Tyndall-effect-enhanced supersensitive naked-eye determination of mercury (II) ions with silver nanoparticles

The surface plasmon resonance (SPR) property of colloidal silver nanoparticles (AgNPs) has been widely adopted for designing colourimetric assays. However, their light scattering property--Tyndall effect (TE)--has been almost completely ignored by analysts to date. Herein, this work initially explores the analytical advantages of the AgNPs' TE as the scattering signal for supersensitive visual point-of-need detection of divalent mercuric (Hg2+) ions within several minutes. An intense red TE signal will be created in the colloidal AgNP solution under the irradiation of a portable laser pointer pen (635 nm). Upon introduction of Hg2+ sample, the degradation of AgNPs takes place because of the specific redox reactions between the analyte ions and the nanoprobes, leading to a significant decrease or even disappearance of the TE in the final reaction mixture. The naked-eye changes in the TE intensity enable qualitative analysis of Hg2+ down to -5 nM. The precise quantitative readout can be further realized by using a smartphone for mobile imaging measurement. The results show that such equipmentfree TE-inspired assay (TEA) can linearly detect Hg2+ in a concentration range from 5 nM to 4 mu M. Its detection limit for the analyte was estimated to be as low as -0.85 nM, offering a -5400-fold enhancement in the assay sensitivity over the traditional SPR-based colourimetric nanosensors using AgNP probes. The satisfactory recovery results of assaying several real polluted pond water samples and patient serum samples additionally demonstrate the accuracy and practicality of the proposed TEA approach.

Automated summary

This study utilizes the light scattering property of colloidal silver nanoparticles to develop a highly sensitive method for detecting divalent mercury ions. By introducing Hg2+ samples, the degradation of AgNPs leads to a decrease in light scattering signal, enabling qualitative analysis of Hg2+ and quantitative detection with a smartphone.