High-throughput experimentation for discovery of biodegradable polyesters.

Area of Science:

• Polymer Science
• Environmental Science
• Biotechnology

Background:

• Growing plastic pollution necessitates biodegradable alternatives.
• Traditional biodegradation testing is slow and expensive, hindering innovation.
• A need exists for efficient methods to screen polymer biodegradability.

Purpose of the Study:

• To develop and implement a high-throughput method for assessing polymer biodegradation.
• To create a comprehensive dataset of biodegradability for diverse polyesters and polycarbonates.
• To identify key chemical structural features influencing polymer biodegradability.

Main Methods:

• High-throughput synthesis of polymers.
• Automated clear-zone assay using *Pseudomonas lemoignei* to measure degradation.
• Generation of a dataset for 642 distinct polyesters and polycarbonates.
• Application of machine learning models for biodegradability prediction.

Main Results:

• Biodegradability is significantly influenced by aliphatic repeat unit length (shorter chains <15 carbons are better).
• Short side chains and backbone ether groups enhance biodegradability.
• Aromatic groups generally reduce biodegradability, with ortho- and para-substitution being more favorable than meta-.
• Other heteroatoms increased degradation rates but not necessarily overall biodegradability.

Conclusions:

• A high-throughput biodegradation assay provides a scalable method for material screening.
• Specific structural features, including aliphatic chain length and aromatic substitution patterns, are critical for designing biodegradable polymers.
• Machine learning models can accurately predict biodegradability from chemical structure, accelerating the development of sustainable plastics.