Interpreting the power spectral density of a fluctuating colloidal current

TL;DR

This study uses a controllable colloidal microfluidic system to analyze the power spectral density of fluctuating particle currents, linking noise characteristics to underlying transport mechanisms, which can inform understanding of molecular transport in nanopores.

Contribution

It introduces a colloidal model system for experimental analysis of current fluctuations, connecting spectral features to particle dynamics and flow profiles, advancing the understanding of noise in transport processes.

Findings

Power spectral density reflects particle speed distributions and flow profiles.

Shot noise with finite transit time models the observed spectral scalings.

Particle velocity distributions are sensitive to confining geometry.

Abstract

The transport of molecules through biological and synthetic nanopores is governed by multiple stochastic processes that lead to noisy, fluctuating currents. Disentangling the characteristics of different noise-generating mechanisms is central to better understanding molecular transport at a fundamental level but is extremely challenging in molecular systems, due to their complexity and relative experimental inaccessibility. Here, we construct a colloidal model microfluidic system for the experimental measurement of particle currents, where the governing physical properties are directly controllable and particle dynamics directly observable, unlike in the molecular case. Currents of hard spheres fluctuate due to the random arrival times of particles into the channel and the distribution of particle speeds within the channel, which results in characteristic scalings in the power spectral density. We rationalise these scalings by quantitatively comparing to a model for shot noise with a finite transit time, extended to include the distribution of particle speeds. Particle velocity distributions sensitively reflect the confining geometry and we interpret and model these in terms of the underlying fluid flow profiles. Finally, we explore the extent to which details of these distributions govern the form of the resulting power spectral density, thereby establishing concrete links between the power spectral density and underlying mechanisms for this experimental system. This paves the way for establishing a more systematic understanding of the links between characteristics of transport fluctuations and underlying molecular mechanisms in driven systems such as nanopores.