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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. 

We performed extensive molecular dynamics simulations using a bead–spring model to investigate the interfacial behavior of blends of linear and cyclic polymer chains confined between two planar, attractive substrates. The model system was studied over a range of chain lengths spanning an order of magnitude in the number of beads for varying blend compositions and for two different levels of substrate affinity. For short chains, we observed the preferential adsorption of linear chains at the substrate interface when they are the majority component (10% cyclic chains) as well as at equimolar composition. In contrast, for longer chains, cyclic chains are preferentially enriched at the interface. These results extend recent findings from neutron reflectivity experiments—where the enrichment of cyclic polystyrene chains at low-energy surfaces was demonstrated—to systems under solid confinement, providing deeper insight into the structural behavior of topologically distinct polymers near interfaces. This work highlights the potential for tuning interfacial composition and properties in polymer blends through topological design, with implications for advanced coatings, membranes, and nanostructured materials. 

This study investigates the role of chain architecture and block asymmetry on the morphology of AB2 miktoarm star block copolymers (AB2 BCPs) in the strongly segregated regime using molecular dynamics simulations. Notably, the cylindrical morphology in AB2 BCPs persists across a broad compositional range, extending close to fA ≈ 0.5, in agreement with both theoretical and experimental findings. The lamellar morphology observed up to fA ≈ 0.8 matches predictions; however, beyond this point, AB2 BCPs continue to exhibit lamellar structures (disk-like micelles), deviating from the expected transitions to cylindrical or spherical morphologies. This behavior, corroborated by dissipative particle dynamics simulations, is attributed to the B arms’ preference to occupying the outer regions of curved interfaces, which hinders the formation of cylindrical or spherical morphologies. Furthermore, domain spacing results exhibit remarkable agreement with strong-stretching theory (SST) across different morphologies, reinforcing the predictive power of SST. Finally, shape parameter analysis, including metrics like asphericity and acylindricity, underscores the significant impact of chain architecture on these morphological transitions. These findings provide molecular-level insights into how chain architecture and block asymmetry dictate phase behavior and morphological stability in linear and miktoarm BCPs. 

This work examines the functional dependence of the efficiency of separation of oil−water emulsions on surfactant adsorption abilities of high surface area polymer gels. The work also develops an understanding of the factors and steps that are involved in emulsion separation processes using polymer gels. The work considers four polymer gels offering different surface energy values, namely, syndiotactic polystyrene (sPS), polyimide (PI), polyurea (PUA), and silica. The data reveal that surfactant adsorption abilities directly control the emulsion separation performance. The gels of sPS and PI destabilize the emulsions due to significant surfactant adsorption. The surfactant-lean oil droplets are then absorbed in the pores of sPS and PI gels due to the preferential wettability of the oil phase. The PUA and silica gels are more hydrophilic and show a lower surfactant adsorption ability. These gels cannot effectively remove the surfactant molecules from the emulsions, leading to a poor emulsion separation performance. The study uses simulation data to understand the adsorption characteristics of two poly(ethylene oxide)- poly(propylene oxide)-poly(ethylene oxide) block copolymer surfactants. The simulation results are used for the interpretation of emulsion separation performance by the gels. 

Theθ-temperature of linear, ring, and poly[n]catenane polymers as characterized by varying LJ cutoff,r_cvalues. Higherr_cvalues correspond to lowerθ-temperature. 

Using an innovative molecular dynamics approach, we observed the case II diffusion behavior of toluene, acetone, and their mixture diffusing within a glassy polystyrene film. 

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.