High-throughput experimentation for discovery of biodegradable polyesters

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.

Keywords: biodegradation; high-throughput; polymers; structure–property relationships.

Fig. 1.

Overview of synthesis techniques used for different polymers in the library. Details for individual reactions can be found in Supplementary Dataset S4.

Fig. 2.

Clear-zone biodegradation test workflow. (A) Homogenize polymer in agar-growth medium. (B) Add polymer agar mixture to 12-well plate and inoculate with microorganism once gelled. (C) Incubate at 30 °C for up to 13 d. (D) Light transmission pictures are taken every 8 h for the first 3 d, if degradation is detected with the naked eye every 8 h, monitoring continues until day 5, then monitoring is done on the 7th, 9th, 10th, and 13th day. (E) Data processing returns shading curves for each sample, which determine the degradability of the polymer.

Fig. 3.

(A–D) Biodegradation behaviors observed from shading curves of clear-zone samples. The different behaviors depend on the enzymatic activity and the rate of biofragmentation. (E) Distribution of all behaviors in the dataset.

Fig. 4.

(A) Biodegradability results for all aliphatic saturated nonsubstituted condensation polymers with no side groups. Marginal histograms show the fraction of polymers degrading for each diol or diacid backbone length. (B) Effect of C or single-bond substitution along the backbone on biodegradation, where a polymer pair identical other than the substitution is compared. The substituted and unsubstituted compared chemistries are listed in SI Appendix, Table S7. (C) Biodegradability of all polymers when separated by presence of rings, distinguishing aromatic and nonaromatic polymers. (D) Biodegradability results for all aliphatic saturated nonsubstituted condensation polymers with and without all carbon side groups. Background color separates the plot by backbone length of the diol repeating units.

Fig. 5.

(A) Biodegradability prediction validation accuracy after a hyperparameter optimization of a fivefold crossvalidation on 83% of the data, and final accuracy determined on the 17% validation data. Polymers are vectorized using three chemical descriptions: Morgan fingerprinting (MFP), RDKit fingerprinting (RFP), and RDKit descriptors (RDS). For polymers that could have physical state or molecular weight values determined, the feature vectors were completed with this information, as indicated by the title of each quadrant. (B) Extrapolation prediction accuracy for the full dataset using only chemical description for the feature vectors. The functionalities studied were substitutions along the backbone of sulfides, disulfides, sulfur oxide, all sulfurs, oxygen, tertiary amine, double-bonded carbons, and triple-bonded carbons (from Left to Right, excluding atoms and bonds of the ester group). In each case, the testing set constituted of all the polymers in the dataset that contained the substitution of interest.