Block copolymers with strong tendency for block demixing have attracted significant attention especially for applications in microelectronics and membranes. Enabling technological advances in these areas critically depends on access to well-defined nanostructures with dimensions near or below 10 nm. Block copolymers bearing polyzwitterionic segments are a promising class of materials for phase separation due to their ion association-induced phase segregation which bestow high-χ. In this work, the synthesis of parent poly(styrene)-block-poly(vinylpyridine) (PS-b-PVP) block copolymers that are subsequently modified to the targeted poly(styrene)-block-poly(vinylpyridine carboxybetaine) using a two-step modification scheme is presented. We study the thermophysical and phase separation properties of the parent, polyelectrolyte intermediate and the final polyzwitterionic block copolymers. Ultra-small, periodic nanostructures with half-spacing as low as 5.3 nm are obtained from the polyzwitterionic copolymers. This work demonstrates a scalable synthetic methodology to access pH responsive polyzwitterions and establishes connectivity between polymer design attributes and phase separation behavior for neutral-block-polyzwitterionic copolymers.

Polyzwitterions, composed of repeating units with equal numbers of anionic and cationic groups, have drawn considerable interest for applications ranging from antimicrobial coatings and antifouling membranes to low-friction interfaces, energy storage media, and actuators. A characteristic feature of many polyzwitterions is the anti-polyelectrolyte effectsalt-induced chain expansion in aqueous solutiona phenomenon often implicated in their functional behavior. In this study, we investigated the conformational behavior of poly-(2-vinylpyridine-N-oxide) (P2VPNO) using small-angle X-ray and neutron scattering (SAXS and SANS). Small-angle X-ray scattering (SAXS) revealed that, in salt-free aqueous solution, P2VPNO adopts an expanded wormlike conformation stabilized by hydration. With increasing concentration, the chains contract due to screening of intrachain excluded-volume interactions, qualitatively consistent with de Gennes' scaling predictions for neutral polymers. Small-angle neutron scattering (SANS) further demonstrated that P2VPNO exhibits the characteristic anti-polyelectrolyte response to added salt, as observed in many other polyzwitterions. At elevated temperatures, chain flexibility increases, leading to a shorter Kuhn length and reduced radius of gyration. Notably, however, salt-induced chain expansion persists, indicating that the anti-polyelectrolyte effect remains operative under elevated thermal conditions. These findings provide the first experimental evidence of scaling behavior in polyzwitterions, as well as the first observation of their altered anti-polyelectrolyte response at elevated temperatures, offering new insights into the solution physics of this important class of polymers.

Designing molecules with specific target properties remains a fundamental challenge in computational chemistry. While existing approaches show promise, most rely on simplified representations like SMILES strings or 2D graphs that lack essential three-dimensional geometric information. We present EvoDiffMol, a computational framework that integrates evolutionary algorithms with three-dimensional diffusion models for property-driven molecular generation. The method operates through adaptive evolutionary optimization, where population-based selection guides the generation process toward desired property landscapes. EvoDiffMol supports both unconstrained molecular design and scaffold-constrained generation that preserves fixed substructures while optimizing complementary regions. Comprehensive evaluation demonstrates exceptional performance, achieving the highest drug-likeness score (0.94) among all compared state-of-the-art methods while maintaining excellent validity, uniqueness, and novelty. Beyond single property optimization, the framework demonstrates flexible multi-property optimization capabilities, simultaneously controlling multiple molecular descriptors including synthetic accessibility, lipophilicity, topological polar surface area, and clinically relevant ADMET properties such as cardiotoxicity (hERG) and intestinal permeability (Caco-2). This adaptability spans from simple descriptors to practical pharmaceutical endpoints without requiring complete model retraining. The framework achieves precise control over target property values, generating molecules with properties closely matching specified targets for both single and multiple descriptors. Scaffold-constrained experiments preserve fixed molecular cores while maintaining effective property optimization. The three-dimensional representation offers advantages in maintaining structural validity during iterative optimization, with potential for geometry-aware applications in materials science and drug discovery. This work presents a novel framework that integrates adaptive evolutionary optimization with 3D equivariant diffusion models, enabling flexible multi-property and scaffold-constrained molecular design without retraining the generative model for new property objectives.

Charge transport in polymers, such as polyacrylonitrile (PAN), is crucial for electronics and energy storage. For instance, PAN can transport cations e.g., Li+, by facilitating dynamic cation-nitrile coordination in batteries. However, little is known regarding the underlying role of complex reactive polymer configurations. Herein, we develop a deep-learning potential, trained on ab initio energies and forces of nonequilibrium reactive PAN configurations, to unravel the kinetics of PAN cyclization initiated by a nucleophile (OH- dissociated from LiOH) attacking the terminal nitrile carbon. We find, based on the reaction free-energetics, rates, and charge analysis, that the nucleophile attack producing the first ring is the rate-limiting step, which subsequently triggers Li+-coupled electron transfer along the PAN backbone, causing ∼104 times faster sequential ring-formation of the remaining nitriles. PAN's extended configurations, where dipolar and H-bonding interactions are minimal, enable such rapid kinetics. By validating our computational findings with IR and NMR experiments, we establish a pathway for designing reactive polymers with enhanced charge transport for energy applications.

The weak welding strength between filament layers in fused deposition modeling (FDM) products results in anisotropic mechanical properties that are also sensitive to print patterns. Furthermore, poor transverse directional alignment coupled with slow printing rates limit their applicability for industrial production. We hypothesize that interfilamentous welding strength can be enhanced by modifying the chemistry of the building blocks and the topological arrangement of the macromolecular structure. To test this, we carried out coarse-grained molecular dynamics simulations to investigate the dynamics of both linear and branched polymer across representative interfaces. We observe that the diffusion controlled interdigitation follows a power law, with the exponent decreasing from 0.34 to 0.11 as grafting density increases from 7.5% to 196% (sidechains are grafted to both sides of a monomer unit). Surprisingly, the addition of sidechains enhances welding efficiency, as dense bottlebrush polymers with high grafting density reach maximum rupture strength faster than linear polymers. However, their saturated rupture strength is lower. This observation is subsequently corroborated by experimental lap shear tests using ungrafted polyethylene and branched polyethylene grafted by octane. Our MD simulations show that while linear polymer welding relies on backbone entanglement, in bottlebrush polymers, sidechains play a dominant role in enhancing interfacial strength, surpassing the contribution of the backbone. And linear polymers require more time to diffuse into neighboring filaments to achieve desired bulk properties. Furthermore, our molecular dynamics simulations reveal a brittle rupture behavior with significant hardening in linear and comb-like (mildly grafted) polymers, while bottlebrush (densely grafted) polymers display elastomeric behavior with a pronounced stress plateau prior to fracture. By comparing the gyration radii of all topological polymers, we found that they exhibit an increase in gyration radius parallel to the stretching direction and a decrease perpendicular to the deformation. The increase becomes more pronounced with higher grafting density. These results not only provide deeper insight into the underlying welding mechanisms of topological polymers but also present a potential approach for mitigating the anisotropy that is inherent in FDM-based additive manufacturing.

Reinforcing polymers with discontinuous fibers improves mechanical properties, such as strength and stiffness, and in some cases achieve isotropic properties, rendering them suitable for various engineering applications. Matrix materials are generally highly engineered thermosets (e.g. crosslinked epoxies), bonded to the fiber periphery by proprietary surface and sizing chemistries. Semicrystalline thermoplastic matrices are less utilized due to poor fiber-matrix bonding resulting in inefficient interfacial load-transfer in reinforced composites. However, flexibility with melt-processing or molding conditions can be leveraged to promote non-covalent interfacial bonding between matrix and fiber via crystallization of the matrix onto fiber surface. In the present study, we utilize a co-mingle chopped carbon and isotactic polypropylene fibers to form isotropic composites, tailoring interfacial immobilized matrix or interphase morphology to optimize performance through precise control of thermal processing/molding windows. Calorimetry and optical microscopy were employed to investigate the impact of carbon fiber at various volume fractions (10, 20, and 30%) on isotactic polypropylene crystallization and mechanical performance. Variations in mechanical properties correspond to the structural evolution of the interfacial region and are correlated to underlying microstructural attributes using wide-angle X-ray scattering, thermal analysis, and low-field nuclear magnetic resonance spectroscopy. These results provide a practical framework for the manufacturing of thermoplastic matrix composites. The results presented provide a guide for the strategic optimization of interphase design, showcasing tailorable tensile strengths which outperform any isotactic polypropylene carbon fiber composites previously reported in literature.

A facile, direct deposition approach that exploits van der Waals interactions between carbonaceous materials is utilized to create unidirectional hybrid carbon fiber composites. Two small molecule crosslinkers, a trifunctional aromatic (TL) and a difunctional aliphatic (DL) acyl chloride, are first utilized to create a crosslinked interphase with a softer and stiffer modulus respectively. TL crosslinked interphase with a higher modulus improved the tensile strength by 50%, despite non-covalent linking between fiber and matrix, elucidating the critical role of the interphase in alleviating modulus mismatch between the high modulus carbon fiber and the rubbery matrix. Fractional quantities of carbon nanotubes are additionally dispersed in the small molecule crosslinkers which behaved as a dispersant, helping introduce nanoasperities on the carbon fiber surface. Strong "pi-pi" interactions between CNTs and CF contributed to tensile properties, which are increased by 66% compared to the control. A cohesive zone model suggests that a stiffer interphase is better able to exploit surface heterogeneities and roughness on the fiber, synergistically enhancing interfacial strength.

To understand how thermoplastic welding strength can be tuned through chemical modifications and macromolecular topology, we combined coarse-grained molecular dynamics (MD) simulations with experimental validation. Our simulations examined the diffusion dynamics of both linear and graft polymers across representative interfaces, revealing that diffusion-controlled interdigitation follows a power law, with the exponent decreasing from 0.34 to 0.11 as grafting density increases from 7.5 to 196% (with side chains grafted to both sides of a monomer unit). The addition of side chains enhances welding efficiency, as dense bottlebrush polymers with high grafting density reach maximum rupture strength faster than linear polymers. However, their saturated rupture strength is lower. This observation is subsequently corroborated by experimental lap-shear tests comparing linear polyethylene with octene grafted polyethylene elastomers. Our MD simulations show that unlike linear polymers, where backbone entanglements dominate, the grafted side chains introduce mechanisms in addition to entanglement dilution. The rapid interdigitation of side chains creates a dense mesh of entropic van der Waals contacts, which can also enhance the film welding. Furthermore, our MD simulations reveal a brittle rupture behavior in linear and comb-like (mildly grafted) polymers, while bottlebrush (densely grafted) polymers display elastomeric behavior with a pronounced stress plateau prior to fracture. Our simulations deconvolute the influence of polymer topology on deformation behavior. The rate of polymer deformation becomes lower than the applied strain rate prior to rupture, and the onset of this deviation is progressively delayed from linear to bottlebrush polymers. This trend highlights the critical role of molecular architecture in governing the mechanical response. These results provide deeper insight into the underlying welding mechanisms of topological polymers and present a potential approach for mitigating the interface anisotropy that is inherent in advanced manufacturing techniques such as fused filament fabrication.

Polymers containing dynamic covalent bonds (DCBs) exhibit thermoplastic‐like flow above their topology freezing temperature ( T v ) while maintaining thermoset‐like properties below it, making them promising for sustainable manufacturing. However, their large‐scale adoption remains limited due to challenges in accurately determining T v and achieving efficient fiber‐matrix bonding in composite applications. Here, hierarchically structured epoxy‐anhydride‐based polyester vitrimer composites reinforced with cellulosic filaments is demonstrated, where hydroxyl groups on fiber surfaces participate directly in transesterification with the matrix. This dynamic interfacial bonding delivers exceptional mechanical properties, including ≈70 MPa shear strength and >10% strain‐to‐failure, while enabling thermal malleability. Using a combination of nuclear magnetic resonance and nano‐infrared spectroscopies, direct evidence is provided that chemical bond exchange begins well below the conventionally measured T v , supporting the hypothesis that rheologically determined T v reflects a combination of chemical exchange and frictional dynamics rather than a discrete transition. The composites demonstrate excellent processability through vacuum‐assisted resin transfer molding and maintain >90% of their mechanical properties after multiple thermal reforming cycles. These findings advance both the fundamental understanding of vitrimeric transitions and the practical development of sustainable, high‐performance composite materials.

Understanding how polymer topology influences melt extrudability is critical for advancing material design in extrusion-based additive manufacturing. In this work, we develop a bottom-up, cross-scale modeling framework that integrates coarse-grained molecular dynamics (CGMD) and continuum-scale computational fluid dynamics (CFD) to quantitatively assess the effects of polymer architecture on extrudability A range of branched polydimethylsiloxane (PDMS) polymers are systematically designed by varying backbone length, sidechain length, grafting density, grafted block ratio, and periodicity of grafted-ungrafted segments. CGMD simulations are used to compute zero-shear viscosity and relaxation times, which are then incorporated into the Phan-Thien-Tanner (PTT) model within a computational fluid dynamics (CFD) model to predict pressure drop of PDMS during extrusion through printer nozzle. Qualitative analysis reveals that polymers with concentrated grafted blocks exhibit significantly higher zero-shear viscosity than stochastically branched analogs, while sidechain inertia drives longer relaxation time. However, for untangled and weakly entangled PDMS, relaxation time remains in the nanosecond range, making shear-thinning and elastic effects negligible. Consequently, zero-shear viscosity emerges as the primary determinant of extrudability. This cross-scale modeling strategy provides a predictive framework for guiding the rational design of extrudable polymer materials with tailored topologies.

Dry processing (DP) is an advanced manufacturing technique for lithium-ion battery (LIB) electrodes. Unlike conventional wet-process-based manufacturing that involves dissolving polyvinylidene fluoride (PVDF) binder in n-methyl-2-pyrrolidone (NMP) solvent for slurry-casting, DP involves fibrillation of polymer binders. This method offers environmental and cost benefits by eliminating the need for expensive and environmentally hazardous organic solvents. However, DP-produced electrode films often lack mechanical stability due to the absence of a current collector substrate during electrode material layer fabrication. This reduced mechanical instability results in difficulty during fabricating of thin electrodes (approximate to 5 mAh/cm(2)). To address this issue, long (>8 mm) carbon fiber (CF) has been incorporated to reinforce the mechanical strength of the electrode films. The study demonstrates that the inclusion of long carbon fiber boosts the mechanical, electrical, thermal, and electrochemical performance of DP electrodes.

To enhance the mechanical properties of carbon fiber‐reinforced polymer composites, a physicochemical scaffold is designed incorporating microscopically architected chemically reactive nanofibers that act as a multiscale bridge between the carbon fibers and the matrix. Thermally activated nanofibers leverage their morphologically driven mechanochemical properties to form covalent bonds with adjacent polymer molecules, creating a co‐continuous network that dramatically enhances fiber‐matrix load transfer. By meticulously controlling the nanofiber architecture through variable surface area, functional group availability, and polymer chain alignment effects, the extent of covalent bonding between nanofibers and the matrix is manipulated ultimately resulting in improved carbon fiber‐matrix adhesion. The concept was validated using polyacrylonitrile nanofibers within an acrylonitrile butadiene styrene matrix in a discontinuous carbon fiber‐reinforced composite system. Nanomechanical studies using atomic force microscopy and low‐field nuclear magnetic resonance spectroscopy confirmed immobilized, chemically transferred, and ordered nanostructures at the interphase. The resulting composites demonstrated ≈56% and ≈175% improvements in tensile strength and toughness, respectively, compared to composites without nanofiber. Comprehensive thermal, rheological, and X‐ray scattering analyses, along side all‐atomic molecular dynamics simulations, revealed the fundamental mechanisms behind these improvements in mechanical behavior. The versatility and efficacy of the approach have the potential to address longstanding interphase challenges in the composite industry.

Mechanical metamaterials (MMs) are engineered structures with unique mechanical properties that arise from their unique spatial arrangement or lattice-like structure. The most commonly designed MMs such as honeycomb and re-entrant auxetics are prone to failure at the sharp corners and weak joints due to the increased stress concentration under deformation. To mitigate this challenge, braided MM structures involving intertwining threads of nylon-forming curved unit cells-have been studied. These textile-inspired cylindrical braided metamaterials (CBMMs) with contrasting unit cells, namely diamond and regular CBMMs, were fabricated by 3D printing. The layer-by-layer deposited structure built by fused filament fabrication delivered an assembly of overlapped threads that are fused at the contact point. To understand deformation behavior of these MMs, finite element models were developed for various load scenarios including quasi-static compression, cyclic and creep loads at room temperature. Stress distribution, deformation mechanisms, and failure modes were analyzed and validated by experiments to analyze the geometries and associated performance. The diamond CBMMs showed stress softening at 30 % compressive strain, withstanding a load of similar to 440 N, whereas the regular CBMMs at 50 % strain experienced similar to 250 N. The diamond CBMMs delivered higher creep resistance under sustained load and better energy absorption under cyclic loading than the regular CBMMs. The latter, however, exhibited 94 % shape recovery in contrast to 88 % recovery in former prototype during their first cyclic load. This study helps design mechanical lightweight devices that endure significant sustained load and exhibit enhanced energy absorption and shape recovery characteristics in cyclic loading.

Inverse molecular design faces significant challenges due to vast chemical space and complex property requirements. While language models show promise for molecular generation, they struggle with validity, multi-property optimization, and structural constraints. This work presents RLMolLM, a reinforcement learning framework combining Proximal Policy Optimization (PPO) with genetic algorithms to address these limitations. Our approach optimizes multiple user-specified properties including quantitative estimates of drug-likeness (QED), synthetic accessibility (SA), and ADMET (absorption, distribution, metabolism, excretion, and toxicity) endpoints without requiring complete model retraining, while maintaining capability for scaffold-constrained generation where specific substructures must be preserved. We outperform state-of-the-art methods for molecular optimization, achieving best QED scores across GDB13, Moses, and Zinc datasets with up to 31% improvement over previous methods while maintaining excellent validity, uniqueness, and novelty metrics. For simultaneous multi-property optimization, our framework achieves substantial improvements in ADMET properties including 4.5-fold reduction in hERG toxicity and enhanced Caco-2 permeability compared to Moses dataset. Under structural constraints, the framework significantly improves molecular validity while preserving scaffolds and effectively optimizing properties. This versatile solution advances pharmaceutical and materials molecular design through effective integration of reinforcement learning and genetic algorithms with multi-property optimization and scaffold preservation.

Polyolefin plastic waste, particularly polypropylene, is one of the most prevalent components in the plastic waste stream generated globally. However, only a small fraction of this waste is recycled and reintegrated into second applications. These materials have significant embedded energy that could be used for additional productivity if recycled properly. Therefore, there is a critical need to develop scalable processing techniques with high throughput to effectively recycle it. We report the continuous manufacturing of microporous fibers using waste polypropylene plastic and melt-processable lignin, a plant biomass constituent produced as a byproduct in paper mills or biorefineries, as a template to create pores. These filaments with hierarchical porosity-created by controlled microphase separation during high-speed extensional flow followed by removal of lignin-exhibit exceptional capillary action for hydrophobic liquids. The resulting porous fibers can be used for various separation applications. For example, the oil uptake of the fibers is 9.59 +/- 0.82 g/g for applications in oil recovery in bodies of water. In addition, polyethylenimine infiltration within these nanoporous fibers introduces cyclic room temperature sorption and thermal desorption potential of acidic gases such as CO2 in a bench-scale experiment. The fibers exhibit multiple cycles of CO2 absorption, with a range of 0.15 to 0.17 mmol/g. Thus, these polyethylenimine-infiltered nanoporous polypropylene fibers can also be used in removing acidic gases from a gas mixture.

Carbon fiber composite performance relies on the fiber-matrix interface for effective load transfer. To enhance interfacial properties between the fiber and matrix, often carbon fiber surfaces are oxidatively and covalently modified to incorporate chemical functional groups. By contrast, here, noncovalent electrodeposition of functional polyelectrolyte is applied onto conducting carbon fibers from aqueous solutions. A natural polymer, chitosan (CS), is electro-deposited onto the fiber surface, which undergoes multi-scale physical interactions. The bound CS layer with abundant amine functionalities reacts with epoxy moieties within the matrix to improve the interfacial properties. The scalable and energy-efficient electrodeposition eliminates traditional functionalization and sizing requirements of carbon fiber while delivering significantly higher mechanical performance with enhanced consistency. For continuous fiber reinforced composites, compared to conventional fibers, apparent interlaminar shear strength increases by 27%, reaching approximate to 86 MPa. The short fiber composites with only 2-11 wt.% fibers exhibit approximate to 20% increase in tensile strength with a peak performance of 120 MPa. Unlike traditionally treated and sized carbon fiber, this approach delivers coated fibers with long shelf-life and allows recovery of both CS in electrolyte form and carbon fiber by continuous electrochemical processing of the modified fibers with inverse polarity; thus, it promotes overall fiber recyclability.

Electrical conductivity in nanocomposites is a complex phenomenon governed by a myriad number of physical and chemical factors. However, the interrelationships between segmental dynamics and its effect on electrical conductivity is less understood. Herein we create a solvent free nanocomposite synthesized in a single step process. Facile covalent bonding is achieved between functionalized nanotubes and the lignin-based matrix using small molecule coupling agents. The covalent bonding and shearing are hypothesized to lead to a breaking of the larger agglomerates, leading to excellent dispersion and thereby percolation at much lower concentrations than can be achieved by traditional blending. We show that while the above process can be utilized to achieve excellent dispersion and thus percolation and conductivity, segmental dynamics also plays a key role in dictating electrical conductivity.

This research examines the correlation between interfacial characteristics and membrane distillation (MD) performance of copper oxide (Cu) nanoparticle-decorated electrospun carbon nanofibers (CNFs) polyvinylidene fluoride (PVDF) mixed matrix membranes. The membranes were fabricated by a bottom-up phase inversion method to incorporate a range of concentrations of CNF and Cu + CNF particles in the polymer matrix to tune the porosity, crystallinity, and wettability of the membranes. The resultant membranes were tested for their application in desalination by comparing the water vapor transport and salt rejection rates in the presence of Cu and CNF. Our results demonstrated a 64% increase in water vapor flux and a salt rejection rate of over 99.8% with just 1 wt % loading of Cu + CNF in the PVDF matrix. This was attributed to enhanced chemical heterogeneity, porosity, hydrophobicity, and crystallinity that was confirmed by electron microscopy, tensiometry, and scattering techniques. A machine learning segmentation model was trained on electron microscopy images to obtain the spatial distribution of pores in the membrane. An Autoregressive Integrated Moving Average with Explanatory Variable (ARIMAX) statistical time series model was trained on MD experimental data obtained for various membranes to forecast the membrane performance over an extended duration.

Polyacrylonitrile (PAN) is an important commercial polymer, bearing atactic stereochemistry resulting from nonselective radical polymerization. As such, an accurate, fundamental understanding of governing interactions among PAN molecular units is indispensable for advancing the design principles of final products at reduced processability costs. While ab initio molecular dynamics (AIMD) simulations can provide the necessary accuracy for treating key interactions in polar polymers, such as dipole-dipole interactions and hydrogen bonding, and analyzing their influence on the molecular orientation, their implementation is limited to small molecules only. Herein, we show that the neural network interatomic potentials (NNIPs) that are trained on the small-scale AIMD data (acquired for oligomers) can be efficiently employed to examine the structures and properties at large scales (polymers). NNIP provides critical insight into intra- and interchain hydrogen-bonding and dipolar correlations and accurately predicts the amorphous bulk PAN structure validated by modeling the experimental X-ray structure factor. Furthermore, the NNIP-predicted PAN properties, such as density and elastic modulus, are in good agreement with their experimental values. Overall, the trend in the elastic modulus is found to correlate strongly with the PAN structural orientations encoded in the Hermans orientation factor. This study enables the ability to predict the structure-property relations for PAN and analogues with sustainable ab initio accuracy across scales.