Influence of Amphiphiles on Percolation of AOT-Based Microemulsions Prediction Using Artificial Neural Networks

Abstract

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, pK a 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.

Is it Necessary for the Use of Fluorinated Compounds to Formulate Reverse Micelles in a Supercritical Fluid? Searching the Best Cosurfactant to Create “Green” AOT Reverse Micelle Media

“…To assess the location and interactions of the alcohol in the RMs, we use DLS and FT-IR as useful techniques for this type of information . In micellar media, we did not use W s values higher than 50 to avoid percolated systems …”

“…42 In micellar media, we did not use W s values higher than 50 to avoid percolated systems. 43 Figure 1A,B shows the values of the diameter obtained by DLS for toluene/AOT/n-pentanol and toluene/AOT/Fpentanol as a function of W s . In the case of n-pentanol (Figure 1A), the d App increases linearly until W s = 10 reaching 18 nm; after this value, the diameter dramatically increases to 53 nm.…”