Decoding Noise in Nanofluidic Systems: Adsorption versus Diffusion Signatures in Power Spectra

TL;DR

This paper derives and validates a model for the power spectral density of particle fluctuations in nanofluidic channels, distinguishing signatures of diffusion and adsorption, aiding interpretation of noisy experimental signals.

Contribution

It introduces an analytical expression for PSD in nanofluidic systems that separates diffusion and adsorption signatures, validated by simulations and applicable to experimental data.

Findings

Identifies a $1/f^{3/2}$ scaling related to diffusion effects.

Detects a $1/f^2$ scaling associated with adsorption processes.

Shows PSD features can reveal underlying transport mechanisms.

Abstract

Adsorption processes play a fundamental role in molecular transport through nanofluidic systems, but their signatures in measured signals are often hard to distinguish from other processes like diffusion. In this paper, we derive an expression for the power spectral density (PSD) of particle number fluctuations in a channel, accounting for diffusion and adsorption/desorption to a wall. Our model, validated by Brownian dynamics simulations, is set in a minimal but adaptable geometry, allowing us to eliminate the effects of specific geometries. We identify distinct signatures in the PSD as a function of frequency $f$, including a $1/f^{3/2}$ scaling related to diffusive entrance/exit effects, and a $1/f^2$ scaling associated with adsorption. These scalings appear in key predicted quantities -- the total number of particles in the channel and the number of adsorbed or unadsorbed particles -- and can dominate or combine in non-trivial ways depending on parameter values. Notably, when there is a separation of timescales between diffusion inside the channel and adsorption/desorption times, the PSD can exhibit two distinct corners with well-separated slopes in some of the predicted quantities. We provide a strategy to identify adsorption and diffusion mechanisms in the shape of the PSD of experimental systems on the nano- and micro-scale, such as ion channels, nanopores, and electrochemical sensors, potentially offering insights into noisy experimental data.