光谱学与光谱分析 |
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Quantitative Analysis of Mn in Soil Samples Using LIBS |
ZHANG Bao-hua1, JIANG Yong-cheng1, ZHANG Xian-yan2, CUI Zhi-feng2 |
1. School of Electronics and Information Engineering, Anhui University, Hefei 230061, China 2. Institute of Atomic and Molecular Physics, Anhui Normal University, Wuhu 241000, China |
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Abstract The trace element of Manganese element in the agricultural farm (Anhui Huaiyuan Nongkang) soil was quantitatively analyzed by Laser-induced breakdown spectroscopy. The line of 403.1 nm was selected as the analysis line of Mn. The matrix element of Fe in soil was chosen as the internal calibration element and the analysis line was 407.2 nm. Ten soil samples were used to construct calibration curves with traditional method and internal standard method, and four soil samples were selected as test samples. The experimental results showed that the fitting correlation coefficient (r) is 0.954 when using the traditional method, the maximum relative error of the measurement samples is 5.72%, and the detection limit of Mn in soil is 93 mg·kg-1. While using the internal standard method to construct the calibration curve, the fitting correlation coefficient (r) is 0.983, the relative error of measurement samples is reduced to 4.1%, and the detection limit of Mn in soil is 71 mg·kg-1. The result indicates that LIBS technique can be used to detect trace element Mn in soil. In a certain extent, the internal standard method can improve the accuracy of measurement.
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Received: 2014-07-25
Accepted: 2014-11-06
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Corresponding Authors:
ZHANG Bao-hua
E-mail: lcphu1983@126.com;zfcui@mail.ahnu.edu.cn
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[1] Feng Yuan, Yang Jiajun, Fan Jianmei, et al. Appl. Opt, 2010, 49: c70. [2] Carvalho G G A D, Nunes M C, Souza P F D, et al. J. Anal. At. Spectrom., 2010, 25: 803. [3] Kemal E Eseller, Markandey M Tripathi, Fang-Yu Yueh, et al. Appl. Opt., 2010, 49: c21. [4] Xie Chengli, Lu Jidong, Li Pengyan, et al. Chin. Opt. Lett., 2009, 7: 545. [5] Kim Gibaek, Kwak Jihyun, Kim Ki-Rak, et al. Journal of Hazardous Materials, 2013, 263: 754. [6] Hussain T, Gondal M A, Yamani Z H, et al. Environ. Monit. Assess., 2007, 124: 131. [7] Yao Shunchun, Lu Jidong, Li Junyan, et al. J. Anal. At. Spectrom., 2010, 25: 1733. [8] Nunes L C, Braga J W B, Trevizan L C, et al. J. Anal. At. Spectrom., 2010, 25: 1453. [9] Madhavi Z Martin, Melanie A Mayes, Katherine R Heal, et al. Spectrochimica Acta Part B, 2013, 87: 100. [10] Lu Cuiping, Wang Liusan, Hu Haiying, et al. Chin. Opt. Lett., 2013, 11(5): 053004. [11] DONG Da-ming, ZHEN Wen-gang, ZHAO Chun-jiang, et al(董大明,郑文刚,赵春江,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2013, 33(3): 785. [12] LI Ting-jun(李廷钧). Emission Spectral Analysis(发射光谱分析). Beijing: Atomic Energy Press(北京: 原子能出版社), 1983. 294. [13] Sabsabi M, Cielo P. Appl. Spectrosc., 1995, 49: 499. |
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