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At least 235 records · Page 13

Continental-pelagic carbonate partitioning and the global carbonate-silicate cycle

A carbonate-silicate geochemical cycle model is developed and used to explore dynamic and climatic consequences of constraints on shallow-water carbonate burial and possible carbon loss to the mantle associated with sea-floor subduction. The model partitions carbonate deposition between shallow-water and deep-water environments and includes carbon fluxes between the mantle and lithosphere. When total lithospheric carbonate mass is constant, there are two stable steady states, one in which the carbonate burial flux is mostly continental and another in which it is mostly pelagic. The continental steady state is characterized by a low metamorphic CO2 flux to the atmosphere and predominantly shallow-water carbonate burial. The pelagic steady state is characterized by a high metamorphic CO2 flux and predominantly deep-water carbonate burial. For reasonable parameter values, when total lithospheric carbonate mass is allowed to vary, the model oscillates between predominantly continental and predominantly pelagic modes. Model results suggest that carbonate deposition patterns established during the Cenozoic may be pushing the Earth system from the continental to the pelagic mode on a time scale of 10(8) yr, with a possible consequent order-of-magnitude increase in the metamorphic CO2 flux to the atmosphere.

NASA Program Exobiology↗

Energetics of Reactions between Ceramic Coating Materials and their Binary Oxide Components with Silicate Melts

This paper summarizes our previous and current studies of using high-temperature calorimetry to investigate the energetics of reactions of ceramic coating materials (e.g., yttrium disilicate and 7-wt% yttria-stabilized zirconia) and their binary oxide components with silicate melts in the CaO–MgO–Al 2 O 3 –SiO 2 (CMAS) system. Such interactions are found to become stronger (more exothermic) with increasing difference in acid–base character between these materials and the melt. Our results suggest that the reactivity between the coating materials and the melt increases with decreasing thermodynamic stability (less exothermic enthalpy of formation from oxide components) of the coating material. They also suggest that ceramic coating materials made from binary oxides that have less exothermic enthalpies of solution and mixing are less susceptible to CMAS melt corrosion when in contact with an acidic, relatively polymerized, melt rich in SiO 2 . Thus, we propose that new coating material formulation and CMAS melt corrosion mitigation strategies should be optimized based on the energetic contributions of their binary oxide components.

CMAS↗

Multiscale Modeling of Reconstructed Tricalcium Silicate using NASA Multiscale Analysis Tool

To study microstructure characteristics of cementitious materials hydrated in space; previously, cement binder formations were processed under microgravity conditions and was further compared against ground-based experiments. For accurate estimation of process-structure-property linkage, particularly on samples hydrated in the microgravity environment, it is desired to have a high-fidelity volumetric representation of the microstructure. However, owing to small sample size and high porosity of the space-returned samples, conventional experimental characterization techniques are not viable. Hence, a deep learning-based reconstruction algorithm was employed to obtain high fidelity 3D volumes from sparse high resolution 2D Scanning Electron Microscopy (SEM) images, as inputs to micromechanics-based modeling. This machine learning-based reconstruction methodology validated against low-order statistical descriptors, captured the microstructural topology of both sample types (ground, 1g and microgravity, μg). Due to the lack of gravity, hydration products of the samples processed in space differed from those processed-on ground. Such AI-generated virtual samples were analyzed in a multiscale recursive micromechanics approach using the NASA Multiscale Analysis Tool (NASMAT). Here, we present a methodology to rapidly integrate and evaluate these AI-generated volumes in NASMAT. The synthesized microstructural volumes are directly employed as Representative Volume Elements (RVEs) to preserve the fidelity (1 pixel = 0.54 m). Invariably, analysis of such largescale problems (5123 voxels) requires huge amount of computational resources. By taking advantage of the NASMAT architecture, we also focused on systematic multiscale integration of these AI-reconstructed virtual volumes to reduce the computational demands. In this work, this methodology is demonstrated on the ground-based, 1g samples. The estimated stiffness value of 15.90 GPa is comparable to experimentally obtained modulus of hydrated tricalcium silicate sample. The workflow presented here paves the way for utilizing the NASMAT tool to perform multiscale analyses of other multi-phase material systems using either 3D virtual datasets synthesized using AI or obtained via micro-CT.

Machine Learning↗