Percolative Behavior Models Based on Artificial Neural Networks for Electrical Percolation of AOT Microemulsions in the Presence of Crown Ethers as Additives
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Óscar A. Moldes
Abstract
A series of models, based on artificial neural networks, of the percolative behaviour of AOT microemulsions in the presence of crown ethers as additives have been developed. Input variables, related to the chemical structure of crown ethers and their packing with surfactant film, have been selected. As a result, a model has been chosen with a good forecast capability for percolation threshold of such microemulsions.
Kurzfassung
Eine auf neuronalen Netzwerken basierende Modellreihe, die das perkolative Verhalten von AOT-Mikroemulsionen in Gegenwart von Kronenethern als Additive beschreibt, wurde entwickelt. Die Eingangsvariablen wurden hinsichtlich der chemischen Struktur der Kronenether und ihrer Packung im Tensidfilm ausgewählt. Es wurde ein Modell bestimmt, mit dem sich die Perkolationsschwelle solcher Mikroemulsionen gut vorhersagen lässt.
References
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© 2014, Carl Hanser Publisher, Munich
Artikel in diesem Heft
- Contents/Inhalt
- Contents
- Application
- Rapid Assessment of Glass Etching/Corrosion using the Quartz Crystal Microbalance with Dissipation Monitoring, QCM-D
- Micelle Based Spectrofluorimetric Determination of Chloroquine Phosphate in Commercial Formulation and Human Plasma
- Novel Surfactants
- Synthesis of Rice Bran Fatty Acids (RBFAs) Based Cationic Surfactants and Evaluation of Their Performance Properties in Combination with Nonionic Surfactant
- Synthesis and Properties of Guerbet Hexadecyl Sulfate
- Synthesis and Characterization of N-alkyl-N′-glucosylhexanediamine Surfactant
- Mizellar Catalysis
- Methanesulfonic Acid as a More Efficient Catalyst for the Synthesis of Lauraldehyde Glycerol Acetal
- Surfactant Assisted Enhancement of Bioremediation Rate for Hexavalent Chromium by Water Extract of Siris (Albizia lebbeck) Sawdust
- Physical Chemistry
- Synergistic Interactions in Mixed W/O Microemulsions of Cationic Gemini and Anionic Surfactants
- Percolative Behavior Models Based on Artificial Neural Networks for Electrical Percolation of AOT Microemulsions in the Presence of Crown Ethers as Additives
- Junction Characteristics System Based on Composite Organic Semiconductors: Polystyrene/Polyaniline Doped by [BMIM] [BF4] Ionic Liquid
Artikel in diesem Heft
- Contents/Inhalt
- Contents
- Application
- Rapid Assessment of Glass Etching/Corrosion using the Quartz Crystal Microbalance with Dissipation Monitoring, QCM-D
- Micelle Based Spectrofluorimetric Determination of Chloroquine Phosphate in Commercial Formulation and Human Plasma
- Novel Surfactants
- Synthesis of Rice Bran Fatty Acids (RBFAs) Based Cationic Surfactants and Evaluation of Their Performance Properties in Combination with Nonionic Surfactant
- Synthesis and Properties of Guerbet Hexadecyl Sulfate
- Synthesis and Characterization of N-alkyl-N′-glucosylhexanediamine Surfactant
- Mizellar Catalysis
- Methanesulfonic Acid as a More Efficient Catalyst for the Synthesis of Lauraldehyde Glycerol Acetal
- Surfactant Assisted Enhancement of Bioremediation Rate for Hexavalent Chromium by Water Extract of Siris (Albizia lebbeck) Sawdust
- Physical Chemistry
- Synergistic Interactions in Mixed W/O Microemulsions of Cationic Gemini and Anionic Surfactants
- Percolative Behavior Models Based on Artificial Neural Networks for Electrical Percolation of AOT Microemulsions in the Presence of Crown Ethers as Additives
- Junction Characteristics System Based on Composite Organic Semiconductors: Polystyrene/Polyaniline Doped by [BMIM] [BF4] Ionic Liquid
