A molecular structure based model for predicting surface tension of organic compounds

Abstract

A Quantitative Structure-Property Relationship (QSPR) model for the prediction of surface tension of organic compounds was derived from a data set of 320 chemicals including N, O, F, Cl, Br, and/or S atoms and covering a range of about 14-45 dyn cm-1. The model, only involving six molecular descriptors obtained solely from the chemical structures, yielded an r2 of 0.96. Its predictive capability was estimated from an external test set containing 55 structures not considered in the training set (r2 = 0.94). It was shown that the selected molecular descriptors presented a physical meaning corresponding to the different intermolecular interactions occurring in the bulk solution. The model is applicable to a wider variety of compounds, includes less parameters and correlates better than other QSPR models reported in literature.

Surface Tension of Liquid Organic Acids: An Artificial Neural Network Model

“…Experimental data for the surface tension of liquid organic acids are scarce, and sometimes the only values available are those predicted by applying Sugden's method [10]. An alternative is to use one of the various more modern methods to calculate and predict those values, methods which generally are based on analytical models [3,[11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26]. These analytical models include purely empirical correlations, as well as those based on the use of the corresponding-states principle or more sophisticated chemical-group-contribution methods, including quantitative-structureproperty models.…”

“…Delgado and Díaz [18] evaluated the performance of their quantitative-structureproperty model when applied to the surface tension at 298 K of some organic compounds. The percentage deviations for the seven carboxylic acids considered were below 2.3%.…”

Predicting the Surface Tension of Liquids: Comparison of Four Modeling Approaches and Application to Cosmetic Oils

“…QSPR models are regression or classification models that predict a property of a molecule from selected descriptors thereof; they were used successfully for the prediction of surface tensions of a wide range of organic compounds. 1 9 Freitas et al decreased the number of descriptors of the model to six to successfully predict the surface tensions of 299 compounds. 10 Delgado et al designed a model with six descriptors corresponding to the different intermolecular interactions in the bulk phase and calculated from the molecular structures 1 for the prediction of the surface tensions of 320 compounds.…”

“…Surface tension (ST) is the force existing at a liquid/gas interface, which tends to minimize the surface area; it is caused by asymmetries in the intermolecular forces between molecules located at the interface. It plays an important role in a number of processes where a liquid/gas interface is present; for instance, it drives the shape of small liquid drops and bubbles or the wetting of a solid surface by a liquid . In cosmetics, the spreadability of oils on the human skin reflects both the sensory qualities and the efficacy of the product; , Parente et al reported that the spreadability of oils and the film forming properties could be partly correlated to the surface tension of the cosmetic oil: the lower the surface tension, the more spreadable the oil.…”

Prediction of Surfactants’ Properties using Multiscale Molecular Modeling Tools: A Review

“…A large number of works dealing with the development of QSPR models for the prediction of the interfacial tension for other chemical families has been reported in the litterature [116][117][118][119][120][121][122][123][124][125][126][127]. These works are summurized in Table 2.…”