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Na induces the formation of acicular RuO2 in the glass melt, and the effect of Cs on the precipitation of RuO2 is not clear. In this study, sodium ruthenate (NaRu) and cesium ruthenate (CsRu) were prepared by quenching experiments, and RuO2-containing waste glass was prepared by calcination of glass surfaces. The results showed that both CsRu and NaRu induced the formation of RuO2 in the glass melts, with rod-like and acicular morphology, respectively. It can be inferred that alkali metals play an important role in the formation of RuO2 with different shapes. [ABSTRACT FROM AUTHOR]

Strong interactions of metal nanoparticles (NPs) with oxide matrices can dramatically enhance the thermal stability of metal NPs. However, how metal-oxide interactions control the growth of metal NPs remains unclear. Here, the growth of Pt NPs with respect to metal-oxide interactions was investigated by encapsulating them in different Al 2 O 3 and SiO 2 oxides. Pt NPs encapsulated in Al 2 O 3 exhibited excellent thermal stability than those in SiO 2. Quantitative thermodynamic calculations revealed that metal-oxide interactions strongly governed the driving force for the coalescence of Pt in Al 2 O 3 and SiO 2. The kinetic analysis further showed that stronger Pt-Al 2 O 3 interactions controlled the Ostwald ripening of Pt NPs by restricting the diffusion of Pt atoms in oxides, leading to a higher growth activation energy of Pt in Al 2 O 3 than SiO 2. These findings explain the different sinter resistance of metal NPs when encapsulated in different oxides, providing valuable insights for enhancing the thermal stability of metal NPs. [Display omitted] [ABSTRACT FROM AUTHOR]

• We would like to demonstrate the successful production of a Cu single crystals by the Czochralski method using a conical die (CD-CZ). • Successfully grown single crystals with a diameter of 2-inches. • The CD-CZ method enables stable growth of copper single crystals by narrowing the distance between the crystal and the die wall, and also suppresses film formation and solidification on the melt surface. Copper (Cu) and other metal single crystals are useful as substrates for the deposition of atomic layer materials for many electronic applications, but the growth of large-sized single crystals is difficult to achieve. Characteristics of the metal material, namely seed elongation and intense cooling radiation at high temperatures during crystal growth, are the main challenges encountered when growing ingots with large diameters. These problems can be resolved by optimizing the crystal growth parameters. By adjusting the shoulder formation angle of the ingot shape to approximately 20° to 40°, we are able to grow a large (1-inch diameter, 30 mm length) single crystal of metal Cu using the Czochralski (CZ) method. However, the generation of suspended solids and film impurities such as reactants and precipitates and their nucleation, growth, and solidification, limited the further increase in size of the Cu crystal. Using the cone-shape die CZ (CD-CZ) method solves this problem and a 2-inch diameter Cu single crystal is successfully grown. This is the world's largest single crystal metal grown using this method and it paves the way for the growth of other metal crystals. [ABSTRACT FROM AUTHOR]

A deep operator network (DeepONet) has been constructed that generates accurate representations of phase-field model simulations for evolving two dimensional metal grain morphology growing from melt. These representations serve as lower resolution, computationally efficient stand-ins for quick parameter space exploration of solutions to the Allen–Cahn equations that dictate the phase-field model simulations. The experimental target for the phase-field model is a uranium casting system cooling a 434 g uranium charge from a maximum temperature of 1400 °C at an average rate of 30 ° C min , traversing the crystallographic phases of the pure metal. Experimental parameters inform the phase-field model, whose higher resolution computational model solutions are used to train the DeepONet in a given parameter space with the aim of developing a faster, more efficient method for predicting the solidifying metal's microstructure at different potential experimental values. The final DeepONet generates high accuracy, lower resolution predictions with cumulative relative approximation error over all timesteps of less than 0.5%, while ensuring solutions remain within physically feasible ranges. These relative error values are comparable with other state-of-the-art DeepONet models for microstructure evolution, while significantly reducing the amount of training data required. Training a convolutional neural network simultaneously with the DeepONet, enforcing realistic values at the complex metal grain boundaries, and mathematically encoding boundary conditions into the structure of the DeepONet improved prediction accuracy and computational efficiency over a standard DeepONet model. [Display omitted] • Developed an ML phase-field model surrogate for solidifying metal grain morphologies. • Predictions have relative errors below 0.5% with low computational overhead. • ML surrogate model allows quick inference for parameter space investigation. [ABSTRACT FROM AUTHOR]