Influence of aza crown ethers on the electric percolation of AOT/isooctane/water (w/o) microemulsions

Vortex-assisted liquid-liquid microextraction of strontium from water samples using 4',4″(5″)-di-(tert-butylcyclohexano)-18-crown-6 and tetraphenylborate

A vortex-assisted liquid-liquid microextraction method was developed for the chromatographic determination of strontium in aqueous samples. In the method, strontium was complexed with 4',4″(5″)-di-(tert-butylcyclohexano)-18-crown-6 in the presence of tetraphenylborate as the counter anion, which increased the hydrophobicity of the ion-association complex, resulting in its improved extraction into 1-octanol. Strontium from the organic phase was stripped with nitric acid back to aqueous solution and determined by ion chromatography. The optimum microextraction conditions were as follows: 2.0 mL aqueous samples with 3 mM tetraphenylborate; 150 μL of 1-octanol as the extractant phase with 10 mM DtBuCH18C6; vortex extraction time for 10 s; centrifugation at 6000 rpm for 4 min; stripping by 0.1 M nitric acid. Under the optimum conditions, the detection limit for strontium was 0.005 mg/L. The calibration curves showed good linearity over the range between 0.01 and 2.5 mg/L. Intra- and interday precisions of the present method were satisfactory with relative standard deviations of 1.7 and 2.1%, respectively.

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.

Percolative Behavior Models Based on Artificial Neural Networks for Electrical Percolation of AOT Microemulsions in the Presence of Crown Ethers as Additives

A series of models, based on artificial neural networks, of the percolative behaviour of AOT microemulsions in the presence of crown ethers as additives have been developed. Input variables, related to the chemical structure of crown ethers and their packing with surfactant film, have been selected. As a result, a model has been chosen with a good forecast capability for percolation threshold of such microemulsions.

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.