Quantifying Population Reversibility of Sensor Performance inMulti-Cycle Single-Sensor Recovery Assay

Abstract

Quantitative chemical imaging requires sensors that reliably recover across repeated exposures. While solution-phase bulk measurements provide an averaged response of the sensor population, imaging at the single-sensor level enables mapping of biological processes with spatiotemporal resolution, revealing localized events and interaction sites. To translate such imaging into calibrated measurements, sensor variability under repeated analyte exposures must be analyzed. This work introduces a generic workflow that combines an automated microfluidic flow imaging platform with systematic characterization of the response, recovery, and reversibility of individual nanosensors across multi-cycle challenges. For representative implementation, three near-infrared fluorescent single-walled carbon nanotube (SWCNT) sensor models are tested, each with a distinct functionalization rendering it optically responsive to a corresponding exemplar target: dopamine, thiocholine, or serotonin. While first-cycle responses averaged over the entire field of view recapitulate ensemble calibration, single-sensor analysis uncovers broad heterogeneity in response magnitude, signal recovery, and reversibility across hundreds of individual SWCNTs under repeated exposure and wash cycles. To compare performance across cycles, a standardized Population Reversibility Score based on Kullback-Leibler Divergence is introduced, condensing response distributions into a single cycle- and concentration-dependent, quantitative metric. This framework is generally applicable to other sensor-analyte systems with transient readouts, guiding optimization for spatiotemporal analyte mapping.