Paper-Based Fluorescent Sensor for Rapid Multi-Channel Detection of Tetracycline Based on Graphene Quantum Dots Coated with Molecularly Imprinted Polymer

Author:

Wang Linzhe123,Hu Jingfang134,Wei Wensong25,Song Yu134,Li Yansheng13,Gao Guowei13,Zhang Chunhui25,Fu Fangting5

Affiliation:

1. Beijing Key Laboratory of Sensor, Beijing Information Science & Technology University, Beijing 100101, China

2. Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences/Key Laboratory of Agricultural Product Processing, Ministry of Agriculture, Beijing 100193, China

3. Key Laboratory of Modern Measurement and Control Technology, Ministry of Education, Beijing Information Science and Technology University, Beijing 100192, China

4. State Key Laboratories of Transducer Technology, Shanghai Institute of Microsystems and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China

5. Zibo Institute for Digital Agriculture and Rural Research, Zibo 255051, China

Abstract

In this paper, we developed a paper-based fluorescent sensor using functional composite materials composed of graphene quantum dots (GQDs) coated with molecularly imprinted polymers (MIPs) for the selective detection of tetracycline (TC) in water. GQDs, as eco-friendly fluorophores, were chemically grafted onto the surface of paper fibers. MIPs, serving as the recognition element, were then wrapped around the GQDs via precipitation polymerization using 3-aminopropyltriethoxysilane (APTES) as the functional monomer. Optimal parameters such as quantum dot concentration, grafting time, and elution time were examined to assess the sensor’s detection performance. The results revealed that the sensor exhibited a linear response to TC concentrations in the range of 1 to 40 µmol/L, with a limit of detection (LOD) of 0.87 µmol/L. When applied to spiked detection in actual water samples, recoveries ranged from 103.3% to 109.4%. Overall, this paper-based fluorescent sensor (MIPs@GQDs@PAD) shows great potential for portable, multi-channel, and rapid detection of TC in water samples in the future.

Funder

Agricultural Science and Technology Innovation Program of the Institute of Food Science and Technology, the Chinese Academy of Agricultural Sciences

National Science Foundation of China

National Key R&D Program of China

Chinese Academy of Agricultural Sciences (CAAS) Digital Agricultural and Rural Research Institute (Zibo) Innovation Team Funding

Publisher

MDPI AG

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