Growth kinetics of enstatite reaction rims studied on nano-scale,Part I: Methodology,microscopic observations and the role of water

The kinetics of (Mg, Fe)SiO₃ pyroxene layer growth within silicate thin films with total thickness <1 μm was studied experimentally at 0.1 MPa total pressure, controlled fO₂ and temperatures from 1,000 to 1,300°C. The starting samples were produced by pulsed laser deposition. Layer thickness before and after the experiments and layer composition as well as microstructures, grain size and shape of the interfaces were determined by Rutherford back scattering and transmission electron microscopy assisted by focused ion beam milling. Due to the miniaturization of the starting samples and the use of high resolution analytical methods the experimentally accessible temperature range for rim growth experiments was extended by about 300°C towards lower temperatures. The thickness of the layers at a given temperature increases proprotional to the square root of time, indicating a diffusion-controlled growth mechanism. The temperature dependence of rim growth yields an apparent activation energy of 426 ± 34 kJ/mol. The small grain size in the orthopyroxene rims implies a significant contribution of grain boundary diffusion to the bulk diffusion properties of the polycrystalline rims. Based on microstructural observations diffusion scenarios are discussed for which the SiO₂ component behaves immobile relative to the MgO component. Volume diffusion data for Mg in orthopyroxene from the literature indicate that the measured diffusivity is probably controlled by the mobility of oxygen. The observed reaction rates are consistent with earlier results from dry high-temperature experiments on orthopyroxene rim growth. Compared to high pressure experiments at 1,000°C and low water fugacities, reaction rates are 3–4 orders of magnitude smaller. This observation is taken as direct evidence for a strong effect of small amounts of water on diffusion in silicate polycrystals. In particular SiO₂ changes from an immobile component at dry conditions to an extremely mobile component even at very low water fugacities.     

Modelling of a medium-term dynamics in a shallow tidal sea, based on combined physical and neural network methods

The paper presents an approach towards a medium-term (decades) modelling of water levels and currents in a shallow tidal sea by means of combined hydrodynamic and neural network models. The two-dimensional version of the hydrodynamic model Delft3D, forced with realistic water level and wind fields, is used to produce a two-year-database of water levels and currents in the study area. The linear principal component analysis (PCA) of the results is performed to reveal dominating spatial patterns in the analyzed dataset and to significantly reduce the dimensionality of the data. It is shown that only a few principal components (PCs) are necessary to reconstruct the data with high accuracy (over 95% of the original variance). Feed-forward neural networks are set up and trained to effectively simulate the leading PCs based on water level and wind speed and direction time series in a single, arbitrarily chosen point in the study area. Assuming that the spatial modes resulting from the PCA are ‘universally’ applicable to the data from time periods not modelled with Delft3D, the trained neural networks can be used to very effectively and reliably simulate temporal and spatial variability of water levels and currents in the study area. The approach is shown to be able to accurately reproduce statistical distribution of water levels and currents in various locations inside the study area and thus can be viewed as a reliable complementary tool e.g., for computationally expensive hydrodynamic modelling. Finally, a detailed analysis of the leading PCs is performed to estimate the role of tidal forcing and wind (including its seasonal and annual variability) in shaping the water level and current climate in the study area.