minerals Article Spectral Tomography for 3D Element Detection and Mineral Analysis Jose R. A. Godinho 1, *, Gabriel Westaway-Heaven 1 , Marijn A. Boone 2 and Axel D. Renno 1   Citation: Godinho, J.R.A.; Westaway-Heaven, G.; Boone, M.A.; Renno, A.D. Spectral Tomography for 3D Element Detection and Mineral Analysis. Minerals 2021, 11, 598. https://doi.org/10.3390/min11060598 Academic Editors: Daniel Sbarbaro, Eduardo Balladares and Jorge Yañez Received: 8 April 2021 Accepted: 25 May 2021 Published: 1 June 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1 Helmholtz Institute Freiberg for Resource Technology, Helmholtz-Zentrum Dresden-Rossendorf, Chemnitzer Straße 40, 09599 Freiberg, Germany; gabriel.westawayheaven@gmail.com (G.W.-H.); renno27@hzdr.de (A.D.R.) 2 TESCAN XRE, Bollebergen 2B Box 1, 9052 Ghent, Belgium; marijn.boone@tescan.com * Correspondence: j.godinho@hzdr.de Abstract: This paper demonstrates the potential of a new 3D imaging technique, Spectral Computed Tomography (sp-CT), to identify heavy elements inside materials, which can be used to classify mineral phases. The method combines the total X-ray transmission measured by a normal poly- chromatic X-ray detector, and the transmitted X-ray energy spectrum measured by a detector that discriminates between X-rays with energies of about 1.1 keV resolution. An analysis of the energy spectrum allows to identify sudden changes of transmission at K-edge energies that are specific of each element. The additional information about the elements in a phase improves the classification of mineral phases from grey-scale 3D images that would be otherwise difficult due to artefacts or the lack of contrast between phases. The ability to identify the elements inside the minerals that compose ore particles and rocks is crucial to broaden the application of 3D imaging in Earth sciences research and mineral process engineering, which will represent an important complement to traditional 2D imaging mineral characterization methods. In this paper, the first applications of sp-CT to classify mineral phases are showcased and the limitations and further developments are discussed. Keywords: spectral tomography; sp-CT; computed tomography; 3D mineral classification; minerals engineering; X-ray imaging; geometallurgy 1. Introduction Benchtop X-ray computed micro-tomography (CT) is the prime technique for non- invasive 3D imaging used across the different fields of research and industry [1,2]. Using CT, the 3D microstructure of a sample can be represented by voxelized images where the intensity of a voxel (grey-scale value) is a function of the electron density of the elements in the microstructure. Thereafter, phases with similar grey-scale values, e.g., minerals and mineral aggregates, may be segmented for individual analyses and quantification [3]. From this analysis, 3D parameters, such as volumes, grain sizes and surface areas can be mea- sured, and also the spatial distribution and associations of individual particles or phases can be determined [4–9]. Other advantages of CT include the minimum requirements of sample preparation and its non-destructive nature permit time-lapse and in situ studies where changes in a sample’s microstructure during a process can be imaged [10]. Despite the many advantages of CT, interpreting 3D grey-scale images can be chal- lenging, especially for complex polyphase samples. For example, classifying a voxel as a specific phase based only on the voxel intensity may not be possible since minerals with different chemical compositions can have similar electron densities, i.e., a similar X-ray attenuation coefficient [11]. Therefore, direct classification of a phase, similar to how it is done with 2D surface imaging techniques that use chemical information, such as mineral liberation analysis, MLA [12] and Hyperspectral [13], is not possible. Additionally, imaging artefacts and partial volume effects broadens the intensity range attributed to a Minerals 2021, 11, 598. https://doi.org/10.3390/min11060598 https://www.mdpi.com/journal/minerals