Soil, one of the most precious natural resources on Earth, gradually accumulates heavy metals, inevitably causing significant damage to the ecological environment. Here, we introduce confocal controlled laser induced breakdown spectroscopy (CCLIBS) technology for the quantitative analysis of the heavy metal cadmium in soil for the first time. CCLIBS offers better spatial consistency and stable plasma temperature during sample ablation compared to traditional LIBS, thereby reducing matrix effects to improve the accuracy of the quantitative results. The fluctuation of the spectrum and limit of detection are reduced by 0.6 times and 0.39 times, respectively. An effective prediction model was established using the partial least squares method, with a determination coefficient increased to 0.96. The root mean square error of prediction and average relative error are reduced to 67.67 and 0.20, respectively. These results indicate that CCLIBS provides consistent ablation conditions for elemental quantification and yields reliable test results, which is significant for monitoring heavy metals in the ecological environment and effectively intervene and mitigate environmental contamination.

Rice is the primary grain crop in China, and the quality of rice is closely related to the external environment, such as soil characteristics, climate, sunshine time, and irrigation water. The high-quality rice-origin area has certain regional limitations. Therefore,the rice can be seen as an apparent geographical marker. There are often some counterfeits or branded famous high-quality rice in the market, which can damage the rice brand, reduce the rice quality guarantee of consumers, and disturb the market stability, so rapid identification technology of rice origin is needed. The rice origin identification models of five sources in Jilin Province (Daan, Gongzhuling, Qianguo, Songyuan and Taoerhe) are done by laser-induced breakdown spectroscopy and machine learning algorithms. The principal component analysis (PCA) algorithm, combined with four machine learning algorithms, Bagged Trees, Weighted KNN, Quadratic SVM, and Coaster Gaussian SVM, has been established. A total of 450 groups of LIBS data are selected. The spectral data of rice LIBS are pretreated with Savitzky-Golay smoothing (SG smoothing) is used for noise reduction and normalisation. The principal component analysis uses the rice LIBS data, which shows that the rice origins had an excellent cluster distribution of clustering spaces. Still, there is spatial overlap in some rice origins. Utilising5x cross-validation, the identification accuracy of rice origins can reachmore than 91.8% by adopting PCA-Bagged Trees, PCA-Weighted KNN, PCA-Quadratic SVM and PCA-Coarse Gaussian SVM, and the recognition accuracy of PCA-Quadratic SVM model is as high as 97.3%. The results show that the combination of LIBS technology and machine learning algorithms can identify rice origin with high precision and high efficiency.

Remote-sensing observations on the surface of airless bodies, such as the Moon and asteroids, have confirmed the presence of hydrogen-bearing materials. However, their spatial distributions at small scales (mm-m) and depth profiles have great uncertainties. In-situ analyses of hydrogen-bearing materials with laser-induced breakdown spectroscopy (LIBS) have been proposed to resolve these problems, as the footprint of LIBS ablation is small (less than or similar to 1 mm) and can penetrate into the subsurface by excavating the surface layer. Nevertheless, the measurement accuracy of hydrogen with LIBS on airless and hydrous planetary bodies has not been evaluated because it requires extensive calibration using hydrogen-rich geologic materials under a high-vacuum condition. In addition, whether hydrogen occurs as hydroxyl or ice has been difficult to ascertain via LIBS analysis because molecular information is typically lost in the ablation plasma. To resolve these problems, we conducted two experiments. First, compressed powders of rocks were measured by LIBS under vacuum (<3 x 10(-2) Pa) to evaluate the calibration accuracies and detection limits in rocks and compacted soils on airless bodies. Several geostandards including basalts and feldspars were doped with various concentrations of hydroxyls (Mg(OH)(2) and Ca(OH)(2)) to prepare hydrogen-rich samples up to 15 wt% in H2O-equivalent concentration (wt%H2O). Our results show that the hydrogen concentration can be accurately calibrated from the LIBS spectra by using multivariate models or matrix-matched calibration curves (i.e., calibration using samples with comparable bulk elemental abundances), facilitating the correction of significant matrix effects observed in the intensities of the 656 nm Ha line. We obtained a measurement accuracy of +/- 0.9 wt%H2O in the 0-12 wt%H2O range via matrix-matched calibration. This level of accuracy is sufficient for many planetary and resource exploration applications, such as designing hardware and operation for mining water on the Moon. We estimate the 2 sigma limit of detection (LOD) to be 0.4 wt %H2O based on the average of all samples, although better LODs were obtained for some individual matrix (e.g., 0.2 wt%H2O for basalt/feldspar-Ca(OH)(2) mixtures). Such LOD shows that exploitable ice on the Moon can be detected with 2 sigma confidence by LIBS. Second, we demonstrate that the molecular structure of hydrogen can be distinguished by operating LIBS in tandem with heating lasers. In this method, the samples are heated prior to LIBS analysis using a continuous-wave laser with adjusted fluence and duration. Our results indicate that ice and hydroxyl can be distinguished because the Ha lines of ice-bearing samples decrease after the laser heating due to sublimation, but those of hydroxyl-bearing samples are retained. In addition, we report an enhancement of hydrogen emission from loose powders, suggesting that hydrogen in lunar soils may be measured with higher sensitivity. The results of this study show that LIBS is a versatile and powerful tool for accurate stand-off measurement of hydrogen-bearing materials on airless planetary bodies.

The project Lunar Volatiles Mobile Instrumentation-Extended (LUVMI-X) developed an initial system design as well as payload and mobility breadboards for a small, lightweight rover dedicated for in situ exploration of the lunar south pole. One of the proposed payloads is the Volatiles Identification by Laser Analysis instrument (VOILA), which uses laser-induced breakdown spectroscopy (LIBS) to analyze the elemental composition of the lunar surface with an emphasis on sampling regolith and the detection of hydrogen for the inference of the presence of water. It is designed to analyze targets in front of the rover at variable focus between 300 mm and 500 mm. The spectrometer covers the wavelength range from 350 nm to 790 nm, which includes the hydrogen line at 656.3 nm as well as spectral lines of most major rock-forming elements. We report here the scientific input that fed into the concept and design of the VOILA instrument configuration for the LUVMI-X rover. Moreover, we present the measurements performed with the breadboard laboratory setup for VOILA at DLR Berlin that focused on verifying the performance of the designed LIBS instrument in particular for the detection and quantification of hydrogen and other major rock forming elements in the context of in situ lunar surface analysis.