Computational protocol for predicting 19F NMR chemical shifts for PFAS and connection to PFAS structure. Issue 20 (6th June 2022)

• Record Type: Journal Article
• Authors: Mifkovic, Maleigh; Pauling, Jessica; Vyas, Shubham
• Is Part Of: Journal of computational chemistry. Volume 43:Issue 20(2022)
• Page Start: 1355
• Page End: 1361
• Publication Date: 2022-06-06
• Subjects: 19F NMR -- density functional theory -- helicity -- PFAS; Chemistry -- Data processing -- Periodicals; 542.85
• DOI: 10.1002/jcc.26939
• Languages: English
• ISSNs: 0192-8651

Abstract

Abstract: Per‐ and polyfluoroalkyl substances (PFAS) are robust "forever" chemicals that have become global environmental contaminants due to their inability to degrade using traditional techniques. In addition to the persistent nature of PFAS, the structural and functional diversity in PFAS creates a unique challenge in identification and remediation. Their identification is further complicated by the absence of standards for many PFAS. This work is aimed at developing a protocol for computing and establishing accurate 19 F NMR chemical shifts for PFAS using density functional theory (DFT), which can aid in the identification of PFAS. The impact of solvation and basis sets was evaluated by comparing the computed data with the experimental measurements. Results showed the addition of dispersion corrections in the methodology improve the accuracy of calculated NMR parameters within 4 ppm of the experimental values. Adding a second diffuse function and additional polarization did not improve the accuracy, likely because of the electronegativity of fluorine which does not allow the electron density of fluorine atoms to be polarized. The inclusion of various implicit solvation (DMSO, chloroform, and water) yielded negligible differences in accuracy, and were overall less accurate than the gas phase calculations. The most accurate methodology was then applied to more environmentally relevant PFAS, and the impact of helical nature on the NMR signatures was evaluated. The implication of this work is to be able to improve the identification of structurally diverse PFAS using the 19 F NMR. Abstract : This work develops a computational protocol to accurately predict 19 F NMR parameters of per‐ and polyfluoroalkyl substances (PFAS), which will transform our ability to identify them. PFAS are the most notorious environmental contaminants of our time. Their structural diversity and lack of available standards makes it near impossible to identify and characterize many of these compounds. Impact of solvation, choice of basis sets and effect of dispersion interaction was evaluated in this study. After benchmarking, the protocol was applied to environmentally relevant PFAS.