Influence of anionic surfactants on the electric percolation of AOT/isooctane/water microemulsions

Aldehydes as Additives in AOT-based Microemulsions: Influence on the Electrical Percolation

The influence of alkyl-aldehydes upon electric percolation of AOT-based microemulsions has been studied. The number of carbons in the hydrocarbon chain was varied between 0 and 5 atoms (chain length between 0 and 7.33 Å). Two different behaviors were found, while the presence in the microemulsion of short chains aldehydes implies a decrease in the percolation temperature, aldehydes with 4 or 5 carbon atoms in the hydrocarbon chain increase the percolation threshold. This opposite behavior has been justified in terms of aldehyde location in the microheterogeneous system.

Viscoelastic wormlike micelles formed by ionic liquid-type surfactant [C16imC8]Br towards template-assisted synthesis of CdS quantum dots

In this paper, viscoelastic wormlike micelles consisting of cationic liquid-type surfactant, 1-hexadecyl-3-octyl imidazolium bromide ([C₁₆imC₈]Br), water and different additives were utilized for the synthesis of CdS quantum dots. First, the influence of different additives, such as [Cd(NH₃)₆]Cl₂ and ethanethioamid (precursors for the synthesis of CdS quantum dots), and temperature on the viscoelasticity of the [C₁₆imC₈]Br aqueous solution was studied by dynamic and steady rheology. Furthermore, the synthesized CdS quantum dots and their photoluminescence properties were characterized by transmission electron microscopy (TEM), UV-Vis absorption spectroscopy, X-ray diffraction (XRD) and energy-dispersive X-ray spectroscopy (EDX). In the end, the mechanism for the synthesis of CdS quantum dots in [C₁₆imC₈]Br wormlike micelles is proposed.

Impact of Alkyl Chain Length on the Transition of Hexagonal Liquid Crystal-Wormlike Micelle-Gel in Ionic Liquid-Type Surfactant Aqueous Solutions without Any Additive

The search for functional supramolecular aggregations with different structure has attracted interest of chemists because they have the potential in industrial and technological application. Hydrophobic interaction has great influence on the formation of these aggregations, such as hexagonal liquid crystals, wormlike micelles, hydrogels, etc. So a systematical investigation was done to investigate the influence of alkyl chain length of surfactants on the aggregation behavior in water. The aggregation behavior of 1-hexadecyl-3-alkyl imidazolium bromide and water has been systematically investigated. These ionic liquid surfactants are denoted as C16-Cn (n = 2, 3, 4, 6, 8, 9, 10, 12, 14, 16). The rheological behavior and microstructure were characterized via a combination of rheology, cryo-etch scanning electron microscopy, polarization optical microscopy, and X-ray crystallography. The alkyl chain has great influence on the formation of surfactant aggregates in water at the molecular level. With increasing alkyl chain length, different aggregates, such as hexagonal liquid crystals, wormlike micelles, and hydrogels can be fabricated: C16-C2 aqueous solution only forms hexagonal liquid crystal; C16-C3 aqueous solution forms wormlike micelle and hexagonal liquid crystal; C16-C4, C16-C6 and C16-C8 aqueous solutions only form wormlike micelle; C16-C9 aqueous solution experiences a transition between wormlike micelle and hydrogel; C16-C10, C16-C12, C16-C14 and C16-C16 only form hydrogel. The mechanism of the transition of different aggregation with increasing alkyl chain length was also proposed.

Influence Prediction of Alkylamines Upon Electrical Percolation of AOT-based Microemulsions Using Artificial Neural Networks

Simulations for the electrical percolation of AOT/iC8/H2O w/o microemulsions added with alkylamines have been carried out by means of multilayer perceptron. Five variables have been elected as inputs: amine concentration, molecular weight, log P, hydrocarbon chain length (as number of carbons), and pKa. As a result, a neural model consisting in five input neurons, two middle layers (with fifteen and ten neurons respectively) and one output neuron was chosen because of its better performance, with a RMSE of 0.54 °C for the prediction set, with R2 = 0.9976.

Volumetric properties of water/AOT/isooctane microemulsions

The densities of AOT/isooctane micelles and water/AOT/isooctane microemulsions with the molar ratios R of water to AOT being 2, 8, 10, 12, 16, 18, 20, 25, 30, and 40 were measured at 303.15 K. The apparent specific volumes of AOT and the quasi-component water/AOT at various concentrations were calculated and used to estimate the volumetric properties of AOT and water in the droplets and in the continuous oil phase, to discuss the interaction between the droplets, and to determine the critical micelle concentration and the critical microemulsion concentrations. A thermodynamic model was proposed to analysis the stability boundary of the microemulsion droplets, which confirms the maximum value of R being about 65 for the stable AOT/water/isooctane microemulsion droplets.

Structure of Microemulsion Formulated with Monoacylglycerols in the Presence of Polyols and Ethanol

The influence of polyols as cosurfactants (propylene glycol PG; glycerol G) and short chain alcohol as a cosolvent (ethanol EtOH) on the formation and solubilization capacity of the systems: hexadecane/monoacylglycerols (MAG)/polyol/water:EtOH, at 60 °C, was investigated. Electrical conductivity measurement, and the DSC method were applied to determine the structure and type of microemulsions formed. The dimension of the droplets was characterized by DLS. It has been stated that concentration of EtOH has a strong influence on the shape and extend the microemulsion areas and helps to avoid rigid structures such as gels, precipitates, and liquid crystals. It was found that, depending on the concentration of five-component systems, it was possible to obtain fully diluted microemulsions with dispersed particles size distribution ranging from 5 to 30 nm. Studied systems are changing the w/o structure into a bicontinuous system. The results of electrical conductivity showed that the electrical percolation threshold is dependent on the hydration of polar head groups in the whole system and the less rigid interfacial film due to the intercalation of ethanol. In addition, the surfactant/alcohol/polyol can strongly bind water in the inner phase so that it freezes below −10 °C and acts in part as ‘bound’ water. In the systems containing more than 50 mass% of polyols, with respect to the water, the all the water was non-freezable. Propylene glycol and glycerol are cryoprotectants protecting biological systems from massive ice crystallization, since they lower the freezing point of water.

Percolation Threshold of AOT Microemulsions with n-Alkyl Acids as Additives Prediction by Means of Artificial Neural Networks

Different artificial neural networks architectures have been assayed to predict percolation temperature of AOT/iC8/H2O microemulsions in the presence of n-alkyl acids with a chain length between 0 and 24 carbons, using a multilayer perceptron with five easy-acquired entrance variables (number of carbons, log P, length of the hydrocarbon chain, pKa and acid concentration). The evaluation of the neural networks was carried out by means of RMSE and IDP, resulting that the architecture with better results consists in five input neurons, two middle layers (with five and ten neuron respectively) and one output neuron. Results prove that Artificial Neural Networks are a useful tool elaborating models to predict percolation temperature of microemulsion systems in the presence of additives.

Artificial Intelligence for Electrical Percolation of AOT-based Microemulsions Prediction

Different Artificial Neural Network architectures have been assayed to predict percolation temperature of AOT/i-C8/H2O microemulsions. A Perceptron Multilayer Artificial Neural Network with five entrance variables (W value of the microemulsions, additive concentration, molecular weight of the additive, atomic radii and ionic radii of the salt components) was used. Best ANN architecture was formed by five input neurons, two middle layers (with eleven and seven neurons respectively) and one output neuron. Root Mean Square Errors (RMSEs) are 0.18°C (R = 0.9994) for the training set and 0.64°C (R = 0.9789) for the prediction set.

Influence Prediction of Small Organic Molecules (Ureas and Thioureas) Upon Electrical Percolation of AOT-Based Microemulsions Using Artificial Neural Networks

In order to predict percolation temperature of AOT-Based microemulsions (AOT/iC8/H2O w/o microemulsions) in the presence of small organic molecules (ureas and thioureas), different Artificial Neural Network architectures (ANN) have been carried out using a Perceptron Multilayer Artificial Neural Network with three entrance variables (W = value of the microemulsion, additive concentration, logP value). Best ANN architecture consists in three input neurons, one middle layer (with two neurons) and one output neuron. Correlation values were R = 0.9251 for the training set and R = 0.9719 for the prediction set.

Behavior of acetyl modified amino acids in reverse micelles: A non-invasive and physiochemical approach

The well-characterized, monodisperse nature of reverse micelles formed by sodium bis-(2-ethylhexyl)sulfosuccinate/water/isooctane and their usefulness in assimilating compounds of varied interests have been exploited to investigate the effect of acetyl modified amino acids (MAA) viz., N-acetyl-L-glycine (NAG), N-acetyl-L-aspartic acid (NAA) and N-acetyl-L-cysteine (NAC), on the water pool and physiochemical properties. Non-invasive techniques such as FTIR and UV-vis absorption spectroscopy have been employed to analyze the interactions of MAA with core water and the AOT headgroup. The micropolarities on both sides of AOT interface have further been investigated by UV-vis absorption probes, methyl orange (MO) and methylene blue (MB). The dynamics of water and temperature induced percolation process have also been studied. The MAA molecules have been found to assist the process with the increase in water content where as a contrary behavior has been observed with the increase in temperature. Conductivity results have been further rationalized in terms of scaling equations, which delineate the dynamic nature of the percolation process. The results have also been analyzed in the light of activation energy of the percolation process and thermodynamics of droplet clustering.