Exploring the Impact of Ambient Gas Property on the Signal of Laser-Induced Breakdown Spectroscopy with Neural Network
Yuzhou Song, Zongyu Hou, Chenyu Yan, Weiran Song, Chenwei Zhang, Zhe Wang
Abstract
Laser-induced breakdown spectroscopy (LIBS) has long been regarded as an ideal analytical technology with the unique capabilities of real-time and multielement sensing. However, the lack of a clear understanding of the impact of ambient gas properties on the LIBS signal has severely hindered LIBS quantification improvement. We proposed an innovative approach by applying neural networks to discover the dependence of the LIBS signal on the ambient gas properties supported with a series of purposely designed experiments. For the first time, the full picture of the dependence of the LIBS signal on the main gas properties was clearly discovered, and the impact mechanism was further clarified. It is not only the first time that AI was used for complicated physical dependence rather than quantification in LIBS and the spectroscopic field but also established a new paradigm for the application of AI in complicated physical dependence by constructing comprehensive data points that are virtually impossible to attain through traditional experimental methods.