Quantitative analysis of Th and U in graphite matrix using femtosecond laser-induced breakdown spectroscopy

被引:2
作者
Fan, Pingping [1 ]
Ren, Shichao [1 ]
Gong, Laiyong [1 ]
Meng, Xiangting [1 ]
Liu, Xiaoliang [1 ]
Jiao, Baobao [1 ]
Sun, Shaohua [2 ]
Guo, Xiaoyang [3 ]
机构
[1] East China Univ Technol, Fundamental Sci Radioact Geol & Explorat Technol L, Nanchang 330013, Peoples R China
[2] Lanzhou Univ, Sch Nucl Sci & Technol, Lanzhou 730000, Peoples R China
[3] Shenzhen Technol Univ, Coll Engn Phys, Shenzhen 518118, Peoples R China
来源
APPLIED PHYSICS B-LASERS AND OPTICS | 2024年 / 130卷 / 06期
基金
中国国家自然科学基金;
关键词
LIBS; THORIUM; CLASSIFICATION; URANIUM; PLASMAS; SAMPLES;
D O I
10.1007/s00340-024-08229-6
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
As one of the six nuclear energy systems selected by the "Fourth Generation Nuclear Reactor International Forum", the Thorium Molten Salt Reactor (TMSR) has garnered significant attention due to its fascinating features, including excellent neutron economy, online fuel reprocessing, and reduced production of actinides. However, real-time online analysis of the nuclear elements remains a crucial technology throughout the fuel reprocessing. In this study, the femtosecond laser-induced breakdown spectroscopy (LIBS) was employed with both univariate and multivariate regression models for the quantitative analysis of Th and U elements in a graphite matrix. The concentrations of ThO2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{2}$$\end{document} in six homemade samples were ranged from 4.998 to 40.012 wt%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}, while the concentrations of U3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{3}$$\end{document}O8\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{8}$$\end{document} were ranged from 0.970 to 2.498 wt%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}. The mean relative error (MRE), root mean square error of prediction (RMSEP), root mean square error of calibration and coefficient of determination (R2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$<^>{2}$$\end{document}) were employed as indicators to evaluate the quantitative accuracy and stability of the LIBS analysis. For the univariate regress, the standard curve method was performed, utilizing a non-interferential emission line (Th II 286.99 nm) as analytical line for Th measurement. However, no suitable analysis line was found for U element. For the multivariate regression, the random forest (RF) method and partial least squares (PLS) regression method utilized three spectral regions (285.00-288.00, 330.00-370.00 and 390.00-425.00 nm) as analytical bands for ThO2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{2}$$\end{document}, while two regions (380.00-389.00 and 514.00-517.00 nm) were employed for U analysis. The results suggest that the PLS regression exhibited the best performance, yielding RMSEPThO2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{ThO_{2}}$$\end{document} = 0.785 wt%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}, RMSEPU3O8\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{U_{3}O_{8}}$$\end{document} = 0.135 wt%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}, MREThO2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{ThO_{2}}$$\end{document} = 4.24%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}, and MREU3O8\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{U_{3}O_{8}}$$\end{document} = 4.90%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}. Therefore, the combination of LIBS with multivariate regression models exhibits significant potential as a robust in-situ analytical approach for the online fuel reprocessing in TMSR.
引用
收藏
页数:10
相关论文
共 31 条
  • [1] A comparison of single shot nanosecond and femtosecond polarization-resolved laser-induced breakdown spectroscopy of Al
    Agnes, Nakimana
    Tao Hai-Yan
    Hao Zuo-Qiang
    Sun Chang-Kai
    Gao Xun
    Lin Jing-Quan
    [J]. CHINESE PHYSICS B, 2013, 22 (01)
  • [2] Characterization of laser induced plasmas by optical emission spectroscopy: A review of experiments and methods
    Aragon, C.
    Aguilera, J. A.
    [J]. SPECTROCHIMICA ACTA PART B-ATOMIC SPECTROSCOPY, 2008, 63 (09) : 893 - 916
  • [3] Strategies for thorium fuel cycle transition in the SD-TMSR
    Ashraf, O.
    Rykhlevskii, Andrei
    Tikhomirov, G., V
    Huff, Kathryn D.
    [J]. ANNALS OF NUCLEAR ENERGY, 2020, 148
  • [4] LIBS development methodology for forensic nuclear materials analysis
    Bhatt, Bobby
    Angeyo, Kalambuka Hudson
    Dehayem-Kamadjeu, Alix
    [J]. ANALYTICAL METHODS, 2018, 10 (07) : 791 - 798
  • [5] Univariate and multivariate analyses of rare earth elements by laser-induced breakdown spectroscopy
    Bhatt, Chet R.
    Yueh, Fang Y.
    Singh, Jagdish P.
    [J]. APPLIED OPTICS, 2017, 56 (08) : 2280 - 2287
  • [6] Characterization of ultrafast laser-ablation plasma plumes at various Ar ambient pressures
    Diwakar, P. K.
    Harilal, S. S.
    Phillips, M. C.
    Hassanein, A.
    [J]. JOURNAL OF APPLIED PHYSICS, 2015, 118 (04)
  • [7] Fortes FJ., 2020, Laser-Induced Breakdown Spectroscopy[M].
  • [8] Microwave-driven inductively coupled plasmas for analytical spectroscopy
    Giersz, J.
    Jankowski, K.
    Ramsza, A.
    Reszke, E.
    [J]. SPECTROCHIMICA ACTA PART B-ATOMIC SPECTROSCOPY, 2018, 147 : 51 - 58
  • [9] Radiative models of laser-induced plasma and pump-probe diagnostics relevant to laser-induced breakdown spectroscopy
    Gornushkin, Igor B.
    Panne, Ulrich
    [J]. SPECTROCHIMICA ACTA PART B-ATOMIC SPECTROSCOPY, 2010, 65 (05) : 345 - 359
  • [10] Discrimination of biological and chemical threat simulants in residue mixtures on multiple substrates
    Gottfried, Jennifer L.
    [J]. ANALYTICAL AND BIOANALYTICAL CHEMISTRY, 2011, 400 (10) : 3289 - 3301