Analyses of Li-Rich Minerals Using Handheld LIBS

Publication: Cécile Fabre, Nour Eddine Ourti, Julien Mercadier, Joana Cardoso-Fernandes, Filipa Dias, Mônica Perrotta, Friederike Koerting, Alexandre Lima, Friederike Kaestner, Nicole Koellner, Robert Linnen, David Benn, Tania Martins and Jean Cauzid. “Analyses of Li-Rich Minerals Using Handheld LIBS Tool,” Data. 2021; 6(6):68.

Researchers used SciAps Z-300 LIBS to provide over 4,000 spectra on lithium-content materials, including minerals, powder pellets, and rocks. High resolution spectrometers combined with low detection limits for light elements make handheld LIBS a powerful option to detect critical elements. The LIBS spectra dataset combined with the Li content dataset can be used to obtain quantitative estimation of Li in Li-rich matrices.

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Abstract: Lithium (Li) is one of the latest metals to be added to the list of critical materials in Europe and, thus, lithium exploration in Europe has become a necessity to guarantee its mid- to long-term stable supply. Laser-induced breakdown spectroscopy (LIBS) is a powerful analysis technique that allows for simultaneous multi-elemental analysis with an excellent coverage of light elements (Z < 13). This data paper provides more than 4,000 LIBS spectra obtained using a handheld LIBS tool on approximately 140 Li-content materials (minerals, powder pellets, and rocks) and their Li concentrations. The high resolution of the spectrometers combined with the low detection limits for light elements make the LIBS technique a powerful option to detect Li and trace elements of first interest, such as Be, Cs, F, and Rb. The LIBS spectra dataset combined with the Li content dataset can be used to obtain quantitative estimation of Li in Li-rich matrices. This paper can be utilized as technical and spectroscopic support for Li detection in the field using a portable LIBS instrument.

Keywords: lithium; LIBS; handheld tool; exploration; Li-mineral

Access to publication: https://doi.org/10.3390/data6060068

‍About this journal: Data is a peer-reviewed, open access journal on data in science, with the aim of enhancing data transparency and reusability. The journal publishes in two sections: a section on the collection, treatment and analysis methods of data in science; a section publishing descriptions of scientific and scholarly datasets (one dataset per paper). The journal is published monthly online by MDPI.