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A Comparative Study on the ATR and TR Methods of Infrared Spectroscopy of Solid Matters |
YANG Shan, CAI Xiu-qin, ZHANG Yi-feng |
College of Chemistry and Materials, Weinan Normal University, Weinan 714099, China |
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Abstract Infrared (IR) spectroscopy is a common tool for material structure analysis, which is widely used in the detection of various solid, liquid and gaseous materials. Since the testing methods and sample preparation process directly affect the accuracy of IR spectra, the comparative study on the attenuated total reflection (ATR) and transmission (TR) methods used in measuring the IR spectroscopy of solid substances was carried out in this paper. Three kinds of common polymers, 3 kinds of inorganic substances, and 3 kinds of organic small-molecule compounds were selected as the research objects, and their IR spectra were measured by both ATR and TR methods, respectively. Various abnormal phenomena in the IR spectra were deeply analyzed combining sample preparation process and spectrum analysis. The TR method is complicated in the sample preparation process and has many interfering factors: the sample is easy to absorb moisture from the air during mixing and grinding process with KBr, which will disturb the analysis of samples having N—H and O—H groups; the spectrum will appear flat-headed peaks and can’t be analyzed as the sample is too thick, and the absorption intensity of the whole spectrum will decrease as the sample is dark colored. Compared with the TR method, the ATR method requires no sample preparation, which saves both time and labor and reduces the possibility of water absorption. Because the method principle is different, the overall absorption intensity of the ATR method is less than that of the TR method. Although the peak wavenumber of the ATR method is generally smaller than that of the TR method among several to dozens of cm-1, the peak position is in a reasonable range, and the qualitative analysis is unaffected. Because the depth of light enters the sample is limited in the ATR method, only 2~15 μm, no flat-headed peaks appear in the ATR-IR spectrum. Due to the short wavelength light cannot penetrate the sample too deep, thus, the absorption intensity in ATR-IR spectrum will weaken along with the decrease of wavelength, which leads to the peak intensity in functional group region decrease and in fingerprint region greatly increases, while this problem can be revised by the “ATR correction” function of software, and also can be used in the quantitative analysis: the original spectra for analyzing the peaks in the fingerprint region, and the revised spectra for analyzing the peaks in functional group region. In comparison, the ATR method is not limited by the color, shape and thickness of the sample, i.e., the measurement is easier, fast, accurate, non-destructive, and recyclable. It has obvious advantages in the IR spectra detection of polymer, dark colored and hygroscopic substances. Thus, it is recommended to widely use the ATR method for detecting IR spectra of solid substances.
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Received: 2019-09-19
Accepted: 2020-01-10
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[1] DENG Qin-ying, LIU Lan, DENG Hui-min(邓芹英, 刘 岚, 邓慧敏). Spectral Analysis Course(波谱分析教程). 2nd Edition(第2版). Beijing: Science Press(北京: 科学出版社), 2007. 45.
[2] Larkin P. Infrared and Raman Spectroscopy: Principles and Spectral Interpretation. Elsevier Science Inc., 2011. 27.
[3] Ogata Y H. Handbook of Porous Silicon. Springer International Publishing, 2014. 474.
[4] LIU Cui-mei, HAN Yu, JIA Wei, et al(刘翠梅, 韩 煜, 贾 薇, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2019, 39(5): 1439.
[5] NI Zhu, JIA Yu-zhen, BAO Jian-chun(倪 竹, 贾玉珍, 包建春). Progress in Biochemistry and Biophysics(生物化学与生物物理进展), 1989, 16(6): 434.
[6] Rozenberg M, Loewenschuss A, Marcus Y. Spectrochimica Acta, Part A: Molecular and Biomolecular, Spectroscopy, 1998, 54(12): 1819.
[7] National Institute of Advanced Industrial Science and Technology (AIST), Japan. Spectral Database for Organic Compounds SDBS [EB/OL]. [2019-8-25]. https://sdbs.db.aist.go.jp/sdbs/cgi-bin/cre_index.cgi
[8] Christopher Blair Crawford, Brian Quinn. Microplastic Pollutants. Elsevier Science Inc., 2017. 219.
[9] JIANG Yuan, WU Li-heng(江 渊, 吴立衡). Polymer Bulletin(高分子通报), 2001, (2): 62.
[10] Richard A N, Ronald O K. Infrared Spectra of Inorganic Compounds (3 800~45 cm<sup>-1</sup>). New York and London: Academic Press, Inc., 1971. 3.
[11] Quintard P, Ramis G, Cauchetier M, et al. Microchimica Acta, 1988, 95(1): 75.
[12] Dunsmuir J T R, Lane A P. Journal of the Chemical Society A: Inorganic, Physical, Theoretical, 1971. 776.
[13] Rao C N R, Venkataraghavan R, Kasturi T R. Canadian Journal of Chemistry, 1964, 42(1): 36.
[14] Sudipta C, Salaün Fabien, Christine C. Marine Drugs, 2014, 12(12): 5801. |
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