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Polymer Biodegradation in Aquatic Environments: A Machine Learning Model Informed by Meta-Analysis of Structure-Biodegradation Relationships
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Researchers compiled a biodegradation dataset of 74 polymers and 1,779 data points to train a machine learning model predicting aquatic polymer biodegradation, finding that ether linkages promote breakdown while high molecular weight, aromatic rings, and ester substructures in polyesters inhibit it, offering a tool for designing more environmentally friendly plastics.
Polymers are widely produced and contribute significantly to environmental pollution due to their low recycling rates and persistence in natural environments. Biodegradable polymers, while promising for reducing environmental impact, account for less than 2% of total polymer production. To expand the availability of biodegradable polymers, research has explored structure-biodegradability relationships, yet most studies focus on specific polymers, necessitating further exploration across diverse polymers. This study addresses this gap by curating an extensive aerobic biodegradation data set of 74 polymers and 1779 data points drawn from both published literature and 28 sets of original experiments. We then conducted a meta-analysis to evaluate the effects of experimental conditions, polymer structure, and the combined impact of polymer structure and properties on biodegradation. Next, we developed a machine learning model to predict polymer biodegradation in aquatic environments. The model achieved an Rtest2 score of 0.66 using Morgan fingerprints, detailed experimental conditions, and thermal decomposition temperature (Td) as the input descriptors. The model's robustness was supported by a feature importance analysis, revealing that substructure R-O-R in polyethers and polysaccharides positively influenced biodegradation, while molecular weight, Td, substructure -OC(═O)- in polyesters and polyalkylene carbonates, side chains, and aromatic rings negatively impacted it. Additionally, validation against the meta-analysis findings confirmed that predictions for unseen test sets aligned with established empirical biodegradation knowledge. This study not only expands our understanding across diverse polymers but also offers a valuable tool for designing environmentally friendly polymers.
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