A rheological model for glassforming silicate melts in the systems CAS, MAS, MCAS
Bibliographic record
Abstract
Viscosity is the single most important property governing the efficacy, rates, and nature of melt transport. Viscosity is intimately related to the structure and thermodynamics properties of the melts and is a reflection of the mechanisms of single atoms slipping over potential energy barriers. The ability to predict melt viscosity accurately is, therefore, of critical importance for gaining new insights into the structure of silicate melts. Simple composition melts, having a reduced number of components, offer an advantage for understanding the relationships between the chemical composition, structural organization and the rheological properties of a melt. Here we have compiled a large database of ∼970 experimental measurements of melt viscosity for the simple chemical systems MAS, CAS and MCAS. These data are used to create a single chemical model for predicting the non-Arrhenian viscosity as a function of temperature ( T ) and composition ( X ) across the entire MCAS system. The T -dependence of viscosity is accounted for by the three parameters in each of the model functions: (i) Vogel–Fulcher–Tamman (VFT); (ii) Adam–Gibbs (AG); and (iii) Avramov (AV). The literature shows that, in these systems, viscosity converges to a common value of the pre-exponential factors ( A ) that can be assumed to be independent of composition. The other two adjustable parameters in each equation are expanded to capture the effects of composition. The resulting models are continuous across T – X space. The values and implications of the optimal parameters returned for each model are compared and discussed. A similar approach is likely to be applicable to a variety of non-silicate multicomponent glassforming systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".