Tungsten hexachloride nanoparticles loaded on montmorillonite K-10: a novel solid acid catalyst in the synthesis of symmetrical and unsymmetrical azines

In the present investigation, we have developed a novel technique to prepare azines using nano-WCl₆ loaded on Montmorillonite K10 clay as a highly active catalyst. A variety of aldehydes and ketones were efficiently converted to the corresponding azines using catalytic amounts of nanosized WCl₆/Mont. K10 under mild conditions. The nanostructures of WCl₆ loaded on Mont. K10 as solid acid catalyst have been prepared by solid dispersion method. The advantages of this catalyst are rapid completion of the reactions, simplicity of performance, lack of pollution and mild and green reaction conditions. The morphologies, structure, and chemical components of parent and modified clay were successfully characterized using SEM, FT-IR, CV, XRD and EDX measurements.     

Experimental investigation and modeling of thermal conductivity of CuO–water/EG nanofluid by FFBP-ANN and multiple regressions

The purpose of this study is to predict the thermal conductivity of copper oxide (CuO) nanofluid by using feed forward backpropagation artificial neural network (FFBP-ANN). Thermal conductivity of CuO nanofluid is measured experimentally using transient hot-wire technique in temperature range of 20–60 °C and in volume fractions of 0.00125, 0.0025, 0.005 and 0.01% for neural network training and modeling. In addition, in order to evaluate accuracy of modeling in predicting the coefficient of nanofluid thermal conductivity, indices of root-mean-square error, coefficient of determination (R ²) and mean absolute percentage error have been used. FFBP-ANN with two input parameters (volume fraction and nanofluid temperature) and one output parameter (nanofluid thermal conductivity) in addition to two hidden layers and one outer layer which purelin, logsig and tansig functions are used was considered as the most optimum structure for modeling with neuron number of 4–10–1. In this study, among common methods of theoretical modeling of nanofluid thermal conductivity, theoretical method of Maxwell and also multivariate linear regression model was used for explaining the importance of modeling and predicting the results using neural network. According to this research, the results of indices and predictions show high accuracy and certainty of ANN modeling in comparison with empirical results and theoretical models.     

One‐Pot Synthesis of Functionalized 2H‐Chromene (=2H‐1‐Benzopyran) Derivatives via a Three‐Component Reaction between a CH‐Acid,a Dialkyl Acetylenedicarboxylate,and Methyl Chloroglyoxylate or Benzyl Carbonochloridate Mediated by Triphenylphosphine

An effective route to functionalized 2H‐chromene (=2H‐1‐benzopyran) derivatives 4 is described (Scheme 1). This involves the reaction of a 1,1‐diactivated alkene, resulting from the reaction of dimedone (=5,5‐dimethylcyclohexane‐1,3‐dione; 1a ) with methyl chloroglyoxylate (ClC(O)COOMe), benzyl carbonochloridate (ClC(O)OCH₂Ph) or 3,5‐dinitrobenzoyl chloride (3,5‐(NO₂)₂C₆H₃C(O)Cl), and a dialkyl acetylenedicarboxylate (=dialkyl but‐2‐ynedioate) in the presence of Ph₃P which undergo intramolecular Wittig reaction to produce 2H‐chromene derivatives (Scheme 1).     

Can commercial peanut assay kits detect peanut allergens?

Peanut is the food group mostly associated with severe and fatal allergic reactions. In the United States, more than 90% of peanut-allergic individuals' serum IgE recognized peanut proteins Ara h 1 and Ara h 2, thus establishing these proteins as major peanut allergens. The amount of Ara h 1 and Ara h 2 in 3 varieties of peanut cultivars that are commonly processed in the industrialized countries was determined to be 12-16 and 6-9%, respectively. Current commercial peanut test kits use polyclonal peanut-specific antibodies to detect soluble or buffer extractable peanut proteins. Because the 2 major peanut allergens Ara h 1 and Ara h 2 are isolated from soluble peanut proteins, it is generally assumed that these commercial kits can detect peanut allergens, although none of these kits claims to detect peanut allergen. This study showed for the first time that the peanut test kits could, in fact, detect major peanut allergens Ara h 1 and Ara h 2 in both native or heat-denatured structures; therefore, these kits qualified to be classified as peanut allergen enzyme-linked immunosorbent assays.     

Magnesium triflate [Mg(OTf)2] a highly stable, non-hygroscopic and a recyclable catalyst for the high yielding preparation of diethyl α-trimethylsilyloxyphosphonates from diethyl α-hydroxyphosphonates by HMDS under solventless conditions

A broad, adaptable, high yielding and convenient procedure for the easy conversion of various α-hydroxyphosphonates to α-trimethylsilyloxyphosphonates under mild conditions with HMDS in the presence of a catalytic amount of magnesium triflate as a highly stable and a non-hygroscopic recyclable catalyst in neat conditions is described. In order to show the general applicability of this method, we have also applied this procedure successfully for the silylation of ordinary alcohols and phenols.     

Biodegradable Cell-Seeded Collagen Based Polymer Scaffolds for Wound Healing and Skin Reconstruction

In the present study a collagen-glycosaminoglycans scaffold, following chemical modifications, was prepared as an analog of the extracellular matrix (ECM) by a freeze drying method. Appropriate tests related to its use for wound healing (evaluation of the structure by scanning electron microscopy, swelling properties, biodegradability properties, Fourier transform infrared spectroscopy and cell culture) were done on the scaffolds. Amniotic fluid-derived stem (AFS) cell seeded scaffolds were shown to be potentially useful for wound healing.     

Optimized ultrasonic assisted extraction-dispersive liquid-liquid microextraction coupled with gas chromatography for determination of essential oil of Oliveria decumbens Vent

Ultrasonic assisted extraction-dispersive liquid-liquid microextraction (UAE-DLLME) coupled with gas chromatography (GC) was applied for extraction and determination of essential oil constituents of the plant Oliveria decumbens Vent. Scanning electron microscopy (SEM) was used to see the effect of ultrasonic radiation on the extraction efficiency. By comparison with hydrodistillation, UAE-DLLME is fast, low cost, simple, efficient and consuming small amount of plant materials (～1.0 g). The effects of various parameters such as temperature, ultrasonication time, volume of disperser and extraction solvents were investigated by a full factorial design to identify significant variables and their interactions. The results demonstrated that temperature and ultrasonication time had no considerable effect on the results. In the next step, a central composite design (CCD) was performed to obtain the optimum levels of significant parameters. The obtained optimal conditions were: 0.45 mL for disperser solvent (acetonitrile) and 94.84 μL for extraction solvent (chlorobenzene). The limits of detection (LODs), linear dynamic range and determination coefficients (R(2)) were 0.2-29 ng mL(-1), 1-2100 ng mL(-1) and 0.995-0.998, respectively. The main components of the essential oil were: thymol (47.06%), carvacrol (23.31%), gamma-terpinene (18.94%), p-cymene (8.71%), limonene (0.76%) and myristicin (0.63%).     

Optimization of dispersive liquid-liquid microextraction coupled with inductively coupled plasma-optical emission spectrometry with the aid of experimental design for simultaneous determination of heavy metals in natural waters

In this study, dispersive liquid-liquid microextraction (DLLME) combined with inductively coupled plasma optical emission spectrometry (ICP-OES) was developed for simultaneous preconcentration and trace determination of chromium, copper, nickel and zinc in water samples. Sodium diethyldithiocarbamate (Na-DDTC), carbon tetrachloride and methanol were used as chelating agent, extraction solvent and disperser solvent, respectively. The effective parameters of DLLME such as volume of extraction and disperser solvents, pH, concentration of salt and concentration of the chelating agent were studied by a (2^f−1) fractional factorial design to identify the most important parameters and their interactions. The results showed that concentration of salt and volume of disperser solvent had no effect on the extraction efficiency. In the next step, central composite design was used to obtain optimum levels of effective parameters. The optimal conditions were: volume of extraction solvent, 113 μL; concentration of the chelating agent, 540 mg L⁻¹; and pH, 6.70. The linear dynamic range for Cu, Ni and Zn was 1-1000 μg L⁻¹ and for Cr was 1-750 μg L⁻¹. The correlation coefficient (R²) was higher than 0.993. The limits of detection were 0.23-0.55 μg L⁻¹. The relative standard deviations (RSDs, C = 200 μg L⁻¹, n = 7) were in the range of 2.1-3.8%. The method was successfully applied to determination of Cr, Cu, Ni and Zn in the real water samples and satisfactory relative recoveries (90-99%) were achieved.