Recent Advances in Laser-induced Breakdown Spectroscopy Quantification: From Fundamental Understanding to Data Processing

Zhe Wang received his Ph.D. in mechanics engineering from The Pennsylvania State University in 2007 and then joined the faculty of Tsinghua University. He has devoted his academic career towards promoting laser-induced breakdown spectroscopy (LIBS) in real industrial applications by focusing on improving its quantification performance. He has published more than 90 peer reviewed papers and holds over 30 patents.  He is the deputy director and general secretary of the LIBS committee of the Chinese Society of Optical Engineering and initiated the LIBS Summit in 2019.

Recent Advances in Laser-induced Breakdown Spectroscopy Quantification: From Fundamental Understanding to Data Processing

Laser-induced breakdown spectroscopy (LIBS) is regarded as a future superstar for chemical analysis. But the relatively high measurement uncertainty and error remain the persistent challenges for its technological development and wide application. In this presentation, I will summarize the generated mechanisms of measurement uncertainty and explain how signal uncertainty and matrix effects impact quantification performance. Furthermore, I will discuss the methods for raw signal improvement including sample preparation, system optimization, and especially plasma modulation, which modulates the laser-induced plasma evolution process for higher signal repeatability and signal-to-noise ratio. I will also discuss different mathematical quantification methods including calibration-free methods and calibration methods, which can be classified into physical-principle based calibration models, data-driven based calibration models, and hybrid models. Finally, I will summarize and recommend a strategy for quantification improvement including key steps and plans for LIBS future development.

Learning objectives:

• Mechanisms of signal uncertainty generation
• Impact of matrix effect and signal uncertainty on quantification performance
• Systematic summary of raw signal improvement methods and mathematical quantification models
• Framework of quantification improvement strategy for future development

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