Chapters authored
Influence of Amphiphiles on Percolation of AOT-Based Microemulsions Prediction Using Artificial Neural Networks
In this chapter, the ability of artificial neural networks was evaluated to predict the influence of amphiphiles as additive upon the electrical percolation of dioctyl sodium sulfosuccinate (AOT)/isooctane/water microemulsions. In particular, water/AOT/isooctane microemulsion behaviour has been modelled. These microemulsions have been developed in presence of 1-n-alcohols, 2-n-alcohols, n-alkylamines and n-alkyl acids. In all cases, a neural network has been obtained to predict with accuracy the experimental behaviour to identify the physico-chemical variables (such as additive concentration, molecular mass, log P, pKa or chain length) that exert a greater influence on the model. All models are valuable tools to evaluate the percolation temperature for AOT-based microemulsions.
Part of the book: Properties and Uses of Microemulsions
Pseudophase Model in Microemulsions
The kinetic behaviours in microemulsions can be easily modelled using an extension of the pseudophase model previously developed for micellar catalysis. This model considers that the microheterogeneous media can be considered as the sum of different conventional reaction media, where the reagents are distributed and in which the reaction can occur simultaneously. The reaction rate observed in the microheterogeneous system will be the sum of the velocities in each one of the pseudophases. This use can be considered as an extension of the pseudophase model, which has been developed for the quantitative analysis of nitrosation reactions in AOT/isooctane/water microemulsions and has been applied successfully in the literature in a large variety of chemical reactions.
Part of the book: Microemulsion
Modeling the Behavior of Amphiphilic Aqueous Solutions
Two types of predictive models based on artificial neural networks (ANN) and quadratic regression model developed in our laboratory will be summarized in this book chapter. Both models were developed to predict the density, speed of sound, kinematic viscosity and surface tension of amphiphilic aqueous solutions. These models were developed taking into account the concentration, the number of carbons and the molecular weight values. The experimental data were compiled from literature and included different surfactants: i) hexyl, ii) octyl, iii) decyl, iv) tetradecyl and v) octadecyl trimethyl ammonium bromide. Neural models present better adjustment values, with R2 values above 0.902 and AAPD values under 2.93% (for all data), than the quadratic regression models. Finally, it is concluded that the quadratic regression and the neural models can be powerful prediction tools for the physical properties of surfactants aqueous solutions.
Part of the book: Deep Learning Applications