Compressive strength and microstructural development of cement paste incorporating nanosilica with different particle sizes
Bibliographic record
Abstract
Nanosilica particles are among the most studied nanomaterials in cementitious mixtures. However, literature on the effect of nanosilica particle size on the performance of these mixtures is still limited, with sometimes inconsistent findings. This study aims to address this gap by including the synthesis and application of different-sized nanosilica particles in one study. A uniform synthesis method was used to achieve nanosilica with four distinct average particle sizes (10, 35, 65, and 90 nm), covering the whole nanoscale range. The nanosilica particles were then fully characterized and utilized in cement paste at 1, 2, and 3 wt% of the cement. The compressive strength, heat evolution, microstructure, and rheological behaviour of the resultant pastes were investigated. The results revealed that the smallest particle size of nanosilica (10 nm) provided the highest compressive strength enhancement (over 100 % enhancement when used at 2 wt% of cement). The high pozzolanic reactivity of such small nanosilica particles at 2 wt%, together with their acceleration effect on cement hydration and densification of the paste microstructure, all contributed to this strength improvement. Overall, the enhancing effects of the nanosilica particles on the compressive strength of the pastes were less substantial when their particle size increased from 10 to 90 nm at any given concentration. All the nanosilica particles also increased the viscosity of the paste. This increasing effect was higher for smaller-sized nanosilica particles and at higher concentrations.
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 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".