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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.

 

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. 

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. 

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). 

The complex physics of glass-forming systems is controlled by the structure of the low-energy portions of their potential energy landscapes. Here we report that a modified metadynamics algorithm efficiently explores and samples low-energy regions of such high-dimensional landscapes. In the energy landscape for a model foam, our algorithm finds and descends meandering canyons in the landscape, which contain dense clusters of energy minima along their floors. Similar canyon structures in the energy landscapes of two model glass formers—hard sphere fluids and the Kob–Andersen glass—allow us to reach high densities and low energies, respectively. In the hard sphere system, fluid configurations are found to form continuous regions that cover the canyon floors up to densities well above the jamming transition. For the Kob–Andersen glass former, our technique samples low-energy states with modest computational effort, with the lowest energies found approaching the predicted Kauzmann limit.