|
|
|
|
|
|
| Study on the Near-Infrared Spectra of Sarin Based on Density
Functional Theory |
| SUN Zhi-shen1, LIU Yong-gang2, 3, ZHANG Xu1, GUO Teng-xiao1*, CAO Shu-ya1* |
1. State Key Laboratory of NBC Protection for Civilian, Beijing 102205, China
2. State Key Laboratory of Environment-Friendly Energy Materials, Southwest University of Science and Technology, Mianyang 621010, China
3. Research Center of Laser Fusion, China Academy of Engineering Physics, Mianyang 621010, China
|
|
|
|
|
Abstract Near-infrared spectroscopy is mainly the overtones and combination bands absorption spectra of organic molecules, which are generated by the overtones and combination bands of hydrogen-containing groups such as C—H, N—H, O—H, etc., which can obtain molecular structure, composition, state and other information. This technology is an important method for studying the vibration information of hydrogen-containing groups in organic matter and is often used for qualitative and quantitative analysis of biological substances such as food and crops. The research objects in the biochemical field also have hydrogen-containing groups. These hydrogen-containing groups have strong absorption frequency characteristics, are less affected by the internal and external environment of the molecule, and have more stable spectral characteristics in the near-infrared spectrum. This technology can be used to detect chemical warfare agents and hazardous chemicals. Sarin is a neurotoxic chemical agent. When studying its structure, chemical properties and spectral properties, in order to ensure safety, simulants are often used in the experiment to substitute for testing, but there is no fair near-infrared simulant for sarin. This paper uses density functional theory (DFT), based on the Gaussian program package, and uses B3LYP/def2-SVP to optimize the ground state structure of the sarin molecule, and calculates the fine structure of the sarin molecule and the fundamental frequency vibration mode of the molecule. The generalized second-order perturbation theory (GVPT2) is introduced to establish a theoretical model for simulating the near-infrared spectrum of biochemical poisons, obtaining the near-infrared vibration peaks and main vibration modes, and the near-infrared spectrum drawn from the vibrations of overtones and combination bands. Analyze the hydrogen-containing groups of sarin in the near-infrared region, use this method to identify its characteristic peaks, obtain three characteristic peaks of sarin molecules at 1 150, 1 362 and 1 500 nm and analyze their vibration modes. Among them, the position at 1 150 nm is produced by the contribution of multiple overtones and combination bands vibration. 1 362 nm is a wide absorption vibration region, mainly caused by the combination bands of atoms connected to C atoms in the molecule and other non-C, H atoms. The near-infrared vibration peak at 1 500 nm is mainly caused by the C8 Related vibration mode contribution. In this paper, the theoretical model of Sarin’s near-infrared spectroscopy is established through density functional theory, and the feasibility of the theoretical model is verified through experiments, which provides theoretical support for finding its near-infrared spectroscopy simulation agent.
|
|
Received: 2021-09-15
Accepted: 2022-06-22
|
|
|
|
Corresponding Authors:
GUO Teng-xiao, CAO Shu-ya
E-mail: guotengxiao@sklnbcpc.cn
|
|
[1] Pasquini C. Analytica Chimica Acta, 2018, 1026: 8.
[2] Salzer R. Angewandte Chemie International Edition, 2008, 47(25): 4628.
[3] Salzer R. Angewandte Chemie International Edition, 2002, 41(22): 4347.
[4] Zheng Y Z, Zhou Y, Deng G, et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2020, 226: 117624.
[5] El-Taher S, Metwaly M. Journal of Molecular Structure, 2017, 1134: 840.
[6] Oliveira L B A, Cardoso W B, Colherinhas G. Journal of Molecular Liquids, 2021, 339: 116815.
[7] Prasad H S N, Ananda A P, Lohith T N, et al. Journal of Molecular Structure, 2022, 1247: 131333.
[8] XU Di, XIN Min-si, LIU Chun-yu, et al(徐 笛, 辛敏思, 刘春宇, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2021, 41(1): 39.
[9] QIN Rui(覃 睿). Chinese Journal of High Pressure Physics(高压物理学报), 2019, 33(3): 4.
[10] Cohen A J, Mori-Sánchez P, Yang W. Chemical Reviews, 2012, 112(1): 289.
[11] Ramakrishnan R, Hartmann M, Tapavicza E, et al. Journal of Chemical Physics, 2015, 143(8): 084111.
[12] Fuks J I, Nielsen S E B, Ruggenthaler M, et al. Physical Chemistry Chemical Physics, 2016, 18(31): 20976.
[13] Hadidi S, Shiri F, Norouzibazaz M. Structural Chemistry, 2020, 31(1): 115.
|
| [1] |
GAO Feng1, 2, XING Ya-ge3, 4, LUO Hua-ping1, 2, ZHANG Yuan-hua3, 4, GUO Ling3, 4*. Nondestructive Identification of Apricot Varieties Based on Visible/Near Infrared Spectroscopy and Chemometrics Methods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 44-51. |
| [2] |
BAO Hao1, 2，ZHANG Yan1, 2*. Research on Spectral Feature Band Selection Model Based on Improved Harris Hawk Optimization Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 148-157. |
| [3] |
HU Cai-ping1, HE Cheng-yu2, KONG Li-wei3, ZHU You-you3*, WU Bin4, ZHOU Hao-xiang3, SUN Jun2. Identification of Tea Based on Near-Infrared Spectra and Fuzzy Linear Discriminant QR Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3802-3805. |
| [4] |
LIU Xin-peng1, SUN Xiang-hong2, QIN Yu-hua1*, ZHANG Min1, GONG Hui-li3. Research on t-SNE Similarity Measurement Method Based on Wasserstein Divergence[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3806-3812. |
| [5] |
BAI Xue-bing1, 2, SONG Chang-ze1, ZHANG Qian-wei1, DAI Bin-xiu1, JIN Guo-jie1, 2, LIU Wen-zheng1, TAO Yong-sheng1, 2*. Rapid and Nndestructive Dagnosis Mthod for Posphate Dficiency in “Cabernet Sauvignon” Gape Laves by Vis/NIR Sectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3719-3725. |
| [6] |
WANG Qi-biao1, HE Yu-kai1, LUO Yu-shi1, WANG Shu-jun1, XIE Bo2, DENG Chao2*, LIU Yong3, TUO Xian-guo3. Study on Analysis Method of Distiller's Grains Acidity Based on
Convolutional Neural Network and Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3726-3731. |
| [7] |
LUO Li, WANG Jing-yi, XU Zhao-jun, NA Bin*. Geographic Origin Discrimination of Wood Using NIR Spectroscopy
Combined With Machine Learning Techniques[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3372-3379. |
| [8] |
ZHANG Shu-fang1, LEI Lei2, LEI Shun-xin2, TAN Xue-cai1, LIU Shao-gang1, YAN Jun1*. Traceability of Geographical Origin of Jasmine Based on Near
Infrared Diffuse Reflectance Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3389-3395. |
| [9] |
YANG Qun1, 2, LING Qi-han1, WEI Yong1, NING Qiang1, 2, KONG Fa-ming1, ZHOU Yi-fan1, 2, ZHANG Hai-lin1, WANG Jie1, 2*. Non-Destructive Monitoring Model of Functional Nitrogen Content in
Citrus Leaves Based on Visible-Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3396-3403. |
| [10] |
HUANG Meng-qiang1, KUANG Wen-jian2, 3*, LIU Xiang1, HE Liang4. Quantitative Analysis of Cotton/Polyester/Wool Blended Fiber Content by Near-Infrared Spectroscopy Based on 1D-CNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3565-3570. |
| [11] |
HUANG Zhao-di1, CHEN Zai-liang2, WANG Chen3, TIAN Peng2, ZHANG Hai-liang2, XIE Chao-yong2*, LIU Xue-mei4*. Comparing Different Multivariate Calibration Methods Analyses for Measurement of Soil Properties Using Visible and Short Wave-Near
Infrared Spectroscopy Combined With Machine Learning Algorithms[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3535-3540. |
| [12] |
KANG Ming-yue1, 3, WANG Cheng1, SUN Hong-yan3, LI Zuo-lin2, LUO Bin1*. Research on Internal Quality Detection Method of Cherry Tomatoes Based on Improved WOA-LSSVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3541-3550. |
| [13] |
HUANG Hua1, LIU Ya2, KUERBANGULI·Dulikun1, ZENG Fan-lin1, MAYIRAN·Maimaiti1, AWAGULI·Maimaiti1, MAIDINUERHAN·Aizezi1, GUO Jun-xian3*. Ensemble Learning Model Incorporating Fractional Differential and
PIMP-RF Algorithm to Predict Soluble Solids Content of Apples
During Maturing Period[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3059-3066. |
| [14] |
CHEN Jia-wei1, 2, ZHOU De-qiang1, 2*, CUI Chen-hao3, REN Zhi-jun1, ZUO Wen-juan1. Prediction Model of Farinograph Characteristics of Wheat Flour Based on Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3089-3097. |
| [15] |
GUO Ge1, 3, 4, ZHANG Meng-ling3, 4, GONG Zhi-jie3, 4, ZHANG Shi-zhuang3, 4, WANG Xiao-yu2, 5, 6*, ZHOU Zhong-hua1*, YANG Yu2, 5, 6, XIE Guang-hui3, 4. Construction of Biomass Ash Content Model Based on Near-Infrared
Spectroscopy and Complex Sample Set Partitioning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3143-3149. |
|
|
|
|
