A new method to measure and quantify the 3D mineralogical composition of particulate materials using X-ray computed micro-tomography (CT) is presented. The new method is part of a workflow designed to standardize the analysis of particles based on their microstructures without the need to segment the individual classes or grains. Classification follows a decision tree with criteria derived from particle histogram parameters that are specific to each microstructure, which in turn can be identified by 2D-based automated quantitative mineralogy. The quantification of mineral abundances is implemented at the particle level according to the complexity of the particle by taking into consideration the partial volume effect at interphases. The new method was tested on two samples with different particle size distributions from a carbonate rock containing various microstructures and phases. The method allowed differentiation and quantification of more mineral classes than traditional 3D image segmentation that uses only the grey-scale for mineral classification. Nevertheless, due to lower spatial resolution and lack of chemical information, not all phases identified in 2D could be distinguished. However, quantification of the mineral classes that could be distinguished was more representative than their 2D quantification, especially for coarser particle sizes and for minor phases. Therefore, the new 3D method shows great potential as a complement to 2D-based methods and as an alternative to traditional phase segmentation analysis of 3D images. Particle-based quantification of mineralogical and 3D geometrical properties of particles opens new applications in the raw materials and particle processing industries.

Characterizing the pathways by which crystals form remains a significant challenge, particularly when multiple pathways operate simultaneously. Here, an imaging‐based strategy is introduced that exploits confinement effects to track the evolution of a population of crystals in 3D and to characterize crystallization pathways. Focusing on calcium sulfate formation in aqueous solution at room temperature, precipitation is carried out within nanoporous media, which ensures that the crystals are fixed in position and develop slowly. The evolution of their size, shape, and polymorph can then be tracked in situ using synchrotron X‐ray computed tomography and diffraction computed tomography without isolating and potentially altering the crystals. The study shows that bassanite (CaSO₄ 0.5H₂O) forms via an amorphous precursor phase and that it exhibits long‐term stability in these nanoscale pores. Further, the thermodynamically stable phase gypsum (CaSO₄ 2H₂O) can precipitate by different pathways according to the local physical environment. Insight into crystallization in nanoconfinement is also gained, and the crystals are seen to grow throughout the nanoporous network without causing structural damage. This work therefore offers a novel strategy for studying crystallization pathways and demonstrates the significant impact of confinement on calcium sulfate precipitation, which is relevant to its formation in many real‐world environments.

This paper demonstrates a new method to classify mineral phases in 3D images of particulate materials obtained by X-ray computed micro-tomography (CT), here named mounted single particle characterization for 3D mineralogical analysis (MSPaCMAn). The method allows minimizing the impact of imaging artefacts that make the classification of voxels inaccurate and thus hinder the use of CT to characterize natural particulate materials. MSPaCMAn consists of (1) sample preparation as particle dispersions; (2) image processing optimized towards the labelling of individual particles in the sample; (3) phase identification performed at the particle level using an interpretation of the grey-values of all voxels in a particle rather than of all voxels in the sample. Additionally, the particle’s geometry and microstructure can be used as classification criteria besides the grey-values. The result is an improved accuracy of phase classification, a higher number of detected phases, a smaller grain size that can be detected, and individual particle statistics can be measured instead of just bulk statistics. Consequently, the method broadens the applicability of 3D imaging techniques for particle analysis at low particle size to voxel size ratio, which is typically limited due to unreliable phase classification and quantification. MSPaCMAn could be the foundation of 3D semi-automated mineralogy similar to the commonly used 2D image-based semi-automated mineralogy methods.

Barite precipitation in fractures and nanopores within a shale sample is analysed in situ, in 3D, and over time. Diffusion of barium and sulphate from opposite sides of the sample creates a supersaturated zone where barium sulphate crystals precipitate. Time-lapse synchrotron-based computed tomography was used to track the growth of precipitates over time, even within the shale’s matrix where the nanopores are much smaller than the resolution of the technique. We observed that the kinetics of precipitation is limited by the type and size of the confinement where crystals are growing, i.e., nanopores and fractures. This has a major impact on the ion transport at the growth front, which determines the extent of precipitation within wider fractures (fast and localised precipitation), thinner fractures (non-localised and slowing precipitation) and nanopores (precipitation spread as a front moving at an approximately constant velocity of 10 ± 3 µm/h). A general sequence of events during precipitation in rocks containing pores and fractures of different sizes is proposed and its possible implications to earth sciences and subsurface engineering, e.g., fracking and mineral sequestration, are discussed.

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 polychromatic 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.