Abstract

In this study, an ultra-sensitive and selective biosensor for the detection of enrofloxacin (ENR) in complex matrices has been established. The study intelligently converges the spectral properties of core-shell upconverted nanoparticles (UCNPs) and the excellent adsorption satiating aptitudes of metal organic frameworks (MOFs). The principle was based on the fluorescence resonance energy transfer (FRET) between aptamer-modified UCNPs as the donors and MOFs MIL-101(Cr) as the effective receptors. The spectral overlap between UCNPs fluorescence emission and MIL-101(Cr) absorption generated the FRET process under the action of π-π stacking interactions. The presence of ENR induced conformational changes in the aptamer via preferential binding, resulting in a decline in the nucleobase exposure on the surface of MIL-101(Cr) and eventually in the fluorescence recovery. The fluorescence intensity (FL) was enhanced as the ENR concentration was increased. The detection limit was 0.034 ng⋅mL−1 and the linear calibration curve between logarithmic ENR concentration and FL was achieved in the range of 0.1–1000 ng⋅mL−1. The applicability of the platform for ENR recognition was confirmed in real fish and shrimp samples and further validated by a standard HPLC technique with no significant difference (p > 0.05). The current UCNPs-MIL-101(Cr) aptasensor proposes a fast, sensitive and quantitative detection approach for ENR in aquatic products.

Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.