Engineering topics
Taron, Mélanie
Publications and source records attributed to Taron, Mélanie.
Predicting the Dissolution Rate of Borosilicate Glasses using QSPR analysis based on molecular dynamics simulations
Quantitative Structure Property Relationship (QSPR) analysis based on molecular dynamics (MD) simulations is a promising approach for establishing the composition-property relationships of glass and other materials with complex structures. A series of 20 borosilicate and boroaluminosilicate glasses have been modeled by using MD simulations with recently developed effective potentials. Short- and medium-range structures of these glasses were analyzed and, based on these structural information, QSPR analysis of the initial dissolution rate (r0) was made and compared with measured r0 at 90°C and pH 9 using various structural descriptors such as percentage of bridging oxygen species, network connectivity and average ring size. The structural descriptors, Fnet, containing energetic information such as single bond strength and other structural information were also used. It was found that overall network connectivity, average ring size and Fnet give reasonable predictions of the r0 of studied glasses, given the conditions that the glasses are homogeneous and dissolve congruently. Modifying glass compositions to account preferential release of modifiers gives a better prediction for incongruently dissolving glasses. The results were compared with our recent work of predicting glass dissolution behavior from compositions using the topological-constraints-based models.
A classical molecular dynamics simulation method for the formation of “dry” gels from boro-aluminosilicate glass structures
In contact with water, glass transforms into amorphous and porous structures called gels. A simulation method based on classical molecular dynamics is proposed here to mimic “dry” gels forming from initial oxide glass structures. Six glass compositions were investigated. Two behaviours were evidenced depending on the initial glass composition, and in particular on the quantity of elements removed. If a large quantity of soluble elements (B, Na) was removed, it induced an increase in the average pore size within the gels, and the time needed to stabilise the gel structure increased because more local atomic rearrangements occurred. The gel network displayed a higher proportion of Si-Q4 at the expense of Si-Q3 and a lower average ring size compared to the glass network, irrespective of the glass composition. Surface effects were also highlighted in the dry gels, such as the presence of 3-coordinated Al and a decrease in the average angle Si-O-Si and Al-O-Al. Our findings will be compared to both wet gels and experimental data in further studies, to help find the best procedure to simulate such structures.
Can a simple topological-constraints-based model predict the initial dissolution rate of borosilicate and aluminosilicate glasses?
Tuning glass composition to obtain targeted properties generally relies on empirical approaches. However, a deep understanding of the physical and chemical mechanisms linking glass composition to its structure and properties would enable developing reliable predictive models. Indeed, although empirical models are usually able to interpolate composition–property relationships within a given compositional envelope, they often fail at extrapolating predictions far from their training domain. Here, as an alternative route to empirical models, we show that a structural descriptor based on the number of topological constraints per atom can be used to predict the initial dissolution rate of aluminosilicate and borosilicate glasses after being parameterized on different families of glasses (specific series of borosilicate glasses). Sixteen glasses belonging to these families were studied and their initial dissolution rates were determined at 90 °C and pH 90 °C = 9, covering rates spanning over 5 orders of magnitude. The model based on topological constraints was trained based on seven select borosilicate glasses (R 2 = 0.997) and used to predict the dissolution rate of nine additional borosilicate and aluminosilicate glasses. We show that, provided that corrections are made for high alkali content glasses that dissolve incongruently (preferential release of Na), the model gives reasonable predictions, even far from its training domain.