Thermal decomposition of nanoparticulate Ca(OH)<sub>2</sub>-anomalous effects
Bibliographic record
Abstract
The degree of crystallinity and particle size of Ca(OH)2inclusions in a hydrated cement matrix influence their rate of dissolution in aggressive media such as distilled water. Durability of cement-based systems (e.g. resistance to leaching and carbonation) is generally dependent on the dissolution characteristics of the products – a process leading to increased permeability and porosity. Relevant features of nano-scale Ca(OH)2particles are explored using thermal analysis. Different forms of Ca(OH)2with varying degrees of crystallinity and surface area were prepared using Ca(OH)2and CaCO3as starting materials. They were decomposed and the hydrated CaO formed at different conditions. The nitrogen surface area values of Ca(OH)2ranged from 3.7 to 31.1m2/g. The presence of two separate and distinct thermal decomposition events (in derivative form of thermogravimetric analysis results) was observed, depending on the degree of crystallinity. Endotherms occurred at temperatures of about 426°C and 454°C. Binary mixtures of Ca(OH)2with substantially different degrees of crystallinity exhibited a well-defined thermal decomposition doublet. The height of each endotherm peak was dependent on the mass proportions of each mixture component. Factors, including thermodynamic considerations, affecting the character of the decomposition behaviour of Ca(OH)2are discussed.
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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".