On-Off-On Fluorescent Chemosensors Based on N/P-Codoped Carbon Dots for Detection of Microcystin-LR

The sensing of microcystin-LR (MC-LR) as a nonfluorescent species is still challenging since it has a negligible influence on the emission of fluorescent nanomaterials. Herein, a well-defined on-off-on indirect chemosensing strategy is developed by means of nitrogen/phosphorus-codoped carbon dots (N/P CDs) for highly sensitive and selective MC-LR determination. The nanoscale N/P CDs were conveniently synthesized by one-step hydrothermal carbonization of adenosine 5'-triphosphate (ATP) in aqueous media. The fluorescence intensity of the N/P CDs was quenched in the presence of Fe3+ due to the formation of Fe-O-P bonds between Fe3+ and the phosphate groups of the N/P CDs. The resulting N/P CD-Fe3+ system was sensitive to MC-LR, and its fluorescence intensity recovered dramatically and quickly since the MC-LR cavity competitively coordinated with Fe3+. Under optimized conditions, the proposed chemosensor exhibited a wide linear response for MC-LR in the range of 0.05-3 mu g/L, with a detection limit down to 17.1 ng/L. Moreover, the chemosensor was successfully applied to accurately detect MC-LR in real water samples and achieved high recoveries in the range of 95.0-109.3%, with relative standard deviations within 1.8-4.7%. By relying on the benefits of fluorescent chemosensors, such as simple operation, low production cost, fast response time, selectivity, and sensitivity, the proposed sensing platform possesses promising applicability for MC-LR determination in complex environmental water samples.

Automated summary

A highly sensitive and selective chemosensing strategy for MC-LR determination was developed using nitrogen/phosphorus-codoped carbon dots. The fluorescence intensity of the carbon dots was quenched by Fe3+ and recovered quickly in the presence of MC-LR. The sensor showed promising applicability for MC-LR determination in complex environmental water samples, with benefits including simple operation, low cost, fast response time, selectivity, and sensitivity.