Effects of Thermal and Pressure Histories on the Chemical Strengthening of Sodium Aluminosilicate Glass

Glasses can be chemically strengthened through the ion exchange process, wherein smaller ions in the glass (e.g., Na+) are replaced by larger ions from a salt bath (e.g., K+). This develops a compressive stress (CS) on the glass surface, which, in turn, improves the damage resistance of the glass. The magnitude and depth of the generated CS depends on the thermal and pressure histories of the glass prior to ion exchange. In this study, we investigate the ion exchange-related properties (mutual diffusivity, CS, and hardness) of a sodium aluminosilicate glass, which has been densified through annealing below the initial fictive temperature of the glass or through pressure-quenching from the glass transition temperature at 1 GPa prior to ion exchange. We show that the rate of alkali interdiffusivity depends only on the density of the glass, rather than on the applied densification method. However, we also demonstrate that for a given density, the increase in CS and increase in hardness induced by ion exchange strongly depends on the densification method. Specifically, at constant density, the CS and hardness values achieved through thermal annealing are larger than those achieved through pressure-quenching. These results are discussed in relation to the structural changes in the environment of the network-modifier and the overall network densification

Predicting the dissolution kinetics of silicate glasses using machine learning

Predicting the dissolution rates of silicate glasses in aqueous conditions is a complex task as the underlying mechanism(s) remain poorly understood and the dissolution kinetics can depend on a large number of intrinsic and extrinsic factors. Here, we assess the potential of data-driven models based on machine learning to predict the dissolution rates of various aluminosilicate glasses exposed to a wide range of solution pH values, from acidic to caustic conditions. Four classes of machine learning methods are investigated, namely, linear regression, support vector machine regression, random forest, and artificial neural network. We observe that, although linear methods all fail to describe the dissolution kinetics, the artificial neural network approach offers excellent predictions, thanks to its inherent ability to handle non-linear data. Overall, we suggest that a more extensive use of machine learning approaches could significantly accelerate the design of novel glasses with tailored properties

Positron prevacancy effects in pure annealed metals

The low-temperature prevacancy effects sometimes observed with positrons in well-annealed high-purity metals are discussed. It is shown that these effects are not experimental artifacts, but are due to trapping of the positrons. It is suggested that dislocations are responsible for these trapping effects. 46 references, 5 figures

Deciphering the controlling factors for phase transitions in zeolitic imidazolate frameworks

Zeolitic imidazolate frameworks (ZIFs) feature complex phase transitions, including polymorphism, melting, vitrification, and polyamorphism. Experimentally probing their structural evolution during transitions involving amorphous phases is a significant challenge, especially at the medium-range length scale. To overcome this challenge, here we first train a deep learning-based force field to identify the structural characteristics of both crystalline and non-crystalline ZIF phases. This allows us to reproduce the structural evolution trend during the melting of crystals and formation of ZIF glasses at various length scales with an accuracy comparable to that of ab initio molecular dynamics, yet at a much lower computational cost. Based on this approach, we propose a new structural descriptor, namely, the ring orientation index, to capture the propensity for crystallization of ZIF-4 (Zn(Im)2, Im = C3H3N2−) glasses, as well as for the formation of ZIF-zni (Zn(Im)2) out of the high-density amorphous phase. This crystal formation process is a result of the reorientation of imidazole rings by sacrificing the order of the structure around the zinc-centered tetrahedra. The outcomes of this work are useful for studying phase transitions in other metal-organic frameworks (MOFs) and may thus guide the development of MOF glasses

Advancing the mechanical performance of glasses: Perspectives and challenges

Glasses are materials that lack a crystalline microstructure and long‐range atomic order. Instead, they feature heterogeneity and disorder on superstructural scales, which have profound consequences for their elastic response, material strength, fracture toughness, and the characteristics of dynamic fracture. These structure–property relations present a rich field of study in fundamental glass physics and are also becoming increasingly important in the design of modern materials with improved mechanical performance. A first step in this direction involves glass‐like materials that retain optical transparency and the haptics of classical glass products, while overcoming the limitations of brittleness. Among these, novel types of oxide glasses, hybrid glasses, phase‐separated glasses, and bioinspired glass–polymer composites hold significant promise. Such materials are designed from the bottom‐up, building on structure–property relations, modeling of stresses and strains at relevant length scales, and machine learning predictions. Their fabrication requires a more scientifically driven approach to materials design and processing, building on the physics of structural disorder and its consequences for structural rearrangements, defect initiation, and dynamic fracture in response to mechanical load. In this article, a perspective is provided on this highly interdisciplinary field of research in terms of its most recent challenges and opportunities.The mechanical performance of glassy materials presents a major challenge in modern glass science and technology. With a focus on visually transparent, inorganic and hybrid glasses, a perspective on the most recent developments in the field is provided herein, emphasizing the importance of translating fundamental insight from glass physics into future applications

Temperature-Modulated Differential Scanning Calorimetry Analysis of High-Temperature Silicate Glasses

Differential scanning calorimetry (DSC) is one of the most versatile probes for silicate glasses, allowing determination of, e.g., transition temperatures (glass, crystallization, melting) and the temperature dependence of heat capacity. However, complications arise for glasses featuring overlapping transitions and low sensitivity, e.g., arising from SiO2-rich compositions with small change in heat capacity during glass transition or the low sensitivity of thermocouples at high temperature. These challenges might be overcome using temperature-modulated DSC (TM-DSC), which enables separation of overlapping signals and improved sensitivity at the expense of increased measurement duration