研究目的
The purpose of this study is to demonstrate the ability of a commercial LIBS analyser to scan drill core samples and quickly identify qualitatively its elemental content and mineralogy.
研究成果
The study successfully demonstrated the capability of performing ultrafast chemical elemental mapping on a platinum/palladium drill core at a scanning speed of 1000 Hz using the LIBS CORIOSITY analyzer. Seven mineral classes were identified through PCA, and their generic formulas matched well with the PCA mineral classes. The elemental mappings revealed that heavy metals are mostly contained in sulfide-based minerals embedded in silicon-rich bytownite. The presence of lithium between silicon-rich and sulfur minerals was reported for the first time.
研究不足
The study is limited by the spectral resolution and range of the LIBS analyzer, which may not capture all elements or minerals present in the sample. Additionally, the identification of minerals is based on spectral fingerprints and may not distinguish between minerals with similar compositions.
1：Experimental Design and Method Selection:
The study utilized laser-induced breakdown spectroscopy (LIBS) for ultrafast chemical mapping of drill cores. A commercial LIBS analyzer was used to perform scans on the flat surface of a drill core with a scanning speed of 1000 Hz and a spatial resolution of 50 μm.
2：Sample Selection and Data Sources:
A drill core containing palladium and platinum from the Stillwater mine in Montana, USA, was analyzed. The core was cut, and a LIBS scan of 40 mm × 30 mm of its flat surface was made.
3：List of Experimental Equipment and Materials:
The LIBS CORIOSITY GEM-III analyzer manufactured by ELEMISSION Inc. was used. It includes a pulsed laser source emitting at 1064 nm, a spectrometer covering the spectral range 252–371 nm, and another spectrometer covering 616–971 nm.
4：Experimental Procedures and Operational Workflow:
The LIBS spectra were collected at 1000 Hz with a step size of 50 μm, forming an image composed of 801 × 601 pixels, in about 8 min. Principal component analysis (PCA) was used to reduce dimensionality of the data matrix and create an image that identifies and locates the different minerals present on the rock surface.
5：Data Analysis Methods:
The intensity of the selected emission line for each element was normalized between 0 and 1 for single-element maps. PCA was used to identify mineral classes based on spectral fingerprints.
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