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Star polymers are an interesting class of polymers to study based on their shape and dynamics. We use complementary techniques to study the polymer dynamics of star polymers with shorter side chains and compare them with linear counterparts.
Polymers with precisely controlled structure and function are in high demand across a diverse array of applications spanning the life sciences and nanotechnology. One prototypical example is a class of branched block copolymers known as miktoarm stars (μ-stars), which contain two or more arm compositions connected at a common junction. Miktoarm stars have attracted considerable attention since their physical properties can be different from conventional linear block copolymers. This perspective highlights the latest developments and historical context in the field of miktoarm star polymers, including design strategies, synthetic techniques, and advanced characterization tools used to avoid common preparation pitfalls and tailor properties for emerging applications. Furthermore, our contemporary perspective on μ-star polymers is a resource for inspiring future research into this exciting class of materials at the intersection of chemistry, physics, and advanced technology.
A library of polyester-based A(BA') n asymmetric miktoarm star polymers was synthesized with A, A' = poly(l-lactide) (PLLA) as the semicrystalline hard blocks and B = poly(4-methylcaprolactone) (PMCL) as the soft segment using a grafting-through platform known as μSTAR. The synthetic versatility of μSTAR enabled a systematic investigation of architectural design parameters, in particular the number of BA' arms (n), while holding the A, A', and B block lengths constant. The value of n has a pronounced impact on the mechanical properties of these high-molecular-weight miktoarm materials. Tensile toughness increases with n, an effect likely related to bridging, as the modulus drops because the hard-block volume fraction decreases. Furthermore, these insights expand our understanding of architecture effects in sustainable thermoplastic elastomers.
We introduce ToPolyAgent, a multi-agent AI framework for performing coarse-grained molecular dynamics (MD) simulations of topological polymers through natural language instructions. By integrating large language models (LLMs) with domain-specific computational tools, ToPolyAgent supports both interactive and autonomous simulation workflows across diverse polymer architectures, including linear, ring, brush, and star polymers, as well as dendrimers. The system consists of four LLM-powered agents: a Config Agent for generating initial polymer–solvent configurations, a Simulation Agent for executing LAMMPS-based MD simulations and conformational analyses, a Report Agent for compiling markdown reports, and a Workflow Agent for streamlined autonomous operations. Interactive mode incorporates user feedback loops for iterative refinements, while autonomous mode enables end-to-end task execution from detailed prompts. We demonstrate ToPolyAgent's versatility through case studies involving diverse polymer architectures under varying solvent conditions, thermostats, and simulation lengths. Furthermore, we highlight its potential as a research assistant by directing it to investigate the effect of interaction parameters on the linear polymer conformation, and the influence of grafting density on the persistence length of the brush polymer. By coupling natural language interfaces with rigorous simulation tools, ToPolyAgent lowers barriers to complex computational workflows and advances AI-driven materials discovery in polymer science. It lays the foundation for autonomous and extensible multi-agent scientific research ecosystems.
A material system for performing layer-spread experiments on brush particle bilayers is presented and used to determine the diffusion constant of brush particles in the melt state. Selective deuteration of the core and shell of organo-silica nanoparticles grafted with poly(methyl methacrylate) was used to match the scattering length density of the core and the polymer canopy layer. This subdued the scattering of particle cores (i.e., formfactor scattering) and enabled the analysis of the interdiffusion kinetics using neutron reflectivity. For low molecular grafts, i.e., grafts with a molecular weight below the entanglement limit, the interdiffusion kinetics revealed both a sub- and Fickian diffusion regime. The former was attributed to the local dynamics that was constrained by the slow-moving cores of neighboring brush particles that acted as long-lived physical cross-links. No transition to Fickian diffusion was observed for entangled systems, even at prolonged annealing times. This suggested a higher level of kinetic restraint in entangled brush particle melts as compared to, for example, star polymers with a comparable chain length for which Fickian diffusion has been reported under similar conditions.
We report the synthesis and temperature-dependent morphologies of a series of polylactide--poly (-decalactone)--polylactide (LDL) triblock copolymers with M n = 16.0 – 18.1 kg / mol and volume fractions f L = 0.27 – 0.31 and associated core-shell bottlebrush (csBB) polymers, which derive from enchaining LDL triblocks through a polymerizable midchain functionality. While the LDL triblocks form micellar Frank-Kasper A15 and phases due to the conformational asymmetry of this monomer pair, the csBB morphologies sensitively depend on the backbone degree of polymerization ( N bb ). At low N bb values, micellar Frank-Kasper phases with the brush backbone situated in the matrix domain are stable, albeit with a modest reduction in the mean interfacial curvature evidenced by a to A15 order-to-order transition. However, larger N bb values drive csBBs to form hexagonally packed cylinders phases. This N bb -dependent phase behavior is rationalized in terms of a star-to-bottlebrush transition. At low N bb values, the csBBs are akin to star polymers with pointlike junctions that can support complex micelle packings. As N bb increases, the csBBs adopt cylindrical molecular geometries with extended backbones situated in the matrix domain that prefer hexagonally packed cylinders morphologies. Published by the American Physical Society 2024
In our previous DOE-supported project (DE-SC0016208), we demonstrated that a particular architecture dubbed ‘randomly end-linked copolymer networks’ (RECNs) led to especially robust self-assembly of disordered cocontinuous nanostructures. Such structures, wherein both microphases are fully percolating in three dimensions, are attractive for a wide-variety of energy-relevant applications including purification membranes, separators in batteries and fuel cells, catalyst supports, and high surface area electrodes. In the current project we sought to substantially extend our fundamental understanding of how different types of randomness in blocky copolymer architecture contribute to the robust formation of disordered cocontinuous nanostructures. To do so, we filled in the sizable gap between the previously studied cases of randomly linked linear multiblock copolymers and RECNs by studying two intermediate cases: (i) random miktoarm star polymers, which contain dispersity in the number of the two chemically-incompatible strands located at each junction and therefore the preferred interfacial curvature of each polymer, and (ii) randomly branched copolymers, which also possess bridging strands that stretch across domains and apply local ‘elastic’ forces to the interfaces. In addition, we studied a different network architecture, statistically crosslinked copolymer networks, that is more straightforward to prepare using chain growth polymerization methods, enabling more facile formation of cocontinuous nanostructures. Finally, we extended our approaches from model systems based on polystyrene and poly(lactic acid) to copolymer architectures containing either (i) polysulfone, an engineering polymer that provides excellent mechanical properties for membrane applications, or (ii) polyacrylonitrile, providing a convenient route to nanoporous carbon materials.
Multiscale systems are ubiquitous in science and technology, but are notoriously challenging to simulate as short spatiotemporal scales must be appropriately linked to emergent bulk physics. When expensive high-dimensional dynamical systems are coarse-grained into low-dimensional models, the entropic loss of information leads to emergent physics which are dissipative, history-dependent, and stochastic. To machine learn coarse-grained dynamics from time-series observations of particle trajectories, we propose a framework using the metriplectic bracket formalism that preserves these properties by construction; most notably, the framework guarantees discrete notions of the first and second laws of thermodynamics, conservation of momentum, and a discrete fluctuation-dissipation balance crucial for capturing non-equilibrium statistics. We introduce the mathematical framework abstractly before specializing to a particle discretization. As labels are generally unavailable for entropic state variables, we introduce a novel self-supervised learning strategy to identify emergent structural variables. We validate the method on benchmark systems and demonstrate its utility on two challenging examples: (1) coarse-graining star polymers at challenging levels of coarse-graining while preserving non-equilibrium statistics, and (2) learning models from high-speed video of colloidal suspensions that capture coupling between local rearrangement events and emergent stochastic dynamics. We provide open-source implementations in both PyTorch and LAMMPS, enabling large-scale inference and extensibility to diverse particle-based systems.
Liquid mixtures composed of colloidal particles and much smaller non-adsorbing linear homopolymers can undergo a gelation transition due to polymer-mediated depletion forces. We now show that the addition of linear polymers to suspensions of soft colloids having the same hydrodynamic size yields a liquid-togel-to-re-entrant liquid transition. In particular, the dynamic state diagram of 1,4-polybutadiene star-linear polymer mixtures was determined with the help of linear viscoelastic and small-angle Xray scattering experiments. While keeping the star polymers below their nominal overlap concentration, a gel was formed upon increasing the linear polymer content. Further addition of linear chains yielded a re-entrant liquid. This unexpected behavior was rationalized by the interplay of three possible phenomena: (i) depletion interactions, driven by the size disparity between the stars and the polymer length scale which is the mesh size of its entanglement network; (ii) colloidal deswelling due to the increased osmotic pressure exerted onto the stars; and (iii) a concomitant progressive suppression of the depletion efficiency on increasing the polymer concentration due to reduced mesh size, hence a smaller range of attraction. Our results unveil an exciting new way to tailor the flow of soft colloids and highlight a largely unexplored path to engineer soft colloidal mixtures.
Polymer single-chain nanoparticles (SCNPs) have found a wide range of applications spanning catalysts, sensors and nanomedicine. The generation of structured SCNPs from star-shaped polymers with diverse architectures and functionalities affords a new avenue to expand the emerging research area. The large-scale synthesis of structured SCNPs is described by the electrostatics-mediated intramolecular crosslinking of three types of 3-armed star-shaped polymers (T-P4VP, T-PS-b-P4VP, and T-P4VP-b-PS), whose configuration is tunable from spherical to cage-shaped to dumbbell-shaped and star-shaped. The structured SCNPs are amphiphilic and can be used as solid surfactants to stabilize different types of emulsions.
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Most lithium batteries offer a wide range of applications. However, safety issues are still an unresolved issue for several applications. To solve the safety issue of Li-ion batteries, solid polymer electrolyte is a promising candidate to replace commercial liquid electrolyte. A 4-arm star poly(ethylene oxide) polymer with LiTFSI salt as an electrolyte was studied. The dynamics of this polymer were explored with the Quasi-Elastic Neutron Scattering technique. Furthermore, the influence of temperature and Li salt concentration on the polymer dynamics was investigated. The dynamics of the polymer ends of the arm show much higher flexibility than the core parts making those types of polymers attractive for further studies in battery research.
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Star block copolymers (s-BCPs), comprised of multiple linear diblock copolymers joined at a central point, are shown to segregate to the interface between two immiscible homopolymers that are identical to the blocks of the s-BCPs. The s-BCPs undergo a configurational transition at the interface, with different blocks of copolymers being embedded in their respective homopolymers, thereby bridging the interface and promoting adhesion. A series of 4-arm s-BCPs were synthesized with hydrogenated or deuterated polystyrene (PS/dPS) as the core block and poly(2-vinylpyridine) (P2VP) as the corona block, which was directly placed at the interface between the two homopolymers. Neutron reflectivity (NR) was used to determine the concentration profiles of the PS homopolymer, s-BCP core blocks, and P2VP total segments under equilibrium. The investigation varies the molecular weight (MW) and the total number of s-BCPs at the interface. Self-consistent-field theory (SCFT) was also employed to calculate the concentration profiles of the components at the interface, which were in excellent agreement with experimental results. The NR showed that the interfacial width between the homopolymers increased with the increasing number of s-BCPs at the interface up to a saturation limit. Beyond this limit, additional s-BCPs were released into the corona-miscible phase as unimolecular micelles. For a comparable interlayer thickness of s-BCPs at the interface, lower MW s-BCPs generated a broader interface. SCFT analysis suggested that, at the same packing density, the arms of the low MW s-BCPs align more parallel to the interface, while the arms of high MW s-BCPs adopt a more normal orientation, like their linear BCP counterparts. Furthermore, it was also observed that the core blocks, constrained by the junction points, were oriented more parallel and closer to the interface than the corona blocks. The phase behavior of the polymer blends revealed that s-BCP additives can efficiently reduce the domain size, with the low MW yielding smaller domain sizes due to the greater reduction in the interfacial energy and the high MW arresting phase separation due to their higher binding energy and a jamming of the interfacial assemblies. Asymmetric double cantilever beam (ADCB) tests demonstrated that s-BCPs promoted adhesion more efficiently than their linear BCP counterparts due to stronger binding energy per molecule, suggesting a more efficient compatibilizer for polymer upcycling. In conclusion, the results from these studies provide fundamental insights into the assembly of s-BCPs at homopolymer interfaces, the reduction of domain size, and promotion of adhesion, providing a strategy for the use of s-BCPs as stealth surfactants and universal compatibilizers.
A novel N-sulfonyl aziridine, N-((2-(trimethylsilyl)ethyl)sulfonyl)-2-methyl-aziridine (SES-MeAz) was synthesized, and its controlled anionic ring-opening polymerization to form poly(SES-MeAz) studied. The desulfonylation of poly(SES-MeAz)s can be achieved under mild conditions, with tetrabutylammonium fluoride (TBAF) to form polypropylene imine (PPI), although purification of the PPI was not completely successful. The block copolymer p(pTs-MeAz)-b-p(SES-MeAz), (pTs-MeAz = N-para-tosyl-2-methyl-aziridine) was prepared by a sequential polymerization. The desulfonylation of the block copolymer is the first example of the selective removal of a sulfonyl protecting group from a poly(sulfonylazirdine) block copolymer and this is the first formation of a polysufonylaziridine-block-polypropylene imine copolymer. Three-armed star-shaped poly(SES-MeAz)s were also successfully synthesized and exhibited improved solubility compared with linear poly(SES-MeAz)s.
Star block copolymers (s-BCPs) have potential applications as novel surfactants or amphiphiles for emulsification, compatibilization, chemical transformations, and separations. s-BCPs have chain architectures where three or more linear diblock copolymer arms comprised of two chemically distinct linear polymers, e.g., solvophobic and solvophilic chains, are covalently joined at one point. The chemical composition of each of the subunit polymer chains comprising the arms, their molecular weights, and the number of arms can be varied to tailor the surface and interfacial activity of these architecturally unique molecules. Further, this makes identification of the optimal s-BCP design nontrivial as the total number of plausible s-BCP architectures is experimentally or computationally intractable. In this work, we use molecular dynamics (MD) simulations coupled with a reinforcement learning-based Monte Carlo tree search (MCTS) to identify s-BCP designs that minimize the interfacial tension between polar and nonpolar solvents. We first validate the MCTS approach for the design of small- and medium-sized s-BCPs and then use it to efficiently identify sequences of copolymer blocks for large-sized s-BCPs. The structural origins of interfacial tension in these systems are also identified by using the configurations obtained from MD simulations. Chemical insights into the arrangement of copolymer blocks that promote lower interfacial tension were mined using machine learning (ML) techniques. Overall, this work provides an efficient approach to solve design problems via fusion of simulations and ML and provides important groundwork for future experimental investigation of s-BCPs for various applications.
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There has been significant interest in the engineering of polymer topology to control rheology and mechanical stability in dilute solutions for applications involving extreme shear rate flows. However, methods to experimentally probe properties at relevant shear rates (≈ 10 4 –10 6 s –1 ) have remained ex situ, obscuring access to measures of polymer deformation and rheology that would otherwise provide mechanistic insight into the topology-dependent properties that control their behavior in extreme shear flows. In this study, we used novel in situ small angle neutron scattering measurements in a capillary rheometer (capillary rheo-SANS) to simultaneously measure solution viscosities and polymer deformations in high shear on a series of chemically homologous topology-defined polymers including linear, randomly branched, and star-shaped molecules. We demonstrate that differences in the onset of chain stretching and shear thinning of these polymers in dilute solution are controlled primarily by differences in their molecular relaxation time. These differences correlate with differences in chain scission inferred from ex situ measurements at more extreme shear rates. Together, the results demonstrate a direct coupling between chain deformation and scission, and suggest that the dominant effect of branching as a means to impart resilience against mechanical degradation is through differences in relaxation dynamics due to branching. We anticipate that these results will provide key insights to engineer topology-controlled polymers for rheological modification, mechanical stability, and controlled mechano-chemistry.