Electrical percolation of AOT-based microemulsions with n-alcohols

A. Moldes; J. Morales; A. Cid; G. Astray; I. A. Montoya; J. C. Mejuto

doi:10.1016/j.molliq.2015.12.021

Electrical percolation of AOT-based microemulsions with n-alcohols

A. Moldes, J. Morales, A. Cid, G. Astray, I. A. Montoya, J. C. Mejuto

Research output: Contribution to journal › Article › peer-review

13 Citations (Scopus)

Abstract

Percolative behaviour of w/o AOT/iC₈/H₂O microemulsions added with different n-alkanols is reported. 1-n-alcohols and 2-n-alkanols presented dissimilarities affecting percolation. Smaller alcohols ease percolation, especially at low concentrations. Greater molecules implied a reinforcement of the surfactant film that delayed the percolation threshold. Also, a neural network based simulation model of the phenomenon has been developed. This single model has only five input variables and can estimate percolation temperature of microemulsions added with the two types of alcohols studied, with an RMSE of 0.98 °C and R² = 0.9740 (validation dataset values). This is considered a successful prediction rate, following previous developments with other families of additives, that confirms neural networks as reliable tools for percolative behaviour modelling.

Original language	English
Pages (from-to)	18-23
Number of pages	6
Journal	Journal of Molecular Liquids
Volume	215
DOIs	https://doi.org/10.1016/j.molliq.2015.12.021
Publication status	Published - 1 Mar 2016

Keywords

1-n-alcohol
2-n-alcohol
Additives
AOT
Artificial neural networks
Conductivity
Microemulsions
Percolation
Prediction
Simulation

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title = "Electrical percolation of AOT-based microemulsions with n-alcohols",

abstract = "Percolative behaviour of w/o AOT/iC8/H2O microemulsions added with different n-alkanols is reported. 1-n-alcohols and 2-n-alkanols presented dissimilarities affecting percolation. Smaller alcohols ease percolation, especially at low concentrations. Greater molecules implied a reinforcement of the surfactant film that delayed the percolation threshold. Also, a neural network based simulation model of the phenomenon has been developed. This single model has only five input variables and can estimate percolation temperature of microemulsions added with the two types of alcohols studied, with an RMSE of 0.98 °C and R2 = 0.9740 (validation dataset values). This is considered a successful prediction rate, following previous developments with other families of additives, that confirms neural networks as reliable tools for percolative behaviour modelling.",

keywords = "1-n-alcohol, 2-n-alcohol, Additives, AOT, Artificial neural networks, Conductivity, Microemulsions, Percolation, Prediction, Simulation",

author = "A. Moldes and J. Morales and A. Cid and G. Astray and Montoya, {I. A.} and Mejuto, {J. C.}",

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AU - Moldes, A.

AU - Morales, J.

AU - Cid, A.

AU - Astray, G.

AU - Montoya, I. A.

AU - Mejuto, J. C.

N1 - info:eu-repo/grantAgreement/FCT/SFRH/SFRH%2FBPD%2F78849%2F2011/PT#

PY - 2016/3/1

Y1 - 2016/3/1

N2 - Percolative behaviour of w/o AOT/iC8/H2O microemulsions added with different n-alkanols is reported. 1-n-alcohols and 2-n-alkanols presented dissimilarities affecting percolation. Smaller alcohols ease percolation, especially at low concentrations. Greater molecules implied a reinforcement of the surfactant film that delayed the percolation threshold. Also, a neural network based simulation model of the phenomenon has been developed. This single model has only five input variables and can estimate percolation temperature of microemulsions added with the two types of alcohols studied, with an RMSE of 0.98 °C and R2 = 0.9740 (validation dataset values). This is considered a successful prediction rate, following previous developments with other families of additives, that confirms neural networks as reliable tools for percolative behaviour modelling.

AB - Percolative behaviour of w/o AOT/iC8/H2O microemulsions added with different n-alkanols is reported. 1-n-alcohols and 2-n-alkanols presented dissimilarities affecting percolation. Smaller alcohols ease percolation, especially at low concentrations. Greater molecules implied a reinforcement of the surfactant film that delayed the percolation threshold. Also, a neural network based simulation model of the phenomenon has been developed. This single model has only five input variables and can estimate percolation temperature of microemulsions added with the two types of alcohols studied, with an RMSE of 0.98 °C and R2 = 0.9740 (validation dataset values). This is considered a successful prediction rate, following previous developments with other families of additives, that confirms neural networks as reliable tools for percolative behaviour modelling.

KW - 1-n-alcohol

KW - 2-n-alcohol

KW - Additives

KW - AOT

KW - Artificial neural networks

KW - Conductivity

KW - Microemulsions

KW - Percolation

KW - Prediction

KW - Simulation

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DO - 10.1016/j.molliq.2015.12.021

M3 - Article

AN - SCOPUS:84950109988

SN - 0167-7322

VL - 215

SP - 18

EP - 23

JO - Journal of Molecular Liquids

JF - Journal of Molecular Liquids

ER -

Electrical percolation of AOT-based microemulsions with n-alcohols

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Keywords

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