Issue 22, 2019

A biomimetic fluorescent nanosensor based on imprinted polymers modified with carbon dots for sensitive detection of alpha-fetoprotein in clinical samples

Abstract

A biomimetic fluorescent nanosensor based on molecularly imprinted polymers modified with carbon dots (CDs@MIPs) has been prepared for rapid, selective and sensitive detection of alpha-fetoprotein (AFP) in clinical samples. The nanosensor was produced using vinyl-functionalized CDs (V-CDs) as transducer elements and support materials, AFP as the template protein, N-isopropylacrylamide (NIPAM) and 4-vinylphenylbronic acid (VPBA) as the thermo-responsive and pH-responsive monomer, respectively, and ammonium peroxodisulphate (APS) and N,N′-methylene bisacrylamide (MBA) as the initiator and cross-linker, respectively. The newly synthesized nanosensor was characterized by FT-IR, TEM, XRD and elemental analysis, which unambiguously confirmed the successful formation of the nanosensor. The fluorescence quenching degree of CDs@MIPs exhibited a good linear response to AFP in a concentration range of 10 to 100 ng mL−1, the limit of detection (LOD) of 0.474 ng mL−1, and high recoveries at three spiking levels of AFP ranging from 97.05% to 102.00%, with relative standard deviations (RSDs) below 4.2% being obtained. Moreover, the proposed CDs@MIPs were successfully exploited to detect AFP in human serum samples. This study successfully established a novel method for rapid, convenient, and highly sensitive and selective detection of AFP, which provides new ideas for the detection of tumor markers.

Graphical abstract: A biomimetic fluorescent nanosensor based on imprinted polymers modified with carbon dots for sensitive detection of alpha-fetoprotein in clinical samples

Article information

Article type
Paper
Submitted
09 Jun 2019
Accepted
01 Sep 2019
First published
17 Sep 2019

Analyst, 2019,144, 6760-6772

A biomimetic fluorescent nanosensor based on imprinted polymers modified with carbon dots for sensitive detection of alpha-fetoprotein in clinical samples

C. Sun, L. Pan, L. Zhang, J. Huang, D. Yao, C. Wang, Y. Zhang, N. Jiang, L. Chen and C. Yuan, Analyst, 2019, 144, 6760 DOI: 10.1039/C9AN01065C

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