Rapid Identification and Quantification of the Octanol–Water Partitioning Coefficients of Polymer Degradation Products

The extensive global use of synthetic polymers has raised concern about their environmental fate, particularly regarding the generation and ecological impact of polymer degradation products. Effective environmental risk assessment requires an understanding of degradation product identity and environmental behavior, yet polymer metabolomics libraries are not well-populated. In this work, mass remainder analysis was used to systematically characterize oligomeric degradation products of polyamide-6 (PA6), polycaprolactone (PCL), and polylactic acid (PLA) using nontarget liquid chromatography-high-resolution mass spectrometry. Distinct homologous series were identified, revealing oligomers of up to seven repeating units for PA6, four for PCL, and 12 for PLA. Among the features detected, up to 70% formed remainder-based clusters (i.e., related by Kendrick mass defects of whole integers and a constant remainder) indicative of plastic-derived oligomerization patterns. To overcome limitations in molecular formula annotations for larger oligomers generated by SIRIUS, this work leveraged retention time variations, MS² fragmentation, and spectral matching for reliable characterization and structural elucidation. Retention time changes across varying mobile-phase pHs (2.7, 5.0, and 9.0) revealed substantial shifts for oligomers with ionizable functional groups, allowing quantitative insights into their acid-base properties (p<i>K</i>_a). These experimentally determined hydrophobicity values (i.e., log <i>K</i>_ow) deviated from computational estimations from a suite of available tools across polymer chemistries, highlighting inadequacies in existing estimation models and the opportunity for the rapid measurement of these important physicochemical properties using liquid chromatography-mass spectrometry workflows. This work demonstrates the necessity of experimentally derived oligomer-specific data to improve computational modeling for assessing the environmental fate of polymer degradation products.