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

Abstract

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.

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

“…In this sense, the best neural model, with topology 10-8-1, presents a good root-mean-squared error around 1.169°C [49]. This error is in concordance with other neural models developed for different additives described above.…”

“…Each variable provide different information for neural model such as: (i) additive concentration provides information about the impact of different additive amounts, (ii) number of atoms, molecular mass, bond and rings provide information about structure that can affect interactions with surfactant and (iii) log P provides information about polarity [49]. With the use of these variables, our research group has been able to simulate the effect of four different families of structurally related compounds (crown ethers, azo-crown ethers, benzo-crown ether and dibenzocrown ethers) [49].…”

“…This error is in concordance with other neural models developed for different additives described above. Different input variables were chosen due its relationship with the nature and structure of the molecule, which influence the packing capabilities of surfactant film [49]. In this sense, the following variables: (i) additive concentration, (ii) number of atoms that conform a ring in a crown ether, (iii) number of heteroatoms, (iv) number of oxygen atoms, (v) number of nitrogen atoms, (vi) number of benzene rings in the molecule, (vii) molecular mass, (viii) log P, (ix) maximum number of bonds between rings and (x) minimum number of bonds between rings were used as input variables for neural model [49].…”

“…Different input variables were chosen due its relationship with the nature and structure of the molecule, which influence the packing capabilities of surfactant film [49]. In this sense, the following variables: (i) additive concentration, (ii) number of atoms that conform a ring in a crown ether, (iii) number of heteroatoms, (iv) number of oxygen atoms, (v) number of nitrogen atoms, (vi) number of benzene rings in the molecule, (vii) molecular mass, (viii) log P, (ix) maximum number of bonds between rings and (x) minimum number of bonds between rings were used as input variables for neural model [49]. Each variable provide different information for neural model such as: (i) additive concentration provides information about the impact of different additive amounts, (ii) number of atoms, molecular mass, bond and rings provide information about structure that can affect interactions with surfactant and (iii) log P provides information about polarity [49].…”

“…In this sense, the following variables: (i) additive concentration, (ii) number of atoms that conform a ring in a crown ether, (iii) number of heteroatoms, (iv) number of oxygen atoms, (v) number of nitrogen atoms, (vi) number of benzene rings in the molecule, (vii) molecular mass, (viii) log P, (ix) maximum number of bonds between rings and (x) minimum number of bonds between rings were used as input variables for neural model [49]. Each variable provide different information for neural model such as: (i) additive concentration provides information about the impact of different additive amounts, (ii) number of atoms, molecular mass, bond and rings provide information about structure that can affect interactions with surfactant and (iii) log P provides information about polarity [49]. With the use of these variables, our research group has been able to simulate the effect of four different families of structurally related compounds (crown ethers, azo-crown ethers, benzo-crown ether and dibenzocrown ethers) [49].…”