Influence of glass network ionicity on the mixed‐alkali effect
Bibliographic record
Abstract
Abstract Most studies of the mixed‐alkali effect (MAE) have focused on relating the differences between the cations to the strength of the MAE; here we examine the effect of the glass former by comparing the MAE in aluminofluorophosphate (FP) and aluminosulfofluorophosphate (FPS) glasses. The sulfate anion in the FPS series does not bond directly to the aluminophosphate network, decreasing connectivity and increasing the ionicity of the FPS glasses. The increased degrees of freedom imparted by the sulfate are evident in the single‐alkali FPS glasses, which have lower E a , hardness, shear moduli, and Young's moduli, and higher T g than the corresponding single‐alkali FP glasses (without sulfate). Conversely, for the mixed‐alkali FPS compositions, the sulfate's mobility is reduced by the mixed cations, resulting in a magnified MAE for the FPS series. For dynamic properties, this phenomenon is explained by reduced plasticity and slippage along ion channels, while understanding T g and elastic properties requires examination of the shape of the sulfate's potential energy well and the resulting regions of compressive and tensile stress. We posit that glass compositions with higher plasticity, that is, less covalent bonding, will exhibit a larger MAE, but, simultaneously, sufficient conventional glass network must be present to constrain the mobile ions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".