DFT and MP2 Study of Intermolecular Interaction of 5‐Aminotetrazole and Hydrazine: Enthalpy of Formation of Hydrazinium 5‐Aminotetrazolate in the Gas Phase

The tautomerism of all possible forms of 5‐aminotetrazole ( AT1 – AT7 ) in the gas phase and continuum solvent was studied theoretically. The calculations were separately performed at the MP2 and CAM‐B3LYP levels of theory, using the 6‐311++G(d,p) basis set. It was found that 5‐aminotetrazole in the 2H form ( AT1) is the most stable isomer in both the gas phase and in solution. In addition, the aggregation of various isomers of 5‐aminotetrazole with hydrazine was investigated in the gas phase and in solution. Finally, the standard enthalpy of formations of the different structures of hydrazinium 5‐aminotetrazolate was determined in the gas phase. Using the calculated standard enthalpy of formation of different structures of hydrazinium 5‐aminotetrazolate in the gas phase and considering their Boltzmann population ratios, a single value has been reported for the standard enthalpy of formation of hydrazinium 5‐aminotetrazolate considering all of the tautomers. The calculated values are in excellent agreement with the experimentally reported heat of formation for the hydrazinium 5‐aminotetrazolate.     

Modeling of viscosity for mixtures of Athabasca bitumen and heavy n-alkane with LSSVM algorithm

The resources of heavy oil and bitumen are more than those of conventional light crude oil in the world. Diluting the bitumen with liquid solvent can decrease viscosity and increase the empty space between molecules. Tetradecane is a candidate as liquid solvent to dilute the bitumen. Owning to the sensitivity of enhanced oil recovery process, the accurate approximation for the viscosity of aforementioned mixture is important to decrease uncertainty. The aim of this study was to develop an effective relation between the viscosity of Athabasca bitumen and heavy n-alkane mixtures based on temperature, pressure, and weight percentage of n-tetradecane using the least square support vector machine. This computational model was compared with the previous developed correlation and its accuracy was confirmed. The value of R² and MSE obtained 1.00 and 1.02 for this model, respectively. This developed predictive tool can be applied as an accurate estimation for any mixture of heavy oil with liquid solvent.     

DRAG REDUCTION IN A TURBULENT CHANNEL FLOW WITH HYDRO- PHOBIC WALL

This paper investigates a theoretical prediction of friction drag reduction in turbulent channel flow which is achieved by using superhydrophobic surfaces. The effect of the hydrophobic surface is considered to be a slip boundary condition on the wall, and this new boundary condition is added to Large Eddy Simulation (LES) equations. The predicted drag reduction at Reτ=180 is approximately 30%, which concurs with results obtained from Direct Numerical Simulation (DNS). An important implication of the present finding is that the near-wall turbulence structures are modified with streamwise slip velocity. In addition, a noticeable effect on the turbulence structure occurs when the slip length is greater than a certain value.     

Evaluation of Data Mining Strategies for Classification of Black Tea Based on Image-Based Features

In this study, a new procedure based on computer vision was developed for qualitative classification of black tea. Images of 240 samples from four different classes of black tea, including Orange Pekoe (OP), Flowery Orange Pekoe (FOP), Flowery Broken Orange Pekoe (FBOP), and Pekoe Dust One (PD-ONE), were acquired and processed using a computer vision system. Eighteen color features, 13 gray-image texture features, and 52 wavelet texture features were extracted and assessed. Two common heuristic feature selection methods: correlation-based feature selection (CFS) and principal component analysis (PCA), were used for selecting the most significant features. Seven of the primary features were selected by CFS as the most relevant ones, while PCA converted the original variables into 11 independent components. These final discriminatory vectors were evaluated by using four different classification methods including decision tree (DT), support vector machine (SVM), Bayesian network (BN), and artificial neural networks (ANN) to predict the qualitative category of tea samples. Among the studied classifiers, the ANN with 7–10–4 topology developed by CFS-selected features provided the best classifier with a classification rate of 96.25%. The other methods assayed provided slightly lower accuracies than ANN from 86.25% for BN till 87.50% for SVM and 88.75% for DT. In all the cases, the accuracy of the classifiers increased when using the CFS-selected features as input variables in front of PCA obtained ones. It can be concluded that image-based features are strong characterizing factors which can be effectively applied for tea quality evaluation.     

Prediction of proton conductivity of graphene oxide-containing polymeric membranes

In the present work we examined the capability of a simple Nernst–Planck based model to predict the proton conductivity of polymeric membranes which contained pristine or functionalized graphene oxide sheets (GO or F-GO, respectively). A wide comparison with experimental data on both fluorinated and non-fluorinated membranes has been made. It was found that the expression employed for calculating the tortuosity factor of the model played an important role in whether the experimental data was accurately predicted. A modification of the tortuosity factor expression was proposed to enable the model to provide the best agreement between the experimental and theoretical values of proton conductivity. The results showed that in the case of fluorinated membranes at low amounts of GO or at high test temperatures the modified model was able to represent the experimental data more accurately than the original model. However, the selection of the best expression for the tortuosity factor calculations in non-fluorinated membrane systems depended on the ion exchange capacity values of the host membranes.     

Chemometrics-Enhanced Micelle-Mediated Extraction Spectrophotometric Method for Simultaneous Determination of Cu<Superscript>2+</Superscript> and Zn<Superscript>2+</Superscript> in Medicinal Plant,Rice and Water Samples Using Continuous Wavelet Transform

A new micelle-mediated extraction method for the pre-concentration of trace amounts of Cu²⁺ and Zn²⁺ as a prior step to their simultaneous spectrophotometric determination has been developed in various samples. The analytes were complexed with new synthesized ligand 4,4′-((4-chlorophenyl)methylene)bis(1,3-diphenyl-1H-pyrazol-5-ol) (CPBMPY) and Triton X-100 was added as a extraction agent. The optimal reaction and extraction conditions were optimized and the analytical characteristics of the method were obtained. The detection limit of the method was 0.90 and 0.30 ng mL^?1 for Cu²⁺ and Zn²⁺, respectively. Continuous wavelet transformation (CWT) of visible spectra as a very simple and accurate method was developed for the simultaneous determination of binary mixtures of Cu²⁺ and Zn²⁺ ions. A zero-crossing technique was applied on the transformed signals and the constructed calibrations were tested by analyzing the composition of the different binary mixtures. The proposed procedure successfully was applied to analysis of water, rice, and medicinal plant and reference material samples. The amounts of metal ions obtained by the proposed methods were in good agreement with those obtained by Graphite furnace atomic absorption spectrometry.