Issue 37, 2023

Application of surface-enhanced Raman scattering to qualitative and quantitative analysis of arsenic species

Abstract

Given the toxicity of arsenic, there is an urgent need for the development of efficient and reliable detection systems. Raman spectroscopy, a powerful tool for material characterization and analysis, can be used to explore the properties of a wide range of different materials. Surface-enhanced Raman spectroscopy (SERS) can detect low concentrations of chemicals. This review focuses on the progress of qualitative and quantitative studies of the adsorption processes of inorganic arsenic and organic arsenic in aqueous media using Raman spectroscopy in recent years and discusses the application of Raman spectroscopy theory simulations to arsenic adsorption processes. Sliver nanoparticles are generally used as the SERS substrate to detect arsenic. Inorganic arsenic is chemisorbed onto the silver surface by forming As–O–Ag bonds, and the Raman shift difference in the As–O stretching (∼60 cm−1) between As(V) and As(III) allows SERS to detect and distinguish between As(V) and As(III) in groundwater samples. For organic arsenicals, specific compounds can be identified based on spectral differences in the vibration modes of the chemical bonds. Under the same laser excitation, the intensity of the Raman spectra for different arsenic concentrations is linearly related to the concentration, thus allowing quantitative analysis of arsenic. Molecular modeling of adsorbed analytes via density functional theory calculation (DFT) can predict the Raman shifts of analytes in different laser wavelengths.

Graphical abstract: Application of surface-enhanced Raman scattering to qualitative and quantitative analysis of arsenic species

Article information

Article type
Tutorial Review
Submitted
10 Мам. 2023
Accepted
23 Там. 2023
First published
24 Там. 2023

Anal. Methods, 2023,15, 4798-4810

Application of surface-enhanced Raman scattering to qualitative and quantitative analysis of arsenic species

X. Nurmamat, Z. Zhao, H. Ablat, X. Ma, Q. Xie, Z. Zhang, J. Tian, H. Jia and F. Wang, Anal. Methods, 2023, 15, 4798 DOI: 10.1039/D3AY00736G

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