|
|
|
|
|
|
| Investigation of the Spectral Characteristics of Laser-Induced Plasma for Non-Flat Samples |
| LEI Bing-ying1, 2, XU Bo-ping1, 2, WANG Yi-shan1, 2, ZHU Xiang-ping1, 2, DUAN Yi-xiang3, ZHAO Wei1, 2, TANG Jie1* |
1. State Key Laboratory of Transient Optics and Photonics, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy Sciences, Xi’an 710119, China
2. School of Future Technology, University of Chinese Academy of Sciences, Beijing 100049, China
3. Key Laboratory of Synthetic and Natural Functional Molecule Chemistry of Ministry of Education, College of Chemistry and Materials Science, Northwest University, Xi’an 710127, China
|
|
|
|
|
Abstract Laser-induced breakdown spectroscopy (LIBS), a fast and real-time tool for elemental analysis, has attracted great attention due to its broad applications in trace detection, geological environment monitoring, and other fields. The sample surface is one of the key environmental factors that affect the generation and characteristics of plasma. In this work, a 1 064 nm-laser beam with a pulse width of 8 ns is used to produce plasma in ambient air and comparatively investigate the emission spectra of a series of natural rock samples under non-flat and flat samples surfaces. Based on the laser-supported detonation wave model, the influence of non-flat sample surface on spectral characteristics of laser-induced plasmais discussed. For time-integrated spectra, the results show that the spectral intensities of the atomic lines of the non-flat sample are reduced by nearly 70% compared to those of the flat sample. This indicates that the negative effect of the non-flat sample surface on the LIBS cannot be ignored. According to the signal intensity of the spectral lines, Fe Ⅰ 404.58 nm and Fe Ⅰ 438.35 nm from limonite sample under different laser energies, the variation of their peak intensities and reduction factor with the change of laser energy were studied under the conditions of flat and non-flat sample surfaces. It is found that the spectral intensity under the condition of the non-flat sample surface is lower than that under the condition of the flat sample surface. It is worth noting that the reduction factor of spectral intensity first decreases gradually with laser energy, reaches the minimum value at 33 mJ, and then increases with the further increase of laser energy. Further observations show that laser-plasma with lower electron density is generated on the non-flat sample surface, and the ratio of the electron density of the non-flat sample to that of the flat sample reaches its minimum at the laser energy of 33 mJ, which is consistent with the changing trend of reduction factor with laser energy. This mainly arises because a thinner energy absorption region in laser-plasma is formed due to the large laser incident angle on the non-flat sample surface, thereby increasing the laser energy threshold corresponding to the plasma shielding. Moreover, it is found that the sample surface and the laser energy have little effect on the plasma temperature.
|
|
Received: 2021-07-30
Accepted: 2022-02-15
|
|
|
|
Corresponding Authors:
TANG Jie
E-mail: tangjie@opt.ac.cn
|
|
[1] Guo L, Zhang D, Sun L, et al. Frontiers of Physics, 2021, 16(2): 22500.
[2] Zhang Y, Sun C, Gao L, et al. Spectrochimica Acta Part B: Atomic Spectroscopy, 2020, 166: 105802.
[3] Li J, Lei B, Wang J, et al. Communications Physics, 2021, 4: 64.
[4] Li J, Wang J, Lei B, et al. Advanced Science, 2020, 7: 1902616.
[5] YUAN Ru-jun, WAN Xiong, WANG Hong-peng（袁汝俊，万 雄，王泓鹏）. Spectroscopy and Spectral Analysis（光谱学与光谱分析）, 2021, 41(4): 1265.
[6] Xue B, Tian Y, Li N, et al. J. Anal. At. Spectrom., 2020, 35: 2880.
[7] Cui M, Deguchi Y, Wang Z, et al. Plasma Sci. Technol., 2019, 21(3): 034007.
[8] Li X, Wang J, Li H, et al. Applied Physics B, 2019, 125: 236.
[9] Han D, Joe Y J, Ryu J S, et al. Spectrochimica Acta Part B, 2018, 146: 84.
[10] National Institute of Standards and Technology. August 20, 2020. https://www.nist.gov/pml/atomic-spectra-database.
[11] Lei B, Wang J, Li J, et al. Optics Express, 2019, 27(15): 20541.
[12] Lei B, Xu B, Wang J, et al. RSC Advances, 2020, 10: 39553.
[13] Lei B, Wang J, Li J, et al. Jpn. J. Appl. Phys., 2018, 57(10): 106001.
|
| [1] |
YANG Kun, CHEN Lei*, CHENG Fan-chong, PEI Huan, LIU Gui-ming, WANG Bao-huai, ZENG Wen. Emission Spectroscopy Diagnosis of Air Gliding Arc Plasma Under
Atmospheric Pressure Condition[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(10): 3006-3011. |
| [2] |
OUYANG Ai-guo, LIN Tong-zheng, HU Jun, YU Bin, LIU Yan-de. Optimization of Hardness Testing Model of High-Speed Iron Wheel by Laser-Induced Breakdown Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(10): 3109-3115. |
| [3] |
LIU Ye-kun, HAO Xiao-jian*, YANG Yan-wei, HAO Wen-yuan, SUN Peng, PAN Bao-wu. Quantitative Analysis of Soil Heavy Metal Elements Based on Cavity
Confinement LIBS Combined With Machine Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(08): 2387-2391. |
| [4] |
ZHANG Peng-peng1, 2, XU Jin-li1, 2*, HU Meng-ying1, 2, ZHANG Ling-huo1, 2, BAI Jin-feng1, 2, ZHANG Qin1, 2*. Determinations of Zr, Hf and Nb Contents in Soil Samples by
Laser-Induced Breakdown Spectroscopy (LIBS)[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(07): 2163-2168. |
| [5] |
ZHANG Xing-long1, LIU Yu-zhu1, 2*, SUN Zhong-mou1, ZHANG Qi-hang1, CHEN Yu1, MAYALIYA·Abulimiti3*. Online Monitoring of Pesticides Based on Laser Induced Breakdown
Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1711-1715. |
| [6] |
YANG Lin-yu1, 2, 3, DING Yu1, 2, 3*, ZHAN Ye4, ZHU Shao-nong1, 2, 3, CHEN Yu-juan1, 2, 3, DENG Fan1, 2, 3, ZHAO Xing-qiang1, 2, 3. Quantitative Analysis of Mn and Ni Elements in Steel Based on LIBS and GA-PLS[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1804-1808. |
| [7] |
HE Ya-xiong1, 2, ZHOU Wen-qi1, 2, ZHUANG Bin1, 2, ZHANG Yong-sheng1, 2, KE Chuan3, XU Tao1, 2*, ZHAO Yong1, 2, 3. Study on Time-Resolved Characteristics of Laser-Induced Argon Plasma[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1049-1057. |
| [8] |
QIN Shuang1, LI Ming-liang1, DAI Yu-jia1, GAO Xun1*, SONG Chao2*, LIN Jing-quan1. The Accuracy Improvement of Fe Element in Aluminum Alloy by Millisecond Laser Induced Breakdown Spectroscopy Under Spatial Confinement Combined With Support Vector Machine[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(02): 582-586. |
| [9] |
LI Ming-liang1, DAI Yu-jia1, QIN Shuang1, SONG Chao2*, GAO Xun1*, LIN Jing-quan1. Influence of LIBS Analysis Model on Quantitative Analysis Precision of Aluminum Alloy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(02): 587-591. |
| [10] |
GONG Zheng1, LIN Jing-jun2*, LIN Xiao-mei3*, HUANG Yu-tao1. Effect of Heating and Cooling on the Characteristic Lines of Al During Melting[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(02): 598-602. |
| [11] |
WANG Ya-wen1,2,3, ZHANG Yong4, CHEN Xiong-fei1,2,3, LIU Ying1,2,3, ZHAO Zhen-yang4, YE Ming-guo5, XU Yu-xing6, LIU Peng-yu1,2,3*. Quantitative Analysis of Nickel-Based Superalloys Based on a Remote LIBS System[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(02): 603-608. |
| [12] |
XU Yu-ting1, SUN Hao-ran2, GAO Xun1*, GUO Kai-min3*, LIN Jing-quan1. Identification of Pork Parts Based on LIBS Technology Combined With PCA-SVM Machine Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(11): 3572-3576. |
| [13] |
DENG Fan1, HU Zhen-lin2, CUI Hao-hao2, ZHANG Deng2, TANG Yun4, ZHAO Zhi-fang2, ZENG Qing-dong2, 3*, GUO Lian-bo2*. Progress in the Correction of Self-Absorption Effect on Laser-Induced Breakdown Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(10): 2989-2998. |
| [14] |
YANG Wen-feng1*, QIAN Zi-ran1, CAO Yu2, WEI Gui-ming1, ZHU De-hua2, WANG Feng3, FU Chan-yuan1. Research on the Controllability of Aircraft Skin Laser Paint Remove Based on Laser-Induced Breakdown Spectrum and Composition Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(10): 3233-3239. |
| [15] |
YOU Wen1, XIA Yang-peng1, HUANG Yu-tao1, LIN Jing-jun2*, LIN Xiao-mei3*. Research on Selection Method of LIBS Feature Variables Based on CART Regression Tree[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(10): 3240-3244. |
|
|
|
|
