Investigation on the influential mechanism of FA and GGBS on the properties of CO2-cured cement paste
Bibliographic record
Abstract
In the present work, the synergetic effect of fly ash (FA) and ground granulated blast furnace slag (GGBS) on the early compressive strength and microstructure development of CO 2 -cured mortars was investigated. A rim of several micrometers was found around carbonated cement particles, which contained not only silica-rich gel but also crystal calcium carbonate . The calcium carbonate formed around the cement particles were surrounded by an amorphous layer of around 3 nm, while no layers were observed around the calcite formed on FA or GGBS particles. The calcite formed on FA particles were hexagonal plate shaped, while the one on GGBS particles were rhombohedral shaped. The use of FA resulted in the increase of crystal size and crystallinity of calcite , while GGBS decreased the crystal size of calcite . The incorporation of FA and GGBS increased the calcite content and polymerization of silica-rich gel. However, this didn't result in a higher compressive strength . This was due to the looser microstructure and nanopores within the carbonation products compared to the pure ordinary Portland cement sample. The compressive strength of the ternary binder system showed linear relationship with capillary pores and the crystal size of calcite. Moreover, the effect of GGBS on the compressive strength reduction was more obvious than that of FA.
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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".