A New General Correlation for the Influence Parameter in Density Gradient Theory and Peng-Robinson Equation of State for -Alkanes.

The Density Gradient Theory (DGT) permits obtaining the surface tension by using an equation of state and the so-called influence parameter. Different correlations of the influence parameter versus temperature have been proposed, with the two-coefficient ones from Zuo and Stenby (full temperature range) and Miqueu et al. (valid for the lower temperature range) being widely used.

Surface Tension for Silanes, Refrigerants, and Carboxylic Acids: Simple Corresponding State Correlations versus DIPPR Data.

A simple corresponding state-based correlation, whose analytical expression contains just one term with an exponential function, is proposed to calculate the surface tension of 36 silanes, 29 carboxylic acids, and 81 refrigerants as a function of temperature. This correlation only requires critical temperature, maximum value of surface tension, and its corresponding temperature in the DIPPR database as inputs for each liquid considered. The correlation allows us to calculate the accepted DIPPR data for silanes with a mean absolute average deviation (AAD) of 2.

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

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.