Solid substrate assisted enhanced laser induced breakdown spectroscopy for metal element analysis in aqueous solution

Abstract

Due to plasma quenching caused by the dense water medium, laser-induced breakdown spectroscopy (LIBS) faces challenges such as strong continuous background radiation, weak and broadened characteristic spectral lines when directly detecting metal elements in liquids. In this work, we introduced a simple approach to improve underwater LIBS signal with solid substrate-assisted method, which requires no sample pre-treatment and simple operation, thus has potential for in-situ marine application. In this method, four submerged solid substrates (Zn, Cu, Ni, Si) were employed to investigate the breakdown characteristics of underwater LIBS and the mechanism of spectral enhancement by using a CaCl2 solution. The results demonstrated significant improvement in the detection sensitivity of Ca with these substrates even at a short laser pulse with a relative low laser energy (10 mJ). Among them, the semiconductor Si substrate exhibited the best enhancement effect, with an enhancement factor over 75 for the Ca ionic lines at 393.4 nm and 396.8 nm, and an enhancement factor of 29 for the Ca atomic line at 422.7 nm, respectively. This mainly because the presence of substrate decreases the breakdown threshold of liquid sample, and higher plasma excitation temperature and electron density are obtained, accordingly leading to higher signal intensity. Furthermore, significant plasma emission enhancements for a wide range of elements are also achieved from seawater. These findings can contribute to the development of compact underwater in-situ LIBS sensors with low power consumption meanwhile ensuring high detection sensitivity.

Article information

Article type
Paper
Submitted
21 ဩ 2024
Accepted
01 အောက် 2024
First published
02 အောက် 2024

Anal. Methods, 2024, Accepted Manuscript

Solid substrate assisted enhanced laser induced breakdown spectroscopy for metal element analysis in aqueous solution

L. Song, J. Han, M. Sui, Z. Wei, Y. Qin, Y. Lu, J. Song, W. Ye and J. Guo, Anal. Methods, 2024, Accepted Manuscript , DOI: 10.1039/D4AY01551G

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