Beyond AI-Driven Curve Fitting: Mechanistic Insight as the Cornerstone of Reliable Sensing and Accurate Fitting

Accurate environmental detection fundamentally depends on reliable calibration. Although AI-assisted curve fitting improves analytical efficiency, it often relies on empirical, concentration-dependent correction coefficients that can introduce deviations from true values and compromise accuracy. Thus, instead of over-relying on AI-driven fitting, rigorous mechanistic understanding of sensing processes and deviation sources is essential to ensure reliable detection. Herein, we constructed a self-calibrating sensing system by functionalizing solid-state nanochannels with rationally designed super-sandwich (SSW) DNA probes, enabling reliable sensing and providing clear insight into the mechanisms underlying signal deviations. By integrating complementary fluorescence and ionic current readouts derived from the same molecular recognition event between the SSW-DNA probe and the target, the negative deviation of the fluorescence signal (exceeding −20.0%) and the positive deviation of the electrical signal (exceeding +20.0%) can effectively offset each other. These deviations, originating from incomplete SSW-DNA disassembly, were reduced to below ± 5.0% through this cancellation mechanism. This work not only significantly improves sensing reliability but also clarifies the underlying deviation mechanisms of nanochannel-based sensors, offering valuable insights for environmental, food, and biomedical applications.