five

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

收藏
科学数据银行2024-08-22 更新2026-04-23 收录
下载链接:
https://www.scidb.cn/detail?dataSetId=8669842021344529ac848c5ade604c98
下载链接
链接失效反馈
官方服务:
资源简介:
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.
提供机构:
Wangquan Ye; Jianwen Han; Mingda Sui; Zihao Wei; Ocean University of China; Yunpeng Qin; Jiaojian Song; Jinjia Guo
创建时间:
2024-08-21
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作