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Depletion-driven assembly has been widely studied for micron-sized colloids, but questions remain at the nanoscale where the governing physics are impacted by the stabilizing surface ligands or wrapping polymers, whose length scales are on the same order as those of the colloidal core and the depletant. Here, we probe how wrapping colloidal tin-doped indium oxide nanocrystals with polymers affects their depletion-induced interactions and assembly in solutions of polyethylene glycol. Copolymers of polyacrylic acid grafted with polyethylene oxide provide nanocrystal wrappings with different effective polymer graft densities and molecular weights. (Ultra) small angle X-ray scattering, coarse-grained molecular dynamics simulation, and molecular thermodynamic theory were combined to analyze how depletant size and polymer wrapping characteristics affect depletion interactions, structure, and phase behavior. The results show how depletant molecular weight, as well as surface density and molecular weight of polymer grafts, set thresholds for assembly. These signatures are unique to depletion-driven assembly of nanoscale colloids and mirror phase behaviors of grafted nanoparticle--polymer composites. Optical and rheological responses of depletion-driven assemblies of nanocrystals with different polymer shell architectures were probed to learn how their structural differences impact properties. We discuss how these handles for depletion-driven assembly at the nanoscale may provide fresh opportunities for designing responsive depletion interactions and dynamically reconfigurable materials.

The influence of dynamical ion-ion correlations and ion pairing on salt transport in ion exchange membranes remain poorly understood. In this study, we use the framework of Onsager transport coefficients within atomistic molecular dynamics simulations to study the impact of ion-ion correlated motion on salt transport in hydrated polystyrene sulfonate membranes and compare with the results from aqueous salt solutions. At sufficiently high salt concentrations, cation-anion dynamical correlations exert a significant influence on both salt diffusivities and conductivities. Anion-anion distinct correlations, arising from the imbalance between the concentration of free (mobile) cations and anions, and the retarding effect of the fixed charge groups on cations, proves to be an additional important feature for polymer membranes. Our results demonstrate that dynamical correlations should become an important consideration in experimental measurements of salt diffusivities and conductivities for non-dilute salt solutions in polymer membranes.

Designing functional materials requires a deep search through multidimensional spaces for system parameters that yield desirable material properties. For cases where conventional parameter sweeps or trial-and-error sampling are impractical, inverse methods that frame design as a constrained optimization problem present an attractive alternative. However, even efficient algorithms require time- and resource-intensive characterization of material properties many times during optimization, imposing a design bottleneck. Approaches that incorporate machine learning can help address this limitation and accelerate the discovery of materials with targeted properties. In this article, we review how to leverage machine learning to reduce dimensionality in order to effectively explore design space, accelerate property evaluation, and generate unconventional material structures with optimal properties. We also discuss promising future directions, including integration of machine learning into multiple stages of a design algorithm and interpretation of machine learning models to understand how design parameters relate to material properties., 43 pages, 4 figures, accepted for publication in Annual Review of Chemical and Biomolecular Engineering

High-performance actuating materials are necessary for advances in robotics, prosthetics and smart clothing. Here we report a class of fibre actuators that combine solution-phase block copolymer self-assembly and strain-programmed crystallization. The actuators consist of highly aligned nanoscale structures with alternating crystalline and amorphous domains, resembling the ordered and striated pattern of mammalian skeletal muscle. The reported nanostructured block copolymer muscles excel in several aspects compared with current actuators, including efficiency (75.5%), actuation strain (80%) and mechanical properties (for example, strain-at-break of up to 900% and toughness of up to 121.2 MJ m

We used atomistic simulations and compared the prediction of three different implementations of force fields, namely, the original full partial charge system, the scaled partial charge system, and the Drude oscillator polarizable force field and its effect on the structural and dynamic properties of a polymeric ionic liquid, poly(1-butyl-3-methyl-imidazolium hexafluorophosphate). We found that both the scaled and the polarizable force field models yield comparable predictions of structural and dynamic properties, although the scaled charge model artificially lowers the first-neighbor peak of the radial distribution function and therefore leads to a slight reduction in density. The full charge model was not accurate in its prediction of the dynamic properties but could reproduce the structural properties. With a refined analysis method for the ion-hopping mechanisms, we found that all three methods produce very similar conclusions, namely, that the mobile anion is associated with three cations from two distinct polymer chains and that the fractions of inter- and intramolecular hopping events are comparable. Our results demonstrate that the scaled charge force fields provide a computationally efficient means to capture polarizability effects on both the structural and dynamic properties of polymeric ionic liquid systems.

Recently, significant interest has arisen on the impact of dynamical ion correlations on the conductivity and transport properties of polymeric electrolyte materials. It has been hypothesized that confining ion motion to narrow channels may reduce such ion correlations and enhance the resulting ionic conductivity. Motivated by such considerations, in this study we used a multiscale simulation framework to study the dynamical ion correlations in the microphase-separated lamella phase of block copolymeric ionic liquids and compare with the corresponding results for homopolymeric systems. We probed the influence of ion correlations through the partial ionicity, Δ, which quantifies the ratio of true conductivity to the ideal, Nernst-Einstein conductivity for the anion-related contributions. Consistent with our original hypothesis, our results demonstrate that the partial ionicity relating to the mobile anions is much larger in the lamella phases of block copolymers compared to that in homopolymers. Analysis of the distinct conductivity contributions demonstrates that such results arise as a result of an intricate compensation among the nonideal dynamical correlations relating to anions in lamella phases. Together, our results suggest that self-assembled phases of block copolymers may provide an avenue to tune the dynamical ion correlations in polymer electrolyte systems.

We use all-atom molecular dynamics simulations to study the effect of polymer polarity, as quantified by the dielectric constant, on the transport properties of lithium bis(trifluoromethylsulfonyl)imide (LiTFSI) doped polyethers. Our results indicate that increasing the host dielectric constant leads to a decrease in ionic cluster sizes and reduction in correlated motion of oppositely charged ions. This causes the ionic conductivity to more closely approach the Nernst-Einstein limit in which ionic conductivity is only limited by the diffusivities of Li+ and TFSI–. We compare our results to recent experimental observations which demonstrate similar qualitative trends in host polarity.

We performed long-time all-atom molecular dynamics simulations of cationic polymerized ionic liquids with eight mobile counterions, systematically varying size and shape to probe their influence on the decoupling of conductivity from polymer segmental dynamics. We demonstrated rigorous identification of the dilatometric glass-transition temperature (Tg) for polymerized ionic liquids using an all-atom force field. Polymer segmental relaxation rates are presumed to be consistent for different materials at the same glass-transition-normalized temperature (Tg/T), allowing us to extract a relative order of decoupling by examining conductivity at the same Tg/T. Size, or ionic volume, cannot fully explain decoupling trends, but within certain geometric and chemical-specific classes, small ions generally show a higher degree of decoupling. This size effect is not universal and appears to be overcome when structural results reveal substantial coordination delocalization. We also reveal a universal inverse correlat...

We probe the ion mobilities, transference numbers, and inverse Haven ratio of ionic liquids and polymerized ionic liquids as a function of their molecular weight using a combination of atomistic equilibrium and nonequilibrium molecular dynamics simulations. In contrast to expectations, we demonstrate that the inverse Haven ratio increases with increasing degree of polymerization (

We present the results of a multiscale simulation framework investigating the ion transport mechanisms in multicomponent polymerized ionic liquids. Three different classes of polymeric ionic liquid systems, namely, random copolymers, lamellae forming block copolymers, and homopolymers, are constructed at the coarse-grained scale, and their atomistic counterparts are derived by using a reverse mapping method. Using such a framework, we investigate the influence of morphology on ion transport properties of such polymerized ionic liquids. Our results for ion mobilities are in qualitative agreement with experimental observations. Further analysis of random copolymer and block copolymer systems reveal that the reduced ion mobilities in such systems arise from the influence of architecture and morphology on ion coordination and intramolecular hopping events.

We use coarse-grained molecular dynamics simulations to study the effect of salt concentration and host polymer molecular weight on ion transport in polymer electrolytes. We find that increasing salt concentration or molecular weight similarly slows polymer dynamics across a wide range of host polarities, and that the resulting relaxation times display a correlation to the product of the salt concentration and polymer molecular weight. However, we find that molar conductivity only decreases with polymer dynamics at high polarities but is uncorrelated with the latter at low polarities. We attribute such differences to the variation in ionic aggregation between high and low polarity electrolytes. At low polarity, ionic dissociation significantly increases with molecular weight and salt concentration, offsetting the slowdown in polymer dynamics and yielding the observed insensitivity of molar conductivity. However, at high polarity, ions are mostly dissociated, independent of either molecular weight or salt concentration, thereby strongly coupling molar conductivity to polymer dynamics.

It is believed that the optimal morphology of an organic solar cell may be characterized by cocontinuous, interpenetrating donor and acceptor domains with nanoscale dimensions and high interfacial areas. One well-known equilibrium morphology that fits these characteristics is the bicontinuous microemulsion achieved by the addition of block copolymer compatibilizers to flexible polymer–polymer blends. However, there does not exist design rules for using block copolymer compatibilizers to produce bicontinuous microemulsion morphologies from the conjugated polymer/fullerene mixtures typically used to form the active layer of organic solar cells. Motivated by these considerations, we use single chain in mean field simulations to study the equilibrium phase behavior of semiflexible polymer + flexible–semiflexible block copolymer + solvent mixtures. Based on our results, we identify design rules for producing large channels of morphologies with characteristics like that of the bicontinuous microemulsion.

The bicontinuous microemulsion (BμE) phase is an equilibrium morphology characterized by cocontinuous domains, high interfacial areas, and nanoscale domain dimensions. These characteristics make the BμE potentially suitable for use in organic photovoltaic applications. Here, we use a combination of simulations and experiments to investigate the equilibrium morphologies formed by a ternary blend of conjugated polymer, all-conjugated diblock copolymer, and fullerene derivative PCBM. Using coarse-grained simulations, we identify the blend compositions that are most likely to result in donor/acceptor morphologies resembling the BμE. Experimentally, we probe these compositions through transmission electron microscopy and grazing-incidence X-ray scattering measurements. We demonstrate that all-conjugated block copolymer additives can be used to produce thermally stable, cocontinuous donor/acceptor morphologies at higher additive contents and longer annealing times than previously reported. These results demonstrate that conjugated BCP compatibilizers can be used as a means to achieve equilibrium, cocontinuous morphologies in donor/acceptor blends.

Recent experiments have reported intriguing trends for the molecular weight (MW) dependence of the conductivity of block copolymer lamellae that contrast with the behavior of homopolymer matrices. By using coarse-grained simulations of the sorption and transport of penetrant cations, we probe the possible mechanisms underlying such behavior. Our results indicate that the MW dependence of conductivity of homopolymeric and block copolymeric matrices arise from different mechanisms. On the one hand, the solvation energies of cations, and, in turn, the charge carrier concentrations, themselves, exhibit a MW dependence in block copolymer matrices. Such trends are shown to arise from variations in the thickness of the conducting phase relative to that of the interfacial zones. Moreover, distinct mechanisms are shown to be responsible for the diffusivities of ions in homopolymer and block copolymer matrices. In the former, diffusivity effects associated with the free ends of the polymers play an important role. In contrast, in block copolymer lamellae, the interfacial zone between the blocks presents a zone of hindered diffusivity for ions and manifests as a molecular weight dependence of the ionic diffusivity. Together, the preceding mechanisms are shown to provide a plausible explanation for the experimentally observed trends for the conductivity of block copolymer matrices.

We report a partial elucidation of the relationship between polymer polarity and ionic conductivity in polymer electrolyte mixtures comprising a homologous series of nine poly(vinyl ether)s (PVEs) and lithium bis(trifluoromethylsulfonyl)imide. Recent simulation studies have suggested that low dielectric polymer hosts with glass transition temperatures far below ambient conditions are expected to have ionic conductivity limited by salt solubility and dissociation. In contrast, high dielectric hosts are expected to have the potential for high ion solubility but slow segmental dynamics due to strong polymer-polymer and polymer-ion interactions. We report results for PVEs in the low polarity regime with dielectric constants of about 1.3 to 9.0. Ionic conductivity measured for the PVE and salt mixtures ranged from about 10

Direct lithium extraction via membrane separations has been fundamentally limited by lack of monovalent ion selectivity exhibited by conventional polymeric membranes, particularly between sodium and lithium ions. Recently, a 12-Crown-4-functionalized polynorbornene membrane was shown to have the largest lithium/sodium permeability selectivity observed in a fully aqueous system to date. Using atomistic molecular dynamics simulations, we reveal that this selectivity is due to strong interactions between sodium ions and 12-Crown-4 moieties, which reduce sodium ion diffusivity while leaving lithium ion mobility relatively unaffected. Moreover, the ion diffusivities in the membrane, when scaled by their respective solution diffusivities and free ion fractions, can be collapsed to an almost universal relationship depending on solvent volume fraction.

We apply a machine learning (ML) technique to the multiobjective design of polymer blend electrolytes. In particular, we are interested in maximizing electrolyte performance measured by a combinati...

Recent experimental results have demonstrated that polymeric ionic liquids doped with Li salts exhibit enhanced ionic mobilities and lithium ion transference numbers with increasing salt concentrat...

Lower critical solution behavior in binary blends of hydrophobic polyethers with 1-hexyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide ([hmim][Tf2N]) exhibited a difference in lower critica...

Block copolymers self-assemble into a variety of morphologies that can serve as templates for preparing conductive or porous materials such as batteries and membranes. These morphologies can be eit...

We develop a multiscale simulation model for diffusion of solutes through porous triblock copolymer membranes. The approach combines two techniques: self-consistent field theory (SCFT) to predict the structure of the self-assembled, solvated membrane and on-lattice kinetic Monte Carlo (kMC) simulations to model diffusion of solutes. Solvation is simulated in SCFT by constraining the glassy membrane matrix while relaxing the brush-like membrane pore coating against the solvent. The kMC simulations capture the resulting solute spatial distribution and concentration-dependent local diffusivity in the polymer-coated pores; we parameterize the latter using particle-based simulations. We apply our approach to simulate solute diffusion through nonequilibrium morphologies of a model triblock copolymer, and we correlate diffusivity with structural descriptors of the morphologies. We also compare the model’s predictions to alternative approaches based on simple lattice random walks and find our multiscale model to be more robust and systematic to parameterize. Our multiscale modeling approach is general and can be readily extended in the future to other chemistries, morphologies, and models for the local solute diffusivity and interactions with the membrane.

Lithium is widely used in contemporary energy applications, but its isolation from natural reserves is plagued by time-consuming and costly processes. While polymer membranes could, in principle, circumvent these challenges by efficiently extracting lithium from aqueous solutions, they usually exhibit poor ion-specific selectivity. Toward this end, we have incorporated host–guest interactions into a tunable polynorbornene network by copolymerizing 1) 12-crown-4 ligands to impart ion selectivity, 2) poly(ethylene oxide) side chains to control water content, and 3) a crosslinker to form robust solids at room temperature. Single salt transport measurements indicate these materials exhibit unprecedented reverse permeability selectivity (∼2.3) for LiCl over NaCl—the highest documented to date for a dense, water-swollen polymer. As demonstrated by molecular dynamics simulations, this behavior originates from the ability of 12-crown-4 to bind Na(+) ions more strongly than Li(+) in an aqueous environment, which reduces Na(+) mobility (relative to Li(+)) and offsets the increase in Na(+) solubility due to binding with crown ethers. Under mixed salt conditions, 12-crown-4 functionalized membranes showed identical solubility selectivity relative to single salt conditions; however, the permeability and diffusivity selectivity of LiCl over NaCl decreased, presumably due to flux coupling. These results reveal insights for designing advanced membranes with solute-specific selectivity by utilizing host–guest interactions.

We study ternary polymer-polymer-salt blend electrolytes using coarse-grained molecular dynamics. We specifically examine the influence of the polymer hosts’ incompatibility and polarity contrast o...

Protic ionic liquids (PILs) have been recently the subject of experiments for lithium-ion batteries (LIBs) applications, showing promising properties, in some cases better performance than aprotic ionic liquids (AILs). With the aim of a direct comparison between the performance of PIL and AIL electrolytes in LIBs, we conducted molecular dynamics simulations of corresponding lithium-salt-doped electrolytes. Our PIL and AIL electrolytes are triethylammonium bis(trifluoromethanesulfonyl)imide ([N222][TFSI]) and triethylmethylammonium bis(trifluoromethanesulfonyl)imide ([N1222][TFSI]), respectively, each doped with LiTFSI. Three fundamental structural changes were observed upon the addition of Li salt into IL solutions: (1) formation of rigid Li-TFSI complexes, (2) cation-anion coordination became larger, and (3) formation of Li aggregates. Moreover, we observed that the density and viscosity of the electrolytes increased with increasing lithium salt mole fraction ( xLi), and correspondingly, the self-diffusivity of ions, ionic conductivity, and ionicity became lower. The Li conductivity exhibits a maximum at xLi = 0.2 due to the competition between increasing Li salt concentration and viscosity. More importantly, the PIL electrolytes display higher Li conductivity and better transport properties over their AIL counterpart.

We develop a convolutional neural network (CNN) model to predict the diffusivity of cations in nanoparticle-based electrolytes and use it to identify the characteristics of morphologies that exhibit optimal transport properties. The ground truth data are obtained from kinetic Monte Carlo (kMC) simulations of cation transport parametrized using a multiscale modeling strategy. We implement deep learning approaches to quantitatively link the diffusivity of cations to the spatial arrangement of the nanoparticles. We then integrate the trained CNN model with a topology optimization algorithm for accelerated discovery of nanoparticle morphologies that exhibit optimal cation diffusivities at a specified nanoparticle loading, and we investigate the ability of the CNN model to quantitatively account for the influence of interparticle spatial correlations on cation diffusivity. Finally, by using data-driven approaches, we explore how simple descriptors of nanoparticle morphology correlate with cation diffusivity, thus providing a physical rationale for the observed optimal microstructures. The results of this study highlight the capability of CNNs to serve as surrogate models for structure-property relationships in composites with monodisperse spherical particles, which can in turn be used with inverse methods to discover morphologies that produce optimal target properties.

We develop a convolutional neural network (CNN) model to predict the diffusivity of cations in nanoparticle-based electrolytes, and use it to identify the characteristics of morphologies which exhibit optimal transport properties. The ground truth data is obtained from kinetic Monte Carlo (kMC) simulations of cation transport parameterized using a multiscale modeling strategy. We implement deep learning approaches to quantitatively link the diffusivity of cations to the spatial arrangement of the nanoparticles. We then integrate the trained CNN model with a topology optimization algorithm for accelerated discovery of nanoparticle morphologies that exhibit optimal cation diffusivities at a specified nanoparticle loading, and we investigate the ability of the CNN model to quantitatively account for the influence of interparticle spatial correlations on cation diffusivity. Finally, using data-driven approaches, we explore how simple descriptors of nanoparticle morphology correlate with cation diffusivity, thus providing a physical rationale for the observed optimal microstructures. The results of this study highlight the capability of CNNs to serve as surrogate models for structure--property relationships in composites with monodisperse spherical particles, which can in turn be used with inverse methods to discover morphologies that produce optimal target properties.

We use direct simulations of particle-polyelectrolyte mixtures using the single chain in mean field framework to extract the phase diagram for such systems. At high charges of the particles and low concentration of polymers, we observe the formation of a coacervate phase involving the particles and polyelectrolytes. At low particle charges and/or high concentration of polymers, the mixture undergoes a segregative phase separation into particle-rich and polymer-rich phases, respectively. We also present results for the influence of particle charge heterogeneity on the phase diagram.

Recent studies have demonstrated the potential of nanoparticle-based single-ion conductors as battery electrolytes. In this work, we introduce a coarse-grained multiscale simulation approach to identify the mechanisms underlying the ion mobilities in such systems and to clarify the influence of key design parameters on conductivity. Our results suggest that for the experimentally studied electrolyte systems, the dominant pathway for cation transport is along the surface of nanoparticles, in the vicinity of nanoparticle-tethered anions. At low nanoparticle concentrations, connectivity of cationic surface transport pathways and conductivity increase with nanoparticle loading. However, cation mobilities are reduced when nanoparticles are in close vicinity, causing conductivity to decrease for suffciently high particle loadings. We discuss the impacts of cation and anion choice as well as solvent polarity within this picture and suggest means to enhance ionic conductivities in single-ion conducting electrolytes based on nanoparticle salts.

Recent studies have demonstrated the potential of nanoparticle-based single-ion conductors as battery electrolytes. In this work, we introduce a coarse-grained multiscale simulation approach to identify the mechanisms underlying the ion mobilities in such systems and to clarify the influence of key design parameters on conductivity. Our results suggest that for the experimentally studied electrolyte systems, the dominant pathway for cation transport is along the surface of nanoparticles, in the vicinity of nanoparticle-tethered anions. At low nanoparticle concentrations, connectivity of cationic surface transport pathways and conductivity increase with nanoparticle loading. However, cation mobilities are reduced when nanoparticles are in close vicinity, causing conductivity to decrease for suffciently high particle loadings. We discuss the impacts of cation and anion choice as well as solvent polarity within this picture and suggest means to enhance ionic conductivities in single-ion conducting electrolytes based on nanoparticle salts.

We employed a combination of single chain in mean field methodology and constant pH Monte Carlo framework to compare the influence of charge regulation and charge heterogeneity on the adsorption and bridging characteristics of polyelectrolytes in solution on proteins. By adopting a coarse-grained representation of the proteins as spherical particles and embedding a simple framework for probing charge heterogeneities, we probe the influence of charge patches, net charge of the particle, the ratio of positive to negative charges on the proteins on the net adsorption, and bridging probabilities of polyelectrolytes. Our results demonstrate that charge regulation increases the probability of bridging between two particles when compared to proteins with the same fixed charge. The influence of charge regulation first increases and then decreases with an increase in the number of charge patches of proteins. An increase in the ratio of positive to negative charges and the net charge of the protein are also seen to increase the propensity for polyelectrolyte adsorption and bridging.

Polymeric ionic liquids (PILs) are an emerging class of materials that combine the attractive properties of ionic liquids with the sequence complexity and mechanical characteristics of macromolecules. While significant advances have occurred in the context of synthesis and characterization of such materials, comparatively less understanding exists on the mechanisms underlying ion transport in such materials. In this perspective article, the status of understanding in related systems of salt-doped polymer electrolytes, (non-ionic-liquid-based) single-ion polymer conductors and room temperature ionic liquids is briefly reviewed. Subsequently, some recent developments in the context of PILs are discussed to identify some open questions confronting the issue of ion transport in such materials.

We used atomistic molecular dynamics simulations to study ion mobilities and the molecular mechanisms of transport in blends of poly(1-butyl-3-vinylimidazolium hexafluorophosphate) electrolytes with 1-butyl-3-methylimidazolium hexafluorophosphate ionic liquids. We characterized the structural and dynamical properties of the blend electrolyte systems through radial distribution functions, diffusivities, conductivities, and different time scales of relaxation and probed the correlations underlying such characteristics. Our results indicate that many features of ion transport in such blend systems mirror those observed in our earlier study of pure polymerized ionic liquids [Mogurampelly et al. J. Am. Chem. Soc. 2017, 139, 9511]. Explicitly, we observe that the anions associated with the polymerized cation move along the polymer backbone via the formation and breakup of ion pairs involving polymerized cationic monomers of different polymer chains. Interestingly, for all blend systems excepting pure polymeric ...

In this work, we use computer simulations to demonstrate that there may be limits to which polymer polarity alone can be used to influence the ionic conductivity of salt-doped polymer electrolytes. Specifically, we use coarse-grained molecular dynamics simulations to probe the effect of the polarity of the polymer electrolyte upon ion mobilities and conductivities of dissolved salts. At low polymer polarities, increasing the polymer dielectric constant reduces ionic aggregation and the resultant correlated ionic motion, and increases the ionic conductivity. At higher polymer polarities, polymer-polymer and polymer-ion interactions slows polymer segmental dynamics, leading to a reduction in the conductivity of the electrolyte. As a consequence, ionic conductivity achieves an optimum at an intermediate polymer polarity.

Recent experimental results have shown that transport properties of salt ions and water in charged polymer membranes exhibit an intriguing dependence on salt concentration that is opposite to that seen in electrolyte solutions. Motivated by such observations, in this study we have used atomistic molecular dynamics simulations to investigate the molecular level mechanisms underlying the transport properties of salt solutions in charged polymer membranes. Our results are in qualitative agreement with experimental results and suggest that the mobility of the salt ions is influenced by electrostatic interactions with the charged groups in the polymer membrane. The mobility of water molecules is shown to arise as a consequence of the influence of salt on the hydration characteristics in solution and polymer electrolyte systems.

We modulated the grafting density (σ) and chain length of polystyrene (PS) brushes on substrates to probe their effect on the glass transition temperature (Tg) in overlaying PS thin films. The Tg of PS films was analyzed as a function of brush thickness (or σRg2), where Rg is the radius of gyration of PS brushes. Our results indicate that PS films below 90 nm exhibit a nonmonotonic dependence of Tg on grafting density with the maximum Tg occurring near the regime of wetting–dewetting transition of the overlaying melt. The maximum Tg was found to be higher than the value of bulk PS system. Computer simulation results suggest that such trends arise as a consequence of the environment of enhanced friction presented by the brush layer and the overlap between the polymer film and the grafted layer.

Coarse-grained molecular dynamics simulations were carried out to identify the conditions under which the nanorods (NRs) side-grafted with polymer chains can assemble in end-to-end configurations in a homopolymer matrix, a structure of significant importance for optimal property characteristics. Our results demonstrate that by adjusting the grafting density and the grafted chain length, three different NR morphologies can be obtained, viz., side-by-side aggregation, end-to-end alignment and homogeneous dispersion. To understand the underlying mechanism, the chain characteristics around the NRs were systematically investigated. We find that the transition of NR morphologies from side-by-side aggregation to others is correlated to the mushroom-to-brush transition of the grafted chain configurations. At high grafting densities corresponding to the brush regime, the entropic steric repulsions between the polymer brushes prevent the NRs from approaching in side-by-side configurations. Instead, end-to-end assem...

An understanding of the structure–property relationship in polymer/nanorod (NR) nanocomposites is of fundamental importance in designing and fabricating polymer nanocomposites (PNCs) with desired properties. Here, we study the structural, mechanical, and viscoelastic properties of polymer-grafted NR filled PNCs, using coarse-grained molecular dynamics simulation. The mechanical reinforcement efficiency is found to be determined by the NR/polymer interfacial properties, which are in turn modulated by the grafting density, the grafted chain length, and the graft–matrix interaction strength. By systematically analyzing the evolution of the polymer-grafted NRs during mechanical deformation, we find that the NRs aligning side by side with each other do not contribute much to the mechanical reinforcement. Simulation results also indicate that the strain-dependent viscoelastic behavior (the Payne effect) originates from the failure of the local filler network and, especially, NR clusters constructed via site-to-...

We employ a combination of the single chain in mean field simulation approach with the solution of Poisson's equation to study the influence of charge heterogeneities on the structure of protein-polyelectrolyte complexes. By adopting a coarse-grained model of representing proteins as charged nanoparticles, we studied the influence of the pattern of charge heterogeneities, net charge, ratio of positive to negative charges on the patches, and the volume fraction of the particles on the structural and aggregation characteristics of proteins in polyelectrolyte solutions. Our results demonstrate that the pattern of charge heterogeneities can exert a significant influence on the resulting characteristics of the aggregates, in some cases leading to a transformation from polymer-bridged complexes into direct particle aggregates driven by the attraction between oppositely charged patches.

We used atomistic molecular dynamics simulations to study the properties of polymerized 1-alkene-3-butylimidazolium-hexafluorophosphate, a polymerized ionic liquid electrolyte, and characterized the influence of the linear alkene length on the mobility of the hexafluorophosphate ions. Consistent with experimental observations, our simulations indicate that as the alkene length increases, the diffusivity of hexafluorophosphate anion monotonically increases. We demonstrate that such a trend arises from the influence of linker segments on the intermolecular ion hopping rates, which is in turn modulated by intermolecular cationic separation distances. © 2017 Wiley Periodicals, Inc. J. Polym. Sci., Part B: Polym. Phys. 2017, 00, 000–000