Seeding Effect of Nano-CaCO <sub>3</sub> on the Hydration of Tricalcium Silicate
Bibliographic record
Abstract
A previous study indicated that the early hydration and strength development of ordinary portland cement (OPC) delayed by the presence of high volumes of supplementary cementitious materials were compensated for by the accelerating effect of nano-CaCO 3 . The mechanism responsible for the accelerating effect on the early hydration and strength development was, however, not fully understood. A study aimed at understanding the accelerating mechanism of the addition of nano-CaCO 3 on the hydration of tricalcium silicate (C 3 S) is presented in this paper. A comparison with the addition of micro-CaCO 3 was made. The hydration mechanism of C 3 S with the addition of micro- or nano-CaCO 3 was studied by conduction calorimetry, thermogravimetric analysis, and scanning electron microscopy. The conduction calorimetry results indicated that the addition of nano-CaCO 3 had an accelerating effect on the hydration of C 3 S as well as on the hydration of OPC. Furthermore, the induction period of C 3 S hydration was significantly shortened by the addition of nano-CaCO 3 . The results of the thermogravimetric analysis indicated that the amount of nano-CaCO 3 decreased as the hydration of C 3 S took place; the decrease was greater with the hydration of OPC. The scanning electron microscopy revealed that the accelerating mechanism in the presence of micro-CaCO 3 was considerably different from that of nano-CaCO 3 . Calcium silicate hydrate growth was observed around the nano-CaCO 3 particles. The observation suggested that the seeding effect due to the addition of nano-CaCO 3 was responsible for the accelerating effect on the hydration of C 3 S.
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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".