Carbon Dot-Decorated Graphite Carbon Nitride Composites for Enhanced Solid-Phase Microextraction of Chlorobenzenes from Water

In this work, carbon dot-decorated graphite carbon nitride composites (CDs/g-C3N4) were synthesized and innovatively used as a SPME coating for the sensitive determination of chlorobenzenes (CBs) from water samples, coupled with gas chromatography-mass spectrometry. The CDs/g-C3N4 coating presented superior extraction performance in comparison to pristine g-C3N4, owing to the enhancement of active groups by CDs. The extraction capacities of as-prepared SPME coatings are higher than those of commercial coatings due to the functions of nitrogen-containing and oxygen-containing group binding, pi-pi stacking, and hydrophobic interactions. Under optimized conditions, the proposed method exhibits a wide linearity range (0.25-2500 ng L-1), extremely low detection of limits (0.002-0.086 ng L-1), and excellent precision, with relative standard deviations of 5.3-9.7% for a single fiber and 7.5-12.6% for five fibers. Finally, the proposed method was successfully applied for the analysis of CBs from real river water samples, with spiked recoveries ranging from 73.4 to 109.1%. This study developed a novel and efficient SPME coating material for extracting organic pollutants from environmental samples.

Automated summary

In this study, carbon dot-decorated graphite carbon nitride composites (CDs/g-C3N4) were synthesized and used as a SPME coating for the sensitive determination of chlorobenzenes (CBs) from water samples. The CDs/g-C3N4 coating exhibited superior extraction performance compared to pristine g-C3N4 due to the presence of active groups introduced by CDs. The as-prepared SPME coatings showed higher extraction capacities than commercial coatings, attributed to the functions of nitrogen-containing and oxygen-containing group binding, pi-pi stacking, and hydrophobic interactions. The proposed method demonstrated a wide linear range, extremely low detection limits, and excellent precision under optimized conditions.