Issue 17, 2016

Reliable SERS detection of nitrite based on pH and laser irradiance-dependent diazotization through a convenient sampling micro-chamber

Abstract

Nitrites (NO2 ions) in food and drink play an important role in human health but require complicated operations before detection. Herein, we present a rationally designed SERS-enabled micro-chamber that comprised a drawn glass capillary with a tiny orifice (∼50 μm) at the distal tip, wherein the gold nanoparticles (Au NPs) are compactly coated on the inner wall surface. In this chamber, nitrites specifically trigger a pH and laser irradiance-dependent diazotization starting from p-aminothiophenol (PATP) absorbed onto the surface of Au NPs to form p,p′-dimercaptoazobenzene (DMAB) molecules, in which the presence of NO2 ions above 30.7 μM (1.38 ppm) in the siphoned liquid sample can be identified relying on SERS peak (1141 cm−1) intensity of the emerging azo moiety. Except for pH conditions, laser irradiance is more important but easily neglected in previous studies, which is capable of preventing generation of errors when the detection sensitivity was pursued through increasing the laser power. In this case, several real samples (rather than simple water samples), including honey, pickled vegetable and fermented bean curd, had been successfully detected accurately through such a convenient sampling micro-chamber. The SERS-enabled device could potentially be facilely incorporated with portable Raman instruments for a special application of food inspection in rapid and field analysis of NO2 ions.

Graphical abstract: Reliable SERS detection of nitrite based on pH and laser irradiance-dependent diazotization through a convenient sampling micro-chamber

Supplementary files

Article information

Article type
Paper
Submitted
15 apr 2016
Accepted
04 iyl 2016
First published
05 iyl 2016

Analyst, 2016,141, 5195-5201

Reliable SERS detection of nitrite based on pH and laser irradiance-dependent diazotization through a convenient sampling micro-chamber

M. Gao, W. Fang, J. Ren, A. Shen and J. Hu, Analyst, 2016, 141, 5195 DOI: 10.1039/C6AN00880A

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