Search

The olivine carbonation reaction, in which carbon dioxide is chemically incorporated to form carbonate, is central to the emerging carbon sequestration method using ultramafic rocks. The rate of this retrograde metamorphic reaction is controlled, in part, by the available reactive surface area: as the solid volume increases during carbonation, the feasibility of this method ultimately depends on the maintenance of porosity and the creation of new reactive surfaces. We conducted in situ dynamic X-ray microtomography and nanotomography experiments to image and quantify the porosity generation during olivine carbonation. We designed a sample setup that included a thick-walled cup (made of porous olivine aggregates with a mean grain size of either ĝ1/4  5 or ĝ1/4  80 μm) filled with loose olivine sands with grain sizes of 100-500 μm. The whole sample assembly was reacted with a NaHCO3 aqueous solution at 200 °C, under a constant confining pressure of 13 MPa and a pore pressure of 10 MPa. Using synchrotron-based X-ray microtomography, the three-dimensional (3-D) pore structure evolution of the carbonating olivine cup was documented until the olivine aggregates became disintegrated. The dynamic microtomography data show a volume reduction in olivine at the beginning of the reaction, indicating a vigorous dissolution process consistent with the disequilibrium reaction kinetics. In the olivine cup with a grain size of ĝ1/4  80 μm (coarse-grained cup), dissolution planes developed within 30 h, before any precipitation was observed. In the experiment with the olivine cup of ĝ1/4  5 μm mean grain size (fine-grained cup), idiomorphic magnesite crystals were observed on the surface of the olivine sands. The magnesite shows a near-constant growth throughout the experiment, suggesting that the reaction is self-sustained. Large fractures were generated as the reaction proceeded and eventually disintegrated the aggregate after 140 h. Detailed analysis show that these are expansion cracks caused by the volume mismatch in the cup walls, between the expanding interior and the near-surface which keeps a nearly constant volume. Nanotomography images of the reacted olivine cup reveal pervasive etch pits and wormholes in the olivine grains. We interpret this perforation of the solids to provide continuous fluid access, which is likely key to the complete carbonation observed in nature. Reactions proceeding through the formation of nano- to micron-scale dissolution channels provide a viable microscale mechanism in carbon sequestration practices. For the natural peridotite carbonation, a coupled mechanism of dissolution and reaction-induced fracturing should account for the observed self-sustainability of the reaction.

Mineral carbonation, a process that binds CO2 in the form of carbonates by silicate weathering, is widespread on the Earth's surface. Because of the abundance of silicate rocks and the permanence of the carbonated solids, sequestering CO2 via mineral carbonation has generated lots of interests. However, it is unclear how the fluid-rock reaction proceeds to completion in spite of an increasing solid volume. We conducted a mineral carbonation experiment in which a sintered olivine aggregate reacted with a sodium bicarbonate solution at reservoir conditions. Time-resolved synchrotron X-ray microtomographic images show cracks in polygonal patterns arising in the surface layers and propagating into the interior of the olivine aggregate. The nanotomography data reveal that the incipient cracks intersect at right angles. We infer that stretching due to nonuniform volume expansion generates polygonal cracking of the surfaces. Our data shed new lights on the processes that control hydration and carbonation of peridotite.

Third- and fourth-generation synchrotron light sources with high fluxes and beam energies enable the use of innovative X-ray translucent experimental apparatus. These experimental devices access geologically relevant conditions whilst enabling in situ characterization using the spatial and temporal resolutions accessible at imaging beamlines. Here, Heitt Mjölnir is introduced, a heated miniature triaxial rig based on the design of Mjölnir, but covering a wider temperature range and larger sample volume at similar pressure capacities. This device is designed to investigate coupled thermal, chemical, hydraulic and mechanical processes from grain to centimetre scales using cylindrical samples of 10 mm × 20 mm (diameter × length). Heitt Mjölnir can simultaneously reach confining (hydraulic) pressures of 30 MPa and 500 MPa of axial stress with independently controlled sample pore fluid pressure < 30 MPa. This internally heated apparatus operates to temperatures up to 573 K with a minimal vertical thermal gradient in the sample of <0.3 K mm-1. This new apparatus has been deployed in operando studies at the TOMCAT (Swiss Light Source), I12 JEEP (Diamond Light Source) and PSICHE' (Synchrotron SOLEIL) beamlines for 4D X-ray microtomography with scan intervals of a few minutes. Heitt Mjölnir is portable and modular, allowing a wide range of 4D characterizations of low-grade metamorphism and deformational processes. It enables spatially and temporally resolved fluid-rock interaction studies at conditions of crustal reservoirs and is suitable for characterization of material properties in geothermal, carbonation or subsurface gas storage applications. Technical drawings and an operation guide are included in this publication. [ABSTRACT FROM AUTHOR]

X-ray computed tomography has established itself as a crucial tool in the analysis of rock materials, providing the ability to visualise intricate 3D microstructures and capture quantitative information about internal phenomena such as structural damage, mineral reactions, and fluid-rock interactions. The efficacy of this tool, however, depends significantly on the precision of image segmentation, a process that has seen varied results across different methodologies, ranging from simple histogram thresholding to more complex machine learning and deep learning strategies. The irregularity in these segmentation outcomes raises concerns about the reproducibility of the results, a challenge that we aim to address in this work. In our study, we employ the mass balance of a metamorphic reaction as an internal standard to verify segmentation accuracy and shed light on the advantages of deep learning approaches, particularly their capacity to efficiently process expansive datasets. Our methodology utilises deep learning to achieve accurate segmentation of time-resolved volumetric images of the gypsum dehydration reaction, a process that traditional segmentation techniques have struggled with due to poor contrast between reactants and products. We utilise a 2D U-net architecture for segmentation and introduce machine learning-obtained labelled data (specifically, from random forest classification) as an innovative solution to the limitations of training data obtained from imaging. The deep learning algorithm we developed has demonstrated remarkable resilience, consistently segmenting volume phases across all experiments. Furthermore, our trained neural network exhibits impressively short run times on a standard workstation equipped with a Graphic Processing Unit (GPU). To evaluate the precision of our workflow, we compared the theoretical and measured molar evolution of gypsum to bassanite during dehydration. The errors between the predicted and segmented volumes in all time-series experiments fell within the 2 % confidence intervals of the theoretical curves, affirming the accuracy of our methodology. We also compared the results obtained by the proposed method with standard segmentation methods and found a significant improvement in precision and accuracy of segmented volumes. This makes the segmented CT images suited for extracting quantitative data, such as variations in mineral growth rate and pore size during the reaction. In this work, we introduce a distinctive approach by using an internal standard to validate the accuracy of a segmentation model, demonstrating its potential as a robust and reliable method for image segmentation in this field. This ability to measure the volumetric evolution during a reaction with precision paves the way for advanced modelling and verification of the physical properties of rock materials, particularly those involved in tectono-metamorphic processes. Our work underscores the promise of deep learning approaches in elevating the quality and reproducibility of research in the geosciences. [ABSTRACT FROM AUTHOR]