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WANG Xin (王鑫), ZHANG Lei (张雷), FAN Juanjuan (樊娟娟), LI Yufang (李郁芳), GONG Yao (弓瑶), DONG Lei (董磊), MA Weiguang (马维光), YIN Wangbao (尹王保), JIA Suotang (贾锁堂). Parameters Optimization of Laser-Induced Breakdown Spectroscopy Experimental Setup for the Case with Beam Expander[J]. Plasma Science and Technology, 2015, 17(11): 914-918. DOI: 10.1088/1009-0630/17/11/04
Citation: WANG Xin (王鑫), ZHANG Lei (张雷), FAN Juanjuan (樊娟娟), LI Yufang (李郁芳), GONG Yao (弓瑶), DONG Lei (董磊), MA Weiguang (马维光), YIN Wangbao (尹王保), JIA Suotang (贾锁堂). Parameters Optimization of Laser-Induced Breakdown Spectroscopy Experimental Setup for the Case with Beam Expander[J]. Plasma Science and Technology, 2015, 17(11): 914-918. DOI: 10.1088/1009-0630/17/11/04

Parameters Optimization of Laser-Induced Breakdown Spectroscopy Experimental Setup for the Case with Beam Expander

  • Improvement of measurement precision and repeatability is one of the issues cur?rently faced by the laser-induced breakdown spectroscopy (LIBS) technique, which is expected to be capable of precise and accurate quantitative analysis. It was found that there was great poten?tial to improve the signal quality and repeatability by reducing the laser beam divergence angle using a suitable beam expander (BE). In the present work, the influences of several experimental parameters for the case with BE are studied in order to optimize the analytical performances: the signal to noise ratio (SNR) and the relative standard deviation (RSD). We demonstrate that by selecting the optimal experimental parameters, the BE-included LIBS setup can give higher SNR and lower RSD values of the line intensity normalized by the whole spectrum area. For validation purposes, support vector machine (SVM) regression combined with principal component analysis (PCA) was used to establish a calibration model to realize the quantitative analysis of the ash content. Good agreement has been found between the laboratory measurement results from the LIBS method and those from the traditional method. The measurement accuracy presented here for ash content analysis is estimated to be 0.31%, while the average relative error is 2.36%.
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