A Simple and Effective Visual Fluorescent Sensing Paper-Based Chip for the Ultrasensitive Detection of Mercury Ions in Environmental Water

Traces of mercury ions in environmental water can harm humans and animals. Paper-based visual detection methods have been widely developed for the rapid detection of mercury ions; however, existing methods are not sensitive enough to be used in real environments. Here, we developed a novel, simple a...

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Published inSensors (Basel, Switzerland) Vol. 23; no. 6; p. 3094
Main Authors Han, Jinglong, Liu, Huajun, Qi, Ji, Xiang, Jiawen, Fu, Longwen, Sun, Xiyan, Wang, Liyan, Wang, Xiaoyan, Li, Bowei, Chen, Lingxin
Format Journal Article
LanguageEnglish
Published Switzerland MDPI AG 14.03.2023
MDPI
Subjects
CdTe quantum dots
Ethanol
Fluorescence
mercury ions
Nanomaterials
Nanoparticles
Onsite
paper-based chips
Quantum dots
rapid detection
Silica
silica nanospheres
Smartphones
Software
China
silica nanospheres
paper-based chips
CdTe quantum dots
rapid detection
mercury ions
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Summary:Traces of mercury ions in environmental water can harm humans and animals. Paper-based visual detection methods have been widely developed for the rapid detection of mercury ions; however, existing methods are not sensitive enough to be used in real environments. Here, we developed a novel, simple and effective visual fluorescent sensing paper-based chip for the ultrasensitive detection of mercury ions in environmental water. CdTe-quantum-dots-modified silica nanospheres were firmly absorbed by and anchored to the fiber interspaces on the paper’s surface to effectively avoid the unevenness caused by liquid evaporation. The fluorescence of quantum dots emitted at 525 nm can be selectively and efficiently quenched with mercury ions, and the ultrasensitive visual fluorescence sensing results attained using this principle can be captured using a smartphone camera. This method has a detection limit of 2.83 µg/L and a fast response time (90 s). We successfully achieved the trace spiking detection of seawater (from three regions), lake water, river water and tap water with recoveries in the range of 96.8–105.4% using this method. This method is effective, low-cost, user-friendly and has good prospects for commercial application. Additionally, the work is expected to be utilized in the automated big data collection of large numbers of environmental samples.
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These authors contributed equally to this work.
ISSN:1424-8220
1424-8220
DOI:10.3390/s23063094