G
Guang Yang
Researcher at Jilin University
Publications - 18
Citations - 418
Guang Yang is an academic researcher from Jilin University. The author has contributed to research in topics: Laser-induced breakdown spectroscopy & Partial least squares regression. The author has an hindex of 8, co-authored 16 publications receiving 302 citations.
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A review of laser-induced breakdown spectroscopy signal enhancement
TL;DR: A review of the methods of signal enhancement in laser-induced breakdown spectroscopy (LIBS) is presented in this paper, where the authors show that conventional LIBS suffers from disadvantages of low sensitivity and high limits of detecti...
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A Review of Laser-Induced Breakdown Spectroscopy for Analysis of Geological Materials
TL;DR: Laser-induced breakdown spectroscopy (LIBS) has been developed into a versatile tool in various fields because of its distinct abilities, especially the simple, rapid, in situ detection of any material (solid, liquid, or gas) as mentioned in this paper.
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A review of laser-induced breakdown spectroscopy for plastic analysis
TL;DR: In this article, a review of laser-induced breakdown spectroscopy (LIBS) applications for coal ranks, combustion efficiency, and environmental protection is presented, together with a description of limitations and the potential developing trend for this topic.
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An effective analytical system based on a pulsed direct current microplasma source for ultra-trace mercury determination using gold amalgamation cold vapor atomic emission spectrometry
TL;DR: In this paper, a low power atmospheric pressure pulsed direct current (Pdc) microplasma was used for the determination of ultra-trace mercury in natural water by cold vapor generation atomic emission spectrometry (CV-AES).
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Rapid classification of plastics by laser-induced breakdown spectroscopy (LIBS) coupled with partial least squares discrimination analysis based on variable importance (VI-PLS-DA)
TL;DR: In this article, an extension of Partial Least Squares Discrimination Analysis (PLS-DA) that uses variable importance to select input variables was presented, which has the highest classification accuracy and shortest classification time.