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We introduce a data-driven potential aimed at the investigation of pressure-dependent phase transitions in bulk germanium, including the estimate of kinetic barriers. This is achieved by suitably building a database including several configurations along minimum energy paths, as computed using the solid-state nudged elastic band method. After training the model based on density functional theory (DFT)-computed energies, forces, and stresses, we provide validation and rigorously test the potential on unexplored paths. The resulting agreement with the DFT calculations is remarkable in a wide range of pressures. The potential is exploited in large-scale isothermal-isobaric simulations, displaying local nucleation in the R8 to β-Sn pressure-induced phase transformation, taken here as an illustrative example. 

The ladle furnace plays a critical role in the secondary steelmaking stage, where many processes take place in the ladle such as steel property and temperature homogenization, inclusion removal, degassing, and desulfurization. Although many research has been conducted to study these aspects, due to the complicated heat and mass transfer process inside the ladle, many details about the physical process are still not quite clear. For example, the efficacy of plug/injector designs in turbulent mixing of molten steel were not fully understood. Due to its complex three dimensional flow phenomena inside the ladle, previous two dimensional flow measurement of water ladle models provided little insight into understanding the three dimensional flow phenomenon of turbulent mixing. Therefore, to achieve a better understanding on the efficacy of plug/injector designs in turbulent mixing, we implemented an advanced volumetric flow measurement instrument of Shake-the-Box system to measure the three-dimensional flow field inside a water ladle model. Totally, three different plug/injector designs were tested under two different flow rates (8 LPM and 11.5 LPM) of gas injection within a volumetric flow measurement region of 4.8 cm × 4.8 cm × 2.4 cm. The flow measurement results suggest the double slits injector produces the highest turbulence kinetic energy comparing the three injectors. 

ABSTRACT The detection of the 11.3$$\, {\rm \mu m}$$ emission feature characteristic of the Si–C stretch in carbon-rich evolved stars reveals that silicon carbide (SiC) dust grains are condensed in the outflows of carbon stars. SiC dust could be a significant constituent of interstellar dust since it is generally believed that carbon stars inject a considerable amount of dust into the interstellar medium (ISM). The presence of SiC dust in the ISM is also supported by the identification of pre-solar SiC grains of stellar origin in primitive meteorites. However, the 11.3$$\,\mu {\rm m}$$ absorption feature of SiC has never been seen in the ISM, and oxidative destruction of SiC is often invoked. In this work, we quantitatively explore the destruction of interstellar SiC dust through oxidation based on molecular dynamics simulations and density functional theory calculations. We find that the reaction of an oxygen atom with SiC molecules and clusters is exothermic and could cause CO-loss. Nevertheless, even if this is extrapolable to bulk SiC dust, the destruction rate of SiC dust through oxidation could still be considerably smaller than the (currently believed) injection rate from carbon stars. Therefore, the lack of the 11.3$$\,\mu{\rm m}$$ absorption feature of SiC dust in the ISM remains a mystery. A possible solution may lie in the currently believed stellar injection rate of SiC (which may have been overestimated) and/or the size of SiC dust (which may actually be considerably smaller than submicron in size). 

This study introduces an Augmented-Reality-based learning system that aims to support young students’ embodied learning in block-based programming activities where they learn computational concepts and create meaningful chunks of codes. Students are going to perform episode-embedded path-finding tasks, which are designed to practice their capacities of applying computational thinking in a reasonable manner to solve problems within different scenarios. Grounded on an embodied cognition approach, the AR integration creates a concrete and tangible environment for young students to understand abstract conceptual knowledge in an engaging and interactive way, with a close connection built between the real and virtual worlds.