Towards designing reactive glasses for alkali activation: Understanding the origins of alkaline reactivity of Na-Mg aluminosilicate glasses
Bibliographic record
Abstract
Alkali-activated materials (AAMs), sometimes called geopolymers, are eco-friendly cementitious materials with reduced carbon emissions when compared to ordinary Portland cement. However, the availability of most precursors used for AAM production may decline in the future because of changes in industrial sectors. Thus, new precursors must be developed. Recently there has been increased interest in synthetic glass precursors. One major concern with using synthetic glasses is ensuring that they react sufficiently under alkaline conditions. Reactivity is a necessary, although not sufficient, requirement for a suitable precursor for AAMs. This work involves the synthesis, characterization, and estimation of alkaline reactivity of Na-Mg aluminosilicate glasses. Structural characterization showed that replacing Na with Mg led to more depolymerization. Alkaline reactivity studies indicated that, as Mg replaced Na, reactivity of glasses increased at first, reached an optimal value, and then declined. This trend in reactivity could not be explained by the conventional parameters used for estimating glass reactivity: the non-bridging oxygen fraction (which predicts similar reactivity for all glasses) and optical basicity (which predicts a decrease in reactivity with an increase in Mg replacement). The reactivity of the studied glasses was found to depend on two main factors: depolymerization (as indicated by structural characterization) and optical basicity. Depolymerization dominated initially, which led to an increase in reactivity, while the effect of optical basicity dominated later, leading to a decrease in reactivity. Hence, while designing reactive synthetic glasses for alkali activation, structural study of glasses should be given due consideration in addition to the conventional factors.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".