Alessandro Sangion

Sep 26, 2024 | pubs.rsc.org | Trevor Brown |James Armitage |Alessandro Sangion |Jon A. Arnot

Improved prediction of PFAS partitioning with PPLFERs and QSPRs Per- and polyfluoroalkyl substances (PFAS) are chemicals of high concern and are undergoing hazard and risk assessment worldwide. Reliable physicochemical property (PCP) data are fundamental to assessments. However, experimental PCP data for PFAS are limited and property prediction tools such as quantitative structure-property relationships (QSPRs) therefore have poor predictive power for PFAS.