Dissolution Kinetics of Hot Compressed Oxide Glasses

Abstract

The chemical durability of oxide glasses is an important property for a wide range of applications and can in some cases be tuned through composition optimization. However, these possibilities are relatively limited because around 3/5 of the atoms in most oxide glasses are oxygens. An alternative approach involves post-treatment of the glass. In this work, we focus on the effect of hot compression on dissolution kinetics because it is known to improve, for example, elastic moduli and hardness, whereas its effect on chemical durability is poorly understood. Specifically, we study the bulk glass dissolution rate of phosphate, silicophosphate, borophosphate, borosilicate, and aluminoborosilicate glasses, which have been compressed at 0.5, 1.0, and 2.0 GPa at the glass transition temperature (T). We perform weight loss and supplementary modifier leaching measurements of bulk samples immersed in acid (pH 2) and neutral (pH 7) solutions. Compression generally improves the chemical durability as measured from weight loss, but the effect is highly composition- and pressure-dependent. As such, we show that the dissolution mechanisms depend on the topological changes induced by permanent densification, which in turn are a function of the changes in the number of nonbridging oxygens and the network cross-linking. We also demonstrate a direct relationship between the chemical durability and the number of chemical topological constraints per atom (n) acting within the molecular network.

Predicting the dissolution kinetics of silicate glasses by topology-informed machine learning

“…8,10,12 (iii) Third, we attempt to identify some relevant reduced-dimensionality descriptors capturing the effect of the atomic structure of the glass on dissolution rate that can be used as inputs. This is based on the idea that, although the dissolution kinetics of glasses is controlled by their composition (at fixed thermal history) for a given set of environment conditions (T, pH, and solution composition [30][31][32][33] ), the knowledge of the structure of the atomic network makes it possible to decipher the relationship between composition and dissolution rate-so that it should be easier for ML algorithms to infer the relationship between "structure and dissolution rate" than between "composition and dissolution rate." In the following, we present how these topology-informed ingredients allow us to derive less complex, yet more accurate predictive models.…”

“…21,[38][39][40][41][42][43] Importantly, the effective activation energy of dissolution for a fixed pH has recently been suggested to be proportional to n c . 31,33,[44][45][46][47][48][49][50] Based on these findings, we compute the number of topological constraints of the rigid aluminosilicate network n c for each glass (see Methods section) and use it as a descriptor of the atomic structure. As shown in Fig.…”

The effect of irradiation on the atomic structure and chemical durability of calcite and dolomite

“…49,50 Coming back to chemical durability (dissolution rates), recently Pignatelli et al and Hsiao et al have shown that mineral and glass dissolution rates are, to the first order, controlled by the topology of their atomic networks. 9,51 Specifically, it has been highlighted that dissolution rates, for a given solution composition, are determined by the number of topological constraints per atom (n c , unitless) as represented by an Arrhenius-like function: [51][52][53][54] constant that depends on the solution chemistry, R is the gas constant, E 0 is the energy required to break a unit topological constraint and T is the thermodynamic temperature. As such, the variation in the dissolution rates before and following irradiation can be expressed as:…”

Can a simple topological-constraints-based model predict the initial dissolution rate of borosilicate and aluminosilicate glasses?

“…To address such difficulties, topological constraint theory (TCT) provides a simplified framework to predict the properties of glasses based on the topology of their atomic network [14][15][16] . Recently, this approach has been used to predict the dissolution rate of silicate minerals and glasses under varying pH conditions [17][18][19][20][21][22] .…”