826 research outputs found

    New aspects on Current enhancement in Brownian motors driven by non Gaussian noises

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    Recent studies on Brownian motors driven by colored non Gaussian noises have shown that the departure of the noise distribution from Gaussian behavior induces an enhancement of its current and efficiency. Here we discuss some new aspects of this phenomenon focusing in some analytical results based in an adiabatic approximation, and in the analysis of the long probability distribution tails' role.Comment: 11 pages, 4 figs. Physica A (in press

    Toy model for molecular motors

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    A hopping model for molecular motors is presented consisting of a state with asymmetric hopping rates with period 2 and a state with uniform hopping rates. State changes lead to a stationary unidirectional current of a particle. The current is explicitly calculated as a function of the rate of state changes, including also an external bias field. The Einstein relation between the linear mobility of the particle and its diffusion coefficient is investigated. The power input into the system is derived, as well as the power output resulting from the work performed against the bias field. The efficiency of this model is found to be rather small.Comment: 11 pages Latex, 7 postscript figures, to be published in Physica

    A Master equation approach to modeling an artificial protein motor

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    Linear bio-molecular motors move unidirectionally along a track by coordinating several different processes, such as fuel (ATP) capture, hydrolysis, conformational changes, binding and unbinding from a track, and center-of-mass diffusion. A better understanding of the interdependencies between these processes, which take place over a wide range of different time scales, would help elucidate the general operational principles of molecular motors. Artificial molecular motors present a unique opportunity for such a study because motor structure and function are a priori known. Here we describe use of a Master equation approach, integrated with input from Langevin and molecular dynamics modeling, to stochastically model a molecular motor across many time scales. We apply this approach to a specific concept for an artificial protein motor, the Tumbleweed.Comment: Submitted to Chemical Physics; 9 pages, 7 figure

    Parrondo's games as a discrete ratchet

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    We write the master equation describing the Parrondo's games as a consistent discretization of the Fokker--Planck equation for an overdamped Brownian particle describing a ratchet. Our expressions, besides giving further insight on the relation between ratchets and Parrondo's games, allow us to precisely relate the games probabilities and the ratchet potential such that periodic potentials correspond to fair games and winning games produce a tilted potential.Comment: 4 pages, 3 figure

    Directed transport in a ratchet with internal and chemical freedoms

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    We consider mechanisms of directed transport in a ratchet model comprising, besides the external freedom where transport occurs, a chemical freedom that replaces the familiar external driving by an autonomous dynamics providing energy input, and an internal freedom representing a functional mode of a motor molecule. The dependence of the current on various parameters is studied in numerical simulations of our model. In particular, we point out the role of the internal freedom as a buffer between energy input and output of mechanical work that allows a temporary storage of injected energy and can contribute to the efficiency of current generation.Comment: 7 pages, 9 figure

    The flashing ratchet and unidirectional transport of matter

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    We study the flashing ratchet model of a Brownian motor, which consists in cyclical switching between the Fokker-Planck equation with an asymmetric ratchet-like potential and the pure diffusion equation. We show that the motor really performs unidirectional transport of mass, for proper parameters of the model, by analyzing the attractor of the problem and the stationary vector of a related Markov chain.Comment: 11 page
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