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Stochastic Inference of Surface-Induced Effects using Brownian Motion

Abstract : Brownian motion in confinement and at interfaces is a canonical situation, encountered from fundamental biophysics to nanoscale engineering. Using the Lorenz-Mie framework, we optically record the thermally-induced tridimensional trajectories of individual microparticles, within salty aqueous solutions, in the vicinity of a rigid wall, and in the presence of surface charges. We construct the time-dependent position and displacement probability density functions, and study the non-Gaussian character of the latter which is a direct signature of the hindered mobility near the wall. Based on these distributions, we implement a novel, robust and self-calibrated multifitting method, allowing for the thermal-noise-limited inference of diffusion coefficients spatially-resolved at the nanoscale, equilibrium potentials, and forces at the femtoNewton resolution.
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Contributor : Thomas Salez <>
Submitted on : Wednesday, April 28, 2021 - 12:24:31 PM
Last modification on : Thursday, July 8, 2021 - 6:18:50 PM


  • HAL Id : hal-03047058, version 2
  • ARXIV : 2012.05512



Maxime Lavaud, Thomas Salez, Yann Louyer, Yacine Amarouchene. Stochastic Inference of Surface-Induced Effects using Brownian Motion. Physical Review Research, American Physical Society, 2021, 3, pp.L032011. ⟨hal-03047058v2⟩



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