Building Chemical Intuition about Physicochemical Properties of C8-Per-/Polyfluoroalkyl Carboxylic Acids through Computational Means

Abstract

We have predicted acid dissociation constants (pK a ), octanol−water partition coefficients (K OW ), and DMPC lipid membrane−water partition coefficients (K lipid-w ) of 150 different eight-carbon-containing poly-/perfluoroalkyl carboxylic acids (C8-PFCAs) utilizing the COnductor-like Screening MOdel for Realistic Solvents (COSMO-RS) theory. Different trends associated with functionalization, degree of fluorination, degree of saturation, degree of chlorination, and branching are discussed on the basis of the predicted values for the partition coefficients. In general, functionalization closest to the carboxylic headgroup had the greatest impact on the value of the predicted physicochemical properties.

A Machine Learning Approach for Predicting Plant Uptake and Translocation of Per- and Polyfluoroalkyl Substances (PFAS) from Hydroponics

“…These models are limited by the diversity of PFAS and the lack of understanding of PFAS uptake mechanisms . Currently, there is a growing interest in applying machine learning (ML) models to predict the property-dependent bioaccumulation of PFAS in plants. − The advantage of using the ML approach over traditional modeling methods is that they do not require assumptions to identify relationships in data. ML models are generally classified into two groups: supervised vs unsupervised.…”

“…Principal component analysis (PCA) and K-means are common examples of unsupervised ML algorithms . Supervised ML algorithms such as ANN have been used in the prediction of the RCF of engineered metallic nanoparticles as well as organic compounds . However, it is the aboveground tissues that are the most likely plant parts for human consumption; therefore, developing predictive models for SCF and TF is more relevant for understanding the health implications of PFAS.…”