Ternary-Emission Molecularly Imprinted Ratiometric Fluorescence Sensor and Kit for the Rapid and Visual Detection of Enrofloxacin

In this study, a RGB based ternary-emission molecularly imprinted ratiometric fluorescence (MI-RFL) sensor was facilely constructed by using a post-imprinting mixing strategy for the sensitive detection of enrofloxacin (ENR). Upon excitation at 365 nm, the MI-RFL sensor exhibited significant emission peaks at 450, 550, and 620 nm. As the ENR concentration increased, the blue fluorescence generated by ENR in the sensing system gradually intensified, while the red and green fluorescence emitted by the quantum dots in the molecularly imprinted polymers (MIPs) was significantly quenched. Sensing conditions were systematically investigated, including the excitation wavelength, mixing ratio of red/green MIPs, the pH of the buffer solution, and the reaction time. Under the optimal conditions, the developed sensor showed a good linear relationship within the range of 0.25-4 ppm along with obvious color change, with a low detection limit of 0.134 ppm. High selectivity was also attained with an imprinting factor up to 11.65. When applied to real samples of seawater and seafood, the sensor showed good recovery rates of 94.3-126.4% and accuracy with a relative standard deviation of less than 3.97%. Furthermore, the sensor-based kit was easily fabricated and, thus, naked-eye detection of ENR was realized onsite. This study can provide a universal approach for the rapid and visual detection of ENR in complicated matrices.

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Publication Details

Journal
Biosensors
Published
2025-04-02
DOI
https://doi.org/10.3390/bios15040226
Citations
2
Primary Topic
Analytical chemistry methods development
Type
article
Field-Weighted Citation Impact
1.50

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article

Ternary-Emission Molecularly Imprinted Ratiometric Fluorescence Sensor and Kit for the Rapid and Visual Detection of Enrofloxacin

Siyuan Peng, Dani Sun, Jinhua Li, Shijie Liu et al.
2 citations
Biosensors
Analytical chemistry methods development
1.50
article

Ternary-Emission Molecularly Imprinted Ratiometric Fluorescence Sensor and Kit for the Rapid and Visual Detection of Enrofloxacin

Siyuan Peng, Dani Sun, Jinhua Li, Shijie Liu, Jingyi Yan, Huaying Fan
article en
2 citations

Abstract

In this study, a RGB based ternary-emission molecularly imprinted ratiometric fluorescence (MI-RFL) sensor was facilely constructed by using a post-imprinting mixing strategy for the sensitive detection of enrofloxacin (ENR). Upon excitation at 365 nm, the MI-RFL sensor exhibited significant emission peaks at 450, 550, and 620 nm. As the ENR concentration increased, the blue fluorescence generated by ENR in the sensing system gradually intensified, while the red and green fluorescence emitted by the quantum dots in the molecularly imprinted polymers (MIPs) was significantly quenched. Sensing conditions were systematically investigated, including the excitation wavelength, mixing ratio of red/green MIPs, the pH of the buffer solution, and the reaction time. Under the optimal conditions, the developed sensor showed a good linear relationship within the range of 0.25-4 ppm along with obvious color change, with a low detection limit of 0.134 ppm. High selectivity was also attained with an imprinting factor up to 11.65. When applied to real samples of seawater and seafood, the sensor showed good recovery rates of 94.3-126.4% and accuracy with a relative standard deviation of less than 3.97%. Furthermore, the sensor-based kit was easily fabricated and, thus, naked-eye detection of ENR was realized onsite. This study can provide a universal approach for the rapid and visual detection of ENR in complicated matrices.

BiosensorsVol. 15(4)
Yantai University (CN), Yantai Institute of Coastal Zone Research (CN)
National Natural Science Foundation of China
Life below water
Openalex Percentile: Top 21%
Analytical chemistry methods development
1.50
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Ternary-Emission Molecularly Imprinted Ratiometric Fluorescence Sensor and Kit for the Rapid and Visual Detection of Enrofloxacin — Siyuan Peng, Dani Sun, et al. · Biosensors (2025) | TGRS Research Map | TGRS