Relaxation time and viscosity of fused silica glass at room temperature

Guiding epitaxial crystallization of amorphous solids at the nanoscale: interfaces, stress, and precrystalline order

The crystallization of amorphous solids impacts fields ranging from inorganic crystal growth to biophysics. Promoting or inhibiting nanoscale epitaxial crystallization and selecting its final products underpins applications in cryopreservation, semiconductor devices, oxide electronics, quantum electronics, structural and functional ceramics, and advanced glasses. As precursors for crystallization, amorphous solids are distinguished from liquids and gases by the comparatively long relaxation times for perturbations of the mechanical stress and for variations in composition or bonding. These factors allow experimentally controllable parameters to influence crystallization processes and to drive materials towards specific outcomes. For example, amorphous precursors can be employed to form crystalline phases, such as polymorphs of Al2O3, VO2, and other complex oxides, that are not readily accessible via crystallization from a liquid or through vapor-phase epitaxy. Crystallization of amorphous solids can further be guided to produce a desired polymorph, nanoscale shape, microstructure, and orientation of the resulting crystals. These effects can enable advances in applications in electronics, magnetic devices, optics, and catalysis. Directions for the future development of the chemical physics of crystallization from amorphous solids can be drawn from the impact of structurally complex and non-equilibrium atomic arrangements in liquids and the atomic-scale structure of liquid-solid interfaces.

Prediction of viscosity behavior in oxide glass materials using cation fingerprints with artificial neural networks

We propose a novel descriptor of materials, named 'cation fingerprints', based on the chemical formula or concentrations of raw materials and their respective properties. To test its performance, this method was used to predict the viscosity of glass materials using the experimental database INTERGLAD. Using artificial neural network models, we succeeded in predicting the temperature required for glass to have a specific viscosity within a root-mean-square error of 33.0°C. We were also able to evaluate the effect of particular target raw materials using a model trained without including the specific target raw material. The results show that cation fingerprints with a neural network model can predict some unseen combinations of raw materials. In addition, we propose a method for estimating the prediction accuracy by calculating cosine similarity of the input features of the material which we want to predict.

Crack Propagation Calculations for Optical Fibers under Static Bending and Tensile Loads Using Continuum Damage Mechanics

Static fatigue behavior is the main failure mode of optical fibers applied in sensors. In this paper, a computational framework based on continuum damage mechanics (CDM) is presented to calculate the crack propagation process and failure time of optical fibers subjected to static bending and tensile loads. For this purpose, the static fatigue crack propagation in the glass core of the optical fiber is studied. Combining a finite element method (FEM), we use the continuum damage mechanics for the glass core to calculate the crack propagation path and corresponding failure time. In addition, three factors including bending radius, tensile force and optical fiber diameter are investigated to find their impacts on the crack propagation process and failure time of the optical fiber under concerned situations. Finally, experiments are conducted and the results verify the correctness of the simulation calculation. It is believed that the proposed method could give a straightforward description of the crack propagation path in the inner glass core. Additionally, the predicted crack propagation time of the optical fiber with different factors can provide effective suggestions for improving the long-term usage of optical fibers.

Absolute measurement of optical flats based on basic iterative methods

Generalized basic iterative methods for absolute measurement of optical flats are presented. They are based on iterative scheme and can be classified into block Jacobi Successive Over-relaxation (SOR) method and block SOR method. Both methods are effective for solving the three-flat problem with pixel-level spatial resolution, without the usage of a fitting procedure. Compared to the block Jacobi SOR method, the block SOR method with an inexpensive choosing of relaxation factor (such as ω = 1.5) converges much faster and saves more computational costs and memory space without reducing accuracy. It has been proved by both simulation results and experimental results. The proposed basic iterative methods are generalized; can correctly reconstruct absolute figures with pixel-level spatial resolution; are easy to understand and implement; and computationally efficient.

Absolute flatness measurement using oblique incidence setup and an iterative algorithm. A demonstration on synthetic data

A method to provide absolute planarity measurements through an interferometric oblique incidence setup and an iterative algorithm is presented. With only three measurements, the calibration of absolute planarity is achieved in a fast and effective manner. Demonstration with synthetic data is provided, and the possible application to very long flat mirrors is pointed out.