Visual detection of lead ions based on nanoparticle-amplified magnetophoresis and Mie scattering

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Original languageEnglish
Article number127564
Journal / PublicationSensors and Actuators, B: Chemical
Online published16 Dec 2019
Publication statusPublished - 1 Mar 2020


Lead poisoning in drinking water is a serious threat to public health as it may cause permanent damage to the human nervous system and developmental disorders in children. Many detection methods were developed based on fluorometric, electrochemical, and chemiluminescent assays, but they require precise instruments for signal quantification, which limit the practicality in resource-limited sites. Here we provide a visual detection of lead ions based on nanoparticle-amplified magnetophoresis and Mie scattering. Lead ions first react with GR-5 DNAzyme to release a DNA fragment that can connect magnetic microparticles (MMPs) and gold nanoparticles (AuNPs) through DNA hybridization. Next, the connected AuNPs are collected by magnetic separation and then released. Benefit from the hundreds of oligonucleotides on the AuNPs, tiny amount of AuNPs, which represent low concentration of lead ions, can effectively connect a second pair of MMPs and polystyrene microparticles (PMPs). Upon magnetophoresis, it changes solution turbidity from milky to clear because of the reduction of Mie scattering when less free PMPs suspend in the solution. Our results achieved a limit of detection (LOD) at 55 pM in 100 μl (reaction concentration) and 1.108 nM in 5 μl (sample concentration), which is much lower than the prescribed level in drinking water suggested by Environmental Protection Agency (72 nM). In addition, it also achieves high selectivity against other metal ions (> 50,000 to 1) and is compatible with detection in tap water, providing a visual, sensitive detection without complicated instrument.

Research Area(s)

  • DNAzyme, Lead ion, Nanoparticles, Visual detection