Geochemical and mineralogical characterization studies play an important role in the definition of mineral deposits. Each mineral system has a unique set of minerals with different chemical makeup and physical properties. Platinum-group elements (PGEs), for example, are scarce resources with many applications. The optimization of extraction process efficiency is therefore crucial to prevent resource shortage and increased bulk prices. To improve the mineral liberation process, high throughput sensors must be added alongside the production line as part of fast process analysis implementation. Current analytical methods are either ineffective to assess PGE content, or unusable in the conditions of the processing facilities. This article shows how Laser-induced breakdown spectroscopy (LIBS) technology, developed by ELEMISSION Inc, can circumvent these drawbacks by enabling automated, ultra-fast, and precise quantitative mineral analyses in any working environment. The drill core samples that were used in this study were collected at the Stillwater platinum group element mine in the United States. The data used for the mineralogical database was validated using the TESCAN Integrated Mineral Analyzer (TIMA) instrument.

This article reports on the quantification of lithium and mineralogical mapping in crushed lithium ore by laser-induced breakdown spectroscopy (LIBS) using two different calibration methods. Thirty crushed ore samples from a pegmatite lithium deposit were used in this study. Representative samples containing the abundant minerals were taken from these crushed ores and mixed with resin to make polished disks. These disks were first analyzed by TIMA (TESCAN Integrated Mineral Analyzer) and then by a LIBS ECORE analyzer to determine the minerals. Afterwards, each of the thirty crushed ore samples (<10 mm) were poured into rectangular containers and analyzed by the ECORE analyzer, then mineral mapping was produced on the scanned surfaces using the mineral library established on the polished sections. For the first method the lithium concentrations were inferred from the empirical mineral chemistry formula, whereas the second one consisted of building a conventional calibration curve with the crushed material to predict the lithium concentration in unknown crushed materials.

This paper demonstrates the capability of performing an ultrafast chemical mapping of drill cores collected from a platinum/palladium mine using laser-induced breakdown spectroscopy (LIBS). A scan of 40 mm × 30 mm was performed, using a commercial LIBS analyzer, onto the flat surface of a drill core with a scanning speed of 1000 Hz, and a spatial resolution of 50 µm, in about 8 min. Maps of the scanned areas for seven chemical elements (platinum, palladium, nickel, copper, iron, silicon, and magnesium), as well as a single map including the seven elements altogether, were then generated using the proprietary software integrated into the LIBS analyzer. Based on the latter image, seven minerals were identified using the principal component analysis (PCA) and correlations with the elemental maps.

Laser-induced breakdown spectroscopy (LIBS) is a valuable tool for the solid-state elemental analysis of battery materials. Key advantages include a high sensitivity for light elements (lithium included), complex emission patterns unique to individual elements through the full periodic table, and record speed analysis reaching 1300 full spectra per second (1.3 kHz acquisition rate). This study investigates deep learning methods as an alternative tool to accurately recognize different compositions of similar battery materials regardless of their physical properties or manufacturer. Such applications are of interest for the real-time digitalization of battery components and identification in automated manufacturing and recycling plant designs.

Scarce platinum group elements (PGE) are mainly concealed in massive sulfides, and finding economically viable ore bodies largely relies on their fast chemical mapping. Most core scanners provide incomplete mineralogical contents, but none also provide a complete chemical analysis including light elements. This study investigates the performance of a fully automated laser-induced breakdown spectroscopy (LIBS) core scanner, the ECORE, by comparing its reliability to a scanning electron microscope-energy dispersive spectroscopy (SEM-EDS) mineral mapper and its speed to infrared diffuse reflectance hyperspectral imagers (IR-HSI). The LIBS elemental imaging has been put to the test in our previous work, as well as the high-resolution mineralogical mapping. This paper reports the scaling up analytical applicability of LIBS as a high performance and high-speed drill core scanner. The analysis of a full core tray in this study is the first and largest 7.62 megapixels image done by a LIBS core scanner to our knowledge. Both high-resolution and low-resolution data are put together to express both mineralogical and chemical content as a function of depth.

What if composition information down to low ppm, even ppb, came as easily as pressing a start button? With recent safety concerns for lithium-ion batteries, quality control units must improve sampling procedures and information gain before releasing lots for commercial purposes. Unfortunately, characterization techniques available on the market bear more cons than pros due to low lithium sensitivities, cumbersome sample preparation, and high maintenance procedures. Laser-induced breakdown spectroscopy (LIBS) elemental analyzers recently entered the synthetic materials characterization market, introducing kHz acquisition rates without loss of spatial information. The scope of this study aims to develop quantitative and visualization tools using LIBS digitalizers applied to the field of battery materials characterization, from raw materials and precursors to composite electrodes. Along with elemental imaging and contents, homogeneity assessments and tomographies can be performed in record processing times (<1 second to a few seconds per sample) to amplify the information gain. Furthermore, AI-powered tools such as composition validation and outlier identification and quantification tools are being developed to fully automate the validation process. The overall goal of this study is to demonstrate the practical uses of LIBS for electrochemical research. Figure 1