Calculating PFAS interfacial adsorption as a function of salt concentration using model parameters determined from chemical structure

Abstract

Per- and polyfluoroalkyl substances (PFAS) are widely-detected environmental contaminants known to concentrate at surfaces and interfaces. Many of the most commonly-detected PFAS function as ionic surfactants under environmental conditions. The interfacial behaviors of ionic surfactants, including PFAS, are strongly dependent on salt concentration and composition, with interfacial affinity potentially varying by orders of magnitude for the same compound under different conditions. The work described here presents a tool for predicting the salt-dependent adsorption of PFAS compounds based entirely on chemical structure, something of great value for predicting the real-world environmental behavior of many of the large numbers of PFAS compounds for which experimental data are not available. The approach combines two different previously-developed models, one a mass-action model designed to predict the effects of salts on interfacial adsorption of ionic PFAS (the UNSW-OU salt model), and the second a group-contribution model designed to predict interfacial adsorption of PFAS in the absence of salt based on chemical structure. The challenge of combining the two models comes from the fact that both are based on different isotherms. The salt model can produce sigmoidal isotherms under salt-limited conditions (an isotherm shape that is supported by experimental evidence), while the group-contribution model can generate Langmuir parameters from calculations based on chemical structure. Equations were derived to determine salt model isotherm parameters from Langmuir parameters (either from the group-contribution model or experimental sources) by matching surface tension curves in the vicinity of the concentration of highest second derivative. Refined group-contribution model parameters were determined based on data from an additional 40 surface tension curves to allow improved structure-based predictions for important classes of PFAS that were not sufficiently well-represented in the original model. The resulting equations provide a tool allowing quantitative predictions of PFAS behavior under realistic environmental conditions for compounds for which little or no experimental data are available.