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Understanding Sensitivity in Nanoscale Sensing Devices.

Dominik Duleba¹, Adria Martínez-Aviñó¹, Andriy Revenko¹

¹School of Chemistry, University College Dublin, Belfield, Dublin 4, Ireland.

ACS Measurement Science Au

|June 25, 2025

Summary

This summary is machine-generated.

Optimizing nanoscale sensor sensitivity involves balancing signal change and random errors. Geometric factors significantly impact sensor output variance, guiding design for improved performance in nanopore sensors.

Keywords:

Sobol nanopipette nanopore optimization sensitivity sensor uncertainty

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Area of Science:

Nanotechnology
Sensor Technology
Physical Chemistry

Background:

Sensor sensitivity is crucial but challenging to predict due to complex physical phenomena.
Sensitivity depends on analyte-induced signal change and random output fluctuations.
Controlling geometric and operating conditions is key to optimizing sensor performance.

Purpose of the Study:

To demonstrate sensitivity optimization in ion-current-rectifying nanopore sensors.
To identify key geometric and operating parameters influencing sensor sensitivity.
To provide a simulation-based framework for optimizing nanoscale sensor sensitivity.

Main Methods:

Utilized finite element analysis (FEA) to simulate sensor output distributions.
Employed Sobol analysis to identify critical parameters affecting sensor output errors.
Integrated signal change magnitude and output spread for sensitivity calculation and optimization.

Main Results:

Geometric parameters were identified as the most significant contributors to output variance.
Smaller pore radii and lower electrolyte concentrations increased the influence of cone angle errors and broadened output.
Highest sensitivity was predicted for larger pores at low electrolyte concentrations.

Conclusions:

Geometric design and operating conditions critically influence nanopore sensor sensitivity.
Simulation-based analysis provides a viable framework for optimizing sensor performance.
Experimental validation confirmed simulation predictions for optimal sensitivity parameters.