Issue 41, 2018

Raman spectroscopy coupled with principal component analysis to quantitatively analyze four crystallographic phases of explosive CL-20

Abstract

The polymorphic quantitative analysis of explosives is very important for national defense and security inspection. However, conventional analytical methods are inaccurate and time-consuming because of the complexity of the polymorphic explosive samples. In this paper, we established a new method of polymorphic quantitative determination in a simple, sensitive, and accurate way. High quality spectra of the four phases of the explosive CL-20 were obtained using a compact Raman spectrometer, and QM calculations were performed to confirm the tentative assignment of the most predominant Raman peaks. Principal component analysis (PCA) of the data was performed to understand the factors affecting the spectral variation across the entire Raman region of the four phases of CL-20 and to calculate the characteristic Raman shift region. In addition, different characteristic peaks were selected according to the PCA and QM calculation results, and a new method for the quantitative determination of polymorphic impurities in ε-CL-20 was also set up. The detection level for the polymorphic impurities was determined to be below 1%, and the standard deviation was less than ±0.5%. This new method is of significant importance for the quality control of synthesis and production not only in explosives, but also in pharmaceuticals, agrochemicals, and optics industries.

Graphical abstract: Raman spectroscopy coupled with principal component analysis to quantitatively analyze four crystallographic phases of explosive CL-20

Supplementary files

Article information

Article type
Paper
Submitted
13 Mar 2018
Accepted
09 Jun 2018
First published
27 Jun 2018
This article is Open Access
Creative Commons BY license

RSC Adv., 2018,8, 23348-23352

Raman spectroscopy coupled with principal component analysis to quantitatively analyze four crystallographic phases of explosive CL-20

X. He, Y. Liu, S. Huang, Y. Liu, X. Pu and T. Xu, RSC Adv., 2018, 8, 23348 DOI: 10.1039/C8RA02189A

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

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