Droplet condensation on surfaces produces patterns, called breath figures. Their evolution into self-similar structures is a classical example of self-organization. It is described by a scaling theory with scaling functions whose universality has recently been challenged by numerical work. Here, we provide a thorough experimental testing, where we inspect substrates with vastly different chemical properties, stiffness, and condensation rates. We critically survey the size distributions, and the related time-asymptotic scaling of droplet number and surface coverage. In the time-asymptotic regime they admit a data collapse: the data for all substrates and condensation rates lie on universal scaling functions.
Strong anomalous diffusion is characterized by asymptotic power-law growth of the moments of displacement, with exponents that do not depend linearly on the order of the moment. The exponents concerning small-order moments are dominated by random motion, while higher-order exponents grow by faster trajectories, such as ballistic excursions or "light fronts". Often such a situation is characterized by two linear dependencies of the exponents on their order. Here, we introduce a simple exactly solvable model, the Fly-and-Die (FnD) model, that sheds light on this behavior and on the consequences of light fronts on displacement autocorrelation functions in transport processes. We present analytical expressions for the moments and derive a scaling form that expresses the long-time asymptotics of the autocorrelation function $\langle x(t_1)\,x(t_2)\rangle$ in terms of the dimensionless time difference $(t_2-t_1)/t_1$. The scaling form provides a faithful collapse of numerical data for vastly different systems. This is demonstrated here for the Lorentz gas with infinite horizon, polygonal billiards with finite and infinite horizon, the Lévy-Lorentz gas, the Slicer Map, and Lévy walks. Our analysis also captures the system-specific corrections to scaling.
Particulate matter, such as foams, emulsions, and granular materials, attain rigidity in a dense regime: the rigid phase can yield when a threshold force is applied. The rigidity transition in particulate matter exhibits \it bona fide scaling behavior near the transition point. However, a precise determination of exponents describing the rigidity transition has raised much controversy. Here, we pinpoint the causes of the controversies. We then establish a conceptual framework to quantify the critical nature of the yielding transition. Our results demonstrate that there is a spectrum of possible values for the critical exponents for which, without a robust framework, one cannot distinguish the genuine values of the exponents. Our approach is two-fold: (\it i) a precise determination of the transition density using rheological measurements and (\it ii) a matching rule that selects the critical exponents and rules out all other possibilities from the spectrum. This enables us to determine exponents with unprecedented accuracy and resolve the long-standing controversy over exponents of jamming. The generality of the approach paves the way to quantify the critical nature of many other types of rheological phase transitions such as those in oscillatory shearing.
The Slicer Map is a one-dimensional non-chaotic dynamical system that shows sub-, super-, and normal diffusion as a function of its control parameter. In a recent paper [Salari et al., CHAOS 25, 073113 (2015)] it was found that the moments of the position distributions as the Slicer Map have the same asymptotic behaviour as the Lévy-Lorentz gas, a random walk on the line in which the scatterers are randomly distributed according to a Lévy-stable probability distribution. Here we derive analytic expressions for the position-position correlations of the Slicer Map and, on the ground of this result, we formulate some conjectures about the asymptotic behaviour of position-position correlations of the Lévy-Lorentz gas, for which the information in the literature is minimal. The numerically estimated position-position correlations of the Lévy-Lorentz show a remarkable agreement with the conjectured asymptotic scaling.
Soft particulate media include a wide range of systems involving athermal dissipative particles both in non-living and biological materials. Characterization of flows of particulate media is of great practical and theoretical importance. A fascinating feature of these systems is the existence of a critical rigidity transition in the dense regime dominated by highly intermittent fluctuations that severely affects the flow properties. Here, we unveil the underlying mechanisms of rare fluctuations in soft particulate flows. We find that rare fluctuations have different origins above and below the critical jamming density and become suppressed near the jamming transition. We then conjecture a time-independent local fluctuation relation, which we verify numerically, and that gives rise to an effective temperature. We discuss similarities and differences between our proposed effective temperature with the conventional kinetic temperature in the system by means of a universal scaling collapse.
We conduct extensive independent numerical experiments considering frictionless disks without internal degrees of freedom (rotation etc.) in two dimensions. We report here that for a large range of the packing fractions below random-close packing, all components of the stress tensor of wet granular materials remain finite in the limit of zero shear rate. This is direct evidence for a fluid-to-solid arrest transition. The offset value of the shear stress characterizes plastic deformation of the arrested state which corresponds to \em dynamic yield stress of the system. Based on an analytical line of argument, we propose that the mean number of capillary bridges per particle, $\nu$, follows a non-trivial dependence on the packing fraction, $\phi$, and the capillary energy, $\vareps$. Most noticeably, we show that $\nu$ is a generic and universal quantity which does not depend on the driving protocol. Using this universal quantity, we calculate the arrest stress, $\sigma_a$, analytically based on a balance of the energy injection rate due to the external force driving the flow and the dissipation rate accounting for the rupture of capillary bridges. The resulting prediction of $\sigma_a$ is a non-linear function of the packing fraction $\phi$, and the capillary energy $\vareps$. This formula provides an excellent, parameter-free prediction of the numerical data. Corrections to the theory for small and large packing fractions are connected to the emergence of shear bands and of contributions to the stress from repulsive particle interactions, respectively.
Unlike macroscopic engines, the molecular machinery of living cells is strongly affected by fluctuations. Stochastic Thermodynamics uses Markovian jump processes to model the random transitions between the chemical and configurational states of these biological macromolecules. A recently developed theoretical framework [Wachtel, Vollmer, Altaner: "Fluctuating Currents in Stochastic Thermodynamics I. Gauge Invariance of Asymptotic Statistics"] provides a simple algorithm for the determination of macroscopic currents and correlation integrals of arbitrary fluctuating currents. Here, we use it to discuss energy conversion and nonequilibrium response in different models for the molecular motor kinesin. Methodologically, our results demonstrate the effectiveness of the algorithm in dealing with parameter-dependent stochastic models. For the concrete biophysical problem our results reveal two interesting features in experimentally accessible parameter regions: The validity of a non-equilibrium Green--Kubo relation at mechanical stalling as well as negative differential mobility for superstalling forces.
Droplet patterns condensing on solid substrates (breath figures) tend to evolve into a self-similar regime, characterized by a bimodal droplet size distribution. The distributions comprise a bell-shaped peak of monodisperse large droplets, and a broad range of smaller droplets. The size distribution of the latter follows a scaling law characterized by a non-trivial polydispersity exponent. We present here a numerical model for three-dimensional droplets on a one-dimensional substrate (fiber) that accounts for droplet nucleation, growth and merging. The polydispersity exponent retrieved using this model is not universal. Rather it depends on the thickness of the fiber and on details of the droplet interaction leading to merging. In addition, its values consistently differ from the theoretical prediction by Blackman (Phys. Rev. Lett., 2000). Possible causes of this discrepancy are pointed out.
The demixing of a binary fluid mixture, under gravity, is a two stage process. Initially droplets, or in general aggregates, grow diffusively by collecting supersaturation from the bulk phase. Subsequently, when the droplets have grown to a size, where their Peclet number is of order unity, buoyancy substantially enhances droplet growth. The dynamics approaches a finite-time singularity where the droplets are removed from the system by precipitation. The two growth regimes are separated by a bottleneck of minimal droplet growth. Here, we present a low-dimensional model addressing the time span required to cross the bottleneck, and we hence determine the time, ∆t, from initial droplet growth to rainfall. Our prediction faithfully captures the dependence of ∆t on the ramp rate of the droplet volume fraction, \xi, the droplet number density, the interfacial tension, the mass diffusion coefficient, the mass density contrast of the coexisting phases, and the viscosity of the bulk phase. The agreement of observations and the prediction is demonstrated for methanol/hexane and isobutoxyethanol/water mixtures where we determined ∆t for a vast range of ramp rates, \xi, and temperatures. The very good quantitative agreement demonstrates that it is sufficient for binary mixtures to consider (i) droplet growth by diffusive accretion that relaxes supersaturation, and (ii) growth by collisions of sedimenting droplets. An analytical solution of the resulting model provides a quantitative description of the dependence of ∆t on the ramp rate and the material constants. Extensions of the model that will admit a quantitative prediction of ∆t in other settings are addressed.
This paper is a comment to M Wilkinson, EPL 106 (2014) 40001, arXiv:1401.4620 [physics.ao-ph,cond-mat.soft], which draws conclusion from our data that are at variance with our observations.
Stochastic Thermodynamics uses Markovian jump processes to model random transitions between observable mesoscopic states. Physical currents are obtained from anti-symmetric jump observables defined on the edges of the graph representing the network of states. The asymptotic statistics of such currents are characterized by scaled cumulants. In the present work, we use the algebraic and topological structure of Markovian models to prove a gauge invariance of the scaled cumulant-generating function. Exploiting this invariance yields an efficient algorithm for practical calculations of asymptotic averages and correlation integrals. We discuss how our approach generalizes the Schnakenberg decomposition of the average entropy-production rate, and how it unifies previous work. The application of our results to concrete models is presented in an accompanying publication.
We explore the evolution of the aggregate size distribution in systems where aggregates grow by diffusive accretion of mass. Supersaturation is controlled in such a way that the overall aggregate volume grows linearly in time. Classical Ostwald ripening, which is recovered in the limit of vanishing overall growth, constitutes an unstable solution of the dynamics. In the presence of overall growth evaporation of aggregates always drives the dynamics into a new, qualitatively different growth regime where ripening ceases, and growth proceeds at a constant number density of aggregates. We provide a comprehensive description of the evolution of the aggregate size distribution in the constant density regime: the size distribution does not approach a universal shape, and even for moderate overall growth rates the standard deviation of the aggregate radius decays monotonically. The implications of this theory for the focusing of aggregate size distributions are discussed for a range of different settings including the growth of tiny rain droplets in clouds, as long as they do not yet feel gravity, and the synthesis of nano-particles and quantum dots.
We investigate the motion of a wedge-shaped object (a granular Brownian motor), which is restricted to move along the x-axis and cannot rotate, as gas particles collide with it. We show that its steady-state drift, resulting from inelastic gas-motor collisions, is dramatically affected by anisotropy in the velocity distribution of the gas. We identify the dimensionless parameter providing the dependence of this drift on shape, masses, inelasticity, and anisotropy: the anisotropy leads to a dramatic breaking of equipartition, which should easily be visible in experimental realizations.
We consider stochastic thermodynamics as a theory of statistical inference for experimentally observed fluctuating time-series. To that end, we introduce a general framework for quantifying the knowledge about the dynamical state of the system on two scales: a fine-grained or microscopic, deterministic and a coarse-grained or mesoscopic, stochastic level of description. For a generic model dynamics, we show how the mathematical expressions for fluctuating entropy changes used in Markovian stochastic thermodynamics emerge naturally. Our ideas are conceptional approaches towards (i) connecting entropy production and its fluctuation relations in deterministic and stochastic systems and (ii) providing a complementary information-theoretic picture to notions of entropy and entropy production in stochastic thermodynamics.
We study the arrest of three-dimensional flow in wet granular matter subject to a sinusoidal external force and a gravitational field confining the flow in the vertical direction. The minimal strength of the external force that is required to keep the system in motion is determined by considering the balance of injected and dissipated power. This provides a prediction whose excellent quality is demonstrated by a data collapse for an extensive set of event-driven molecular dynamics simulations where we varied the system size, particle number, the energy dissipated upon rupturing capillary bridges, and the bridge length where rupture occurs. The three parameters of the theoretical prediction all lie within narrow margins of theoretical estimates.
Nanoscale hydrogen particles in superfluid helium track the motions of quantized vortices. This provides a way to visualize turbulence in the superfluid. Here, we trace the evolution of the hydrogen from a gas to frozen particles migrating toward the cores of quantized vortices. Not only are the intervening processes interesting in their own right, but understanding them better leads to more revealing experiments.
The analysis of the size distribution of droplets condensing on a substrate (breath figures) is a test ground for scaling theories. Here, we show that a faithful description of these distributions must explicitly deal with the growth mechanisms of the droplets. This finding establishes a gateway connecting nucleation and growth of the smallest droplets on surfaces to gross features of the evolution of the droplet size distribution.
We present a novel computational method for direct measurement of yield stress of discrete materials. The method is well-suited for the measurement of jamming phase diagram of a wide range of discrete particle systems such as granular materials, foams, and colloids. We further successfully apply the method to evaluate the jamming phase diagram of wet granular material in order to demonstrates the applicability of the model.
Finite stochastic Markov models play a major role for modelling biochemical pathways. Such models are a coarse-grained description of the underlying microscopic dynamics and can be considered mesoscopic. The level of coarse-graining is to a certain extend arbitrary since it depends on the resolution of accomodating measurements. Here, we present a way to simplify such stochastic descriptions, which preserves both the meso-micro and the meso-macro connection. The former is achieved by demanding locality, the latter by considering cycles on the network of states. Using single- and multicycle examples we demonstrate how our new method preserves fluctuations of observables much better than naïve approaches.
Demixing of binary fluids subjected to slow temperature ramps shows repeated waves of nucleation which arise as a consequence of the competition between generation of supersaturation by the temperature ramp and relaxation of supersaturation by diffusive transport and flow. Here, we use an advection-reaction-diffusion model to study the oscillations in the weak- and strong-diffusion regime. There is a sharp transition between the two regimes, which can only be understood based on the probability distribution function of the composition rather than in terms of the average composition. We argue that this transition might be responsible for some yet unclear features of experiments, like the appearance of secondary oscillations and bimodal droplet size distributions.
Non-equilibrium steady states (NESS) of Markov processes give rise to non-trivial cyclic probability fluxes. Cycle decompositions of the steady state offer an effective description of such fluxes. Here, we present an iterative cycle decomposition exhibiting a natural dynamics on the space of cycles that satisfies detailed balance. Expectation values of observables can be expressed as cycle "averages", resembling the cycle representation of expectation values in dynamical systems. We illustrate our approach in terms of an analogy to a simple model of mass transit dynamics. Symmetries are reflected in our approach by a reduction of the minimal number of cycles needed in the decomposition. These features are demonstrated by discussing a variant of an asymmetric exclusion process (TASEP). Intriguingly, a continuous change of dominant flow paths in the network results in a change of the structure of cycles as well as in discontinuous jumps in cycle weights.
We discuss the interplay between a slow continuous drift of temperature, which induces continuous phase separation, and the non-linear diffusion term in the $\phi^4$-model for phase separation of a binary mixture. This leads to a bound for the stability of diffusive demixing. It is argued that these findings are not specific to the $\phi^4$ model, but that they always apply up to slight modifications of the bound. In practice stable diffusive demixing can only be achieved when special precautions are taken in experiments on real mixtures. Therefore, the recent observations on complex dynamical behavior in such systems should be considered as a new challenge for understanding generic features of phase-separating systems.
A one-dimensional continuum description of growth on vicinal surfaces in the presence of immobile impurities predicts that the impurities can induce step bunching when they suppress the diffusion of adatoms on the surface. In the present communication we verify this prediction by kinetic Monte-Carlo simulations of a two-dimensional solid-on-solid model. We identify the conditions where quasi one-dimensional step flow is stable against island formation or step meandering, and analyse in detail the statistics of the impurity concentration profile. The sign and strength of the impurity-induced step interactions is determined by monitoring the motion of pairs of steps. Assemblies containing up to 20 steps turn out to be unstable towards the emission of single steps. This behavior is traced back to the small value of the effective, impurity-induced attachment asymmetry for adatoms. An analytic estimate for the critical number of steps needed to stabilize a bunch is derived and confirmed by simulations of a one-dimensional model.
We consider the demixing of a binary fluid mixture, under gravity, which is steadily driven into a two phase region by slowly ramping the temperature. We assume, as a first approximation, that the system remains spatially isothermal, and examine the interplay of two competing nonlinearities. One of these arises because the supersaturation is greatest far from the meniscus, creating inversion of the density which can lead to fluid motion; although isothermal, this is somewhat like the Benard problem (a single-phase fluid heated from below). The other is the intrinsic diffusive instability which results either in nucleation or in spinodal decomposition at large supersaturations. Experimental results on a simple binary mixture show interesting oscillations in heat capacity and optical properties for a wide range of ramp parameters. We argue that these oscillations arise under conditions where both nonlinearities are important.
A generalized multibaker map with periodic boundary conditions is shown to model boundary-driven transport, when the driving is applied by a ``perturbation'' of the dynamics localised in a macroscopically small region. In this case there are sustained density gradients in the steady state. A non-uniform stationary temperature profile can be maintained by incorporating a heat source into the dynamics, which deviates from the one of a bulk system only in a (macroscopically small) localized region such that a heat (or entropy) flux can enter an attached thermostat only in that region. For these settings the relation between the average phase-space contraction, the entropy flux to the thermostat and irreversible entropy production is clarified for stationary and non-stationary states. In addition, thermoelectric cross-effects are described by a multibaker chain consisting of two parts with different transport properties, modelling a junction between two metals.
A consistent description of simultaneous heat and particle transport, including cross effects, and the associated entropy balance is given in the framework of a deterministic dynamical system. This is achieved by a multibaker map where, besides the phase-space density of the multibaker, a second field with appropriate source terms is included in order to mimic a spatial temperature distribution and its time evolution. Conditions are given to ensure consistency in an appropriately defined continuum limit with the thermodynamic entropy balance. They leave as the only free parameter of the model the entropy flux let directly into a surroundings. If it vanishes in the bulk, the transport properties of the model are described by the thermodynamic transport equations. Another choice leads to a uniform temperature distribution. It represents transport problems treated by means of a thermostatting algorithm, similar to the one considered in non-equilibrium molecular dynamics.
We give a thermodynamically consistent description of simultaneous heat and particle transport, as well as of the associated cross-effects, in the framework of a chaotic dynamical system, a generalized multibaker map. Besides the density, a second field with appropriate source terms is included in order to mimic, after coarse graining, a spatial temperature distribution and its time evolution. A new expression is derived for the irreversible entropy production in a steady state, as the average of the growth rate of the relative density, a unique combination of the two fields.
Braneshop, and
the US National Science Foundation.
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