While nearly all theoretical and computational studies of entangled polymer melts have focused on uniform samples, polymer synthesis routes always result in some dispersity, albeit narrow, of distribution of molecular weights (${Đ}_M=M_w/M_n\sim 1.02–1.04$). Here, the effects of dispersity on chain mobility are studied for entangled, disperse melts using a coarse-grained model for polyethylene. Polymer melts with chain lengths set to follow a Schulz-Zimm distribution for the same average $M_w=36\mathrm{kg}/\mathrm{mol}$ with ${Đ}_M=1.0$ to 1.16, were studied for times of $600–800\mu \mathrm{s}$ using molecular dynamics simulations. This time frame is longer than the time required to reach the diffusive regime. We find that dispersity in this range does not affect the entanglement time or tube diameter. However, while there is negligible difference in the average mobility of chains for the uniform distribution ${Đ}_M=1.0$ and ${Đ}_M=1.02$, the shortest chains move significantly faster than the longest ones offering a constraint release pathway for the melts for larger ${Đ}_M$.

We investigate the kinetics of a polymer collapse due to the formation of irreversible cross-links between its monomers. Using the contact probability $P(s)$ as a scale-dependent order parameter depending on the chemical distance $s$, our simulations show the emergence of a cooperative pearling instability. Namely, the polymer undergoes a sharp conformational transition to a set of absorbing states characterized by a length scale $\xi$ corresponding to the mean pearl size. This length and the transition time depend on the polymer equilibrium dynamics and the cross-linking rate. We confirm experimentally this transition using a DNA conformation capture experiment in yeast.

A deep understanding of the dynamical properties of natural time-varying images is essential for interpreting how they are efficiently processed in the brain. Here we examine natural time-varying images from the perspective of their spatiotemporal patterns and find evidence of dynamical thermodynamic criticality. We further demonstrate that these spatiotemporal patterns ubiquitously organize as localized, propagating patterns. By studying these propagating patterns and their spreading dynamics over time, we demonstrate that the critical dynamics of natural time-varying images belong to the universality class of directed percolation. These critical dynamics have important implications for understanding neural processing of time-varying stimuli.

We study a system composed of like-charged cylinders and dumbbell-like counterions, with the focus laid on the role of the internal structure of counterions, using Monte Carlo simulations. The dumbbell ions are found to exhibit novel counterion condensation behavior governed by their length. Effective electrostatic interactions mediated between charged parallel cylinders also turn out significantly different from the case of pointlike ions, as a result of the complex interplay between the spatially separated charge distribution in the dumbbell counterions, their orientation, and the curvature of the charged cylinder. We show that at a weak-to-moderate electrostatic coupling strength, where effective like-charge interactions are usually found to be repulsive, the intercylinder interaction can become attractive and display a distinctive sensitivity to the cylinder curvature and dumbbell size, proving the significant effect of ion structure.

Crackling noise, which occurs in a wide range of situations, is characterized by discrete events of various sizes, often correlated in the form of avalanches. We report experimental evidence that the mechanical response of a knitted fabric displays such broadly distributed events both in the force signal and in the deformation field, with statistics analogous to that of earthquakes or soft amorphous materials. A knit consists of a regular network of frictional contacts, linked by the elasticity of the yarn. When deformed, the fabric displays spatially extended avalanchelike yielding events resulting from collective interyarn contact slips. We measure the size distribution of these avalanches, at the stitch level from the analysis of nonelastic displacement fields and externally from force fluctuations. The two measurements yield consistent power law distributions reminiscent of those found in other avalanching systems. Our study shows that a knitted fabric is not only a thread-based metamaterial with highly sought after mechanical properties, but also an original, model system, with topologically protected structural order, where an intermittent, scale-invariant response emerges from minimal ingredients, and thus a significant landmark in the study of out-of-equilibrium universality.

The collective motion of membrane lipids over hundreds of nanometers and nanoseconds plays an essential role in the formation of submicron complexes of lipids and proteins in the cell membrane. These dynamics are difficult to access experimentally and are currently poorly understood. One of the conclusions of the celebrated Saffman-Debrück (SD) theory is that lipid disturbances smaller than the Saffman length (microns) are not affected by the hydrodynamics of the embedding solvent. Using molecular dynamics and coarse-grained models with implicit hydrodynamics we show that this is not true. Hydrodynamic interactions between the membrane and the solvent strongly enhance the short-time collective diffusion of lipids at all scales. The momentum transferred between the membrane and the solvent in the normal direction (not considered by the SD theory) propagates tangentially over the membrane inducing long-ranged repulsive forces amongst lipids. As a consequence, the lipid collective diffusion coefficient increases proportionally to the disturbance wavelength. We find quantitative agreement with the predicted anomalous diffusion in quasi-two-dimensional dynamics, observed in colloids confined to a plane but embedded in a three-dimensional solvent.

Nonlinear extensional flows are common in polymer processing, but they remain challenging theoretically because dramatic stretching of chains deforms the entanglement network far from equilibrium. Here, we present coarse-grained simulations of extensional flows in entangled polymer melts for Rouse-Weissenberg numbers ${\mathrm{Wi}}_R=0.06–52$ and Hencky strains $\epsilon \ge 6$. Simulations reproduce experimental trends in extensional viscosity with time, rate, and molecular weight. Studies of molecular structure reveal an elongation and thinning of the confining tube with increasing ${\mathrm{Wi}}_R$. The rising stress is quantitatively consistent with the decreasing entropy of chains at the equilibrium entanglement length. Molecular weight dependent trends in viscosity are related to a crossover from the Newtonian limit to a high rate limit that scales differently with chain length.

The Leidenfrost effect occurs when a liquid or stiff sublimable solid near a hot surface creates enough vapor beneath it to lift itself up and float. In contrast, vaporizable soft solids, e.g., hydrogels, have been shown to exhibit persistent bouncing—the elastic Leidenfrost effect. By carefully lowering hydrogel spheres towards a hot surface, we discover that they are also capable of floating. The bounce-to-float transition is controlled by the approach velocity and temperature, analogously to the “dynamic Leidenfrost effect.” For the floating regime, we measure power-law scalings for the gap geometry, which we explain with a model that couples the vaporization rate to the spherical shape. Our results reveal that hydrogels are a promising pathway for controlling floating Leidenfrost objects through shape.

We numerically investigate the behavior of a phase-separating mixture of a blue phase I liquid crystal with an isotropic fluid. The resulting morphology is primarily controlled by an inverse capillary number, $\chi$, setting the balance between interfacial and elastic forces. When $\chi$ and the concentration of the isotropic component are both low, the blue phase disclination lattice templates a cubic array of fluid cylinders. For larger $\chi$, the isotropic phase arranges primarily into liquid emulsion droplets which coarsen very slowly, rewiring the blue phase disclination lines into an amorphous elastic network. Our blue phase-simple fluid composites can be externally manipulated: an electric field can trigger a morphological transition between cubic fluid cylinder phases with different topologies.

We propose a novel Bayesian methodology which uses random walks for rapid inference of statistical properties of undirected networks with weighted or unweighted edges. Our formalism yields high-accuracy estimates of the probability distribution of any network node-based property, and of the network size, after only a small fraction of network nodes has been explored. The Bayesian nature of our approach provides rigorous estimates of all parameter uncertainties. We demonstrate our framework on several standard examples, including random, scale-free, and small-world networks, and apply it to study epidemic spreading on a scale-free network. We also infer properties of the large-scale network formed by hyperlinks between Wikipedia pages.

Within a confining tube section, the multithreads of a strongly confined, backfolding polymer exert the excluded-volume repulsions on each other and produce physical properties that are very different from those of a confined ideal chain. The conformational properties of a such confined wormlike chain are of fundamental interest and are also practically useful in understanding the DNA confinement problems. Here, the excluded-volume effects are added to the standard wormlike-chain model by a self-consistent field theory. The numerical solutions are examined in light of their scaling properties.

Measuring and quantifying nonequilibrium dynamics in active biological systems is a major challenge because of their intrinsic stochastic nature and the limited number of variables accessible in any real experiment. We investigate what nonequilibrium information can be extracted from noninvasive measurements using a stochastic model of soft elastic networks with a heterogeneous distribution of activities, representing enzymatic force generation. In particular, we use this model to study how the nonequilibrium activity, detected by tracking two probes in the network, scales as a function of the distance between the probes. We quantify the nonequilibrium dynamics through the cycling frequencies, a simple measure of circulating currents in the phase space of the probes. We find that these cycling frequencies exhibit power-law scaling behavior with the distance between probes. In addition, we show that this scaling behavior governs the entropy production rate that can be recovered from the two traced probes. Our results provide insight into how internal enzymatic driving generates nonequilibrium dynamics on different scales in soft biological assemblies.

Using coarse-grained molecular dynamics simulations and an umbrella sampling method that uses local surfactant density as a reaction coordinate, we directly calculate, for the first time, both the scission and branching free energies of a model charged micelle [cationic cetyltrimethylammonium chloride (CTAC)] in the presence of inorganic and organic salts (hydrotropes). We find that while inorganic salt only weakly affects the micelle scission energy, organic hydrotropes produce a strong, nonmonotonic dependence of both scission energy and branching on salt concentration. The nonmonotonicity in scission energy is traced to a competition between electrostatic screening of the repulsions among the surfactant head groups and thinning of the micellar core, which result from attachment of the hydrotropes to the micelle surface. We are able to correlate the nonmonotonicity in the scission energy of CTAC micelles with the peak observed experimentally in viscosity versus hydrotrope concentration and the location of this peak in CTAC solutions.

Systems of independent active particles embedded into a fluctuating environment are relevant to many areas of soft-matter science. We use a minimal model of noninteracting spin-carrying Brownian particles in a Gaussian field and show that activity-driven spin dynamics leads to patterned order. We find that the competition between mediated interactions and active noise alone can yield such diverse behaviors as phase transitions and microphase separation, from lamellar up to hexagonal ordering of clusters of opposite magnetization. These rest on complex multibody interactions. We find regimes of stationary patterns, but also dynamical regimes of relentless birth and growth of lumps of magnetization opposite of the surrounding one. Our approach combines Monte Carlo simulations with analytical methods based on dynamical density functional approaches.

We use x-ray tomography to investigate the translational and rotational dynamical heterogeneities of a three dimensional hard ellipsoid granular packing driven by oscillatory shear. We find that particles which translate quickly form clusters with a size distribution given by a power law with an exponent that is independent of the strain amplitude. Identical behavior is found for particles that are translating slowly, rotating quickly, or rotating slowly. The geometrical properties of these four different types of clusters are the same as those of random clusters. Different cluster types are considerably correlated or anticorrelated, indicating a significant coupling between translational and rotational degrees of freedom. Surprisingly, these clusters are formed already at time scales that are much shorter than the $\alpha$-relaxation time, in stark contrast to the behavior found in glass-forming systems.