Decoding noise in nanofluidic systems: Adsorption vs diffusion signatures in power spectra

Area of Science:

• Physics
• Physical Chemistry
• Nanotechnology

Background:

• Adsorption significantly impacts molecular transport in nanofluidic systems.
• Distinguishing adsorption signatures from diffusion in experimental data is challenging.
• Understanding these processes is crucial for applications in nano- and micro-scale devices.

Purpose of the Study:

• To derive a model for the power spectral density (PSD) of particle number fluctuations in a channel.
• To differentiate signatures of adsorption/desorption from diffusion.
• To provide a strategy for analyzing experimental data from nanofluidic systems.

Main Methods:

• Derivation of an analytical expression for the PSD.
• Validation using Brownian dynamics simulations.
• Analysis of particle number fluctuations in a minimal, adaptable channel geometry.

Main Results:

• Identified distinct PSD scaling behaviors: 1/f3/2 for diffusion and 1/f2 for adsorption.
• Demonstrated that these scalings can combine non-trivially based on system parameters.
• Observed two distinct corners in the PSD when timescales of diffusion and adsorption are separated.

Conclusions:

• The derived PSD model effectively distinguishes adsorption and diffusion mechanisms.
• The identified scaling signatures offer a strategy for interpreting experimental data from nanofluidic systems.
• This work provides insights into noisy experimental data from systems like ion channels and nanopores.