Prediction of critical micelle concentration for per- and polyfluoroalkyl substances

• Published in: SAR and QSAR in environmental research Vol. 35; no. 4; pp. 309 - 324
• Main Authors: Creton, B., Barraud, E., Nieto-Draghi, C.
• Format: Journal Article
• Language: English
• Published: England Taylor & Francis 02.04.2024; Taylor & Francis Ltd
• Subjects: Algorithms; Chemical Physics; Comparative analysis; critical micelle concentration; Datasets; Fluorination; Micelles; per- and polyfluoroalkyl substances; Perfluoroalkyl & polyfluoroalkyl substances; Perfluorochemicals; Physics; QSPR; Support vector machines; surfactant; SVM; QSPR; per- and polyfluoroalkyl substances; SVM; critical micelle concentration; surfactant
• ISSN: 1062-936X; 1029-046X; 1029-046X
• DOI: 10.1080/1062936X.2024.2337011

In this study, we focus on the development of Quantitative Structure-Property Relationship (QSPR) models to predict the critical micelle concentration (CMC) for per- and polyfluoroalkyl substances (PFASs). Experimental CMC values for both fluorinated and non-fluorinated compounds were meticulously compiled from existing literature sources. Our approach involved constructing two distinct types of models based on Support Vector Machine (SVM) algorithms applied to the dataset. Type (I) models were trained exclusively on CMC values for fluorinated compounds, while Type (II) models were developed utilizing the entire dataset, incorporating both fluorinated and non-fluorinated compounds. Comparative analyses were conducted against reference data, as well as between the two model types. Encouragingly, both types of models exhibited robust predictive capabilities and demonstrated high reliability. Subsequently, the model having the broadest applicability domain was selected to complement the existing experimental data, thereby enhancing our understanding of PFAS behaviour.