|
|
|
|
|
|
Determination of Silicon and Iron Contents in Primary Aluminum by Laser-Induced Breakdown Spectroscopy |
LU Hui1, 2, HU Xiao-jun1*, CAO Bin2, MA Liang2, LI Meng2, SUN Lan-xiang3 |
1. State Key Laboratory of Advanced Metallurgy,University of Science and Technology Beijing, Beijing 100083,China
2. National Engineering and Technology Research Center of Aluminum and Magnesium Electrolysis Equipment, Guiyang 550081, China
3. Shenyang Institute of Automation, Chinese Academy of Sciences,Shenyang 110016,China |
|
|
Abstract The content of silicon and iron in primary aluminum were detected by self-built LIBS device. The micro-morphology analysis of the primary aluminum sample was carried out before the experiment, It was found the distribution of silicon element is relatively uniform in the primary aluminum except little silicon has agglomerated in partial areas, the iron elements mostly appeared in agglomerated form, and there was no obvious distribution rule. The effects of laser energy on plasma spectrum characteristics were investigated in the paper. It was found that the signal-to-noise ratio of silicon and iron analytical lines increased firstly and then decreased with the increasing of laser energy,when the laser energy reached 160mJ, the signal-to-noise ratio was maximized, so the laser energy 160 mJ is the more reasonable experimental condition. The calibration model was established based on the CC method using two standard samples (pure aluminum standard samples and self-selected standard samples) under the above reasonable experimental conditions. The results showed that the calibration curve established by the self-selected sample was not ideal compared with the calibration curve established by standard samples, and there were large errors in the results, The fit goodness of the iron element calibration model is only 0.821 3, and the relative standard deviation is also large. When the pure aluminum standard samples were used, under the condition of fixed sample, the fit goodness of the calibration curves for silicon and iron elements was 0.961 1 and 0.974 1, respectively, and the relative standard deviations were 8.85% and 9.43%, respectively. Errors expressed by error bars increased with the increasing of silicon and iron contents in the pure aluminum standard samples. Under the condition of rotating sample pool, the fit goodness of the calibration curves for silicon and iron was 0.978 5 and 0.988, respectively, and the relative standard deviations were 3.78% and 3.4%, respectively. The calibration results showed that the fit goodness was significantly improved and the relative standard deviation was also reduced compared with fixed sample pool condition. The calibration model was significantly better than the model established by the self-selected samples. The 25 samples were detected using two different calibration models by LIBS, the relative errors for the results obtained from different models were compared, and the content of pure aluminum samples has a larger concentration gradient and a wider distribution, so the models obtained from pure aluminum samples have relatively poor adaptability to low-iron primary aluminum samples, while the calibration model established by the self-selected samples is not ideal, but the measurement adaptability for low-iron primary aluminum samples is relatively good. The plasma generated by laser-induced primary aluminum was diagnosed. The plasma temperature was calculated to be approximately 34 100.14 K from the Boltzmann diagram of several magnesium ion lines, The plasma electron density was estimated to be 1.69×1017 cm-3 by the Stark broadening of a line of magnesium, which confirms that the assumption that the plasma obtained from laser induced raw aluminium is in a local thermodynamic equilibrium state is valid.
|
Received: 2018-08-29
Accepted: 2019-01-16
|
|
Corresponding Authors:
HU Xiao-jun
E-mail: huxiaojun@ustb.edu.cn
|
|
[1] QIU Zhu-xian(邱竹贤). Aluminium Electrolysis by Prebaked Anode Cell(预焙槽炼铝). Beijing: Metallurgical Industry Press(北京:冶金工业出版社),2005.
[2] FENG Nai-xiang(冯乃祥). Auminium Eectrolysis(铝电解). Beijing:Chemical Industry Press(北京:化学工业出版社),2006. 7.
[3] GB/T 1196—2017. Unalloyed Aluminium Ingots for Remelting(重熔用铝锭). Beijing:Standards Press of China(北京:中国标准出版社), 2017. 7.
[4] Kim Gibaek, Yoon Young-Jun, Kim Hyun-A, et al. Spectrochimica Acta Part B: Atomic Spectroscopy, 2017, 134: 17.
[5] Anna P M. Michel, Frederick Sonnichsen. Spectrochimica Acta Part B: Atomic Spectroscopy, 2016, 125: 177.
[6] Xiao X, Berre S L, Hartig K C. Spectrochimica Acta Part B: Atomic Spectroscopy, 2017, 130: 67.
[7] Sari Romppanen, Heikki Häkkänen, Saara Kaski. Spectrochimica Acta Part B: Atomic Spectroscopy, 2017, 134: 69.
[8] Sun Lanxiang, Zhong Zhibo, Yu Haibin, et al. Spectrochimica Acta Part B: Atomic Spectroscopy,2018, 142: 29.
[9] XIE Cheng-li, LU Ji-dong, LI Yong, et al(谢承利, 陆继东, 李 勇, 等). Journal of Huazhong University of Science and Technology·Nature Science, 2008, (10):114.
[10] SUN Dui-xiong, SU Mao-gen, DONG Chen-zhong, et al(孙对兄, 苏茂根, 董晨钟, 等). Acta Physica Sinica(物理学报), 2010, 59(7): 4571.
[11] ZHENG Pei-chao, LIU Hong-di, WANG Jin-mei, et al(郑培超, 刘红弟, 王金梅, 等). Acta Photonica Sinica(光子学报), 2014, (9): 82.
[12] Gruber J, Heitz J, Arnold N, et al. Applied Spectroscopy, 2004, 58(4): 457.
[13] ZHOU Xiu-qi, LI Run-hua, DONG Bo, et al(周秀杞,李润华,董 博,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析),2018,38(5): 1577. |
[1] |
LIU Jia1, 2, GUO Fei-fei2, YU Lei2, CUI Fei-peng2, ZHAO Ying2, HAN Bing2, SHEN Xue-jing1, 2, WANG Hai-zhou1, 2*. Quantitative Characterization of Components in Neodymium Iron Boron Permanent Magnets by Laser Induced Breakdown Spectroscopy (LIBS)[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 141-147. |
[2] |
YANG Wen-feng1, LIN De-hui1, CAO Yu2, QIAN Zi-ran1, LI Shao-long1, ZHU De-hua2, LI Guo1, ZHANG Sai1. Study on LIBS Online Monitoring of Aircraft Skin Laser Layered Paint Removal Based on PCA-SVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3891-3898. |
[3] |
SUN Cheng-yu1, JIAO Long1*, YAN Na-ying1, YAN Chun-hua1, QU Le2, ZHANG Sheng-rui3, MA Ling1. Identification of Salvia Miltiorrhiza From Different Origins by Laser
Induced Breakdown Spectroscopy Combined with Artificial Neural
Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3098-3104. |
[4] |
LIU Shu1, JIN Yue1, 2, SU Piao1, 2, MIN Hong1, AN Ya-rui2, WU Xiao-hong1*. Determination of Calcium, Magnesium, Aluminium and Silicon Content in Iron Ore Using Laser-Induced Breakdown Spectroscopy Assisted by Variable Importance-Back Propagation Artificial Neural Networks[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3132-3142. |
[5] |
LI Chang-ming1, CHEN An-min2*, GAO Xun3*, JIN Ming-xing2. Spatially Resolved Laser-Induced Plasma Spectroscopy Under Different Sample Temperatures[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2032-2036. |
[6] |
ZHAO Yang1, ZHANG Lei2, 3*, CHENG Nian-kai4, YIN Wang-bao2, 3*, HOU Jia-jia5, BAI Cheng-hua1. Research on Space-Time Evolutionary Mechanisms of Species Distribution in Laser Induced Binary Plasma[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2067-2073. |
[7] |
WANG Bin1, 2, ZHENG Shao-feng2, GAN Jiu-lin1, LIU Shu3, LI Wei-cai2, YANG Zhong-min1, SONG Wu-yuan4*. Plastic Reference Material (PRM) Combined With Partial Least Square (PLS) in Laser-Induced Breakdown Spectroscopy (LIBS) in the Field of Quantitative Elemental Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2124-2131. |
[8] |
HU Meng-ying1, 2, ZHANG Peng-peng1, 2, LIU Bin1, 2, DU Xue-miao1, 2, ZHANG Ling-huo1, 2, XU Jin-li1, 2*, BAI Jin-feng1, 2. Determination of Si, Al, Fe, K in Soil by High Pressure Pelletised Sample and Laser-Induced Breakdown Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2174-2180. |
[9] |
WU Shu-jia1, 2, YAO Ming-yin2, 3, ZENG Jian-hui2, HE Liang2, FU Gang-rong2, ZENG Yu-qi2, XUE Long2, 3, LIU Mu-hua2, 3, LI Jing2, 3*. Laser-Induced Breakdown Spectroscopy Detection of Cu Element in Pig Fodder by Combining Cavity-Confinement[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1770-1775. |
[10] |
YUAN Shu, WU Ding*, WU Hua-ce, LIU Jia-min, LÜ Yan, HAI Ran, LI Cong, FENG Chun-lei, DING Hong-bin. Study on the Temporal and Spatial Evolution of Optical Emission From the Laser Induced Multi-Component Plasma of Tungsten Carbide Copper Alloy in Vacuum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1394-1400. |
[11] |
WANG Qiu, LI Bin, HAN Zhao-yang, ZHAN Chao-hui, LIAO Jun, LIU Yan-de*. Research on Anthracnose Grade of Camellia Oleifera Based on the Combined LIBS and Fourier Transform NIR Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1450-1458. |
[12] |
CHAI Shu1, PENG Hai-meng1, WU Wen-dong1, 2*. Acoustic-Based Spectral Correction Method for Laser-Induced Breakdown Spectroscopy in High Temperature Environment[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1401-1407. |
[13] |
NING Qian-qian, YANG Jia-hao, LIU Xiao-lin, HE Yu-han, HUANGFU Zhi-chao, YU Wen-jing, WANG Zhao-hui*. Design and Study of Time-Resolved Femtosecond Laser-Induced
Breakdown Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 1083-1087. |
[14] |
DING Kun-yan1, HE Chang-tao2, LIU Zhi-gang2*, XIAO Jing1, FENG Guo-ying1, ZHOU Kai-nan3, XIE Na3, HAN Jing-hua1. Research on Particulate Contamination Induced Laser Damage of Optical Material Based on Integrated Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 1234-1241. |
[15] |
SU Yun-peng, HE Chun-jing, LI Ang-ze, XU Ke-mi, QIU Li-rong, CUI Han*. Ore Classification and Recognition Based on Confocal LIBS Combined With Machine Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(03): 692-697. |
|
|
|
|