Synergistic Influence of Micro-and Nano-Reactive Additives on Cement Mortar Performance
Bibliographic record
Abstract
There are several drivers for the continuous development of concrete technology, among them concerns about greenhouse gas emissions and the depletion of natural resources resulting from the high production of Portland cement.One sustainable solution is to use fly ash as an additional cementitious material to partially replace cement on a different scale, which has shown beneficial effects on the performance of cement-based composites.The present study investigates and compares the influence of fly ash (FA) and nano-fly ash (NFA) on the mechanical and physical properties of cement mixtures.The cement mortar mixtures were prepared by replacing 1-6% NFA or 15% fly ash (FA).All mixtures were designed with a fixed binder content of 900 Kg/m³ and a constant water-to-binder ratio of 0.30.The workability, apparent density, water absorption, compressive strength, and flexural strength of all mortar mixes were evaluated and compared with the control mixture containing no mineral admixtures.The indication of FA incorporation improved the performance of mortar, whereas NFA exhibited significantly greater enhancements in both physical and mechanical properties.The observed enhancement may be attributed to pore refinement, the micro-filling effect, and the high reactivity of NFA.The optimum NFA level was observed to be 4% resulting in an 83.4% and 58.6% increase in mechanical properties, compressive strength and modulus of rupture, respectively, over the reference mixture at 4 weeks.
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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) | 0.001 | 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".