A time-dependent Ginzburg-Landau (TDGL) model is proposed to simulate the ordering of linear $ABC$ triblock terpolymers. The model, in its current form, is applicable to nonfrustrated triblock systems, with the specific condition that ${\chi }_{AC}\gg {\chi }_{AB}\approx {\chi }_{BC}$. Simulations are presented that demonstrate the model's ability to evolve a wide variety of morphologies throughout time, including tetragonal, core-shell hexagonal, three-phase lamellar, and beads-in-lamellar phases. The model also incorporates an interaction term to study templated substrates for directed self-assembly. The efficiency of the TDGL model enables large-scale simulations that allow investigation of self-assembly, and directed self-assembly, processes that may exhibit very small defect concentrations.

The surface corrugation plays an important role in single polymer diffusion on attractive surfaces. However, its effect on dynamics of surface adsorption-induced polymer translocation through a nanopore is not clear. Using three-dimensional Langevin dynamics simulations, we investigate the dynamics of a flexible polymer chain translocation through a nanopore induced by the selective adsorption of translocated segments onto the trans side of the membrane. The translocation probability $P_{trans}$ increases monotonically, while the mean translocation time $\tau$ has a minimum as a function of the adsorption strength $\varepsilon$, which are explained from the perspective of the effective driving force for the translocation. With the surface being smoother, $\tau$ as well as the scaling exponent $\alpha$ of $\tau$ with the chain length $N$ decreases. Finally, we show that the distributions of the translocation time are non-Gaussian even for strong adsorption at a moderate surface corrugation. A nearly Gaussian distribution of the translocation time is observed only for the smoothest surface we studied.

A series of nanopores with diameters ranging from 2.5 to 63 nm are fabricated on a reduced $\mathrm{S}{\mathrm{i}}_3{\mathrm{N}}_4$ membrane by focused ion beam and high energy electron beam. Through measuring the blocked ionic currents for DNA strands threading linearly through those solid-state nanopores, it is found that the blockade ionic current is proportional to the square of the hydrodynamic diameter of the DNA strand. With the nanopore diameter reduced to be comparable with that of DNA strands, the hydrodynamic diameter of the DNA becomes smaller, which is attributed to the size confinement effects. The duration time for the linear DNA translocation events increases monotonically with the nanopore length. By comparing the spatial configurations of DNA strands through nanopores with different diameters, it is found that the nanopore with large diameter has enough space to allow the DNA strand to translocate through with complex conformation. With the decrease of the nanopore diameter, the folded part of the DNA is prone to be straightened by the nanopore, which leads to the increase in the occurrence frequency of the linear DNA translocation events. Reducing the diameter of the nanopore to 2.5 nm allows the detection and discrimination of three nucleotide “G” and three nucleotide “T” homopolymer DNA strands based on differences in their physical dimensions.

Networks are topological and geometric structures used to describe systems as different as the Internet, the brain, or the quantum structure of space-time. Here we define complex quantum network geometries, describing the underlying structure of growing simplicial 2-complexes, i.e., simplicial complexes formed by triangles. These networks are geometric networks with energies of the links that grow according to a nonequilibrium dynamics. The evolution in time of the geometric networks is a classical evolution describing a given path of a path integral defining the evolution of quantum network states. The quantum network states are characterized by quantum occupation numbers that can be mapped, respectively, to the nodes, links, and triangles incident to each link of the network. We call the geometric networks describing the evolution of quantum network states the quantum geometric networks. The quantum geometric networks have many properties common to complex networks, including small-world property, high clustering coefficient, high modularity, and scale-free degree distribution. Moreover, they can be distinguished between the Fermi-Dirac network and the Bose-Einstein network obeying, respectively, the Fermi-Dirac and Bose-Einstein statistics. We show that these networks can undergo structural phase transitions where the geometrical properties of the networks change drastically. Finally, we comment on the relation between quantum complex network geometries, spin networks, and triangulations.

Nodes in real-world networks are repeatedly observed to form dense clusters, often referred to as communities. Methods to detect these groups of nodes usually maximize an objective function, which implicitly contains the definition of a community. We here analyze a recently proposed measure called surprise, which assesses the quality of the partition of a network into communities. In its current form, the formulation of surprise is rather difficult to analyze. We here therefore develop an accurate asymptotic approximation. This allows for the development of an efficient algorithm for optimizing surprise. Incidentally, this leads to a straightforward extension of surprise to weighted graphs. Additionally, the approximation makes it possible to analyze surprise more closely and compare it to other methods, especially modularity. We show that surprise is (nearly) unaffected by the well-known resolution limit, a particular problem for modularity. However, surprise may tend to overestimate the number of communities, whereas they may be underestimated by modularity. In short, surprise works well in the limit of many small communities, whereas modularity works better in the limit of few large communities. In this sense, surprise is more discriminative than modularity and may find communities where modularity fails to discern any structure.

Visibility algorithms are a family of methods to map time series into networks, with the aim of describing the structure of time series and their underlying dynamical properties in graph-theoretical terms. Here we explore some properties of both natural and horizontal visibility graphs associated to several nonstationary processes, and we pay particular attention to their capacity to assess time irreversibility. Nonstationary signals are (infinitely) irreversible by definition (independently of whether the process is Markovian or producing entropy at a positive rate), and thus the link between entropy production and time series irreversibility has only been explored in nonequilibrium stationary states. Here we show that the visibility formalism naturally induces a new working definition of time irreversibility, which allows us to quantify several degrees of irreversibility for stationary and nonstationary series, yielding finite values that can be used to efficiently assess the presence of memory and off-equilibrium dynamics in nonstationary processes without the need to differentiate or detrend them. We provide rigorous results complemented by extensive numerical simulations on several classes of stochastic processes.

We discuss the nonlinear dynamics and fluctuations of interfaces with bending rigidity under the competing attractions of two walls with arbitrary permeabilities. This system mimics the dynamics of confined membranes. We use a two-dimensional hydrodynamic model, where membranes are effectively one-dimensional objects. In a previous work [T. Le Goff et al., Phys. Rev. E 90, 032114 (2014)], we have shown that this model predicts frozen states caused by bending rigidity-induced oscillatory interactions between kinks (or domain walls). We here demonstrate that in the presence of tension, potential asymmetry, or thermal noise, there is a finite threshold above which frozen states disappear, and perpetual coarsening is restored. Depending on the driving force, the transition to coarsening exhibits different scenarios. First, for membranes under tension, small tensions can only lead to transient coarsening or partial disordering, while above a finite threshold, membrane oscillations disappear and perpetual coarsening is found. Second, potential asymmetry is relevant in the nonconserved case only, i.e., for permeable walls, where it induces a drift force on the kinks, leading to a fast coarsening process via kink-antikink annihilation. However, below some threshold, the drift force can be balanced by the oscillatory interactions between kinks, and frozen adhesion patches can still be observed. Finally, at long times, noise restores coarsening with standard exponents depending on the permeability of the walls. However, the typical time for the appearance of coarsening exhibits an Arrhenius form. As a consequence, a finite noise amplitude is needed in order to observe coarsening in observable time.

We study control of synchronization in weakly coupled oscillator networks by using a phase-reduction approach. Starting from a general class of limit-cycle oscillators we derive a phase model, which shows that delayed feedback control changes effective coupling strengths and effective frequencies. We derive the analytical condition for critical control gain, where the phase dynamics of the oscillator becomes extremely sensitive to any perturbations. As a result the network can attain phase synchronization even if the natural interoscillatory couplings are small. In addition, we demonstrate that delayed feedback control can disrupt the coherent phase dynamic in synchronized networks. The validity of our results is illustrated on networks of diffusively coupled Stuart–Landau and FitzHugh–Nagumo models.

We analyze the recurrence-time statistics (RTS) in three-dimensional non-Hamiltonian volume-preserving systems (VPS): an extended standard map and a fluid model. The extended map is a standard map weakly coupled to an extra dimension which contains a deterministic regular, mixed (regular and chaotic), or chaotic motion. The extra dimension strongly enhances the trapping times inducing plateaus and distinct algebraic and exponential decays in the RTS plots. The combined analysis of the RTS with the classification of ordered and chaotic regimes and scaling properties allows us to describe the intricate way trajectories penetrate the previously impenetrable regular islands from the uncoupled case. Essentially the plateaus found in the RTS are related to trajectories that stay for long times inside trapping tubes, not allowing recurrences, and then penetrate diffusively the islands (from the uncoupled case) by a diffusive motion along such tubes in the extra dimension. All asymptotic exponential decays for the RTS are related to an ordered regime (quasiregular motion), and a mixing dynamics is conjectured for the model. These results are compared to the RTS of the standard map with dissipation or noise, showing the peculiarities obtained by using three-dimensional VPS. We also analyze the RTS for a fluid model and show remarkable similarities to the RTS in the extended standard map problem.

We found two stationary solutions of the cubic complex Ginzburg-Landau equation (CGLE) with an additional term modeling the delayed Raman scattering. Both solutions propagate with nonzero velocity. The solution that has lower peak amplitude is the continuation of the chirped soliton of the cubic CGLE and is unstable in all the parameter space of existence. The other solution is stable for values of nonlinear gain below a certain threshold. The solutions were found using a shooting method to integrate the ordinary differential equation that results from the evolution equation through a change of variables, and their stability was studied using the Evans function method. Additional integration of the evolution equation revealed the basis of attraction of the stable solutions. Furthermore, we have investigated the existence and stability of the high amplitude branch of solutions in the presence of other higher order terms originating from complex Raman, self-steepening, and imaginary group velocity.

Phase response curves (PRCs) have become an indispensable tool in understanding the entrainment and synchronization of biological oscillators. However, biological oscillators are often found in large coupled heterogeneous systems and the variable of physiological importance is the collective rhythm resulting from an aggregation of the individual oscillations. To study this phenomena we consider phase resetting of the collective rhythm for large ensembles of globally coupled Sakaguchi-Kuramoto oscillators. Making use of Ott-Antonsen theory we derive an asymptotically valid analytic formula for the collective PRC. A result of this analysis is a characteristic scaling for the change in the amplitude and entrainment points for the collective PRC compared to the individual oscillator PRC. We support the analytical findings with numerical evidence and demonstrate the applicability of the theory to large ensembles of coupled neuronal oscillators.

Considering a wall turbulence as a chaotic dynamical system, we study regeneration cycles in a minimal wall turbulence from the viewpoint of orbital instability by employing the covariant Lyapunov analysis developed by [F. Ginelli et al. Phys. Rev. Lett. 99, 130601 (2007)]. We divide the regeneration cycle into two phases and characterize them with the local Lyapunov exponents and the covariant Lyapunov vectors of the Navier-Stokes turbulence. In particular, we show numerically that phase (i) is dominated by instabilities related to the sinuous mode and the streamwise vorticity, and there is no instability in phase (ii). Furthermore, we discuss a mechanism of the regeneration cycle, making use of an energy budget analysis.

The experiments of Leptos et al. [Phys. Rev. Lett. 103, 198103 (2009)] show that the displacements of small particles affected by swimming microorganisms achieve a non-Gaussian distribution, which nevertheless scales diffusively—the “diffusive scaling.” We use a simple model where the particles undergo repeated “kicks” due to the swimmers to explain the shape of the distribution as a function of the volume fraction of swimmers. The net displacement is determined by the inverse Fourier transform of a single-swimmer characteristic function. The only adjustable parameter is the strength of the stresslet term in our spherical squirmer model. We give a criterion for convergence to a Gaussian distribution in terms of moments of the drift function and show that the experimentally observed diffusive scaling is a transient related to the slow crossover of the fourth moment from a ballistic to a linear regime with path length. We also present a simple model, with logarithmic drift function, that can be solved analytically.

A new regime of oblique jet collision, characterized by low impact inertia and jet merging through bridge formation, is observed and thereby completes the entire suite of possible jet collision outcomes of (soft) merging, bouncing, and (hard) merging with increasing inertia. These distinct regimes, together with the observed dependence of the collision outcome on the impact angle and liquid properties, are characterized through scaling analysis by considering the competing effects of impact inertia, surface tension, and viscous thinning of the interfacial air gap leading to merging.

The dynamics of particle transport under the influence of localized high energy anomalies (explosions) is a complicated phenomenon dependent on many physical parameters of both the particle and the medium it resides in. Here we present a conceptual model that establishes simple scaling laws for particle dispersion in relation to the energy released in a blast, properties of the medium, physical properties of particles, and their initial position away from a blast epicenter. These dependencies are validated against numerical simulations and we discuss predictions of the model which can be validated experimentally. Other applications and extensions to the framework are also considered.

Formation, spontaneous spin-up, and stability of θ-pinch formed field-reversed configurations are studied self-consistently in three dimensions with a multiscale hybrid model that treats all plasma ions as full-orbit collisional macroparticles and the electrons as a massless quasineutral fluid. The end-to-end hybrid simulations reveal poloidal profiles of implosion-driven fast toroidal plasma rotation and demonstrate three well-known discharge regimes as a function of experimental parameters: the decaying stable configuration, the tilt unstable configuration, and the nonlinear evolution of a fast growing tearing mode.

Experimental investigations of dusty plasma parameters of a dc glow discharge were performed in a vertically oriented discharge tube. Under certain conditions, dust-free regions (voids) were formed in the center of the dust particle clouds that levitated in the strong electric field of a stratified positive column. A model for radial distribution of dusty plasma parameters of a dc glow discharge in inert gases was developed. The behavior of void formation was investigated for different discharge conditions (type of gas, discharge pressure, and discharge current) and dust particle parameters (particle radii and particle total number). It was shown that it is the ion drag force radial component that leads to the formation of voids. Both experimental and calculated results show that the higher the discharge current the wider dust-free region (void). The calculations also show that more pronounced voids are formed for dust particles with larger radii and under lower gas pressures.

We show that the expression for the equilibrium thermodynamic entropy contains an integral over a surface in phase space, and in so doing, we confirm that negative temperature is a valid thermodynamic concept. This demonstration disproves the claims of several recent papers that the Gibbs entropy, which contains an integral over a volume in phase space, is the correct definition and that thermodynamics cannot be extended to include negative temperatures. We further show that the Gibbs entropy fails to satisfy the postulates of thermodynamics and that its predictions for systems with nonmonotonic energy densities of states are incorrect.

Dense suspensions display complex flow properties, intermediate between solid and liquid. When sheared, a suspension self-organizes and forms particle clusters that are likely to percolate, possibly leading to significant changes in the overall behavior. Some theoretical conjectures on percolation in suspensions were proposed by de Gennes some 35 years ago. Although still used, they have not received any validations so far. In this Rapid Communication, we use three-dimensional detailed numerical simulations to understand the formation of percolation clusters and assess de Gennes conjectures. We found that sheared noncolloidal suspensions do show percolation clusters occurring at a critical volume fraction in the range 0.3–0.4 depending on the system size. Percolation clusters are roughly linear, extremely transient, and involve a limited number of particles. We have computed critical exponents and found that clusters can be described reasonably well by standard isotropic percolation theory. The only disagreement with de Gennes concerns the role of percolation clusters on rheology which is found to be weak. Our results eventually validate de Gennes conjectures and demonstrate the relevance of percolation concepts in suspension physics.

Feedback control schemes are a promising way to manipulate transport properties of driven colloidal suspensions. In the present article, we suggest a feedback scheme to enhance the collective transport of colloidal particles with repulsive interactions through a one-dimensional tilted washboard potential. The control is modeled by a harmonic confining potential, mimicking an optical “trap,” with the center of this trap moving with the (instantaneous) mean particle position. Our theoretical analysis is based on the Smoluchowski equation combined with dynamical density functional theory for systems with hard-core or ultrasoft (Gaussian) interactions. For either type of interaction, we find that the feedback control can lead to an enhancement of the mobility by several orders of magnitude relative to the uncontrolled case. The largest effects occur for intermediate stiffness of the trap and large particle numbers. Moreover, in some regions of the parameter space the feedback control induces oscillations of the mean velocity. Finally, we show that the enhancement of mobility is robust against a small time delay in implementing the feedback control.