3 years ago

Subnanomolar Sensitivity of Filter Paper-Based SERS Sensor for Pesticide Detection by Hydrophobicity Change of Paper Surface

Subnanomolar Sensitivity of Filter Paper-Based SERS
Sensor for Pesticide Detection by Hydrophobicity Change of Paper Surface
As a cost-effective approach for detecting trace amounts of pesticides, filter paper-based SERS sensors have been the subject of intensive research. One of the hurdles to overcome is the difficulty of retaining nanoparticles on the surface of the paper because of the hydrophilic nature of the cellulose fibers in paper. This reduces the sensitivity and reproducibility of paper-based SERS sensors due to the low density of nanoparticles and short retention time of analytes on the paper surface. In this study, filter paper was treated with alkyl ketene dimer (AKD) to modify its property from hydrophilic to hydrophobic. AKD treatment increased the contact angle of the aqueous silver nanoparticle (AgNP) dispersion, which consequently increased the density of AgNPs. The retention time of the analyte was also increased by preventing its rapid absorption into the filter paper. The SERS signal was strongly enhanced by the increased number of SERS hot spots owing to the increased density of AgNPs on a small contact area of the filter surface. The reproducibility and sensitivity of the SERS signal were optimized by controlling the distribution of AgNPs on the surface of the filter paper by adjusting the concentration of the AgNP solution. Using this SERS sensor with a hydrophobicity-modified filter paper, the spot-to-spot variation of the SERS intensity of 25 spots of 4-aminothiophenol was 6.19%, and the limits of detection of thiram and ferbam as test pesticides were measured to be 0.46 nM and 0.49 nM, respectively. These proof-of-concept results indicate that this paper-based SERS sensor can serve for highly sensitive pesticide detection with low cost and easy fabrication.

Publisher URL: http://dx.doi.org/10.1021/acssensors.7b00782

DOI: 10.1021/acssensors.7b00782

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