Aluminium catalysed oligomerisation in cement-forming silicate systems
Bibliographic record
Abstract
Alumino-silicates form the backbone of structural materials including cements and the concrete they form. However, the nanoscale aspects of the oligomerisation mechanisms elongating the (alumino-)silicate chains is not fully clarified; the role of aluminium in particular. Herein, we explore and contrast the growth of silicate and alumino-silicate oligomers by both neutral and anionic mechanisms, with focus on the influence of Al on oligomer structure and stability. Further, the spontaneity of chain lengthening in the absence and presence of Al of differing coordination (Al-IV, V, VI) was characterised. Result trends showed Al-IV facilitating oligomerisation in neutral conditions, with respect to Si only systems, effectively promoting longer chain formation and stabilisation. The anionic pathway similarly showed Al reducing the overall energetic barriers to oligomerisation. In both conditions, Al's coordinative and structural flexibility, at O-Al-O hinge points in particular, was responsible for the lowering of the energetic expense for oligomerisation. The results and implications resolved herein are informative for chain formation and stability for bulk material properties of alumino-silicate materials such as cements, where the aluminosilicate systems are dominated by short chains of 2-5 units in length.
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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) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".