Abstract

Abstract. With the recurring interest in rare earth elements (REEs), laser-induced fluorescence (LiF) may provide a powerful tool for their rapid and accurate identification at different stages along their value chain. Applications to natural materials such as minerals and rocks could complement the spectroscopy-based toolkit for innovative, non-invasive exploration technologies. However, the diagnostic assignment of detected emission lines to individual REEs remains challenging because of the complex composition of natural rocks in which they can be found. The resulting mixed spectra and the large amount of data generated demand automated approaches of data evaluation, especially in mapping applications such as drill core scanning. LiF reference data provide the solution for robust REE identification, yet they usually remain in the form of tables of published emission lines. We show that a complete reference spectra library could open manifold options for innovative automated analysis. We present a library of high-resolution LiF reference spectra using the Smithsonian rare earth phosphate standards for electron microprobe analysis. We employ three standard laser wavelengths (325, 442, 532 nm) to record representative spectra in the UV-visible to near-infrared spectral range (340–1080 nm). Excitation at all three laser wavelengths yielded characteristic spectra with distinct REE-related emission lines for EuPO4, TbPO4, DyPO4 and YbPO4. In the other samples, the high-energy excitation at 325 nm caused unspecific, broad-band defect emissions. Here, lower-energy laser excitation is shown to be successful for suppressing non-REE-related emission. At 442 nm excitation, REE reference spectra depict the diagnostic emission lines of PrPO4, SmPO4 and ErPO4. For NdPO4 and HoPO4 the most efficient excitation was achieved with 532 nm. Our results emphasise the possibility of selective REE excitation by changing the excitation wavelength according to the suitable conditions for individual REEs. Our reference spectra provide a database for the transparent and reproducible evaluation of REE-bearing rocks. The LiF spectral library is available at zenodo.org and the registered DOI https://doi.org/10.5281/zenodo.4054606 (Fuchs et al., 2020). Primarily addressing the raw material exploration sector, it aids particularly the development of advanced data processing routines for LiF analysis but can also support further research on the REE luminescence in natural rocks or artificial compounds. It gives access to traceable data for the comparison of emission line positions, emission line intensity ratios and splitting into emission line sub-levels or can be used as reference or training data for automated approaches of component assignment.

Highlights

  • The exploding demand for rare earth elements (REEs) for the high-tech industry, e-mobility and the energy transition justifies the need for efficient detection methods all along the value chain, especially in raw material exploration and recycling and in science and processing or production monitoring (e.g. National Research Council, 2008; Lima and Filho, 2015; Barakos et al, 2016; European Commission, 2014, 2018)

  • Prominent emission lines with high signal to noise ratios allow for an unequivocal assignment of transitions and represent the diagnostic features needed for a laser-induced fluorescence (LiF)-based REE identification

  • The REE phosphate reference spectra presented above show that adequate excitation conditions can be achieved when selecting one out of the three laser wavelengths used in this study

Read more

Summary

Introduction

The exploding demand for rare earth elements (REEs) for the high-tech industry, e-mobility and the energy transition justifies the need for efficient detection methods all along the value chain, especially in raw material exploration and recycling and in science and processing or production monitoring (e.g. National Research Council, 2008; Lima and Filho, 2015; Barakos et al, 2016; European Commission, 2014, 2018). Spectroscopy-based methods are of paramount importance in overcoming time- and cost-intensive exploration routines in a world of depleting, increasingly complex and more remote raw material deposits. Key to result validation are reference spectra. Established spectral libraries such as from the USGS (Kokaly et al, 2017) give access to the necessary reference data for automated identification routines. This trend is recognised by Fasnacht et al (2019), who released a new library explicitly dedicated to serve for advanced automated data processing including machine learning approaches

Methods
Results
Discussion
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call