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Polymer infiltration is studied in a bicontinuous, nanoporous gold (NPG) scaffold. For poly(2-vinylpyridine) (P2VP) with molecular weights (M_w) from 51k to 940k Da, infiltration is investigated in a NPG with fixed pore radius (R_p= 34 nm) under moderate confinement (Γ = R_g/R_p ) 0.18 to 0.78. The time for 80% infiltration (τ_(80%)) scales as M_w^1.43, similar to PS, but weaker than bulk behavior. Infiltration of P2VP is slower than PS due to stronger P2VP-wall interactions resulting in a physisorbed P2VP layer. This interpretation is supported by the similar scaling of τ_(80%) for P2VP and PS, as well as Molecular Dynamics (MD) simulations. Simulations show that infiltration time scales as M_w^1.43and that infiltration slows as the polymer-wall attraction increases. As M_w increases, the effective viscosity transitions from greater than to less than the bulk viscosity due to pore narrowing and a reduction entanglement density. These studies provide new insight for polymer behavior under confinement and a new route for preparing nanocomposites at high filler loadings. 

Understanding the dynamics of polymers in confined environments is pivotal for diverse applications ranging from polymer upcycling to bioseparations. In this study, we develop an entropic barrier model using self-consistent field theory that considers the effect of attractive surface interactions, solvation, and confinement on polymer kinetics. In this model, we consider the translocation of a polymer from one cavity into a second cavity through a single-segment-width nanopore. We find that, for a polymer in a good solvent (i.e., excluded volume, u0 > 0), there is a nonmonotonic dependence of mean translocation time (τ) on surface interaction strength, ɛ. At low ɛ, excluded volume interactions lead to an energetic penalty and longer translocation times. As ɛ increases, the surface interactions counteract the energetic penalty imposed by excluded volume and the polymer translocates faster through the nanopore. However, as ɛ continues to increase, an adsorption transition occurs, which leads to significantly slower kinetics due to the penalty of desorption from the first cavity. The ɛ at which this adsorption transition occurs is a function of the excluded volume, with higher u0 leading to an adsorption transition at higher ɛ. Finally, we consider the effect of translocation across different size cavities. We find that the kinetics for translocation into a smaller cavity speeds up while translocation to a larger cavity slows down with increasing ɛ due to higher surface contact under stronger confinement. 

Polymer infiltrated nanoporous gold is prepared by infiltrating polymer melts into a bicontinuous, nanoporous gold (NPG) scaffold. Polystyrene (PS) films with molecular weights (Mw) from 424 to 1133 kDa are infiltrated into a NPG scaffold (∼120 nm), with a pore radius (Rp) and pore volume fraction of 37.5 nm and 50%, respectively. The confinement ratios (Γ=RgRp) range from 0.47 to 0.77, suggesting that the polymers inside the pores are moderately confined. The time for PS to achieve 80% infiltration (τ80%) is determined using in situ spectroscopic ellipsometry at 150 °C. The kinetics of infiltration scales weaker with Mw, τ80%∝Mw1.30±0.20, than expected from bulk viscosity Mw3.4. Furthermore, the effective viscosity of the PS melt inside NPG, inferred from the Lucas–Washburn model, is reduced by more than one order of magnitude compared to the bulk. Molecular dynamics simulation results are in good agreement with experiments predicting scaling as Mw1.4. The reduced dependence of Mw and the enhanced kinetics of infiltration are attributed to a reduction in chain entanglement density during infiltration and a reduction in polymer–wall friction with increasing polymer molecular weight. Compared to the traditional approach involving adding discrete particles into the polymer matrix, these studies show that nanocomposites with higher loading can be readily prepared, and that kinetics of infiltration are faster due to polymer confinement inside pores. These films have potential as actuators when filled with stimuli-responsive polymers as well as polymer electrolyte and fuel cell membranes. 

Coacervation of charged polymer chains has been a topic of major interest both in polymer and biological sciences, as it is a subset of a phenomenon called liquid-liquid (LLPS) phase separation. In this process a polymer-rich phase separates from the polymer-lean supernatant while still maintaining its liquid-like properties. LLPS has been shown to play a crucial role in cellular homeostasis by driving the formation of membraneless organelles. It also has the potential to be harnessed to aid in novel therapeutical applications. Recent studies have demonstrated that there is no one simple mechanism which drives LLPS, which is instead a result of the combined effect of electrostatic, dipolar, hydrophobic, and other weak interactions. Using coarse-grained polymer simulations we investigate the relatively unexplored effects of monomer polarizability and spatially varying dielectric constant on LLPS propensity, and these factors affect the properties of the resulting condensates. In order to produce spatial variations in the dielectric constant, all our simulations include explicit solvent and counterions. We demonstrate that polarizability has only a minor effect on the bulk behaviour of the condensates but plays a major role when ion partitioning and microstructure are considered. We observe that the major contribution comes from the nature of the neutral blocks as endowing them with an induced dipole changes their character from hydrophobic to hydrophilic. We hypothesize that the results of this work can aid in guiding future studies concerned with LLPS by providing a general framework and by highlighting important factors which influence LLPS. 

There are empirical strategies for tuning the degree of strain localization in disordered solids, but they are system-specific and no theoretical framework explains their effectiveness or limitations. Here, we study three model disordered solids: a simulated atomic glass, an experimental granular packing, and a simulated polymer glass. We tune each system using a different strategy to exhibit two different degrees of strain localization. In tandem, we construct structuro-elastoplastic (StEP) models, which reduce descriptions of the systems to a few microscopic features that control strain localization, using a machine learning-based descriptor, softness, to represent the stability of the disordered local structure. The models are based on calculated correlations of softness and rearrangements. Without additional parameters, the models exhibit semiquantitative agreement with observed stress–strain curves and softness statistics for all systems studied. Moreover, the StEP models reveal that initial structure, the near-field effect of rearrangements on local structure, and rearrangement size, respectively, are responsible for the changes in ductility observed in the three systems. Thus, StEP models provide microscopic understanding of how strain localization depends on the interplay of structure, plasticity, and elasticity. 

We explore the relationship between a machine-learned structural quantity (softness) and excess entropy in simulations of supercooled liquids. Excess entropy is known to scale well the dynamical properties of liquids, but this quasi-universal scaling is known to breakdown in supercooled and glassy regimes. Using numerical simulations, we test whether a local form of the excess entropy can lead to predictions similar to those made by softness, such as the strong correlation with particles’ tendency to rearrange. In addition, we explore leveraging softness to compute excess entropy in the traditional fashion over softness groupings. Our results show that the excess entropy computed over softness-binned groupings is correlated with activation barriers to rearrangement. 

In this paper, we announce the public release of a massively parallel, graphics processing unit (GPU)-accelerated software, which is the first to combine both coarse-grained particle simulations and field-theoretic simulations in one simulation package. MATILDA.FT (Mesoscale, Accelerated, Theoretically Informed, Langevin, Dissipative particle dynamics, and Field Theory) was designed from the ground-up to run on CUDA-enabled GPUs with Thrust library acceleration, enabling it to harness the possibility of massive parallelism to efficiently simulate systems on a mesoscopic scale. It has been used to model a variety of systems, from polymer solutions and nanoparticle-polymer interfaces to coarse-grained peptide models and liquid crystals. MATILDA.FT is written in CUDA/C++ and is object oriented, making its source-code easy to understand and extend. Here, we present an overview of the currently available features, and the logic of parallel algorithms and methods. We provide the necessary theoretical background and present examples of systems simulated using MATILDA.FT as the simulation engine. The source code, along with the documentation, additional tools, and examples, can be found on the GitHub MATILDA.FT repository. 

Porous materials possess numerous useful functions because of their high surface area and ability to modulate the transport of heat, mass, fluids, and electromagnetic waves. Unlike highly ordered structures, disordered porous structures offer the advantages of ease of fabrication and high fault tolerance. Bicontinuous interfacially jammed emulsion gels (bijels) are kinetically trapped disordered biphasic materials that can be converted to porous materials with tunable features. Current methods of bijel fabrication result in domains that are micrometers or larger, and non-uniform in size, limiting their surface area, mechanical strength, and interaction with electromagnetic waves. In this work, scalable synthesis of bijels with uniform and sub-micrometer domains is achieved via a two-step solvent removal process. The resulting bijels are characterized quantitatively to verify the uniformity and sub-micrometer scale of the domains. Moreover, these bijels have structures that resemble the microstructure of the scale of the white beetle Cyphochilus. We find that such bijel films with relatively small thicknesses (<150 μm) exhibit strong solar reflectance as well as high brightness and whiteness in the visible range. Considering their scalability in manufacturing, we believe that VIPS-STRIPS bijels have great potential in large-scale applications including passive cooling, solar cells, and light emitting diodes (LEDs). 

Many soft and biological materials display so-called ‘soft glassy’ dynamics; their constituents undergo anomalous random motions and complex cooperative rearrangements. A recent simulation model of one soft glassy material, a coarsening foam, suggested that the random motions of its bubbles are due to the system configuration moving over a fractal energy landscape in high-dimensional space. Here we show that the salient geometrical features of such high-dimensional fractal landscapes can be explored and reliably quantified, using empirical trajectory data from many degrees of freedom, in a model-free manner. For a mayonnaise-like dense emulsion, analysis of the observed trajectories of oil droplets quantitatively reproduces the high-dimensional fractal geometry of the configuration path and its associated local energy minima generated using a computational model. That geometry in turn drives the droplets’ complex random motion observed in real space. Our results indicate that experimental studies can elucidate whether the similar dynamics in different soft and biological materials may also be due to fractal landscape dynamics.