed calculating the Stern–Volmer fluorescence quenching constants (K_sv) that resulted in the same consequence, obtained by calculating the K_f of complexation values. In addition, the UV–vis spectroscopy was applied for the determination of K_f only for the Ln ions that had interactions with DMNP. Finally, the DFT studies were done on Er³⁺ and the DMNP complex for distinguishing the active sites and estimating the pair wise interaction energy. It can be concluded that this derivative of Dansyl chloride with inherent high fluorescence intensity is a suitable reagent for the selective determination of the Er³⁺ ion which can be used in constructing selective Er³⁺ sensors.     

A novel strategy to increase performance of solid‐phase microextraction fibers: Electrodeposition of sol‐gel films on highly porous substrate

In the present work, the effect of substrate porosity for preparation of solid‐phase microextraction (SPME) fibers was investigated. The fibers were prepared by electrodeposition of sol‐gel coatings using negative potentials on porous Cu wire and compared with previous reported technique for preparation of SPME fibers using positive potentials on smooth gold wire. Porous substrate was prepared by electrodeposition of a thin layer of Cu on a Cu wire. The extraction capability of prepared fibers was evaluated through extraction of some aromatic hydrocarbons from the headspace of aqueous samples. The effect of substrate porosity and some operating parameters on extraction efficiency was optimized. The results showed that extraction efficiency of SPME fibers highly depends on porosity of the substrate. The LOD ranged from 0.005 to 0.010 ng/mL and repeatability at the 1 ng/mL was below 12%. Electrodeposited films were characterized for their surface morphology and thermal stability using SEM and thermogravimetric analysis, respectively. SEM analysis revealed formation of porous substrate and subsequently porous coating on the wire surface and thermogravimetric analysis showed high thermal stability of the prepared fiber.     

H2TPP organocatalysis in mild and highly regioselective ring opening of epoxides to halo alcohols by means of halogen elements

We found that elemental iodine and bromine are converted to trihalide nucleophiles (triiodine and tribromide anion, respectively) in the presence of catalytic amounts of meso-tetraphenylporphyrins (H2TPP). Therefore a highly regioselective method for the synthesis of beta-haloalcohols through direct ring opening of epoxides with elemental iodine and bromine in the presence of H2TPPs as new catalysts is described. At room temperature a series of epoxide derivatives were converted into the corresponding halohydrins resulting from an attack of trihalide species anion atoms at the less substituted carbon atom. This method occurs under neutral and mild conditions with high yields in various aprotic solvents, even when sensitive functional groups are present.     

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.     

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.