High-throughput experimentation for discovery of biodegradable polyesters

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sword-2025-11-17T21:54:53.original.xml (130 B)
Original SWORD entry document
Author(s)
Fransen, Katharina A
•
Av-Ron, Sarah HM
•
Buchanan, Tess R
•
Walsh, Dylan J
•
Rota, Dechen T
•
Van Note, Lana
•
Olsen, Bradley D
Date Issued
May 30, 2023
Journal
Proceedings of the National Academy of Sciences
Publisher
Proceedings of the National Academy of Sciences
Citation
K.A. Fransen,S.H.M. Av-Ron,T.R. Buchanan,D.J. Walsh,D.T. Rota,L. Van Note, & B.D. Olsen, High-throughput experimentation for discovery of biodegradable polyesters, Proc. Natl. Acad. Sci. U.S.A. 120 (23) e2220021120.
Version
Final published version
Abstract
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
Pseudomonas lemoignei 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.
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
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Creative Commons Attribution-NonCommercial-NoDerivatives
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DOI of Published Version
https://doi.org/10.1073/pnas.2220021120
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