Effect of nano-CaCO<sub>3</sub> on hydration of cement containing supplementary cementitious materials
Bibliographic record
Abstract
The efficacy of the addition of nano-CaCO 3 in accelerating the hydration of ordinary Portland cement (OPC) delayed by the presence of high volumes of supplementary cementitious materials including fly ash and slag was investigated. The conduction calorimetry indicated that the early hydration of OPC was significantly accelerated by the addition of the nano-CaCO 3 and the higher the amount of CaCO 3 addition, the greater was the accelerating effect. The thermogravimetric analysis results showed that the amounts of added CaCO 3 became slightly lower as the hydration took place; however, any new reaction products were not detected by the X-ray diffractometry analysis. The engineering properties, including microhardness and modulus of elasticity, in the early stage of the hydration were remarkably improved by the addition of nano-CaCO 3 . It was suggested that the seeding effect of the nano-CaCO 3 particles and the nucleation of C–S–H caused the enhanced strength development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Research integrity | 0.000 | 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".