We present results from the first $d=3$ molecular dynamics (MD) study of phase-separating fluid mixtures (AB) with simple chemical reactions ($\mathrm{A}\rightleftharpoons \mathrm{B}$). We focus on the case where the rates of forward and backward reactions are equal. The chemical reactions compete with segregation, and the coarsening system settles into a steady-state mesoscale morphology. However, hydrodynamic effects destroy the lamellar morphology which characterizes the diffusive case. This has important consequences for the phase-separating structure, which we study in detail. In particular, the equilibrium length scale $\left({\ell }_{\mathrm{eq}}\right)$ in the steady state suggests a power-law dependence on the reaction rate $\epsilon :{\ell }_{\mathrm{eq}}\sim {\epsilon }^{-\theta }$ with $\theta \simeq 1.0$.

In this paper, we investigate effects of counterion connectivity (i.e., association of the counterions into a chain molecule) on the electrostatic potential of mean force (EPMF) between two similarly charged cylinder rods in a primitive model electrolyte solution by solving a classical density functional theory. The main findings include the following: (i) The counterion connectivity helps in inducing a like-charge-attractionlike (LCA-like) phenomenology even in a monovalent counterion solution wherein the LCA-like observation generally does not occur without the counterion connectivity. (ii) For divalent counterion solutions, the counterion connectivity can reinforce or weaken the LCA-like observation depending on the chain length $N$, and simply increases the equilibrium nearest surface separation of the rods corresponding to the minimum EPMF to nearly three times the counterion site diameter, whether $N$ is large or small. (iii) If $N$ is large enough, the LCA-like strength tends to be negatively correlated with the electrolyte concentration $c$ over the entire range of the rod surface charge magnitude $|{\sigma }^{*}|$ considered; whereas if $N$ drops, the correlation tends to become positive with decrease of the $|{\sigma }^{*}|$ value, and particularly for modest $|{\sigma }^{*}|$ values, the correlation relationship exhibits an extreme value phenomenon. (iv) In the case of a 1:1 electrolyte, the EPMF effects of the diameters of counterion and coion sites are similar in both situations with and without the counterion connectivity. All of these findings can be explained self-consistently by a recently proposed hydrogen-bonding style mechanism reinforced by one additional concept: flexibility of the counterion chain and the factors affecting it, like $N$ and counterion site valence.

The transition from ballistic to electrodiffusive transport of ions through thin polymer membranes has been investigated by recording single transport events via time-correlated single-particle detection. At the highest kinetic energies investigated, ballistic transport of potassium ions is observed with no discernible energy loss to the membrane. At the lowest kinetic energies investigated (several 100 eV) ions are demonstrated to lose the entire kinetic energy to the membrane. Transport there occurs by electrodiffusion. A transition regime is observed. The transition energy is shown to depend on the thickness of the membrane.

We have applied a combined fluorescence microscopy and single-ion-channel electric current recording approach, correlating with molecular dynamics (MD) simulations, to study the mechanism of voltage-sensor domain translocation across a lipid bilayer. We use the colicin Ia ion channel as a model system, and our experimental and simulation results show the following: (1) The open-close activity of an activated colicin Ia is not necessarily sensitive to the amplitude of the applied cross-membrane voltage when the cross-membrane voltage is around the resting potential of excitable membranes; and (2) there is a significant probability that the activation of colicin Ia occurs by forming a transient and fluctuating water pore of ∼15 Å diameter in the lipid bilayer membrane. The location of the water-pore formation is nonrandom and highly specific, right at the insertion site of colicin Ia charged residues in the lipid bilayer membrane, and the formation is intrinsically associated with the polypeptide conformational fluctuations and solvation dynamics. Our results suggest an interesting mechanistic pathway for voltage-sensitive ion channel activation, and specifically for translocation of charged polypeptide chains across the lipid membrane under a transmembrane electric field: the charged polypeptide domain facilitates the formation of hydrophilic water pore in the membrane and diffuses through the hydrophilic pathway across the membrane; i.e., the charged polypeptide chain can cross a lipid membrane without entering into the hydrophobic core of the lipid membrane but entirely through the aqueous and hydrophilic environment to achieve a cross-membrane translocation. This mechanism sheds light on the intensive and fundamental debate on how a hydrophilic and charged peptide domain diffuses across the biologically inaccessible high-energy barrier of the hydrophobic core of a lipid bilayer: The peptide domain does not need to cross the hydrophobic core to move across a lipid bilayer.

Using methods from condensed matter and statistical physics, we examine the transport of excitons through the photosynthetic complex from a receiving antenna to a reaction center. Writing the equations of motion for the exciton creation-annihilation operators, we are able to describe the exciton dynamics, even in the regime when the reorganization energy is of the order of the intrasystem couplings. We determine the exciton transfer efficiency in the presence of a quenching field and protein environment. While the majority of the protein vibrational modes are treated as a heat bath, we address the situation when specific modes are strongly coupled to excitons and examine the effects of these modes on the energy transfer efficiency in the steady-state regime. Using the structural parameters of the Fenna-Matthews-Olson complex, we find that, for vibrational frequencies below 16 meV, the exciton transfer is drastically suppressed. We attribute this effect to the formation of a “mixed exciton-vibrational mode” where the exciton is transferred back and forth between the two pigments with the absorption or emission of vibrational quanta, instead of proceeding to the reaction center. The same effect suppresses the quantum beating at the vibrational frequency of 25 meV. We also show that the efficiency of the energy transfer can be enhanced when the vibrational mode strongly couples to the third pigment only, instead of coupling to the entire system.

We simulate a phase-separating bilayer in which the leaflets experience a direct coupling favoring local compositional symmetry (“registered” bilayer phases), and an indirect coupling due to hydrophobic mismatch that favors strong local asymmetry (“antiregistered” bilayer phases). For wide ranges of overall leaflet compositions, multiple competing states are possible. For estimated physical parameters, a quenched bilayer may first evolve toward a metastable state more asymmetric than if the leaflets were uncorrelated; subsequently, it must nucleate to reach its equilibrium, more symmetric, state. These phase-transition kinetics exhibit characteristic signatures through which fundamental and opposing interleaflet interactions may be probed. We emphasize how bilayer phase diagrams with a separate axis for each leaflet can account for overall and local symmetry or asymmetry, and capture a range of observations in the experiment and simulation literature.

In this paper we study the quantum transport on networks with a temporal evolution governed by the fractional Schrödinger equation. We generalize the dynamics based on continuous-time quantum walks, with transitions to nearest neighbors on the network, to the fractional case that allows long-range displacements. By using the fractional Laplacian matrix of a network, we establish a formalism that combines a long-range dynamics with the quantum superposition of states; this general approach applies to any type of connected undirected networks, including regular, random, and complex networks, and can be implemented from the spectral properties of the Laplacian matrix. We study the fractional dynamics and its capacity to explore the network by means of the transition probability, the average probability of return, and global quantities that characterize the efficiency of this quantum process. As a particular case, we explore analytically these quantities for circulant networks such as rings, interacting cycles, and complete graphs.

The detrended cross-correlation coefficient ${\rho }_{\mathrm{DCCA}}$ has recently been proposed to quantify the strength of cross-correlations on different temporal scales in bivariate, nonstationary time series. It is based on the detrended cross-correlation and detrended fluctuation analyses (DCCA and DFA, respectively) and can be viewed as an analog of the Pearson coefficient in the case of the fluctuation analysis. The coefficient ${\rho }_{\mathrm{DCCA}}$ works well in many practical situations but by construction its applicability is limited to detection of whether two signals are generally cross-correlated, without the possibility to obtain information on the amplitude of fluctuations that are responsible for those cross-correlations. In order to introduce some related flexibility, here we propose an extension of ${\rho }_{\mathrm{DCCA}}$ that exploits the multifractal versions of DFA and DCCA: multifractal detrended fluctuation analysis and multifractal detrended cross-correlation analysis, respectively. The resulting new coefficient ${\rho }_q$ not only is able to quantify the strength of correlations but also allows one to identify the range of detrended fluctuation amplitudes that are correlated in two signals under study. We show how the coefficient ${\rho }_q$ works in practical situations by applying it to stochastic time series representing processes with long memory: autoregressive and multiplicative ones. Such processes are often used to model signals recorded from complex systems and complex physical phenomena like turbulence, so we are convinced that this new measure can successfully be applied in time-series analysis. In particular, we present an example of such application to highly complex empirical data from financial markets. The present formulation can straightforwardly be extended to multivariate data in terms of the $q$-dependent counterpart of the correlation matrices and then to the network representation.

Complex network null models based on entropy maximization are becoming a powerful tool to characterize and analyze data from real systems. However, it is not easy to extract good and unbiased information from these models: A proper understanding of the nature of the underlying events represented in them is crucial. In this paper we emphasize this fact stressing how an accurate counting of configurations compatible with given constraints is fundamental to build good null models for the case of networks with integer-valued adjacency matrices constructed from an aggregation of one or multiple layers. We show how different assumptions about the elements from which the networks are built give rise to distinctively different statistics, even when considering the same observables to match those of real data. We illustrate our findings by applying the formalism to three data sets using an open-source software package accompanying the present work and demonstrate how such differences are clearly seen when measuring network observables.

We study the probability distribution of the number of synchronous action potentials (spike count) in a model network consisting of a homogeneous neural population that is driven by a common time-dependent stimulus. We derive two analytical approximations for the count statistics, which are based on linear response theory and hold true for weak input correlations. Comparison to numerical simulations of populations of integrate-and-fire neurons in different parameter regimes reveals that our theory correctly predicts how much a weak common stimulus increases the probability of common firing and of common silence in the neural population.

We consider a nonlinear kinetic equation of Boltzmann type, which takes into account the influence of conviction during the formation of opinion in a system of agents, which interact through the binary exchanges, introduced by Toscani [G. Toscani, Commun. Math. Sci. 4, 481 (2006)]. The original exchange mechanism, which is based on the human tendency to compromise and change of opinion through self-thinking, is here modified in the parameters of the compromise and diffusion terms, which now are assumed to depend on the personal degree of conviction. The numerical simulations show that the presence of conviction has the potential to break symmetry, and to produce clusters of opinions. The model is partially inspired by the recent work [L. Pareschi and G. Toscani, Phil. Trans. R. Soc. A 372, 20130396 (2014)], in which the role of knowledge in the formation of wealth distribution has been investigated.

We report a collective dynamical state, namely the mixed-mode oscillation suppression state where the steady states of the state variables of a system of coupled oscillators show heterogeneous behaviors. We identify two variants of it: The first one is a mixed-mode death (MMD) state, which is an interesting oscillation death state, where a set of variables show dissimilar values, while the rest arrive at a common value. In the second mixed death state, bistable and monostable nontrivial homogeneous steady states appear simultaneously to a different set of variables (we refer to it as the MNAD state). We find these states in the paradigmatic chaotic Lorenz system and Lorenz-like system under generic coupling schemes. We identify that while the reflection symmetry breaking is responsible for the MNAD state, the breaking of both the reflection and translational symmetries result in the MMD state. Using a rigorous bifurcation analysis we establish the occurrence of the MMD and MNAD states, and map their transition routes in parameter space. Moreover, we report experimental observation of the MMD and MNAD states that supports our theoretical results. We believe that this study will broaden our understanding of oscillation suppression states; subsequently, it may have applications in many real physical systems, such as laser and geomagnetic systems, whose mathematical models mimic the Lorenz system.

We examine the prototypical Gray-Scott model, which mimics cubic autocatalytic reaction with linear decay of the autocatalyst, to model the kinetics of a reaction-diffusion system subjected to advective streamline flow. For a proper choice of boundary conditions and parameter space, the system admits wave-induced spatiotemporal chaos in the absence of flow. We show that flow above a critical value leads to an arrest of the spatiotemporal chaos due to a change in the instability from absolute to convective type. Furthermore, stationary spatial structures are borne out of a second successive bifurcation for yet another critical flow value. The theoretical formulations are corroborated by extensive numerical simulation of the full reaction-diffusion-advection system in one dimension.

In a network of nonlocally coupled Stuart-Landau oscillators with symmetry-breaking coupling, we study numerically, and explain analytically, a family of inhomogeneous steady states (oscillation death). They exhibit multicluster patterns, depending on the cluster distribution prescribed by the initial conditions. Besides stable oscillation death, we also find a regime of long transients asymptotically approaching synchronized oscillations. To explain these phenomena analytically in dependence on the coupling range and the coupling strength, we first use a mean-field approximation, which works well for large coupling ranges but fails for coupling ranges, which are small compared to the cluster size. Going beyond standard mean-field theory, we predict the boundaries of the different stability regimes as well as the transient times analytically in excellent agreement with numerical results.

The localized mode propagation in binary nonlinear kagome ribbons is investigated with the premise to ensure controlled light propagation through photonic lattice media. Particularity of the linear system characterized by the dispersionless flat band in the spectrum is the opening of new minigaps due to the “binarism.” Together with the presence of nonlinearity, this determines the guiding mode types and properties. Nonlinearity destabilizes the staggered rings found to be nondiffracting in the linear system, but can give rise to dynamically stable ringlike solutions of several types: unstaggered rings, low-power staggered rings, hour-glass-like solutions, and vortex rings with high power. The type of solutions, i.e., the energy and angular momentum circulation through the nonlinear lattice, can be controlled by suitable initial excitation of the ribbon. In addition, by controlling the system “binarism” various localized modes can be generated and guided through the system, owing to the opening of the minigaps in the spectrum. All these findings offer diverse technical possibilities, especially with respect to the high-speed optical communications and high-power lasers.

We report on a switching synchronization phenomenon in one-dimensional memristive networks, which occurs when several memristive systems with different switching constants are switched from the high- to low-resistance state. Our numerical simulations show that such a collective behavior is especially pronounced when the applied voltage slightly exceeds the combined threshold voltage of memristive systems. Moreover, a finite increase in the network switching time is found compared to the average switching time of individual systems. An analytical model is presented to explain our observations. Using this model, we have derived asymptotic expressions for memory resistances at short and long times, which are in excellent agreement with results of our numerical simulations.

This article analyzes the status of two classical one-particle probability density function (PDF) descriptions of the dynamics of discrete particles dispersed in turbulent flows. The first PDF formulation considers only the process made up by particle position and velocity ${\mathbf{Z}}_p=({\mathbf{x}}_p,{\mathbf{U}}_p)$ and is represented by its PDF $p(t;{\mathbf{y}}_p,{\mathbf{V}}_p)$ which is the solution of a kinetic PDF equation obtained through a flux closure based on the Furutsu-Novikov theorem. The second PDF formulation includes fluid variables into the particle state vector, for example, the fluid velocity seen by particles ${\mathbf{Z}}_p=({\mathbf{x}}_p,{\mathbf{U}}_p,{\mathbf{U}}_s)$, and, consequently, handles an extended PDF $p(t;{\mathbf{y}}_p,{\mathbf{V}}_p,{\mathbf{V}}_s)$ which is the solution of a dynamic PDF equation. For high-Reynolds-number fluid flows, a typical formulation of the latter category relies on a Langevin model for the trajectories of the fluid seen or, conversely, on a Fokker-Planck equation for the extended PDF. In the present work, a new derivation of the kinetic PDF equation is worked out and new physical expressions of the dispersion tensors entering the kinetic PDF equation are obtained by starting from the extended PDF and integrating over the fluid seen. This demonstrates that, under the same assumption of a Gaussian colored noise and irrespective of the specific stochastic model chosen for the fluid seen, the kinetic PDF description is the marginal of a dynamic PDF one. However, a detailed analysis reveals that kinetic PDF models of particle dynamics in turbulent flows described by statistical correlations constitute incomplete stand-alone PDF descriptions and, moreover, that present kinetic-PDF equations are mathematically ill posed. This is shown to be the consequence of the non-Markovian characteristic of the stochastic process retained to describe the system and the use of an external colored noise. Furthermore, developments bring out that well-posed PDF descriptions are essentially due to a proper choice of the variables selected to describe physical systems and guidelines are formulated to emphasize the key role played by the notion of slow and fast variables.

On the basis of the ellipsoidal vortex model and a Monte-Carlo-type diffusion simulation, we examine the flux and ensuing distribution of passive fluid particles through the boundary of an idealized geophysical vortex. Our focus is on features that the horizontal and vertical diffusion components introduce into the fluid particle transport. We examine the concurrent effect of both components, and we compare it with the only horizontal diffusion impact. We analyze the ellipsoid vortex model in two cases: (i) the steady state when the ellipsoid is motionless, i.e., there is no variation in its axes’ lengths, and consequently the exterior fluid is not being stirred; (ii) the perturbed case when the ellipsoid rotates periodically, varying it axes’ lengths, which results in the appearance of stirred fluid outside the ellipsoid. Influenced by diffusion, a fluid particle is now permitted to move to another vertical horizon, thus there is an increased possibility that the particle will eventually be located in the exterior stirred region rather than in the ellipsoid vortex with the regular dynamics. This is because the area of the horizontal section of the ellipsoid vortex decreases with depth, but the region of stirred exterior fluid extends significantly deeper. Numerical calculations show that factoring in the vertical component of diffusion significantly affects scalar spreading in the horizontal plane in the perturbed case, while in the steady state the vertical component of diffusion only induces dispersion linear growth according to a Gaussian distribution.

The impact of droplets on a deep pool has applications in cleaning up oil spills, spray cooling, painting, inkjet printing, and forensic analysis, relying on the changes in properties such as viscosity, interfacial tension, and density. Despite the exhaustive research on different aspects of droplet impact, it is not clear how liquid properties can affect the instabilities leading to Rayleigh jet breakup and number of daughter drops formed after its pinch-off. In this article, through systematic experiments we investigate the droplet impact phenomena by varying viscosity and surface tension of liquids as well as impact speeds. Further, using numerical simulations, we show that Rayleigh-Plateau instability is influenced by these parameters, and capillary time scale is the appropriate scale to normalize the breakup time. Based on Ohnesorge number (Oh) and impact Weber number (We), a regime map for no breakup, Rayleigh jet breakup, and crown splash is suggested. Interestingly, crown splash is observed to occur at all Ohnesorge numbers; however, at high Oh, a large portion of kinetic energy is dissipated, and thus the Rayleigh jet is suppressed regardless of high impact velocity. The normalized required time for the Rayleigh jet to reach its peak varies linearly with the critical height of the jet.