The parameter optimization of lasers' energy ratio of the double-pulse laser induced breakdown spectrometry for heavy metal elements in the soil.

2021 
Laser-induced breakdown spectroscopy (LIBS) is a rapid, no-sample preparation, remote detection method that has been applied widely in the area of heavy metal detection in the soil. However, the promotion of LIBS is limited by its disadvantages, such as low precision analysis, a high detection limit, and so on. In recent years, many studies have been conducted to improve the LIBS spectral intensity. The double-pulse LIBS (DP-LIBS) is a representative technology in this area. Most of the research work focuses on the analytical methods of DP-LIBS, including the spatial configuration, the inter-pulse time, and the effect of signal enhancement of the DP-LIBS. However, there are few reports about the effect of the energy proportion of the two lasers and the contribution of different laser energies on the signal enhancement, and the inter-pulse time under the conditions of different laser energies. Moreover, DP-LIBS is mostly evaluated by the enhancement factor of the spectral signal, and there are few reports on the quantitative analysis of double-pulse LIBS. This study, which mainly detects Cu, Ni, and Pb in the soil, focuses on the contribution of the signal enhancement by adjusting the energy ratio of the two lasers and the best inter-pulse time under the conditions of different laser energies. Then, quantitative analysis of spectral signals obtained by single-pulse LIBS (SP-LIBS) and DP-LIBS are performed based on the random forest (RF) model. The results demonstrate that DP-LIBS shows better analytical performance than SP-LIBS, the coefficients of determination (R2) of the test have great improvement, the root-mean-squared error (RMSE) is much decreased and the relative error is much improved. Thus, this study shows that DP-LIBS is an effective method for the quantitative analysis of heavy metals in the soil.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    50
    References
    0
    Citations
    NaN
    KQI
    []