In previous papers, specific correlations were proposed to reproduce the surface tension values selected for several families of fluids and for wide ranges of temperatures. In this paper, the surface tension of n-alkanes is considered. For that, the data available in DIPPR and DETHERM databases and in Wohlfarth and Wohlfarth [“Surface tension of pure liquids and binary liquid mixtures,” in Landolt-Börnstein, New Series Group IV Physical Chemistry Vol. 16, edited by M. D. Lechner (Springer-Verlag, Berlin, 1997)] and its updated supplements (2008 and 2016) have been compiled. In most cases, a significant number of new data have been added, which were published elsewhere during the last few years and that were not included in the previously mentioned sources. All the data and values available for each fluid have been carefully screened and subsequently fitted to the Guggenheim–Katayama model, which includes two to six adjustable coefficients for each fluid. As a result, recommended correlations for 33 n-alkanes are proposed, providing absolute deviations below 1.65 mN/m and mean absolute percentage deviations below 2.1%.

In previous papers, we have proposed specific correlations to reproduce the surface tension values for several sets of fluids and for wide ranges of temperatures. In this paper, we focus our attention on organic fatty (aliphatic, carboxylic, and polyfunctional) acids. We have taken into account the available data and values in the DIPPR and DETHERM databases and also Wohlfarth and Wohlfarth’s (1997) book. In some cases we have also considered new data published elsewhere. All the data and values have been carefully filtered and subsequently fitted with the use of the model currently implemented in NIST’s REFPROP program, calculating two or four adjustable coefficients for each fluid. As a result, we propose recommended correlations for 99 acids, providing mean absolute percentage deviations below 1.6% in all cases.

An artificial neural network model is proposed for the surface tension of liquid organic fatty acids covering a wide temperature range. A set of 2051 data collected for 98 acids (including carboxylic, aliphatic, and polyfunctional) was considered for the training, testing, and prediction of the resulting network model. Different architectures were explored, with the final choice giving the best results, in which the input layer has the reduced temperature (temperature divided by the critical point temperature), boiling temperature, and acentric factor as an independent variable, a 41-neuron hidden layer, and an output layer consisting of one neuron. The overall absolute percentage deviation is 1.33%, and the maximum percentage deviation is 14.53%. These results constitute a major improvement over the accuracy obtained using corresponding-states correlations from the literature.

Surface tension values for 80 esters have been compiled from databases, books, and papers in the literature. The data have been carefully screened and selected, and the final dataset for each fluid has been fitted as a function of the temperature by using the Guggenheim–Katayama model, which requires two or four adjustable coefficients for each fluid. As a result, recommended correlations are proposed for each of the 80 esters, providing mean absolute deviations below 0.55 mN/m, mean absolute percentage deviations below 2.2%, and percentage deviations below 10% except for 3 data out of 1846 selected. These correlations are added to the collection of those previously proposed for different kinds of fluids, including common fluids, alcohols, refrigerants, organic acids, and n-alkanes.

A large data set of surface tension values for binary n-alkane mixtures was fitted to ten correlation models. The Winterfeld–Scriven–Davis and Redlich–Kister correlations performed best, with predicted pure fluid values yielding accurate results.