Zhang, Mengru; (2025) Computational study of PFAS sorptive removal from water in porous materials. Doctoral thesis (Ph.D), UCL (University College London).

Text Thesis_Final_Mengru_Zhang.pdf - Accepted Version Access restricted to UCL open access staff until 1 March 2026. Download (40MB)

Abstract

Widespread use of per- and polyfluoroalkyl substances (PFASs) poses ecological and health risks due to their non-degradability in the environment and consequent bioaccumulation in humans. It is crucial to remove PFASs from the environment and their selective capture from natural water remains a daunting challenge given their low concentrations in natural waters. Sorptive separation by porous materials is one of the promising techniques for PFAS removal from water. In this thesis, we studied the role of surface thermodynamics and kinetics in the removal of perfluorooctanoic acid (PFOA), one of the most widely encountered PFASs in ground water, from water by using molecular simulations and enhanced sampling techniques. We concluded that interfacial sorption constitutes the rate limiting step and PFOA sorption is orientation competitive. Computational screening of all-silica zeolites based on PFOA ideal permeabilities revealed that three zeolites, MTW, VET and GON, are top performers. Besides, our research unravelled the effect of perfluorinated carbon chain length and the type of acid head group on the sorption mechanism of PFASs. Perfluoroalkyl carboxylic acids (PFCAs) and perfluoroalkyl sulfonic acids (PFSAs) with chain lengths of three to eight perfluorinated carbons were considered, which are the most widely encountered PFASs in ground water. We concluded that hydrophobic interactions dominate PFCA sorption whereas polar interactions dominate PFSA sorption, as such, hydrophobic sorbents are expected to be more effective in capturing PFCAs from water whereas polar sorbents work better for PFSAs. Moreover, we used high-throughput screening to find the quantitative structure-property relationship between covalent organic frameworks (COFs) and perfluorobutanoic acid (PFBA) sorption selectivity. Subsequently, we employed machine learning (ML) methods to accurately and quickly predict PFBA sorption selectivity for COFs, making them highly accessible for future use.

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