Masonry mortar with nanoparticles at a low temperature
Bibliographic record
Abstract
This study explores a possible approach for alleviating the effects of low temperatures on masonry construction through the addition of nano-alumina (NA) and nano-silica (NS) in masonry mortar mixtures (used for joints) mixed and cured at 5 ± 1°C. Fresh (flow, air content, setting time) and hydration, hardened (compressive strength) and microstructural characteristics of the mortar mixtures were determined at different ages to capture the behaviour of masonry mortar at the low temperature. The type (NA or NS) and dosage (2, 4 and 6% by mass of masonry cement) of the nanoparticles had very pronounced effects on the trends of the various tests. The overall trends suggest that either 6% NA or 6% NS can alleviate the effects of low temperature on masonry mortar mixtures in terms of accelerating the kinetics of hydration and rate of hardening, and inducing (especially NS) progressive microstructural and strength development with time.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".