Accessible Requires Authentication Published by De Gruyter August 22, 2013

Predicting Critical Micelle Concentration Values of Non-Ionic Surfactants by Using Artificial Neural Networks

Voraussage von kritischen Mizellbildungskonzentrationen nichtionischer Tenside mit Hilfe von künstlichen neuronalen Netzwerken
Gonzalo Astray, Manuel A. Iglesias-Otero, Oscar A. Moldes and Juan C. Mejuto

Abstract

Critical Micelle Concentration is a fundamental property on studying behaviour of surfactants. In general terms it depends on temperature, pressure and on the existence and concentration of other surface-active substances and electrolytes. In this work it is presented a model based on Artificial Neural Networks to obtain predictive values of Critical Micelle Concentration (CMC) of some non-ionic surfactants. ANN model works using topological descriptors of the molecules involved together with already known CMC values and provides predictive values for new cases. It is proposed a specific architecture for ANN consisting of an input layer with seven neurons, one intermediate layer with fourteen neurons and one neuron in the output layer. This ANN model seems to be a good method for forecast CMC.

Kurzfassung

Die kritische Mizellbildungskonzentration ist eine wichtige Eigenschaft bei der Untersuchung des Tensidverhaltens. Sie hängt grundsätzlich von der Temperatur, dem Druck und von der Anwesenheit und Konzentration anderer grenzflächenaktiver Substanzen und Elektrolyte ab. In dieser Arbeit wird ein Modell vorgestellt, das auf künstlichen neuronalen Netzwerken (ANN) beruht und zur Vorhersage von kritischen Mizellbildungskonzentrationen (CMC) einiger nichtionischer Tenside verwendet wird. Das ANN arbeitet mit topologischen Deskriptoren der Moleküle und bereits bekannten CMC-Werten und sagt so Werte für neue Fälle voraus. Es wird eine spezielle Bauweise des ANN vorgeschlagen, die aus einer Eingangslage mit sieben Neutronen einer Zwischenschicht mit vierzehn Neuronen und einer Abschlusslage mit einem Neutron besteht. Dieses ANN-Modell scheint ein gutes Verfahren zu sein, um die CMC vorauszusagen.


1 Prof. Dr. Juan C. Mejuto, Department of Physical Chemistry, Faculty of Science, University of Vigo at Ourense, Ourense, Galicia, Spain, E-Mail:

Juan Carlos Mejuto actually is Full Professor in the Physical Chemistry Department of University of Vigo at Ourense Campus. He is the head of the Colloids group at Ourense Campus. His research interest comprises (i) physical organic and physical inorganic chemistry, (ii) reactivity mechanisms in homogeneous and microheterogeneous media, (iii) stability of self-assembly aggregates and (iv) supramolecular chemistry.

Gonzalo Astray take his PhD at University of Vigo, actually he has got a Post-Doctoral Position at Vigo University. His research interest is focused in the applications of Artificial Neural Networks to chemical and biological problems.

Oscar Adrían Moldes is a PhD student at Colloid Chemistry Group at the Faculty of Sciences at Ourense (University of Vigo).

Manuel Angel Iglesias-Otero is a PhD student at Colloid Chemistry Group at the Faculty of Sciences at Ourense (University of Vigo).


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Received: 2012-10-11
Revised: 2012-11-26
Published Online: 2013-08-22
Published in Print: 2013-03-15

© 2013, Carl Hanser Publisher, Munich