Detection of Chlorpyrifos Based on Surface-Enhanced Raman Spectroscopy and Density Functional Theory
TAN Ai-ling1, ZHAO Rong1, SUN Jia-lin1, WANG Xin-rui1, ZHAO Yong2*
1. School of Information Science and Engineering, Yanshan University, The Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province, Qinhuangdao 066004, China
2. School of Electrical Engineering, Yanshan University, The Key Laboratory of Measurement Technology and Instruments of Hebei Province, Qinhuangdao 066004, China
Abstract：Chlorpyrifos, a broad-spectrum and highly effective organophosphorus pesticide, is widely used in agriculture and other fields. However, environmental toxicology studies have found that chlorpyrifos can be directly applied to the soil, firmly binds to soil particles, hardly migrate or volatilize, and has low water solubility, which is likely to cause drug residues, thus affects the safety of agricultural and sideline products. Many countries have strict regulations on the residual amount of chlorpyrifos in agricultural products. Therefore, detecting the ecological risk of chlorpyrifos residues is a top priority. Surface-enhanced Raman spectroscopy has the advantages of fast, high efficiency and high sensitivity, and has become a hot technology in the spectroscopy research field. Density functional theory is widely used in theoretical simulation calculations and spectral analysis of molecular structure and properties. This paper, based on the surface-enhanced Raman spectroscopy technology and density functional theory, the theoretical study of chlorpyrifos Raman and surface-enhanced Raman spectroscopy is carried out. First, GaussView5.0 was used to configure the insecticide chlorpyrifos molecule and the molecular structure added to the silver cluster base. Second, the 6-31G basis set was used for the chlorpyrifos molecule, and the structure was optimized based on density functional theory, and then the Raman and surface-enhanced Raman spectra were calculated by Gaussian09 simulation. The Raman spectrum peak attributions were determined. Finally, the enhancement effect of silver clusters Ag2 and Ag3 on the Raman spectrum of chlorpyrifos was analysed from the frequency shift perspective, and the frequency shift was compared. The study found that the peak intensity of Raman spectrum at 326, 463, 741, 781, 1 068, 1 294, 1 435, and 1 602 cm-1 wavenumber has a significant increase with the action of the silver clusters, and with the increase of the size of the silver cluster structure, the enhancement was more effective. Besides, the position of some characteristic peaks shifted and the frequency shift was related to the structure of silver cluster Correlatively. Raman spectrum of 463, 741 to 781 cm-1 wavenumber produced a large frequency shift and the frequency shifts at other characteristic peak wavenumbers were all smaller than 20 cm-1. The frequency shifts of the surface enhanced spectra of Ag2 invasion with the chlorpyrifos molecule were in agreement with the shifts of Ag3 invasion with the chlorpyrifos molecule. The results of this article provide a theoretical basis for applying surface-enhanced Raman spectroscopy for pesticide residue detection.
谈爱玲，赵 荣，孙嘉林，王鑫蕊，赵 勇. 基于表面增强拉曼光谱及密度泛函理论的农药毒死蜱检测研究[J]. 光谱学与光谱分析, 2021, 41(11): 3462-3467.
TAN Ai-ling, ZHAO Rong, SUN Jia-lin, WANG Xin-rui, ZHAO Yong. Detection of Chlorpyrifos Based on Surface-Enhanced Raman Spectroscopy and Density Functional Theory. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(11): 3462-3467.
[1] TA Na, SUN Cheng(塔 娜, 孙 成). Environmental Science & Technology(环境科学与技术), 2011, 34(1): 8, 40.
[2] ZOU Ru-bing, LIU Ying, WANG Shuang-jie, et al(邹茹冰, 柳 颖, 王双节, 等). Chinese Journal of Pesticide Science(农药学学报), 2017, 19(1): 37.
[3] XU Zhi-yi, CHEN Meng-ting, HOU Xi-ai, et al(徐芷怡, 陈梦婷, 侯锡爱, 等). Chinese Journal of Analytical Chemistry(分析化学), 2020, 48(7): 928.
[4] DI Ling, CHEN Fang, FU Rong-rong, et al(狄 玲, 陈 放, 付荣荣, 等). CIESC Jorunal(化工学报), 2020, 71(8): 3830.
[5] SUN Li-si, WANG Na, KONG De-yang, et al(孙立思, 王 娜, 孔德洋, 等). Journal of Ecology and Rural Environment(生态与农村环境学报), 2017, 33(9): 860.
[6] ZHU Xiao-hua, YANG Yong-liang, LU Guo-hui, et al(朱晓华, 杨永亮, 路国慧, 等). Research of Environmental Sciences(环境科学研究), 2012, 25(7): 778.
[7] HU Fu-zhao, CHEN Zheng-xing, LI He, et al(胡付照, 陈正行, 李 鹤, 等). Journal of Food Science and Biotechnology(食品与生物技术学报) , 2018, 37(10): 1009.
[8] JIANG Xue-song, WANG Wei-qin, XU Lin-yun, et al(蒋雪松, 王维琴, 许林云, 等). Journal of Agricultural Engineering(农业工程学报), 2016, 32(20): 267.
[9] Guo Huiyuan, Leigh C Hamlet, He Lili, et al. Science of the Total Environment, 2019, 653: 1034.
[10] ZHOU Yun-quan, LIU Chun-yu, QU Guan-nan, et al(周云全, 刘春宇, 曲冠男, 等). Journal of Atomic and Molecular Physics(原子与分子物理学报), 2020, 37(3): 331.
[11] Birke R L, Lombardi J R. J. Phys. Chem. C, 2018, 122: 4908.
[12] Zajac A, Dyminska L, Lorence J, et al. JBIC J. Bio. Inor. Chem., 2019, 24: 11.
[13] FENG Ao-ran, JIA Qiang, TAN Yong(丰傲然, 贾 强, 谭 勇). The Journal of Light Scattering(光散射学报), 2019, 31(3): 248.
[14] LI Yuan-yuan, HU Zhu-bin, SUN Hai-tao, et al（李媛媛，胡竹斌，孙海涛，等）. Acta Physica Sinica(物理学报), 2020, 69(16): 163101.
[15] WANG Xin-qiang, GE Hao-ran, XIONG Wei, et al（王新强, 葛浩然, 熊 伟, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2020, 40 (7): 2110.
