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Surface tension is a critical property that influences polymer behavior at interfaces and affects applications ranging from coatings to biomedical devices. Traditional experimental methods for measuring polymer surface tension are time-consuming, costly, and sensitive to environmental conditions. Computational approaches such as molecular dynamics (MD) simulations are valuable but computationally intensive, especially for polymers with long chains. This study investigates the use of machine learning (ML) techniques to predict polymer surface tension using different levels of molecular representation, focusing on multilinear regression (MLR), random forest (RF), and graph neural networks (GNNs). A data set of 317 homopolymers collected from the PolyInfo database is used to train and evaluate these models. Descriptors are derived at various levels of complexity, ranging from manually calculated features to graph-based representations. The GNN approach captures the intrinsic connectivity of polymer structures, while the MLR and RF models rely on manually crafted descriptors. The performance of these models is compared with experimental data, with the GNN model demonstrating superior accuracy due to its ability to directly learn from molecular graphs. Our results show that GNNs can better capture complex nonlinear relationships in polymer structures than traditional descriptorbased methods, suggesting their significant potential for accelerating polymer design and development. The study also includes validation of model predictions against molecular dynamics simulations, highlighting the potential of GNNs to accurately model polymer interfacial properties. 

Three-dimensional (3D) printing is becoming increasingly prevalent in tissue engineering, driving the demand for low-modulus, high-performance, biodegradable, and biocompatible polymers. Extrusion-based direct-write (EDW) 3D printing enables printing and customization of low-modulus materials, ranging from cell-free printing to cell-laden bioinks that closely resemble natural tissue. While EDW holds promise, the requirement for soft materials with excellent printability and shape fidelity postprinting remains unmet. The development of new synthetic materials for 3D printing applications has been relatively slow, and only a small polymer library is available for tissue engineering applications. Furthermore, most of these polymers require high temperature (FDM) or additives and solvents (DLP/SLA) to enable printability. In this study, we present low-modulus 3D printable polyester inks that enable low-temperature printing without the need for solvents or additives. To maintain shape fidelity, we incorporate physical and chemical cross-linkers. These 3D printable polyester inks contain pendant amide groups as the physical cross-linker and coumarin pendant groups as the photochemical cross-linker. Molecular dynamics simulations further confirm the presence of physical interactions between different pendants, including hydrogen bonding and hydrophobic interactions. The combination of the two types of cross-linkers enhances the zero-shear viscosity and hence provides good printability and shape fidelity. 

Three-dimensional (3D) printing allows for creation of patient-specific implants. However, development of new synthetic materials for 3D printing has been relatively slow with only a few polymers available for tissue engineering applications. Most of these polymers require harsh processing conditions like high temperatures and pressures or are mixed with a combination of leachable additives like plasticizers, initiators, crosslinkers, and solvents to enable 3D printing. Therefore, to propel the development of new polymers for ambient temperature, additive-free 3D printing it is necessary to systematically understand the relationship between the structure of a polymer with its 3D printability. Herein, three homopolyesters were synthesized, each with a common backbone but differing in the length of their saturated, aliphatic pendant chains with 2, 6, or 15 carbons. The physical properties such as the glass transition temperature ( T g ) and the rheological properties like shear thinning, temperature response, and stress relaxation were correlated to the individual polymer's 3D printability. The 3D printability of the polymers was assessed based on four criteria: ability to be extruded as continuous filaments, shape fidelity, the retention of printed shape, and the ability to form free hanging filaments. We observed that the polymers with longer side chains can be extruded at low temperature and pressure because the long side chains act as internal diluents and increase the flowability of the polymer. However, their ability to retain the 3D printed shape is adversely affected by the increase in side chain length, unless the side chains form ordered structures leading to immediate recovery of viscosity. The insight derived from the systematic investigation of the effect of polymer structure on their rheology and 3D printability can be used to rationally design other polymers for extrusion-based direct-write 3D printing. 

Abstract Each year, thousands of patients die from antimicrobial‐resistant bacterial infections that fail to respond to conventional antibiotic treatment. Antimicrobial polymers are a promising new method of combating antibiotic‐resistant bacterial infections. We have previously reported the synthesis of a series of narrow‐spectrum peptidomimetic antimicrobial polyurethanes that are effective against Gram‐negative bacteria, such asEscherichia coli; however, these polymers are not effective against Gram‐positive bacteria, such asStaphylococcus aureus. With the aim of understanding the correlation between chemical structure and antibacterial activity, we have subsequently developed three structural variants of these antimicrobial polyurethanes using post‐polymerization modification with decanoic acid and oleic acid. Our results show that such modifications converted the narrow‐spectrum antibacterial activity of these polymers into broad‐spectrum activity against Gram‐positive species such asS. aureus, however, also increasing their toxicity to mammalian cells. Mechanistic studies of bacterial membrane disruption illustrate the differences in antibacterial action between the various polymers. The results demonstrate the challenge of balancing antimicrobial activity and mammalian cell compatibility in the design of antimicrobial polymer compositions. © 2019 Society of Chemical Industry