A group contribution method is proposed to predict
surface tension of liquid organic solvents. The proposed
model is developed from a training set of 349 chemicals and
validated with an external testing set of 44 chemicals.
For the training set, the experimental surface tension values
and the values fitted by this model agreed well with r
2
= 0.75 at p = 0.0001. The predictions of this model for the
external testing set of 44 chemicals were within an
average factor of error of 1.07 showing good agreement
between experimental and predicted values with r
2 = 0.89
at p = 0.0001. A comparison of the model developed in
this study against five other empirical models reported in
the literature is also presented.
Multivariate analysis was used to explore physicochemical properties of organic chemicals that would characterize and identify degreasing solvents. The exploratory techniques used in this study include cluster analysis, discriminant function analysis, and canonical discriminant analysis. Out of a compilation of 16 physicochemical properties evaluated, aqueous solubility, Henry's constant, and surface tension were identified as relevant properties that could effectively screen degreasing solvents from among 30 chemicals of similar chemical classes. The suitability of these three properties and the multivariate techniques used in classifying degreasing solvents were demonstrated on an external testing set of 10 solvent-and nonsolventtype chemicals. On the basis of the results of these studies, canonical discriminant analysis is recommended as a potential tool for screening purposes. The cluster analysis procedure was informative for explorative purposes; the discriminant function analysis procedure was not efficient in separating solvents from others.
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