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 journalArticlepeer-review

13 Citations (Scopus)


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 languageEnglish
Pages (from-to)18-23
Number of pages6
JournalJournal of Molecular Liquids
Publication statusPublished - 1 Mar 2016


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


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