A modified spectral standardization method for uranium ores measurement using laser induced breakdown spectroscopy.

Area of Science:

• Analytical Chemistry
• Spectroscopy
• Materials Science

Background:

• High signal uncertainty limits the quantification performance of laser-induced breakdown spectroscopy (LIBS).
• Existing spectral standardization methods struggle with elements lacking sufficient independent spectral lines, such as uranium in ores.
• This limitation hinders accurate elemental analysis in complex matrices.

Purpose of the Study:

• To develop an updated spectral standardization method to reduce signal uncertainty for LIBS analysis.
• To improve the quantification performance for elements with limited spectral lines.
• To provide a more robust method for analyzing uranium in ores.

Main Methods:

• A modified spectral standardization approach was developed, utilizing information from a matrix element (silicon) to estimate the total number density of the measured element (uranium).
• Multiple spectral lines of silicon were used to characterize its total number density.
• The total number density of uranium was then inferred through the correlation between silicon and uranium, compensating for plasma parameter fluctuations.

Main Results:

• The modified method significantly reduced the relative standard deviation (RSD) for U II 409.01 nm from 23.03% to 9.41%.
• The quantitative model determination coefficient (R_C²) for uranium increased from 0.9129 to 0.9921.
• The root mean square error of prediction (RMSE_P) decreased from 0.2586 to 0.0670, indicating improved accuracy.

Conclusions:

• The modified spectral standardization method effectively reduces signal uncertainty and enhances quantitative analysis performance for uranium in ores.
• This approach offers a novel solution for signal uncertainty reduction in LIBS analysis of elements lacking sufficient independent spectral lines or exhibiting spectral interference.
• The expanded applicability of spectral standardization broadens its use in complex sample analysis.