Stochastic Inference of Surface-Induced Effects using Brownian Motion

TL;DR

This paper develops a new method to analyze Brownian motion near surfaces, enabling precise inference of diffusion, potential, and force profiles at the nanoscale using optical tracking data.

Contribution

It introduces a robust, self-calibrated multifitting approach for spatially-resolved inference of nanoscale diffusion and forces from Brownian trajectories near surfaces.

Findings

Accurate inference of diffusion coefficients near surfaces.

Resolution of femtoNewton forces and potentials.

Validation of the method with experimental data.

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.