This Letter introduces unexpected diffusion properties in dense granular flows and shows that they result from the development of partially jammed clusters of grains, or granular vortices. Transverse diffusion coefficients $D$ and average vortex sizes $\ell$ are systematically measured in simulated plane shear flows at differing inertial numbers $I$ revealing (i) a strong deviation from the expected scaling $D\propto d^2\dot{\gamma }$ involving the grain size $d$ and shear rate $\dot{\gamma }$ and (ii) an increase in average vortex size $\ell$ at low $I$, following $\ell \propto d{I}^{-\frac{1}{2}}$ but limited by the system size. A general scaling $D\propto \ell d\dot{\gamma }$ is introduced that captures all the measurements and highlights the key role of vortex size. This leads to establishing a scaling for the diffusivity in dense granular flow as $D\propto d^2\sqrt{\dot{\gamma }/t_i}$ involving the geometric average of shear time $1/\dot{\gamma }$ and inertial time $t_i$ as the relevant time scale. Analysis of grain trajectories is further evidence that this diffusion process arises from a vortex-driven random walk.

Colloidal particles with short-ranged attractions, e.g., micron-scale spheres functionalized with single-stranded DNA oligomers, are susceptible to becoming trapped in disordered configurations even when a crystalline arrangement is the ground state. Moreover, for reasons that are not well understood, seemingly minor variations in the particle formulation can lead to dramatic changes in the crystallization outcome. We demonstrate, using a combination of equilibrium and nonequilibrium computer simulations, that interaction heterogeneity—variations in the energetic interactions among different particle pairs in the population—may favorably impact crystal nucleation. Specifically, interaction heterogeneity is found to lower the free energy barrier to nucleation via the formation of clusters comprised preferentially of strong-binding particle pairs. Moreover, gelation is inhibited by “spreading out over time” the nucleation process, resulting in a reduced density of stable nuclei, allowing each to grow unhindered and larger. Our results suggest a simple and robust approach for enhancing colloidal crystallization near the “sticky sphere” limit, and support the notion that differing extents of interaction heterogeneity arising from various particle functionalization protocols may contribute to the otherwise unexplained variations in crystallization outcomes reported in the literature.

Interfaces are of critical importance to many materials and phenomena yet are difficult to probe. This difficulty is compounded in three-dimensional nanostructures and with delicate organic materials. Here we demonstrate a quantitative spectral analysis of resonant soft x-ray scattering that can accurately measure properties of buried nonplanar interfaces within polymeric systems. We measure the scattering invariant on an absolute scale to quantify the interfacial volume and width involved in mixing at the interface of block copolymer nanostructures. Using continuous contrast tuning, this spectral analysis enables the separation and identification of any number of unique scatterers in complex nanostructures.

We analyze the collective behavior of hydrodynamically coupled molecular motors. We show that the local fluxes induced by motor displacement can induce the experimentally observed bidirectional motion of cargoes and vesicles. By means of a mean-field approach we show that sustained oscillations as well as bistable collective motor motion arise even for very large collection of motors, when thermal noise is irrelevant. The analysis clarifies the physical mechanisms responsible for such dynamics by identifying the relevant coupling parameter and its dependence on the geometry of the hydrodynamic coupling as well as on system size. We quantify the phase diagram for the different phases that characterize the collective motion of hydrodynamically coupled motors and show that sustained oscillations can be reached for biologically relevant parameters, hence, demonstrating the relevance of hydrodynamic interactions in intracellular transport.

We present an approach to generate chimera dynamics (localized frequency synchrony) in oscillator networks with two populations of (at least) two elements using a general method based on a delayed interaction with linear and quadratic terms. The coupling design yields robust chimeras through a phase-model-based design of the delay and the ratio of linear and quadratic components of the interactions. We demonstrate the method in the Brusselator model and experiments with electrochemical oscillators. The technique opens the way to directly bridge chimera dynamics in phase models and real-world oscillator networks.

We study the statistics, in stationary conditions, of the work $W_{\tau }$ done by the active force in different systems of self-propelled particles in a time $\tau$. We show the existence of a critical value $W_{\tau }^{†}$ such that fluctuations with $W_{\tau }>W_{\tau }^{†}$ correspond to configurations where interaction between particles plays a minor role whereas those with $W_{\tau }<W_{\tau }^{†}$ represent states with single particles dragged by clusters. This twofold behavior is fully mirrored by the probability distribution $P(W_{\tau })$ of the work, which does not obey the large-deviation principle for $W_{\tau }<W_{\tau }^{†}$. This pattern of behavior can be interpreted as due to a phase transition occurring at the level of fluctuating quantities and an order parameter is correspondingly identified.

We use extremely bright and ultrashort pulses from an x-ray free-electron laser (XFEL) to measure correlations in x rays scattered from individual bioparticles. This allows us to go beyond the traditional crystallography and single-particle imaging approaches for structure investigations. We employ angular correlations to recover the three-dimensional (3D) structure of nanoscale viruses from x-ray diffraction data measured at the Linac Coherent Light Source. Correlations provide us with a comprehensive structural fingerprint of a 3D virus, which we use both for model-based and ab initio structure recovery. The analyses reveal a clear indication that the structure of the viruses deviates from the expected perfect icosahedral symmetry. Our results anticipate exciting opportunities for XFEL studies of the structure and dynamics of nanoscale objects by means of angular correlations.

To assess the role of particle roughness in the rheological phenomena of concentrated colloidal suspensions, we develop model colloids with varying surface roughness length scales up to 10% of the particle radius. Increasing surface roughness shifts the onset of both shear thickening and dilatancy towards lower volume fractions and critical stresses. Experimental data are supported by computer simulations of spherical colloids with adjustable friction coefficients, demonstrating that a reduction in the onset stress of thickening and a sign change in the first normal stresses occur when friction competes with lubrication. In the quasi-Newtonian flow regime, roughness increases the effective packing fraction of colloids. As the shear stress increases and suspensions of rough colloids approach jamming, the first normal stresses switch signs and the critical force required to generate contacts is drastically reduced. This is likely a signature of the lubrication films giving way to roughness-induced tangential interactions that bring about load-bearing contacts in the compression axis of flow.

We investigate the universal scaling of protein fluctuation dynamics with a site-specific diffusive model of protein motion, which predicts an initial subdiffusive regime in the configurational relaxation. The long-time dynamics of proteins is controlled by an activated regime. We argue that the hierarchical free energy barriers set the time scales of biological processes and establish an upper limit to the size of single protein domains. We find it compelling that the scaling behavior for the protein dynamics is in close agreement with the Kardar-Parisi-Zhang scaling exponents.

Environment plays a fundamental role in the competition for resources, and hence in the evolution of populations. Here, we study a well-mixed, finite population consisting of two strains competing for the limited resources provided by an environment that randomly switches between states of abundance and scarcity. Assuming that one strain grows slightly faster than the other, we consider two scenarios—one of pure resource competition, and one in which one strain provides a public good—and investigate how environmental randomness (external noise) coupled to demographic (internal) noise determines the population’s fixation properties and size distribution. By analytical means and simulations, we show that these coupled sources of noise can significantly enhance the fixation probability of the slower-growing species. We also show that the population size distribution can be unimodal, bimodal, or multimodal and undergoes noise-induced transitions between these regimes when the rate of switching matches the population’s growth rate.

Inspired by Kubo-Anderson Markov processes, we introduce a new class of transfer matrices whose largest eigenvalue is determined by a simple explicit algebraic equation. Applications include the free energy calculation for various equilibrium systems and a general criterion for perfect harmonicity, i.e., a free energy that is exactly quadratic in the external field. As an illustration, we construct a “perfect spring,” namely, a polymer with non-Gaussian, exponentially distributed subunits which, nevertheless, remains harmonic until it is fully stretched. This surprising discovery is confirmed by Monte Carlo and Langevin simulations.

The chemotactic motion of eukaryotic cells such as leukocytes or metastatic cancer cells relies on membrane protrusions driven by the polymerization and depolymerization of actin. Here we show that the response of the actin system to a receptor stimulus is subject to a threshold value that varies strongly from cell to cell. Above the threshold, we observe pronounced cell-to-cell variability in the response amplitude. The polymerization time, however, is almost constant over the entire range of response amplitudes, while the depolymerization time increases with increasing amplitude. We show that cell-to-cell variability in the response amplitude correlates with the amount of $\text{Arp}2/3$, a protein that enhances actin polymerization. A time-delayed feedback model for the cortical actin concentration is consistent with all our observations and confirms the role of $\text{Arp}2/3$ in the observed cell-to-cell variability. Taken together, our observations highlight robust regulation of the actin response that enables a reliable timing of cell movement.

Using molecular dynamics simulation of a standard coarse-grained polymer glass model, we investigate by means of the stress-fluctuation formalism the shear modulus $\mu$ as a function of temperature $T$ and sampling time $\mathrm{\Delta }t$. While the ensemble-averaged modulus $\mu (T)$ is found to decrease continuously for all $\mathrm{\Delta }t$ sampled, its standard deviation $\delta \mu (T)$ is nonmonotonic, with a striking peak at the glass transition. Confirming the effective time-translational invariance of our systems, $\mu (\mathrm{\Delta }t)$ can be understood using a weighted integral over the shear-stress relaxation modulus $G(t)$. While the crossover of $\mu (T)$ gets sharper with an increasing $\mathrm{\Delta }t$, the peak of $\delta \mu (T)$ becomes more singular. It is thus elusive to predict the modulus of a single configuration at the glass transition.

Under certain conditions, two samples of fluid at different initial temperatures present a counterintuitive behavior known as the Mpemba effect: it is the hotter system that cools sooner. Here, we show that the Mpemba effect is present in granular fluids, both in uniformly heated and in freely cooling systems. In both cases, the system remains homogeneous, and no phase transition is present. Analytical quantitative predictions are given for how differently the system must be initially prepared to observe the Mpemba effect, the theoretical predictions being confirmed by both molecular dynamics and Monte Carlo simulations. Possible implications of our analysis for other systems are also discussed.

The process of pattern formation for a multispecies model anchored on a time varying network is studied. A nonhomogeneous perturbation superposed to an homogeneous stable fixed point can be amplified following the Turing mechanism of instability, solely instigated by the network dynamics. By properly tuning the frequency of the imposed network evolution, one can make the examined system behave as its averaged counterpart, over a finite time window. This is the key observation to derive a closed analytical prediction for the onset of the instability in the time dependent framework. Continuously and piecewise constant periodic time varying networks are analyzed, setting the framework for the proposed approach. The extension to nonperiodic settings is also discussed.

Surface polarization in ion-containing heterogeneous dielectric media such as cell media and emulsions is determined by and determines the positions of the ions. We compute the surface polarization self-consistently as the ions move and analyze their effects on the interactions between electro-neutral, ion-containing droplets using coarse-grained molecular dynamics simulations based on the true energy functional. For water droplets immersed in oil, the interdroplet interaction is attractive, and the surface polarization makes the major contribution. By contrast, for oil droplets in water, the ion-surface induced charge interaction is repulsive and counteracts the attraction between the ions, leading to a small attractive interaction between the droplets. This research improves our understanding of self-assembly in mixed phases such as metal extraction for recovering rare earth elements and nuclear waste as well as water purification.

By combining confocal microscopy and stress assessment from local structural anisotropy, we directly measure stresses in 3D quiescent colloidal liquids. Our noninvasive and nonperturbative method allows us to measure forces $\lesssim 50\mathrm{fN}$ with a small and tunable probing volume, enabling us to resolve the stress fluctuations arising from particle thermal motions. We use the Green-Kubo relation to relate these measured stress fluctuations to the bulk Brownian viscosity at different volume fractions, comparing against simulations and conventional rheometry measurements. We find that the Green-Kubo analysis gives excellent agreement with these prior results, suggesting that similar methods could be applied to investigations of local flow properties in many poorly understood far-from-equilibrium systems, including suspensions that are glassy, strongly sheared, or highly confined.

Single-molecule Förster resonance energy transfer is widely applied to study helicases by detecting distance changes between a pair of dyes anchored to overhangs of a forked DNA. However, it has been lacking single-base pair (1-bp) resolution required for revealing stepping kinetics of helicases. We designed a nanotensioner in which a short DNA is bent to exert force on the overhangs, just as in optical or magnetic tweezers. The strategy improved the resolution of Förster resonance energy transfer to 0.5 bp, high enough to uncover differences in DNA unwinding by yeast Pif1 and E. coli RecQ whose unwinding behaviors cannot be differentiated by currently practiced methods. We found that Pif1 exhibits 1-bp-stepping kinetics, while RecQ breaks 1 bp at a time but sequesters the nascent nucleotides and releases them randomly. The high-resolution data allowed us to propose a three-parameter model to quantitatively interpret the apparently different unwinding behaviors of the two helicases which belong to two superfamilies.