Time-series profiling of structure–property relationships in biodegradable polymers via NMR-driven data science

Abstract Understanding and controlling the marine degradation of biodegradable polymers is crucial for sustainable materials engineering and pollution prevention. However, a unified interpretive method that links multiscale molecular dynamics to time-course, stage-resolved degradation outcomes has remained largely unrealized. In the present work, a data-driven analytical approach that couples time-domain nuclear magnetic resonance (TD-NMR), solution-state nuclear magnetic resonance, mechanical testing, and thermal analysis to study seven representative biodegradable polyesters relevant to marine and estuarine use—PBS, PBSA, PCL, PBAT, PES, P(3HB), and PHBH—across time-course degradation stages was developed. A Random-Forest model (R² = 0.81) delivered accurate, interpretable predictions under marine/estuarine conditions, and explainable AI attributions revealed a time-course degradation process of time shift. Overall, this study demonstrates how a time-course viewpoint fundamentally reshapes the interpretation of degradation mechanisms, enabling comparative insights across polymers. These findings have practical implications for ecological design and process decisions in coastal applications, while also indicating the possibility of future integration with lifecycle or risk assessment considerations.