Thermophoresis of Brownian particles driven by coloured noise

Hottovy, Scott; Volpe, Giovanni; Wehr, Jan

doi:10.1209/0295-5075/99/60002

Cited by 26 publications

(39 citation statements)

References 27 publications

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“…Important examples include Brownian motors [38,39], active Brownian motion of self-propelled particles [40][41][42][43][44][45][46], hot Brownian motion [47], and Brownian motion in shear flows [48]. Recent theoretical studies also found that the inertias of particles and surrounding fluids can significantly affect the Brownian motion in nonequilibrium systems [49][50][51][52][53][54].…”

Section: Introductionmentioning

confidence: 99%

Brownian motion at short time scales

2013

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Brownian motion has played important roles in many different fields of science since its origin was first explained by Albert Einstein in 1905. Einstein's theory of Brownian motion, however, is only applicable at long time scales. At short time scales, Brownian motion of a suspended particle is not completely random, due to the inertia of the particle and the surrounding fluid. Moreover, the thermal force exerted on a particle suspended in a liquid is not a white noise, but is colored. Recent experimental developments in optical trapping and detection have made this new regime of Brownian motion accessible. This review summarizes related theories and recent experiments on Brownian motion at short time scales, with a focus on the measurement of the instantaneous velocity of a Brownian particle in a gas and the observation of the transition from ballistic to diffusive Brownian motion in a liquid.

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Section: Introductionmentioning

confidence: 99%

Brownian motion at short time scales

2013

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“…[13,14]. Similar considerations hold also in the case of diffusion gradients induced by, e.g., chemical gradients or temperature gradients [15,16,17].…”

Section: Diffusion Gradientsmentioning

confidence: 59%

Numerical simulation of optically trapped particles

Volpe

2014

12th Education and Training in Optics and Photonics Conference

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Some randomness is present in most phenomena, ranging from biomolecules and nanodevices to financial markets and human organizations. However, it is not easy to gain an intuitive understanding of such stochastic phenomena, because their modeling requires advanced mathematical tools, such as sigma algebras, the Itô formula and martingales. Here, we discuss a simple finite difference algorithm that can be used to gain understanding of such complex physical phenomena. In particular, we simulate the motion of an optically trapped particle that is typically used as a model system in statistical physics and has a wide range of applications in physics and biophysics, for example, to measure nanoscopic forces and torques.

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“…The particle is driven to regions with lower temperature so c T in Table I is negative. In liquids or in gases when small particles are considered, the interactions are more complex and both signs of c T can hold, see [46,47] and references therein.…”

Section: Phoresis In Turbulent Flowsmentioning

confidence: 99%

Clustering of particles in turbulence due to phoresis

et al. 2016

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We demonstrate that diffusiophoretic, thermophoretic and chemotactic phenomena in turbulence lead to clustering of particles on multi-fractal sets that can be described using one single framework, valid when the particle size is much smaller than the smallest length scale of turbulence l0. To quantify the clustering, we derive positive pair correlations and fractal dimensions that hold for scales smaller than l0. For scales larger than l0 the pair correlation function is predicted to show a stretched exponential decay towards 1. In the case of inhomogeneous turbulence we find that the fractal dimension depends on the direction of inhomogeneity. By performing experiments with particles in a turbulent gravity current we demonstrate clustering induced by salinity gradients in conformity to the theory. The particle size in the experiment is comparable to l0, outside the strict validity region of the theory, suggesting that the theoretical predictions transfer to this practically relevant regime. This clustering mechanism may provide the key to the understanding of a multitude of processes such as formation of marine snow in the ocean and population dynamics of chemotactic bacteria.

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Thermophoresis of Brownian particles driven by coloured noise

Cited by 26 publications

References 27 publications

Brownian motion at short time scales

Brownian motion at short time scales

Numerical simulation of optically trapped particles

Clustering of particles in turbulence due to phoresis

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