Fluorescence detection of three types of pollutants based on fluorescence resonance energy transfer and its comparison with colorimetric detection

基于荧光共振能量转移的三类污染物荧光检测及其与比色检测的比较

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作者:Yifei Kong, Dan Liu, Xinran Guo, Xinyue Chen

Abstract

This study aimed at three representative pollutants, benzidine, cyromazine, and streptomycin, which were commonly used and posed a great threat to both environment and human health, mainly to explore a fast, simple, sensitive, visible naked-eye detection method. Colorimetric detection by gold nanoparticles (AuNPs) was first attempted. The cross-linking reaction occurred owing to the strong forces between the targets and AuNPs, leading to aggregation and color change. However, large-scale aggregation was easily formed and settled, which failed to achieve accurate quantification. Thus, AuNPs are considered to be used in fluorescence detection as reaction bridges. The introduction of AuNPs could effectively quench the fluorescence of Rhodamine B based on fluorescence resonance energy transfer (FRET). Moreover, a classical "on-off-on" fluorescence detection system was constructed based on nanomaterials. When AuNPs were added, the red fluorescence of the Rhodamine B solution could be effectively quenched (the "off" reaction). However, the tight cross-linking reaction between the three targets and AuNPs occurred through the strong affinity, causing Rhodamine B to dissociate in the solution. The fluorescence was rapidly restored, accompanied by a significant enhancement of fluorescence intensity (the "on" reaction). The fluorescent responses toward the three targets were established, resulting in good linearity in a wide range with low detection limits. Moreover, through the investigation of specificity, the fluorescence sensor exhibited satisfying selectivity and high binding affinity to the detected targets among the same types of inferences, indicating great potential for practical application. This simple, fast and sensitive fluorescence detection system was first used for simultaneously detecting three types of pollutants and finally successfully applied to real samples.

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