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Structure–Property Relationships for Biodegradability in Copolyesters

Original title: Structure–Property Relationships for Biodegradabilityin Copolyesters

Figshare 2026
Katharina A. Fransen, Julia Casey, Gabrielle Godbille‐Cardona, Natalie Mamrol, Jiale Shi (7126757), Alex Zappi, Jignesh S. Mahajan, J Lu, Debra J. Audus, Bradley D. Olsen

Summary

Scientists tested 300 different plastic blends and found that mixing two plastic ingredients together made biodegradable plastic far more likely (over 80% broke down) compared to using just one ingredient alone (only 50% broke down). This matters because it gives manufacturers a practical roadmap for designing plastics that actually break down in the environment instead of persisting as pollution and microplastics that can end up in our food, water, and bodies.

With growing concerns about increasing plastic pollution, interest in biodegradable polymers, particularly polyesters, continues to grow. Copolymerization is an important molecular handle to tune properties to achieve material performance and biodegradation simultaneously. To better understand the structure–property relationships that govern biodegradability, a 300-member copolymer library was synthesized and tested using a high-throughput clear-zone biodegradation assay; testing shows over 80% of the copolymer library is biodegradable even though only 50% of the homopolymers from which they are derived are degradable. Repeat units with longer carbon backbones decreased biodegradability, though copolymers showed biodegradability at all average repeat unit lengths examined. For both homopolymers and copolymers, oxygen substitution of backbone carbons was established as a lever to improve biodegradability. A novel chemical similarity-informed embedding that considers polymer chemical structure and composition was developed and implemented for the simultaneous quantitative structure–property relationship modeling of homopolymers and copolymers. Random forest models could simultaneously capture homopolymer and copolymer behavior with 78% and 95% accuracy, respectively; however, models trained only on homopolymer data could not predict copolymer biodegradability. Unlike random forest models, linear models were not able to capture both homopolymer and copolymer biodegradability.

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