Micellization and mixed micellization of cationic gemini (dimeric) surfactants and cationic conventional (monomeric) surfactants: Conductometric,dye solubilization,and surface tension studies

In the present study, we have investigated the self-association, mixed micellization, and thermodynamic studies of a cationic gemini (dimeric) surfactant, hexanediyl-1,6-bis(dimethylcetylammonium bromide (16-6-16)) and a cationic conventional (monomeric) surfactant, cetyltrimethylammonium bromide (CTAB). The critical micelle concentration (CMC) of pure (16-6-16 and CTAB) and mixed (16-6-16+CTAB) surfactants was measured by electrical conductivity, dye solubilization, and surface tension measurements. The surface properties (viz., C₂₀ (the surfactant concentration required to reduce the surface tension by 20 mN/m), Π_CMC (the surface pressure at the CMC), Γ_max (maximum surface excess concentration at the air/water interface), A_min (the minimum area per surfactant molecule at the air/water interface), etc.) of micellar (16-6-16 or CTAB) and mixed micellar (16-6-16+CTAB) surfactant systems were evaluated. The thermodynamic parameters of the micellar (16-6-16 and CTAB) and mixed micellar (16-6-16+CTAB) surfactant systems were also evaluated.     

Bacilli as Biological Nano-factories Intended for Synthesis of Silver Nanoparticles and Its Application in Human Welfare

The search of eco-friendly technologies for nano-synthesis is significant to expand their applications in human welfare. Nowadays, various inorganic nanoparticles with beneficial features have been synthesized via physical, chemical, and biological means. Significant biological applications of silver nanoparticles include on-infectious microbes, target drug delivery, cancer and vector-borne disease control. Their syntheses have been tested from plant fungi, bacteria, and viruses. The bacterial mediated synthesis of silver, gold, zinc and other metal leads to a milestone in nano-medicines. Thus, in this review, we focus on the contribution of Bacilli in the synthesis of silver nanoparticles, the mechanism of action and their potential application in the welfare of human beings.     

Crown ether complexes of potassium and thallium(I) 2- and 4-nitrophenoxides

18-Crown-6 and dicyclohexano-18-crown-6 complexes of potassium 2- and 4-nitrophenoxide, and 18-crown-6 complexes of thallium(I) 2- and 4-nitrophenoxide have been synthesized. Solvent effects on the visible spectra of the nitrophenoxide anions are independent of the nature of the cation and the nature of the complexing agent. The 18-crown-6 complex of thallium(I) 2-nitrophenoxide is a 1:2 complex. All the other complexes are 1:1. X-ray crystallographic examination of the potassium dicyclohexano-18-crown-6 complexes showed the potassium ion is octacoordinated in the 2-nitrophenoxide and heptacoordinated in the 4-nitrophenoxide.     

A Müntz space having no complement in

in . In the present paper, we prove that there is a Müntz space not complemented in .

     

Regression and ANN models for estimating minimum value of machining performance

Surface roughness is one of the most common performance measurements in machining process and an effective parameter in representing the quality of machined surface. The minimization of the machining performance measurement such as surface roughness (R_a) must be formulated in the standard mathematical model. To predict the minimum R_a value, the process of modeling is taken in this study. The developed model deals with real experimental data of the R_a in the end milling machining process. Two modeling approaches, regression and Artificial Neural Network (ANN), are applied to predict the minimum R_a value. The results show that regression and ANN models have reduced the minimum R_a value of real experimental data by about 1.57% and 1.05%, respectively.     

Optimization of neural network for ionic conductivity of nanocomposite solid polymer electrolyte system (PEO-LiPF6-EC-CNT)

In this study, the ionic conductivity of a nanocomposite polymer electrolyte system (PEO-LiPF₆-EC-CNT), which has been produced using solution cast technique, is obtained using artificial neural networks approach. Several results have been recorded from experiments in preparation for the training and testing of the network. In the experiments, polyethylene oxide (PEO), lithium hexafluorophosphate (LiPF₆), ethylene carbonate (EC) and carbon nanotubes (CNT) are mixed at various ratios to obtain the highest ionic conductivity. The effects of chemical composition and temperature on the ionic conductivity of the polymer electrolyte system are investigated. Electrical tests reveal that the ionic conductivity of the polymer electrolyte system varies with different chemical compositions and temperatures. In neural networks training, different chemical compositions and temperatures are used as inputs and the ionic conductivities of the resultant polymer electrolytes are used as outputs. The experimental data is used to check the system’s accuracy following the training process. The neural network is found to be successful for the prediction of ionic conductivity of nanocomposite polymer electrolyte system.     

Two-Equation Turbulence Modelling of a Transitional Separation Bubble

Calculations of the Reynolds averaged equations using two different turbulence models have been compared with direct numerical simulation of a transitional separation bubble. Three methods of transition modelling were investigated. The first had no transition adjustment, the second involved fixing the transition point at the location observed in the simulation and the third was a direct transformation of a method proposed by Wilcox [1] which involved sensitising the eddy viscosity and transport equations to the local turbulent Reynolds number. The models captured the general features of the flow but were unable to show the recovery behaviour of the flow behind the bubble. Reasons for the failure are discussed using apriori analysis of terms in the model equations. This revised version was published online in July 2006 with corrections to the Cover Date.     

Hybrid GR-SVM for prediction of surface roughness in abrasive water jet machining

This paper presents a hybridization model of support vector machine (SVM) and grey relational analysis (GRA) in predicting surface roughness value of abrasive water jet (AWJ) machining process. The influential factors of five process parameters in AWJ, namely traverse speed, water jet pressure, standoff distance, abrasive grit size and abrasive flow rate, need to be analyzed using GRA approach. Then, the irrelevance factors of process parameters are eliminated. There is a need of determining the influential factors of process parameters to the surface roughness as to develop a robust prediction model. GRA acts as feature selection method in preprocessing process of hybrid grey relational-support vector machine (GR-SVM) prediction model. Efficiency of the proposed model is demonstrated. GR-SVM presents more accurate result than conventional SVM as it removes the redundant features and irrelevant element from the experimental datasets.