A Molecularly Imprinted Polymer Capped Nitrogen‐Doped Graphene Quantum Dots System for Sensitive Determination of Tetracycline in Animal‐Derived Food. Issue 2 (16th January 2020)

• Record Type: Journal Article
• Authors: Zhang, Lei; Wang, Junping; Fang, Guozhen; Deng, Jiankang; Wang, Shuo
• Is Part Of: ChemistrySelect. Volume 5:Issue 2(2020)
• Page Start: 839
• Page End: 846
• Publication Date: 2020-01-16
• Subjects: Fluorescence -- Graphene quantum dots -- Molecular imprinted polymers -- Sensors -- Tetracyclines; Chemistry -- Periodicals; 540.5
• DOI: 10.1002/slct.201903868
• Languages: English
• ISSNs: 2365-6549

Abstract

Abstract: A novel, highly sensitive and selective fluorescent sensor based on water‐soluble nitrogen‐doped graphene quantum dots (N‐GQDs) coupled with molecularly imprinted polymers (MIPs) was developed to detect tetracycline (TC). Unique N‐GQDs@MIPs nanomaterials were synthesized via the simple sol‐gel method due to its advantages of saving time and costs. The prepared N‐GQDs were fairly uniform and the fluorescence quantum yield (QY) was up to 58.2%. Results of transmission electron microscopy (TEM) and Fourier transform infrared (FTIR) confirmed that the N‐GQDs@MIPs nanomaterial was prepared successfully. The imprinted factor (IF) of the synthesized N‐GQDs@MIPs was 3.42. Under the optimized experimental conditions, the fluorescence intensity and concentration of TC maintained a good linear range. The recovery experiment indicated this method can be applied to detecting TC in animal‐derived food. In summary, the proposed method provided a new and effective way for simple and rapid analysis of specific components in complex matrix samples. Abstract : A simple, rapid, highly sensitive and selective fluorescent sensor based on excellent fluorescent probe (water‐soluble nitrogen‐doped graphene quantum dots, N‐GQDs) coupled with highly selective recognition part (molecularly imprinted polymers, MIPs) was first developed to detect tetracycline (TC) in four animal‐derived food samples with complex matrix. Results showed that the proposed method confirmed good detection performance, including low detection limit, reasonable quantitative recoveries, low relative standard deviation and high reliability.