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We study the deterministic dynamics of N point particles moving at a constant speed in a 2D table made of two polygonal urns connected by an active rectangular channel, which applies a feedback control on the particles, inverting the horizontal component of their velocities when their number in the channel exceeds a fixed threshold. Such a bounce-back mechanism is non-dissipative: it preserves volumes in phase space. An additional passive channel closes the billiard table forming a circuit in which a stationary current may flow. Under specific constraints on the geometry and on the initial conditions, the large N limit allows nonequilibrium phase transitions between homogeneous and inhomogeneous phases. The role of ergodicity in making a probabilistic theory applicable is discussed for both rational and irrational urns. The theoretical predictions are compared with the numerical simulation results. Connections with the dynamics of feedback-controlled biological systems are highlighted.

We present results about the shape, size, structure, conformational stability, and hydrodynamics of alpha-conotoxin AuIB (a disulfide-rich peptide from the venom of Conus aulicus, recognized as a nicotinic acetylcholine antagonist with great pharmaceutical potential) from very long (0.5 mus) massively parallel molecular dynamics (MD) simulations in full atomistic detail. We extract coarse-grained descriptors of protein shape (ellipsoid), and of translational and rotational mobilities, i.e., the basic components at the lowest hierarchical level in a multiscale modeling strategy. Structural analysis reveals the folded conformation and asymmetric shape to be strongly favored for conotoxin. In accordance with experimental findings, conformational stability is observed and found to be linked to the presence of the alpha-helix along the 15 residues and to the existence of the two disulfide bonds. We find rotational (D(r)) and translational (D(t)) diffusivities to be suitable descriptors of coarse-grained dynamics, i.e., of the hydrodynamic behavior, and obtain D(r) = 3.62 (+/-0.17) x 10(8) s(- 1) and D(t) = 1.08 (+/-0.4) x 10(- 10) m(2) s(- 1). We further compare the MD-computed coarse-grained descriptors with first principles theoretical predictions based on the extended Hess-Doi Fokker-Planck approach which relates particle shape and dimensions to diffusion coefficients. An excellent agreement between simulation data and analytical predictions is observed for both dynamical descriptors. This comparison strongly suggests that diffusivities of rigid biomolecules much larger than the alpha-conotoxin AuIB studied here can be obtained from the coarse-grained shape descriptor (ellipsoid) derived from relatively short MD simulations.

The field-dependent relaxation dynamics of suspended magnetic nanoparticles continues to present a fascinating topic of basic science that at the same time is highly relevant for several technological and biomedical applications. Renewed interest in the intriguing behavior of magnetic nanoparticles in response to external fields has at least in parts be driven by rapid advances in magnetic fluid hyperthermia research. Although a wealth of experimental, theoretical, and simulation studies have been performed in this field in recent years, several contradictory findings have so far prevented the emergence of a consistent picture. Here, we present a dynamic mean-field theory together with comprehensive computer simulations of a microscopic model system to systematically discuss the influence of several key parameters on the relaxation dynamics, such as steric and dipolar interactions, the external magnetic field strength and frequency, as well as the ratio of Brownian and Néel relaxation time. We also discuss the specific and intrinsic loss power as measures of the efficiency of magnetic fluid heating and discuss optimality conditions in terms of fluid and field parameters. Our results are helpful to reconcile contradictory findings in the literature and provide an important step towards a more consistent understanding. In addition, our findings also help to select experimental conditions that optimize magnetic fluid heating applications. [ABSTRACT FROM AUTHOR]

Over the past two decades polymer nanocomposites have received tremendous interest from industry and academia due to their advanced properties comparative to polymer blends. Many computational studies have revealed that the macroscopic properties of polymer nanocomposites depend strongly on the microscopic polymer structure and conformations. In this article we review computer simulation studies of the fundamental problem of homopolymers structure and dimensions in nanocomposites containing bare or grafted spherical or rod nanoparticles. Experimentally, there is controversy over whether the addition of nanoparticles in a polymer matrix can perturb the polymer chains. [ABSTRACT FROM PUBLISHER]

We present a method to map the full equilibrium distribution of the primitive-path (PP) length, obtained from multi-chain simulations of polymer melts, onto a single-chain mean-field ‘target’ model. Most previous works used the Doi–Edwards tube model as a target. However, the average number of monomers per PP segment, obtained from multi-chain PP networks, has consistently shown a discrepancy of a factor of two with respect to tube-model estimates. Part of the problem is that the tube model neglects fluctuations in the lengths of PP segments, the number of entanglements per chain and the distribution of monomers among PP segments, while all these fluctuations are observed in multi-chain simulations. Here we use a recently proposed slip-link model, which includes fluctuations in all these variables as well as in the spatial positions of the entanglements. This turns out to be essential to obtain qualitative and quantitative agreement with the equilibrium PP-length distribution obtained from multi-chain simulations. By fitting this distribution, we are able to determine two of the three parameters of the model, which govern its equilibrium properties. This mapping is executed for four different linear polymers and for different molecular weights. The two parameters are found to depend on chemistry, but not on molecular weight. The model predicts a constant plateau modulus minus a correction inversely proportional to molecular weight. The value for well-entangled chains, with the parameters determined ab initio, lies in the range of experimental data for the materials investigated. [ABSTRACT FROM AUTHOR]

Abstract: Structural formation mechanisms of carbon nanotube (CNT) buckypaper and their effects on its mechanical properties are studied with numerical simulations. A bond swap algorithm, resulting from coupling the molecular dynamics and Monte Carlo methods, has been developed to equilibrate initial structures of buckypaper, generated by a random walk approach. Entanglement and bundling mechanisms are found to affect major structural features of buckypaper. Both mechanisms are evaluated quantitatively by calculating the entanglement network and pore size of buckypaper. Compared with (8,8)-(12,12) double-walled CNT, the structure of (5,5) single-walled CNT buckypaper is mainly dominated by entanglement, due to its smaller adhesion energy. We show that the pore size of modeled buckypaper, containing both types of CNTs, can be tuned from 7nm to 50nm by increasing the double-walled CNT content from 0wt% to 100wt%, due to the transformation from entanglement-dominated to bundling-dominated structures. Such an observation agrees exceptionally well with experimental results. Both entanglement and bundling mechanisms are also found to play important roles in the mechanical properties of buckypaper. The findings open a way to tailor both structural and mechanical properties of buckypaper, such as Young’s modulus or Poisson’s ratio, by using different CNTs and their mixtures. [Copyright &y& Elsevier]

This review is concerned with the nonequilibrium dynamics and structure of complex fluids based on simple micro- and mesoscopic physical models which are not rigorously solvable by analytic methods. Special emphasis is placed on the finitely extendable nonlinear elastic (FENE) chain models which account for molecular stretch, bending, and topology. More coarse-grained descriptions such as primitive path models, and elongated particle models are reviewed as well. We focus on their inherently anisotropic material—in particular rheological—properties via deterministic and stochastic approaches. A number of representative examples are given on how simple (often high-dimensional) models can, and have been implemented in order to enable the analysis of the microscopic origins of the nonlinear viscoelastic behavior of polymeric materials. These examples are shown to provide us with a number of routes for developing and establishing coarse-grained (low-dimensional) models devoted to the prediction of a reduced number of significant material properties. At this stage approximations which allow for an analytical treatment are discussed as well. Concerning the types of complex fluids, we cover the range from flexible to semiflexible polymers in melts and solutions, wormlike micelles, structural suspensions including ferrofluids in field-induced anisotropic or liquid crystalline phases. [Copyright &y& Elsevier]

The embedded atom method is adapted to study solid friction and the mechanical behavior of a model metal which incorporates the effect of electronic glue in its structure. The elastic properties of real metals are reproduced by a set of basic model potentials as revealed by analytic considerations. A slightly modified version of a classical nonequilibrium molecular dynamics computer simulation is employed to study the dynamics and structural changes of the model metal undergoing a process of solid friction and an uniaxial compression in order to analyze, e.g., plastic yield, transient friction coefficients, and the underlying structure. Under the appropriate choice of parameters, the model turns out to also be applicable for studying multiscale structures in porous metals. [ABSTRACT FROM AUTHOR]