Aldehydes as additives in AOT-based microemulsions: influence upon electrical percolation

Tenside, Surfactants & Detergents, 2018

The influence of alkyl-aldehydes upon electric percolation of AOT-based microemulsions has been studied. The number of carbons in the hydrocarbon chain was varied between 0 and 5 atoms (chain length between 0 and 7.33Å). Two different behaviors were found, while the presence in the microemulsion of short chains aldehydes implies a decrease in the percolation temperature, aldehydes with 4 or 5 carbon atoms in the hydrocarbon chain increase the percolation threshold. These opposite behavior has ben justified in terms of aldehyde location in the microheterogeneous system.

Electrical Percolation of AOT-based Microemulsions with n-Alcohols

Journal of Molecular Liquids

DOI: 10.1016/j.molliq.2015.12.021

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 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 0.98oC 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.

Influence Prediction of Alkylamines Upon Electrical Percolation of AOT-based Microemulsions Using Artificial Neural Networks

Tenside Surfactants Detergents, 52, 473-476 (2015)

doi: 10.3139/113.110399

Simulations for the electrical percolation of AOT/iC8/H2O w/o microemulsions added with alkylamines have been carried out by means of multilayer perceptron. Five variables have been elected as inputs: amine concentration, molecular weight, log P, hydrocarbon chain length (as number of carbons), and pKa. As a result, a neural model consisting in five input neurons, two middle layers (with fifteen and ten neurons respectively) and one output neuron was chosen because of its better performance, with a RMSE of 0.54 °C for the prediction set, with R2 = 0.9976.

Linear Polyethers as Additives for AOT-Based Microemulsions: Prediction of Percolation Temperature Changes Using Artificial Neural Networks

Tenside Surfactants Detergents: 52 (2015) 264-270.
doi: 10.3139/113.110374

 

Predictive models based on artificial neural networks have been developed for the percolation threshold of AOT based microemulsions with addition of either glymes or polyethylene glycols. Models have been built according to the multilayer perceptron architecture, with five input variables (concentration, molecular mass, log P, number of C and O of the additive). Best model for glymes has a topology of five input neurons, five neurons in a single hidden layer and one output neuron. Polyethylene glycol model's architecture consists in five input neurons, three hidden layers with eight neurons in both first two and five in the last, and a neuron in the last output layer. All of them have a good predictive power according to several quality parameters.

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