|
|
|
|
|
|
Optimization of a Near-Concentric Cavity Raman Spectroscopy System for Liquid Sample and Preliminary Results of CO2-3/HCO-3 |
SI Gan-shang, YANG De-wang, GUO Jin-jia*, LIU Qing-sheng, YE Wang-quan, ZHENG Rong-er |
Optics and Optoelectronics Laboratory, Ocean University of China, Qingdao 266100, China |
|
|
Abstract It is of great significance to study the carbon cycle in the ocean for environmental monitoring and resource detecting. In this field, one of the most important topics is to study carbonate. There is no direct detection method to monitor carbonate in seawater, and most traditional detection methods for carbonate are indirect. For example: with seawater sample acidified by phosphoric acid, the carbonate in the sample can be converted into CO2 and then be detected. Raman spectroscopy can be used in in-situ detection and has great potential to detect the carbonate directly. But it’s sensitivity is still a limitation in the practical use of ocean detection. In the hope of developing an approach to directly detect the carbonate in the seawater, we build a near-concentric cavity Raman spectroscopy system and optimize the main parameters of the cavity (diameter=25.4 mm, reflectivity=99.66%@532 nm) including optical windows thickness of the liquid cell, the optical windows distance at two sides, and the focal length of the mirrors with simulation software. The results are listed as follows: (1)The number of the reflection is at a maximum when the focal length is 25 mm for the mirrors with diameter of 25.4 mm; (2) For the optical windows of the liquid cell, with smaller thickness, the light would be denser in the center of the cell, and the totally luminous intensity in the center plane of the near-concentric cavity would be larger; (3) with smaller distance between the optical windows, the light would be denser in the center of the cell, and the totally luminous intensity in the center plane of the near-concentric cavity would be larger; After optimization, the measurement of CO2-3 and HCO-3 solutions on different concentration levels is carried out using the optimized near-concentric cavity Raman spectroscopy system. The spectral signal was pretreated using second order differential and Gaussian filter, and then calibration curves were established using the peak intensity of the corresponding concentrations. The results showed good linear relationship between concentration of solution and signal intensity of Raman spectrum, with R2 of 0.994 and 0.998 for CO2-3 and HCO-3, respectively. We calculated the LODs using the 3 times signal-to-noise ratio. The results showed that the LOD for CO2-3 and HCO-3 is about 0.06 and 0.38 mmol·L-1 respectively. The LODs are lower than the typical concentrations of CO2-3 and HCO-3 in seawater, which are about 0.2 and 2 mmol·L-1 respectively. Compared to the current reported of the Raman spectroscopy system of in-suit ocean detection, the sensitivity of the system has increased by nearly ten times. So it is hoped to apply the system to the in-situ CO2-3 and HCO-3 detection inseawater.
|
Received: 2018-03-09
Accepted: 2018-08-11
|
|
Corresponding Authors:
GUO Jin-jia
E-mail: opticsc@ouc.edu.cn
|
|
[1] King S, Butcher A C, Rosenoern T, et al. Environmental Science & Technology, 2012, 46(19): 10405.
[2] Wang Z A, Sonnichsen F N, Bradley A M, et al. Environmental Science & Technology, 2015, 49(7): 4441.
[3] Doney S C, Fabry V J, Feely R A, et al. Annual Review of Marine Science, 2009, 1: 169.
[4] Bell R J, Timothy S R, Byrne R H. Limnology & Oceanography Methods, 2011, 9(4): 164.
[5] Brewer P G,Peltzer E T,Walz P M. MBARI, 2012: 1.
[6] Yang Dewang, Guo Jinjia, Liu Qingsheng, et al. Applied Optics, 2016, 55(27): 7744.
[7] Malard L M, Pimenta M A, Dresselhaus G, et al. Physics Reports, 2009, 473(5): 51.
[8] Kirtley J D, Halat D M, Mcintyre M D, et al. Analytical Chemistry, 2012, 84(22): 9745.
[9] Zhang Xin, Du Zengfeng, et al. Deep Sea Research Part Ⅰ Oceanographic Research Papers, 2017, 123: 1.
[10] White S N, Dunk R M, Peltzer E T, et al. Geochemistry, Geophysics, Geosystems, 2006, 7(5): Q05023.
[11] ZHANG Xin, DU Zeng-feng, ZHENG Rong-er, et al(张 鑫,杜增丰,郑荣儿,等). EGU General Assembly Conference Abstracts(EGU大会会议摘要),2016, 18: 3428.
[12] Breier J A, White S N, German C R. Mathematical, Physical and Engineering Sciences, 2010, 368(1922): 3067.
[13] Pasteris J D, Wopenka B, Freeman J J, et al. Applied Spectroscopy, 2004, 58(7): 195A.
[14] Brewer P G, Dunk R M, Whie S N, et al. AGU Fall Meeting Abstracts, 2004,86.
[15] YANG De-wang, GUO Jin-jia, DU Zeng-feng, et al(杨德旺,郭金家,杜增丰,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2015, 35(3): 645. |
[1] |
LI Jie, ZHOU Qu*, JIA Lu-fen, CUI Xiao-sen. Comparative Study on Detection Methods of Furfural in Transformer Oil Based on IR and Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 125-133. |
[2] |
WANG Fang-yuan1, 2, HAN Sen1, 2, YE Song1, 2, YIN Shan1, 2, LI Shu1, 2, WANG Xin-qiang1, 2*. A DFT Method to Study the Structure and Raman Spectra of Lignin
Monomer and Dimer[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 76-81. |
[3] |
XING Hai-bo1, ZHENG Bo-wen1, LI Xin-yue1, HUANG Bo-tao2, XIANG Xiao2, HU Xiao-jun1*. Colorimetric and SERS Dual-Channel Sensing Detection of Pyrene in
Water[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 95-102. |
[4] |
WANG Xin-qiang1, 3, CHU Pei-zhu1, 3, XIONG Wei2, 4, YE Song1, 3, GAN Yong-ying1, 3, ZHANG Wen-tao1, 3, LI Shu1, 3, WANG Fang-yuan1, 3*. Study on Monomer Simulation of Cellulose Raman Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 164-168. |
[5] |
WANG Lan-hua1, 2, CHEN Yi-lin1*, FU Xue-hai1, JIAN Kuo3, YANG Tian-yu1, 2, ZHANG Bo1, 4, HONG Yong1, WANG Wen-feng1. Comparative Study on Maceral Composition and Raman Spectroscopy of Jet From Fushun City, Liaoning Province and Jimsar County, Xinjiang Province[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 292-300. |
[6] |
LI Wei1, TAN Feng2*, ZHANG Wei1, GAO Lu-si3, LI Jin-shan4. Application of Improved Random Frog Algorithm in Fast Identification of Soybean Varieties[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3763-3769. |
[7] |
WANG Zhi-qiang1, CHENG Yan-xin1, ZHANG Rui-ting1, MA Lin1, GAO Peng1, LIN Ke1, 2*. Rapid Detection and Analysis of Chinese Liquor Quality by Raman
Spectroscopy Combined With Fluorescence Background[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3770-3774. |
[8] |
LIU Hao-dong1, 2, JIANG Xi-quan1, 2, NIU Hao1, 2, LIU Yu-bo1, LI Hui2, LIU Yuan2, Wei Zhang2, LI Lu-yan1, CHEN Ting1,ZHAO Yan-jie1*,NI Jia-sheng2*. Quantitative Analysis of Ethanol Based on Laser Raman Spectroscopy Normalization Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3820-3825. |
[9] |
LU Wen-jing, FANG Ya-ping, LIN Tai-feng, WANG Hui-qin, ZHENG Da-wei, ZHANG Ping*. Rapid Identification of the Raman Phenotypes of Breast Cancer Cell
Derived Exosomes and the Relationship With Maternal Cells[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3840-3846. |
[10] |
LI Qi-chen1, 2, LI Min-zan1, 2*, YANG Wei2, 3, SUN Hong2, 3, ZHANG Yao1, 3. Quantitative Analysis of Water-Soluble Phosphorous Based on Raman
Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3871-3876. |
[11] |
GUO He-yuanxi1, LI Li-jun1*, FENG Jun1, 2*, LIN Xin1, LI Rui1. A SERS-Aptsensor for Detection of Chloramphenicol Based on DNA Hybridization Indicator and Silver Nanorod Array Chip[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3445-3451. |
[12] |
ZHU Hua-dong1, 2, 3, ZHANG Si-qi1, 2, 3, TANG Chun-jie1, 2, 3. Research and Application of On-Line Analysis of CO2 and H2S in Natural Gas Feed Gas by Laser Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3551-3558. |
[13] |
LIU Jia-ru1, SHEN Gui-yun2, HE Jian-bin2, GUO Hong1*. Research on Materials and Technology of Pingyuan Princess Tomb of Liao Dynasty[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3469-3474. |
[14] |
LI Wen-wen1, 2, LONG Chang-jiang1, 2, 4*, LI Shan-jun1, 2, 3, 4, CHEN Hong1, 2, 4. Detection of Mixed Pesticide Residues of Prochloraz and Imazalil in
Citrus Epidermis by Surface Enhanced Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3052-3058. |
[15] |
ZHAO Ling-yi1, 2, YANG Xi3, WEI Yi4, YANG Rui-qin1, 2*, ZHAO Qian4, ZHANG Hong-wen4, CAI Wei-ping4. SERS Detection and Efficient Identification of Heroin and Its Metabolites Based on Au/SiO2 Composite Nanosphere Array[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3150-3157. |
|
|
|
|