Issue 4, 2019

Deposition of CdTe quantum dots on microfluidic paper chips for rapid fluorescence detection of pesticide 2,4-D

Abstract

Rapid detection of pesticides in fruits is an ongoing challenge. The objective of the present study was to develop novel fluorescent microfluidic paper chips for specific recognition and sensitive detection of the pesticide 2,4-D through the electron-transfer-induced fluorescence quenching mechanism. CdTe quantum dots (QDs) were deposited onto cellulose paper (base material) to yield imprinted paper chips (paper@QDs@MIPs). This method allows the transferability of the molecularly imprinted fluorescence sensor from the liquid phase to the solid phase (paper base) for rapid and portable analysis. The resultant imprinted paper chips were effectively characterized, and they exhibited ideal ordered spatial network structure, chemical stability, and fluorescence property. The paper@QDs@MIPs showed that 2,4-D binding significantly reduced the fluorescence intensity within less than 18 min, and it achieved satisfactory linearity in the range of 0.83–100 μM and high detectability of 90 nM. The recognition specificity for 2,4-D relative to its analogues was shown, and the imprinting factor was 2.13. In addition, the recoveries of the spiked bean sprouts at three concentration levels ranged within 94.2–107.0%, with a relative standard deviation of less than 5.9%. Collectively, the device provided an effective platform for rapid recognition, convenience, and detection of trace food pollutants in complex matrices, thereby ensuring food safety and further promoting surface imprinting studies.

Graphical abstract: Deposition of CdTe quantum dots on microfluidic paper chips for rapid fluorescence detection of pesticide 2,4-D

Supplementary files

Article information

Article type
Paper
Submitted
25 Okt. 2018
Accepted
01 Dec. 2018
First published
03 Dec. 2018

Analyst, 2019,144, 1282-1291

Deposition of CdTe quantum dots on microfluidic paper chips for rapid fluorescence detection of pesticide 2,4-D

Z. Zhang, X. Ma, M. Jia, B. Li, J. Rong and X. Yang, Analyst, 2019, 144, 1282 DOI: 10.1039/C8AN02051E

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