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One-step hydrothermal preparation of biomass-derived carbon dots as fluorescent probes for the detection of enrofloxacin in aquatic products

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Abstract

Enrofloxacin (ENR) residue is harmful to humans and the ecosystem. Therefore, it is necessary to develop a method for the detection of ENR residues in food. Herein, we have prepared novel biomass-derived carbon dots (BCDs) by a one-step hydrothermal method to detect ENR. The precursor material is cicada slough, which is one of the traditional crude drugs. Transmission electron microscopy (TEM) and X-ray diffraction (XRD) results confirmed the formation of BCDs. The average particle size of the prepared BCDs is 19.6 ± 0.2 nm, and the maximum excitation/emission wavelength is 355/436 nm. Under the optimal conditions, the detection linear of ENR ranged from 0.13 to 16.67 µM, and the detection limit was 0.069 µM. According to the experimental results, it can be applied in the detection of ENR in aquatic products with a good recovery rate (88.6%–104.7%), demonstrating its potential in detecting ENR in aquatic products.

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The datasets generated during and analyzed during the current study are available from the corresponding author upon reasonable request.

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Acknowledgements

The authors thank Professor Longshan Zhao from Shenyang Pharmaceutical University for proofreading this article.

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The authors did not receive support from any organization for the submitted work.

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Zhili Xiong and Longshan Zhao contributed to the conception of the study and helped perform the analysis with constructive discussions. Jiaxue Su, Chunyu Zhou, and Jialing Du experimented; Chunyu Zhou contributed significantly to the analysis and manuscript preparation; Jiaxue Su wrote the main manuscript text and prepared tables. Chunyu Zhou prepared figures. All authors reviewed the manuscript.

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Correspondence to Zhili Xiong or Longshan Zhao.

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Su, J., Zhou, C., Du, J. et al. One-step hydrothermal preparation of biomass-derived carbon dots as fluorescent probes for the detection of enrofloxacin in aquatic products. Food Measure 17, 4565–4574 (2023). https://doi.org/10.1007/s11694-023-01988-4

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