New insights on the mechanism of vapour phase hydrolysis of sodium borohydride in a fed-batch reactor

This study aims to produce H₂ from sodium borohydride (NaBH₄) and to initiate its hydrolysis at elevated temperature in the absence of a catalyst. Experimental results indicated that the hydrogen generation yield increased up to %99 at 150 °C in the NaBH₄ concentration of %5 wt in the acidic medium. It can be concluded that experimental characterization of the by-products is quite important since they affected the reaction mechanism or pathway. When the experiments are carried out under aqueous condition, the primary by- product is sodium metaborate while it is boric acid under acidic condition. It is postulated that by-product boric acid decreased the mass transfer limitation due to its higher solubility that prevents the formation of shell and thus increases the contact area between NaBH₄ and vapor. A series of fed-batch reactions were performed to confirm the hypothesis, and the conversions of NaBH₄ reached 99% under the acidic condition.     

Polyurethanes as solid–solid phase change materials for thermal energy storage

Polyurethane polymers (PUs) have been synthesized as solid–solid phase change materials for thermal energy storage using three different kinds of diisocyanate molecules and polyethylene glycols (PEGs) at three different molecular weights. PEGs and their derivatives are usually used as phase change units in polymeric solid–solid phase change materials due to the hydroxyl functional groups. 1000, 6000, and 10,000 g/mol number average molecular weight PEGs are used as working element as hexamethylene, isophorone, and toluene diisocyanates are used as hard segment at the backbone. The effects of molecular weight of PEG and type of diisocyanate on the thermal energy storage properties have been discussed. Only two of the produced polymers show solid–liquid phase change as the rest show solid–solid phase transitions. The produced PUs with a solid–solid phase transitions have potential to be used in thermal energy storage systems.     

Investigating hydrophobicity and the effect of exopolysaccharide on aggregation properties of dairy propionibacteria isolated from Turkish homemade cheeses

Propionic acid bacteria have been used widely as starter cultures. However, their potential as probiotics has received little attention. The ability to auto- and coaggregate is a desirable property for probiotics in health-promoting foods. Therefore, in the current study, we assessed the effect of exopolysaccharides produced by dairy propionibacteria strains on the aggregative and hydrophobicity properties. All propionibacteria strains tested showed auto- and coaggregation ability with Escherichia coli ATCC 11229, but the results were strain specific and dependent on exopolysaccharides production and incubation conditions. In addition, propionibacteria strains tested were determined to be highly hydrophilic. Our results indicate that the ability to autoaggregate, together with cell-surface hydrophobicity and coaggregation abilities with an E. coli strain, can be used for preliminary screening in order to identify potentially probiotic bacteria suitable for human or animal use.     

Electrocatalytic hydrogen production on GCE/RGO/Au hybrid electrode

We have prepared a nanocomposite hybrid film to produce a collaborative network of gold (Au) nanoparticles that are highly dispersed on reduced graphene oxide (RGO) sheets, and tested it for electrocatalytic hydrogen production. The RGO/Au nanocomposite film synthesized on glassy carbon electrode (GCE) allows significant improvements to the electron-transfer process. The Au nanoparticles decorated on the surface of graphene increases the electron density, which synergistically promote the adsorption of hydrogen atoms on the graphene sheets and consequently enhance the hydrogen evolution reaction (HER) activity. The surface properties of the composite was characterized by field-emission scanning electron microscopy (FE-SEM) and the electrocatalytical performances evaluated as-prepared electrocatalyst toward (HER) by linear sweep voltammetry (LSV), Tafel polarization curves and electrochemical impedance spectroscopy (EIS) analyses. The GCE/RGO/Au nanohybrid electrode exhibited good catalytic activity for HER with an onset potential of ?0.3 V and a Tafel slope of 136 mV dec^?1, achieving a current density of 10 mA cm^?2 at an overpotential of ?0.43 V.     

An expert system based on Generalized Discriminant Analysis and Wavelet Support Vector Machine for diagnosis of thyroid diseases

Nowadays, there are many persons, which suffer from thyroid diseases. Therefore, the correct diagnosis of these diseases are very important topic. In this study, a Generalized Discriminant Analysis and Wavelet Support Vector Machine System (GDA_WSVM) method for diagnosis of thyroid diseases is presented. This proposed system includes three phases. These are feature extraction – feature reduction phase, classification phase, and test of GDA_WSVM for correct diagnosis of thyroid diseases phase, respectively. The correct diagnosis performance of this GDA_WSVM expert system for diagnosis of thyroid diseases is estimated by using classification accuracy and confusion matrix methods, respectively. The classification accuracy of this expert system for diagnosis of thyroid diseases was obtained about 91.86%.     

Neural network analysis of ophthalmic arterial doppler signals with Uveitis disease

Doppler ultrasound is known as a reliable technique, which demonstrates the flow characteristics and resistance of ophthalmic arteries. In this study, ophthalmic arterial Doppler signals were obtained from 95 subjects—that 45 of them had suffered from Uveitis disease and the rest of them had been healthy subjects. Multilayer perceptron neural network (MLPNN) employing quick propagation training algorithm was used to detect the presence of Uveitis disease. Spectral analysis of ophthalmic arterial Doppler signals was performed by autoregressive moving average (ARMA) method for determining the MLPNN inputs. The MLPNN was trained with training set, cross validated with cross validation set and tested with testing set. All these data sets were obtained from ophthalmic arteries of healthy subjects and subjects suffering from Uveitis disease. Performance indicators and statistical measures were used for evaluating the MLPNN. The correct classification rate was 95.83% for healthy subjects and 91.30% for subjects suffering from Uveitis disease. Based on the accuracy of the MLPNN detections, it can be mentioned that the classification of ophthalmic arterial Doppler signals with Uveitis disease is feasible by the MLPNN employing quick propagation training algorithm.