Selective colorimetric sensors based on the monitoring of an unmodified silver nanoparticles (AgNPs) reduction for a simple and rapid determination of mercury

In this work, selective calorimetric sensors for simple and rapid detection of Hg(II) ions based on the monitoring of an unmodified silver nanoparticles (AgNPs) reduction were developed. The average diameter of synthesized AgNPs was 8.3 +/- 1.4 nm which was characterized by transmission electron microscopy (TEM). The abrupt change in absorbance of the unmodified AgNPs was observed which progressively decreased and slightly shifted to the blue wavelength as the concentration of Hg(II) increased, indicating the oxidation of Ag(0) to Ag(I) occurred. It appears that the AgNPs were oxidized by Hg(II), resulting in disintegration of the AgNPs into smaller particles as well as mediating the reduction of Hg(II) to Hg(0) adsorbed onto the surface of AgNPs. The adsorption of Hg(0) resulted in the lack of sufficient charges on AgNPs surfaces due to the decrease in the surface coverage of negatively charged citrate molecules, which then leaded to enlargement of AgNPs. The calibration curve of this technique was demonstrated from 0.5 to 7 ppm (r(2) = 0.995), the limit of detection (LOD) was 0.06 ppm (SDblank/slope of calibration curve) with the precision (RSD, n = 4) of 3.24-4.53. Interestingly, the results show a significant enhance in the Hg(II) analytical sensitivity when Cu(II) is doped onto the unmodified AgNPs, which improves the quantitative detection limit to 0.008 ppm. In addition, greater selectivity toward Hg(II) compared with the other metal ions tested was observed. Furthermore, the percentage recoveries of spiked drinking water, tap water and SRM1641d (mercury in water) were in acceptable range with a good precision (RSD) which were in agreement with the values obtained from graphite furnace atomic absorption spectrometer (GFAAS). The technique proposed in this study provides a rapid, simple, sensitive and selective detection method for Hg(II) in water samples. (C) 2015 Elsevier B.V. All rights reserved.