High-throughput experimentation for discovery of biodegradable polyesters

• Published in: Proceedings of the National Academy of Sciences - PNAS 2023-06, Vol.120 (23), p.e2220021120
• Main Authors: Fransen, Katharina A, Av-Ron, Sarah H M, Buchanan, Tess R, Walsh, Dylan J, Rota, Dechen T, Van Note, Lana, Olsen, Bradley D
• Format: Article
• Language: English
• Subjects: Automation; Benzene; Biodegradability; Biodegradable Plastics; Biodegradation; Biodegradation, Environmental; Bioplastics; Chemical synthesis; Datasets; Machine learning; Measurement methods; Physical Sciences; Plastic pollution; Plastics - chemistry; Polyester resins; Polyesters; Polyesters - chemistry; Polymers; Research Design
• ISSN: 0027-8424; 1091-6490; 1091-6490
• DOI: 10.1073/pnas.2220021120

The consistent rise of plastic pollution has stimulated interest in the development of biodegradable plastics. However, the study of polymer biodegradation has historically been limited to a small number of polymers due to costly and slow standard methods for measuring degradation, slowing new material innovation. High-throughput polymer synthesis and a high-throughput polymer biodegradation method are developed and applied to generate a biodegradation dataset for 642 chemically distinct polyesters and polycarbonates. The biodegradation assay was based on the clear-zone technique, using automation to optically observe the degradation of suspended polymer particles under the action of a single bacterial colony. Biodegradability was found to depend strongly on aliphatic repeat unit length, with chains less than 15 carbons and short side chains improving biodegradability. Aromatic backbone groups were generally detrimental to biodegradability; however, ortho- and para-substituted benzene rings in the backbone were more likely to be degradable than metasubstituted rings. Additionally, backbone ether groups improved biodegradability. While other heteroatoms did not show a clear improvement in biodegradability, they did demonstrate increases in biodegradation rates. Machine learning (ML) models were leveraged to predict biodegradability on this large dataset with accuracies over 82% using only chemical structure descriptors.