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Modified linear solvation energy relationships for adsorption of perfluorocarboxylic acids by polystyrene microplastics
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Researchers developed modified linear solvation energy relationship models to predict how perfluorocarboxylic acids (PFAS) adsorb onto polystyrene microplastics in water, accounting for the ionic state of PFAS at environmental pH and improving the accuracy of fate predictions.
Microplastics (MPs) could act as vectors of organic pollutants such as per- and polyfluoroalkyl substances (PFAS). Therefore, understanding adsorptive interactions are essential steps towards unraveling the fate of PFAS in the natural waters where MPs are ubiquitous. Linear solvation energy relationships (LSER)-based predictive models are utilitarian tools to delineate the complexity of adsorption interactions. However, commonly studied PFAS are in their ionic forms at environmentally relevant conditions and LSER modeling parameters do not account for their ionization. This study aims to develop the first LSER model for the adsorption of PFAS by MPs using a subset of ionizable perfluoroalkyl carboxylic acids (PFCA). The adsorption of twelve PFCAs by polystyrene (PS) MPs was used for model training. The study provided mechanistic insights regarding the impacts of PFCA chain length, PS oxidation state, and water chemistry. Results show that the polarizability and hydrophobicity of anionic PFCA are the most significant contributors to their adsorption by MPs. In contrast, van der Waals interactions between PFCA and water significantly decrease PFCA binding affinity. Overall, LSER is demonstrated as a promising approach for predicting the adsorption of ionizable PFAS by MPs after the correction of Abraham's solute descriptors to account for their ionization.
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