Reactivity-Guided Depercolation Processes Determine Fracture Behavior in End-Linked Polymer Networks.

The fracture of polymer networks is tied to the molecular behavior of strands within the network, yet the specific molecular-level processes that determine the mechanical limits of a network remain elusive. Here, the question of reactivity-guided fracture is explored in otherwise indistinguishable end-linked networks by tuning the relative composition of strands with two different mechanochemical reactivities. Increasing the substitution of less mechanochemically reactive ("strong") strands into a network comprising more reactive ("weak") strands has a negligible impact on the fracture energy until the strong strand content reaches approximately 45%, at which point the fracture energy sharply increases with strong strand content. This aligns with the measured strong strand percolation threshold of 48 ± 3%, revealing that depercolation, or the loss of a percolated network structure, is a necessary criterion for crack propagation in a polymer network. Coarse-grained fracture simulations agree closely with the tearing energy trend observed experimentally, confirming that weak strand scissions dominate the failure until the strong strands approach percolation. The simulations further show that twice as many strands break in a mixture than in a pure network.

High-throughput experimentation for discovery of biodegradable polyesters.

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.