An assessment of the role of morphology in thermal/thermo-oxidative degradation mechanism of PP/EVA/clay nanocomposites

In this study, morphological properties of polypropylene (PP)/ethylene vinyl acetate copolymer (EVA) (75/25 wt/wt) blend-based nanocomposites containing various amounts of organically modified montmorillonite (OMMT) were primarily investigated. The incorporation of compatibilizer into nanocomposites decreased EVA droplet size in PP matrix while increasing compatibilizer/OMMT ratio showed a dual behavior with respect to the variations of OMMT interlayer spacing. By a rough estimation it was found that at EVA droplet size of D_n = 0.43 μm, the highest OMMT interlayer spacing would be acheived. Increasing D_n had a negative effect on the OMMT interlayer spacing. Activation energy of thermal/thermo-oxidative degradation based on Flynn model was obtained. Isothermal degradation test was also performed and desired temperature range for predicting degradation behavior was obtained by means of a free prediction model. An attempt was made to establish a correlation between morphological and thermal/thermo-oxidative parameters and also charred residue morphology. A mechanism for degradation process was proposed according to the changes of chemical bonds during the degradation process probed by FTIR analysis.     

Titanium tetrachloride incorporated crosslinked polystyrene copolymer as an efficient and recyclable polymeric Lewis acid catalyst for the synthesis of Β-amino carbonyl compounds at room temperature

An efficient and eco-friendly procedure for one-pot synthesis of β-amino carbonyl compounds by three-component reaction of aromatic aldehydes, acetophenone, and aromatic amines via a Mannich type reaction using a stable tightly bound complex, titanium tetrachloride incorporated crosslinked polystyrene copolymer (PS/TiCl₄), as a water tolerant, recoverable, and reusable polymeric Lewis acid catalyst in ethanol at room temperature is reported. The complex of the polystyrene and TiCl₄ provides a shelf-stable acidic, water tolerant material, and it is not easily hydrolyzed by water. Our findings thus show a novel and improved modification of the Mannich type reaction in terms of mild reaction conditions, clean reaction profiles, applicability to various substrates using a simple workup procedure which makes this reaction an interesting alternative to previously applied procedures. The recyclability of the catalyst makes this protocol environmentally benign.     

Comparing various machine learning approaches in modeling the dynamic viscosity of CuO/water nanofluid

Nanofluids are broadly employed in heat transfer mediums to enhance their efficiency and heat transfer capacity. Thermophysical properties of nanofluids play a crucial role in their thermal behavior. Among various properties, the dynamic viscosity is one of the most crucial ones due to its impact on fluid motion and friction. Applying appropriate models can facilitate the design of nanofluidics thermal devices. In the present study, various machine learning methods including MPR, MARS, ANN-MLP, GMDH, and M5-tree are used for modeling the dynamic viscosity of CuO/water nanofluid based on the temperature, concentration, and size of nanostructures. The input data are extracted from various experimental studies to propose a comprehensive model, applicable in wide ranges of input variables. Moreover, the relative importance of each variable is evaluated to figure out the priority of the variables and their influences on the dynamic viscosity. Finally, the accuracy of the models is compared by employing the statistical criteria such as R-squared value. The models’ outputs disclosed that employing ANN-MLP approach leads to the most precise model. R-square value and average absolute percent relative error (AAPR) value of the model by using ANN-MLP model are 0.9997 and 1.312%, respectively. According to these values, ANN-MLP is a reliable approach for predicting the dynamic viscosity of the studied nanofluid. Additionally, based on the relative importance of the input variables, it is concluded that concentration has the highest relative importance; while the influence of size is the lowest one.

     

Investigation of the effect of using various HFC refrigerants in geothermal heat pump with residential heating applications

Journal of Thermal Analysis and Calorimetry - In the present investigation, the impact of various refrigerants on the efficiency of the geothermal heat pump operation is investigated. Appropriate...     

A computational investigation on the potential energy surface of thiosulfeno with O(3P) reaction

The reaction paths of thiosulfeno radical (HS₂) with O(³P) have been investigated at the UB3LYP/aug-cc-pV(T + d)Z and UCCSD(T)/aug-cc-pV(T + d)Z//B3LYP levels. Two stable collision intermediates, HSSO and SS(H)O, have been considered for the HS₂ + O(³P) reaction. Four products of S + HSO, H + SSO, HS + SO, and S₂ + OH are obtained by starting from HSSO and SS(H)O. The calculated results show that the most feasible paths for the formation of S + HSO, H + SSO, and HS + SO products include no transition states in reaction path, while that of S₂ + OH product includes relatively high energy barriers of 23.0 kcal/mol. Therefore, S + HSO, H + SSO, and HS + SO are main products (with the stability other of HS + SO > H + SSO > S + HSO) and S₂ + OH is the second product in HS₂ + O(³P) reaction. Because, all intermediates, transition states, and products involved in the reaction paths lie below the initial reactants, the HS₂ + O(³P) reaction is expected to be rapid even at low temperatures.     

Generalized φ-contraction for a pair of mappings on cone metric spaces

Using the setting of cone metric space, a fixed point theorem is proved for two maps, and several corollaries are obtained. In these cases, the cone does not need to be normal. These results generalize several well known compatible recent and classical results in the literature. As an application, the existence of solution of an integral equation is presented.     

Prediction of the logarithmic of partition coefficients (log P) of some organic compounds by least square-support vector machine (LS-SVM)

A new method least square-support vector machine (LS-SVM) was used to develop quantitative structure–property relationship (QSPR) models for predicting the logarithmic of n-octanol/water partition coefficient (log P) of some derivatives phenolic compounds. The calibration and predictive ability of LS-SVM were investigated and compared with those of three other methods; multiple linear regression (MLR), support vector linear regression (SVR) and artificial neural network (ANN). The results showed that the log P values calculated by LS-SVM were in good agreement with experimental values, and the performances of the LS-SVM models were comparable or superior to those of MLR, SVR and ANN methods. The root-mean-square errors of the training set and the predicting set for the LS-SVM model were 0.0855, 0.0746 and the squares of the correlation coefficients were 0.9960 and 0.9728, respectively. These values and other statistical parameters obtained for the LS-SVM model show the reliability of this model. LS-SVM is a new and effective method for predicting log P of some organic compounds, and can be used as a powerful chemometrics tool for QSPR studies.