Influence of SiO2, TiO2 and Fe2O3 nanoparticles on the properties of fly ash blended cement mortars
Bibliographic record
Abstract
This study explores the effects of different types of nanoparticles, namely nano-SiO2 (NS), nano-TiO2 (NT), and nano-Fe2O3 (NF) on the fresh properties, mechanical properties, and microstructure of cement mortar containing fly ash as a supplementary cementitious material. These nanoparticles existed in powder form and were incorporated into the mortar at the dosages of 1%, 3%, and 5% wt.% of cement. Also, fly ash has been added into in mortars with a constant dosage of 30% wt.% of cement. Compressive and flexural strength tests were performed to evaluate the mechanical properties of the mortar specimens with different nanoparticles at three curing ages, 7, 14, and 28 days. Scanning Electron Microscopy (SEM) and Energy Dispersive X-ray (EDX) tests were conducted to study the microstructure and the hydration products of the mortars. To elucidate the effects of nanoparticles on the binder phase, additional experiments were performed on accompanying cement pastes: nanoindentation and open porosity measurements. The study shows that, if added in appropriate amounts, all nanoparticles investigated can result in significantly improved mechanical properties compared to the reference materials. However, exceeding of the optimal concentration results in clustering of the nanoparticles and reduces the mechanical properties of the composites, which is accompanied with increasing the porosity. This study provides guidelines for further improvement of concretes with blended cements through use of nanoparticles.
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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".