Influence of polyethylene glycols on percolative phenomena in AOT microemulsions

Arias-Barros, Serxio I.; Cid, Antonio; García‐Río, Luis; Mejuto, J. C.; Morales, Juan J.

doi:10.1007/s00396-009-2122-0

Cited by 19 publications

(11 citation statements)

References 16 publications

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“…Moderate or low concentrations of additives have a significant effect on the percolation threshold (García-Río, L. 1994). In the literature there are a large amount of studies about the influence of different additives in general (García-Río et al, 1997;Moulik et al, 1999;Hait et al, 2001;Hait et al, 2002a;Dasilva-Carvalhal et al, 2003;Dasilva-Carvalhal et al, 2005;Dasilva-Carvalhal et al, 2006;Arias-Barros et al, 2010) and, in particular, amphiphilic additives upon the percolation phenomena and the internal dynamics of microemulsions (Nazario et al, 1996;Nazario et al, 2000;García-Río et al, 2000;García-Río et al, 2005;Cid-Samamed et al, 2008). The additives that increase the "rigidity" of the membranes increase the value of the percolation threshold of a microemulsion, (Ray et al, 1993), while those that make them more flexible favour the process (Mathew et al, 1988;Ray et al, 1993).…”

Section: Methodsmentioning

confidence: 99%

Influence of Linear Aliphatic Alcohols upon the Electric Percolation of AOT-Based Microemulsions

Cid¹,

Manso²,

Mejuto³

et al. 2012

Microemulsions - An Introduction to Properties and Applications

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Section: Methodsmentioning

confidence: 99%

Influence of Linear Aliphatic Alcohols upon the Electric Percolation of AOT-Based Microemulsions

Cid¹,

Manso²,

Mejuto³

et al. 2012

Microemulsions - An Introduction to Properties and Applications

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“…An experimental procedure to determine percolation temperature has been described previously [20][21][22]. A Crison GPL 32 conductivity meter was used.…”

Section: Percolation Temperature Determinationmentioning

confidence: 99%

“…In the last decade, our research group has studied the effects of different additives on the electrical conductivity, and other properties, for water/AOT/isooctane microemulsions (aerosol OT or dioctyl sodium sulfosuccinate, isooctane and water) [5,[15][16][17][18][19][20][21][22]. The influence of different additives was explained on the basis of changes in the surfactant film structure and different solubility of the complex system.…”

Section: Introductionmentioning

confidence: 99%

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

Astray¹,

Cid²,

Moldes³

et al. 2017

Properties and Uses of Microemulsions

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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.

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“…This behavior is determined by the rigidity of the surfactant film that is determined by the way in which the surfactant molecules fit one with another and the presence of other substances, for example, additives [1,6,7]. It has been reported that the presence of additives in AOT microemulsions, for example, glymes and polyethylene glycols, facilitates the percolation and as a result reduces the percolation threshold [1,2,8], due to that additive are incorporated in the surfactant film and reduces their rigidity and the stabilizing forces [1].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Models to Predict the Influence of Linear and Cyclic Polyethers on the Electric Percolation of Microemulsions

Alonso-Ferrer¹,

Astray²,

Mejuto³

2020

Application of Expert Systems - Theoretical and Practical Aspects

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This book chapter presents three predictive models, based on artificial neural networks, to determine the percolation temperature of different AOT microemulsions in the presence of different additives (crown ethers, glymes, and polyethylene glycols), which were developed in our laboratory by different authors. An artificial neural network model has been developed for each additive. The models developed, multilayer perceptron, were implemented with different input variables (chosen among the variables that define the packing or its chemical properties) and different intermediate layers. The best model for crown ethers has a topology of 10-8-1, for glymes the selected topology is 5-5-1, and for polyethylene glycol, the best topology was 5-8-8-5-1. The selected models are capable of predicting the electrical percolation temperature with good adjustments in terms of the root mean square error (RMSE), presenting values below 1°C for glymes and polyethylene glycols. According to these results, it can be concluded that the models presented good predictive capacity for percolation temperature. Nevertheless, the adjustments obtained for the crown ethers model indicate that it would be convenient to study new input variables, increase the number of cases, and even use other training algorithms and methods.

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Influence of polyethylene glycols on percolative phenomena in AOT microemulsions

Cited by 19 publications

References 16 publications

Influence of Linear Aliphatic Alcohols upon the Electric Percolation of AOT-Based Microemulsions

Influence of Linear Aliphatic Alcohols upon the Electric Percolation of AOT-Based Microemulsions

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

Artificial Intelligence Models to Predict the Influence of Linear and Cyclic Polyethers on the Electric Percolation of Microemulsions

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