Study on the photoionization and dissociative photoionization of ortho-, meta-, para-bromofluorobenzenes using VUV synchrotron radiation

Zhang, Qihang; Zhu, Liuyang; Wang, Zhandong; Liu, Yuzhu

doi:10.1016/j.cplett.2021.139045

Cited by 2 publications

(1 citation statement)

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“…However, most of the current researches on isomers are focused on the identification of isomeric carbohydrates [14][15][16], and research on the isomers of VOCs has received little attention. At present, the main techniques used in the analysis and identification of atmospheric VOCs and their isomers are mass spectrometry and synchrotron radiation [17,18]. However, these techniques all require complex sample preparation and cannot achieve realtime monitoring.…”

Section: Introductionmentioning

confidence: 99%

Rapid identification of volatile organic compounds and their isomers in the atmosphere

Zhang¹,

Ge²,

Wan³

et al. 2022

Plasma Sci. Technol.

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Isomers are widely present in volatile organic compounds (VOCs), and it is a tremendous challenge to rapidly distinguish the isomers of VOCs in the atmosphere. In this paper, laser induced breakdown spectroscopy (LIBS) technology was developed to online distinguish VOCs and their isomers in the atmosphere. First, LIBS was used to directly detect halogenated hydrocarbons (typical class of VOCs) in the atmosphere and the characteristic peaks of the related halogens were observed in the LIBS spectra. Then, comparing the LIBS spectra of various samples, it was found that for the VOCs with different molecular formulas, although the spectra are completely the same in element composition, there are still significant differences in the relative intensity of the spectral lines and other information. Finally, in light of the shortcomings of traditional LIBS technology in identifying isomers, machine learning algorithms were introduced to develop the LIBS technology to identify the isomers of VOCs in the atmosphere, and the recognition results were very good. It is proved that LIBS technology combined with machine learning algorithms is promising for online traceability of VOCs in the atmosphere.

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