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ABSTRACT The introduction of degradable units into the backbone of commodity vinyl polymers represents a major opportunity to address the societal challenge of plastic waste and polymer recycling. Previously, we reported the facile copolymerization ofα‐lipoic acid derivatives containing 1,2‐dithiolane rings with vinyl monomers leading to the incorporation of degradable S–S disulfide bonds along the backbone at relatively high dithiolane monomer feed ratios. To further enhance the recyclability of these systems, here we describe a facile and user‐friendly strategy for backbone degradation at significantly lower dithiolane loading levels through cleavage of both SS and SC backbone units. Copolymers ofn‐butyl acrylate (nBA) or styrene (St) with small amounts of eitherα‐lipoic acid (LA) or ethyl lipoate (ELp) dissolved in DMF were observed to undergo efficient degradation when heated at 100°C under air. For example, at only 5 mol% ELp, a high molecular weight poly(ELp‐co‐nBA) (M_n = 62 kg mol⁻¹) degraded to low molecular weight oligomers (M_n = 3.2 kg mol⁻¹) by simple heating in DMF. In contrast, extended heating of either poly(nBA) or poly(St) homopolymers under the same conditions did not lead to any change in molecular weight or cleavage of the C–C backbone. This novel approach allows for the effective degradation of vinyl‐based polymers with negligible impact on properties and performance due to the low levels of dithiolane incorporation. 

ABSTRACT Block copolymers play a vital role in materials science due to their diverse self‐assembly behavior. Traditionally, exploring the block copolymer self‐assembly and associated structure–property relationships involve iterative synthesis, characterization, and theory, which is labor‐intensive both experimentally and computationally. Here, we introduce a versatile, high‐throughput workflow toward materials discovery that integrates controlled polymerization and automated chromatographic separation with a novel physics‐informed machine‐learning algorithm for the rapid analysis of small‐angle X‐ray scattering data. Leveraging the expansive and high‐quality experimental data sets generated by fractionating polymers using automated chromatography, this machine‐learning method effectively reduces data dimensionality by extracting chemical‐independent features from SAXS data. This new approach allows for the rapid and accurate prediction of morphologies without repetitive and time‐consuming manual analysis, achieving out‐of‐sample predictive accuracy of around 95% for both novel and existing materials in the training data set. By focusing on a subset of samples with large predictive uncertainty, only a small fraction of the samples needs to be inspected to further improve accuracy. Collectively, the synergistic combination of controlled synthesis, automated chromatography, and data‐driven analysis creates a powerful workflow that markedly expedites the discovery of structure–property relationships in advanced soft materials. 

Abstract Hydrogels hold much promise for 3D printing of functional living materials; however, challenges remain in tailoring mechanical robustness as well as biological performance. In addressing this challenge, the modular synthesis of functional hydrogels from 3‐arm diblock copolypeptide stars composed of an inner poly(l‐glutamate) domain and outer poly(l‐tyrosine) or poly(l‐valine) blocks is described. Physical crosslinking due to ß‐sheet assembly of these star block copolymers gives mechanical stability during extrusion printing and the selective incorporation of methacrylate units allows for subsequent photocrosslinking to occur under biocompatible conditions. This permits direct ink writing (DIW) printing of bacteria‐based mixtures leading to 3D objects with high fidelity and excellent bacterial viability. The tunable stiffness of different copolypeptide networks enables control over proliferation and colony formation for embeddedEscherichia colibacteria as demonstrated via isopropyl ß‐d‐1‐thiogalactopyranoside (IPTG) induction of green fluorescent protein (GFP) expression. This translation of molecular structure to network properties highlights the versatility of these polypeptide hydrogel systems with the combination of writable structures and biological activity illustrating the future potential of these 3D‐printed biocomposites.