Electrical percolation of AOT-based microemulsions with n-alcohols

Moldes, O.; Morales, Juan J.; Cid, Antonio; Astray, Gonzalo; Montoya, Iago Antonio; Mejuto, J. C.

doi:10.1016/j.molliq.2015.12.021

Cited by 13 publications

(11 citation statements)

References 40 publications

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“…In order to evaluate the effect of amphiphiles on percolation threshold, the influence of 1-n-alcohols [59], 2-n-alcohols [59], n-alkylamines [60][61][62][63] and n-alkyl acids [64,65] had been analysed. This allowed us to estimate the influence of the chain length of the molecule with constant head group and also the influence of the head group while the chain length remains invariant (see Figure 9).…”

Section: Percolation Prediction In Aot-based Microemulsions In the Prmentioning

confidence: 99%

“…The alcohols (both 1-n-alcohols and 2-n-alcohols) were modelled using the same ANN [59]. The best neural model presents a topology with five input nodes, a single intermediate layer with 11 neurons and one node in the output layer to predict percolation temperature (5-11-1) [59] (Figure 10).…”

Section: Percolation Prediction In Aot-based Microemulsions In the Prmentioning

confidence: 99%

“…The best neural model presents a topology with five input nodes, a single intermediate layer with 11 neurons and one node in the output layer to predict percolation temperature (5-11-1) [59] (Figure 10). This model was developed with five input neurons: (i) additive concentration [Add], (ii) molecular weight (M w ), (iii) number of carbon atoms (n°C), (iv) pK a , and (v) log P [59]. This neural model was trained with 41 microemulsion compositions (67.2% of the total cases) and 20 compositions for valuation phase (32.8% of the total cases) [59].…”

Section: Percolation Prediction In Aot-based Microemulsions In the Prmentioning

confidence: 99%

“…This model was developed with five input neurons: (i) additive concentration [Add], (ii) molecular weight (M w ), (iii) number of carbon atoms (n°C), (iv) pK a , and (v) log P [59]. This neural model was trained with 41 microemulsion compositions (67.2% of the total cases) and 20 compositions for valuation phase (32.8% of the total cases) [59]. Best neural model, 5-11-1, presents a root-mean-squared error of 0.73°C (R = 0.9939) in the training phase and 0.98°C (R =0.9869) for the validation phase [59] (see Figure 11 and Figure 12).…”

Section: Percolation Prediction In Aot-based Microemulsions In the Prmentioning

confidence: 99%

“…This neural model was trained with 41 microemulsion compositions (67.2% of the total cases) and 20 compositions for valuation phase (32.8% of the total cases) [59]. Best neural model, 5-11-1, presents a root-mean-squared error of 0.73°C (R = 0.9939) in the training phase and 0.98°C (R =0.9869) for the validation phase [59] (see Figure 11 and Figure 12). In the case of carboxylic acids [65], the best neural model presents a topology with five neurons in the input layer, two intermediate layers with five and 10 neurons and an output layer with one node [65].…”

Section: Percolation Prediction In Aot-based Microemulsions In the Prmentioning

confidence: 99%

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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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Section: Percolation Prediction In Aot-based Microemulsions In the Prmentioning

confidence: 99%

Section: Percolation Prediction In Aot-based Microemulsions In the Prmentioning

confidence: 99%

Section: Percolation Prediction In Aot-based Microemulsions In the Prmentioning

confidence: 99%

Section: Percolation Prediction In Aot-based Microemulsions In the Prmentioning

confidence: 99%

Section: Percolation Prediction In Aot-based Microemulsions In the Prmentioning

confidence: 99%

See 3 more Smart Citations

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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Enhanced Hydrodynamic Radius of AOT/n-heptane/Water Reverse Micellar System Through Altered Electrostatic Interactions and Molecular Self-Assemblies

Das

Mandal

et al. 2021

J Fluoresc

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We have demonstrated a unique approach to alter the aqueous pool size of an AOT/n-heptane/water reverse micellar system. A positively charged dye Rhodamine B (RhB) and negatively charged Rose Bengal (RB) were incorporated in the reverse micellar pool to investigate the effect of electrostatic interactions and stacking effects among the dye molecules on the AOT/nheptane/water interface. Dynamic light scattering revealed increase in reverse micellar pool size in presence of positively charged dye aggregates at the oil-water interface. However, less expansion was observed in presence of negatively charged dye aggregates (RB). This confirms the role of electrostatic interaction in modulating the hydrodynamic radius. A headto-tail type of stacking of RhB molecules at the interface favors this expansion. The differences in stacking of the two dyes inside the reverse micelles and their torsional mobility indicated the role of the reverse micellar interface and H-bonding ability of the microenvironment on dye aggregation. Conductivity measurements demonstrated a significant drop in percolation temperature of the reverse micellar system in presence of dye aggregates. This confirms the effect of dye aggregation and electrostatic interaction on such expansion. This strategy can be exploited for solubilizing greater amounts and a wider variety of drug molecules in microemulsions.

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Transport Phenomena in Reverse Micelles: An Overview on Electrical Conductivity, Viscosity, Dielectric Constant, Diffusion Coefficient and Heat apacity

2022

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Electrical percolation of AOT-based microemulsions with n-alcohols

Cited by 13 publications

References 40 publications

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

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

Enhanced Hydrodynamic Radius of AOT/n-heptane/Water Reverse Micellar System Through Altered Electrostatic Interactions and Molecular Self-Assemblies

Transport Phenomena in Reverse Micelles: An Overview on Electrical Conductivity, Viscosity, Dielectric Constant, Diffusion Coefficient and Heat apacity

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