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

noise coefficient and the white-noise coefficient, together with a compact measure of overall low-frequency noise and the applied voltage, formed a compact and interpretable feature set for distinguishing wetted and unwetted pores. A logistic regression classifier trained on these features achieved high performance across a mixed-voltage dataset (median F1-score = 98%) and generalized effectively to unseen applied voltages and different pore dimensions. Segment-wise evaluation on one-second windows that mimic real-time operation demonstrated that the classifier reliably distinguished wetted and unwetted pores across all applied voltages. This physics-informed, data-driven framework enables automated wettedness prediction and stability assessment, indicating a pathway toward reliable real-time quality control for multiplexed, high-throughput solid-state nanopore sensing platforms.