Sensitive detection of mercury using the fluorescence resonance energy transfer between CdTe/CdS quantum dots and Rhodamine 6G
Source abstract
Abstract
The sensitive and selective detection of mercury in aqueous solution is of paramount importance as the mercury concentration in drinking water above the threshold level set by world health organization can cause serious health issues to humans. We demonstrate a simple, facile, and cost-effective one pot synthesis route to synthesize MPA (3-mercaptopropionic acid) stabilized CdTe/CdS core-shell quantum dots and its application for detection of mercury using the fluorescence resonance energy transfer with a cationic dye, Rhodamine 6G. The quantum dots prepared via chemical reduction strategy using a combination of reducing agents, namely sodium borohydride and citric acid exhibit a high quantum efficiency (> 20% for solid state). Structural as well as luminescence studies of the prepared quantum dots were found to depend on the pH as well as the size of the quantum dots (hydrodynamic diameter ranging from 9 to 16 nm). Analysis of the fluorescence resonance energy transfer (FRET) between the prepared quantum dots and Rhodamine 6G elucidate that efficient energy transfer happens in the presence of a cetyl trimethyl ammonium bromide (CTAB) surfactant. Though the prepared CdTe/CdS quantum dots exhibit fluorescence quenching with an increase in mercury concentration and act as an “OFF-sensor,” the Rhodamine 6G-quantum dot pair employed here found to be a better approach as the inherent fluorescence of Rh6G is insensitive to mercury concentration. Our studies elucidate that the fluorescence ratio of Rh6G in a FRET pair follows a linear nature for the Stern-Volmer plot in the concentration range of Hg2+ ions (0.1 nM to 2 μM) and provide a LOD of 3.8 nM.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Quantum Dots Synthesis And Properties · Carbon and Quantum Dots Applications · Advanced Photocatalysis Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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