Search

Rare earth elements are greatly valued for their crucial role in many high-tech fields. Highly sensitive and precise determination technology of these elements in aqueous solution is essential for the extraction and recycling processes of these elements. Currently, conventional measurement methods for rare earth elements, such as ICP-MS and ICP-OES, often suffer from issues related to complex operations, lengthy procedures, and low economic efficiency. While laser-induced breakdown spectroscopy (LIBS) provides fast and convenient detection capability, due to the matrix effect of the water LIBS technology itself, the reported sensitivities of these elements have not met the application requirements for detection capabilities. In this work, we propose a method that utilizes a micro-hole array sprayer to rapidly prepare rare earth aqueous solution samples into densely and uniformly adhered particles on an Al substrate, combined with a computer-controlled three-dimensional displacement platform to facilitate fully automated LIBS detection. Through optimization of system parameters, the detection limits for La, Pr, Nd, and Sm reach 8.6, 22.3, 13.6, and 6 μg L−1, respectively, with a stable repeatability of 4.96%, within a total sample preparation and detection time of no more than 15 minutes. Compared to existing LIBS-based detection methods, the average detection limits have been reduced by two orders of magnitude, essentially meeting the ppb-level requirements for practical production. This demonstrates the potential of our system to develop into a fast, low-cost, standardized detection technology in the field of rare earth element exploration, mining, and recovery. [ABSTRACT FROM AUTHOR]

Moving the laser focus to the vicinity of the gas-liquid interface is the key point for many new enhanced and new methods to improve the quality of spectral signals in water Laser induced breakdown spectroscopy (LIBS) detection. Understanding the generation and evolution characteristics of the plasma induced by pulsed laser near the gas–liquid interface is of great significance for the establishment of evolution models and improvement of these new LIBS methods. In this paper, a set of slow horizontal flow auxiliary system is established to provide an ideal flat gas–liquid two-phase interface experimental condition. Experimental research on vertical incidence flat system was conducted using techniques such as time-resolved imaging, plasma characterization diagnosis, and spectral analysis. And the detection capabilities of the system were also tested. The characteristics and mechanisms of LIBS near the gas-liquid two-phase interface were investigated with the laser incident on the sample along the vertical direction. Simulation of the laser beam focusing process and observation of laser beam spot images show that the shift of plasma generation position relative to the focal point results from the refraction of the laser beam entering the solution from the air and the 'interface effect' of propagation on the vertical direction. Moreover, the plasma forms only the optical power density surpasses the breakdown threshold. In this work, plasma with smaller size, rounder shape, stronger radiation, higher temperature, and higher density can be produced when the focus position is in the liquid column 0.3 mm away from the upper interface. Simultaneously, for example, the Mg ion line at 285.213 nm, the obtained spectral intensity to signal-to-background ratio reaches the maximum value, and a better spectral signal can be obtained, which is 2–4 times of other positions, and the detection limits of the elements Na, Mg, and Ca also reach the lowest level, with 1.6–2.4 times of the detection limit of other focusing positions for Mg and 1.4–1.7 times for Ca, respectively. [ABSTRACT FROM AUTHOR]

A novel approach, named guided conversion enhancement, has been established to improve the laser-induced breakdown spectroscopy (LIBS) sensitivity of cation detection in water. Two processes were involved in this approach: the main part was replacement reaction that converted the cations in water to solid granules on the surface of an immersed metallic sheet; the other was electric assistance that increased local cation concentration and strengthened the reaction. With the aid of replacement reaction and an electric field, a detection limit of 16 ppb was achieved for copper cation ([Cu.sup.2+]) detection in a water solution of CuS[O.sub.4]. The obtained results suggest that this approach has significant potential to be developed as an effective method for underwater cation detection. [c] 2010 Optical Society of America OCIS codes: 300.6365, 300.6210, 140.3440, 350.5400.

Sea ice type is the key parameter of Arctic sea ice monitoring. Microwave remote sensors with medium incidence and normal incidence modes are the primary detection methods for sea ice types. The Surface Wave Investigation and Monitoring instrument (SWIM) on the China-France Oceanography Satellite (CFOSAT) is a new type of sensor with a small incidence angle detection mode that is different from traditional remote sensors. The method of sea ice detection using SWIM data is also under development. The research reported here concerns ice classification using SWIM data in the Arctic from October 2019 to April 2020. Six waveform features are extracted from the SWIM echo data at small incidence angles, then the distinguishing capabilities of a single feature are analyzed using the Kolmogorov-Smirnov distance. The classifiers of the k-nearest neighbor and support vector machine are established and chosen based on single features. Moreover, sea ice classification based on multi-feature combinations is carried out using the chosen KNN classifier, and optimal combinations are developed. Compared with sea ice charts, the overall accuracy is up to 81% using the optimal classifier and a multi-feature combination at 2°. The results reveal that SWIM data can be used to classify sea water and sea ice types. Moreover, the optimal multi-feature combinations with the KNN method are applied to sea ice classification in the local regions. The classification results are analyzed using Sentinel-1 SAR images. In general, it is concluded that these multifeature combinations with the KNN method are effective in sea ice classification using SWIM data. Our work confirms the potential of sea ice classification based on the new SWIM sensor, and highlight the new sea ice monitoring technology and application of remote sensing at small incidence angles. [ABSTRACT FROM AUTHOR]