A Model of Microemulsion Formation and Percolation: Experimental Validation

Naouli, Nabil; Rosano, Henri L.; Kanouni, Mouhcine

doi:10.1080/01932691003662365

Cited by 7 publications

(4 citation statements)

References 15 publications

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“…In this equation, T is the absolute temperature, k the Boltzmann constant, 3 the dielectric constant, h the Planck's constant, n e the characteristic absorption frequency and n the refractive index. When bodies 1 and 2 are identical, eqn (7) reduces to: 31 A ls-i ðTÞ ¼…”

Section: Model Developmentmentioning

confidence: 99%

“…31 In this case, it establishes the separation distance between the oil drop and the tail domain. A SO is the surfactant-oil Hamaker constant computed on the basis of the Lifshitz theory using eqn (7). It is important to note that swollen micelles not only contain surfactant tails in their tail domain but also oil occluded in between them, as discussed in subsequent sections.…”

Section: Surfactant-oil W So (R O ) Interactionmentioning

confidence: 99%

“…4,5 Some of these contributions were conceived conceptually or estimated through phenomenological models that incorporated molecular congurations and some form of molecular interactions. [6][7][8][9][10][11] Most of the phenomenological approaches emphasized the inter-aggregate interactions over the intramicellar energetic contributions to the total free energy of the aggregate formation. More recent efforts also considered intra-micellar contributions, indirectly, via the analysis of the bending energy of the droplet at the interface.…”

Section: Introductionmentioning

confidence: 99%

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Van der Waals free energy model for solubilization of oil in micelles

Troncoso

Acosta

2015

Soft Matter

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This work introduces the first of a two part thermodynamic framework to estimate the solubilization of nonpolar oils in micelles conformed by nonionic surfactants with linear alkyl tails, considering their configuration and the molecular properties of the constituents. This first part introduces a formal approach to account for the lipophilic (van der Waals) contributions to the free energy of solubilization in spherical micelles. To this end, this work uses two recently developed integration methods for sphere-shell and cone-shell VDW interactions that allow the calculation of surfactant-oil and surfactant-surfactant interactions that take place within the micelles of the solubilization process studied here. The method consists in calculating the free energy of transferring a normal alkane from its continuum, and surfactants monomers from empty micelles to produce an oil swollen micelle. The lipophilic interactions are estimated using the microscopic approach of Hamaker with Lifshitz-based Hamaker constants. The influence of n-alkane and surfactant tail length on the solubilization capacity predicted by the van der Walls free energy model (VDW-FEM) are consistent with experimental trends and it is also consistent with the lipophilic terms included in the semi-empirical Hydrophilic-Lipophilic-Difference + Net-Average-Curvature's (HLD-NAC) equation that predicts the phase behavior of microemulsions. As a result, these lipophilic terms can now be defined in terms of molecular interactions and molecular properties.

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Section: Model Developmentmentioning

confidence: 99%

Section: Surfactant-oil W So (R O ) Interactionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Van der Waals free energy model for solubilization of oil in micelles

Troncoso

Acosta

2015

Soft Matter

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“…Internal dynamics of microemulsions has been largely studied, especially on the phenomenon of electrical percolation [1][2][3][4]. Electrical percolation is characterized by an increment in electrical conductivity when the temperature, or the volume fraction of the dispersed phase, reaches a critical value [5].…”

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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