Effects of Nanosilica as Suspensions on the Hydration and the Microstructure of Hardened Cement Paste
Bibliographic record
Abstract
Nanosilica (NS) has shown significant beneficial effects on concrete. However, the utilization of NS to achieve its maximum benefits is limited by the agglomeration of nanoparticles, which is associated with production methods and the method of NS dispersion in concrete. In this study, the effects of the utilization of NS as a suspension in calcium hydroxide (CH) on the hydration characteristics and the microstructure of the cement pastes were investigated with different levels of cement replacements (1%, 2%, 4%, and 6% NS) at 2, 7, and 28 days. The hydration of the cement pastes was investigated by isothermal calorimetry, and the measurement of CH content by thermogravimetry. The microstructures were analyzed by scanning electron microscopy and by energy dispersive X-ray spectroscopic mapping. The microstructure of the pastes was characterized by analyzing the pore size and the pore size distribution using mercury intrusion porosimetry (MIP). The calorimetric studies indicated that the replacing cement by NS derived by this method leads to faster hydration up to 4% replacement. The CH contents could be reduced by the incorporation of NS. The pore structures revealed that the pastes with NS had become comparably denser than the pastes without NS. A positive insight into the durability characteristics was shown by the results of the MIP when using NS as suspensions.
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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".