ABSTRACT
BACKGROUND: Variations in plasma properties among spectra and samples lead to significant signal uncertainty and matrix effects in laser-induced breakdown spectroscopy (LIBS). To address this issue, direct compensation for plasma property variations is considered highly desirable. However, reliably compensating for the total number density variation is challenging due to inaccurate spectroscopic parameters. For reliable compensation, a total number density compensation (TNDC) method was presented in our recent work, but its applicability is limited to simple samples because of its strict assumptions. In this study, we propose a new pre-processing method, namely extended TNDC (ETNDC), to reduce signal uncertainty and matrix effects in the more complex analytical task of uranium determination. RESULTS: ETNDC reflects the total number density variation with a weighted combination of spectral lines from all major elements and incorporates temperature and electron density compensation into the weighting coefficients. The method is evaluated on yellow cake samples and combined with regression models for uranium determination. Using the typical validation set and line combination, the mean relative standard deviation (RSD) of U II 417.159 nm in validation samples decreases from 4.92% to 2.27%, and the root mean square error of prediction (RMSEP) and the mean RSD of prediction results decrease from 4.81% to 1.93% and from 1.92% to 1.56%, respectively. Furthermore, the results of 10 validation sets and 216 line combinations show that ETNDC outperforms baseline methods in terms of average performance and robustness. SIGNIFICANCE: For the first time, ETNDC explicitly addresses the temperature and electron density variations while compensating for the total number density variation, where the inaccurate spectroscopic parameters are avoided by fitting related quantities using concentration information. The method demonstrates effective and robust improvement in signal repeatability and analytical performance in uranium determination, facilitating accurate quantification of the LIBS technique.
ABSTRACT
The Yellow River in Inner Mongolia was selected as the study area in this study. In July (wet season) and October (dry season) of 2021, the acquisition of seasonal rivers, the Yellow River tributaries and precipitation, the Yellow River, Wuliangsuhai, Lake Hasuhai, Lake Daihai, an irrigation canal system, and underground water and sea water samples were collected to test the water chemical composition and hydrogen and oxygen isotopic values of different water types. Using the Piper triplot, Gibbs plot, ion ratio, and MixSIAR model methods, the evolution of water chemistry in the Mongolian section of the Yellow River Basin was analyzed, and the transformation relationship between precipitation, surface water, and groundwater was revealed. The results showed that both groundwater and surface water in the study area were slightly alkaline; the dominant anion in water was Cl-, and the dominant cation was Na+. The main hydrochemical types of surface water were Cl·SO4-Na·Mg and SO4·HCO3-Na·Mg, whereas those of groundwater were Cl·SO4-Na·Mg and SO4·HCO3-Na·Ca. Groundwater Ca2+ and Mg2+ were primarily derived from the dissolution of silicate and evaporite, and surface water Ca2+ and Mg2+ were primarily derived from carbonate karst dissolution and carbonate and sulfuric acid in water participating in the dissolution process of carbonate and sulfide minerals. Na+ and Cl- in different water bodies were all affected by anthropogenic pollution sources. Owing to the seasonal effect, δD and δ18O of surface water and groundwater were higher in the wet season than in the dry season. The results showed that surface water was affected by evaporative fractionation after receiving precipitation recharge, and the groundwater recharge sources were complex. The MixSIAR model revealed that surface water was the main recharge source of groundwater, accounting for 52.4%-62.2% of the total recharge, and atmospheric precipitation was the main recharge source of surface water, accounting for 85.4%-97.1% of the total recharge.
ABSTRACT
Real-time quantitative detection of uranium in ores is one of the major challenges for uranium exploration. Laser-induced breakdown spectroscopy (LIBS) has been regarded as a most promising technique for this application. However, due to the matrix complexity as well as low uranium concentration of ore, the detection sensitivity of LIBS for uranium in ores is still unsatisfactory. This work explored the potential of a beam-shaping plasma modulation method to improve the limit of detection of uranium in ores. By shaping the profile of laser beam from normally Gaussian distribution to flat-top, the plasma was modulated to be more excited with reduced peak electron density at the laser-plasma interaction point for plasma shielding reduction especially at high laser energy as well as to be more morphologically stable for LIBS signal repeatability improvement. It was further found that this method enhanced LIBS signal intensity mainly by increasing the plasma temperature, while the electron density was almost unchanged, which was very attractive for uranium detection in ore since one of the major problem for uranium detection was that it is hard to find clear uranium spectral lines for analysis due to the high dense emission lines of ore samples in real cases and lower electron density, indicated less line broadening and less line overlap or interferences. A clear uranium emission line has been found in crowded ore spectra, and the intensity of U II 409.013 nm based on flat-top beam was about 5 times higher than that of Gaussian beam, and the relative standard deviation (RSD) of the signal was reduced by about 50%. Moreover, the LOD of uranium in ores was estimated to be 21.2 ppm with flat-top beam, indicating that beam shaping is a promising method for rapid and accurate detection of uranium in ores.
