Quantifying Population Reversibility of Sensor Performance in Multi-Cycle Single-Sensor Recovery Assay

Area of Science:

• Analytical Chemistry
• Materials Science
• Biotechnology

Background:

• Quantitative chemical imaging demands sensors with reliable recovery across repeated exposures.
• Single-sensor imaging offers spatiotemporal resolution for biological processes, but requires analysis of sensor variability for calibration.

Purpose of the Study:

• To introduce a generic workflow for characterizing individual nanosensor response, recovery, and reversibility under multi-cycle challenges.
• To assess sensor variability in quantitative chemical imaging applications.

Main Methods:

• An automated microfluidic flow imaging platform was combined with systematic characterization of individual nanosensors.
• Three near-infrared fluorescent single-walled carbon nanotube (SWCNT) sensor models targeting dopamine, thiocholine, and serotonin were tested.
• A Population Reversibility Score based on Kullback-Leibler Divergence was introduced to quantify performance across cycles.

Main Results:

• Single-sensor analysis revealed broad heterogeneity in response magnitude, signal recovery, and reversibility across hundreds of SWCNTs.
• While first-cycle ensemble averages matched bulk calibration, individual sensor behavior varied significantly under repeated exposure and wash cycles.
• The Population Reversibility Score provided a quantitative metric for cycle- and concentration-dependent performance.

Conclusions:

• Nanosensor variability is a critical factor in quantitative chemical imaging that is often masked by ensemble averaging.
• The developed workflow and Population Reversibility Score enable detailed analysis of sensor performance, guiding optimization for spatiotemporal analyte mapping.
• This framework is applicable to various sensor-analyte systems with transient readouts.