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Power Spectral Density Analysis of Solid-State Nanopore Signals: Application to Stability Estimation.

Pratima Upretee1, Eric Beamish2, Wouter Botermans2

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Summary

We developed a quantitative method using power spectral density (PSD) analysis to automatically assess nanopore stability and predict wettedness. This data-driven framework improves the reliability of solid-state nanopore sensing platforms.

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Area of Science:

  • Nanotechnology
  • Biosensing
  • Physical Chemistry

Background:

  • Solid-state nanopore sensing demands stable, low-noise ionic current baselines for reliable measurements.
  • Current methods for evaluating nanopore stability often rely on subjective visual inspection, limiting throughput and consistency.

Purpose of the Study:

  • To establish a robust, quantitative framework for assessing nanopore wettedness and stability using power spectral density (PSD) analysis.
  • To develop an automated method for predicting nanopore wettedness, moving beyond subjective evaluations.

Main Methods:

  • Utilized power spectral density (PSD) analysis to characterize the noise floor of ionic current measurements.
  • Compared multiple PSD fitting models and weighting strategies, identifying a five-component, five-parameter (5C5P) model with high-frequency, low-PSD (HFLS) weighting as optimal.
  • Applied logistic regression using noise coefficients (1/f noise, white-noise, low-frequency noise) and applied voltage to predict nanopore wettedness.

Main Results:

  • The optimized PSD analysis framework accurately characterized nanopore noise.
  • A logistic regression classifier trained on noise features achieved a high median F1-score of 98% for wettedness prediction across varied voltages and pore dimensions.
  • The classifier demonstrated reliable performance in segment-wise evaluations, mimicking real-time operation.

Conclusions:

  • The proposed physics-informed, data-driven framework enables automated, quantitative wettedness prediction and stability assessment for solid-state nanopores.
  • This approach offers a pathway toward reliable real-time quality control for high-throughput nanopore sensing platforms.