Rheological, Fresh State, and Strength Characteristics of Alkali-Activated Mortars Incorporating MgO and Carbon Nanoparticles
Bibliographic record
Abstract
This study presents a comprehensive assessment of the fresh state, rheological, and mechanical properties of alkali-activated mortars (AAMs) developed by incorporating magnesium oxide (MgO) and nanomaterials. A total of 24 AAM mixes with varying content of MgO, multi-walled carbon nanotube (MWCNT), and reduced graphene oxide (rGO) were developed following the one-part dry mix technique using powder-based activators/reagents. The effects of the types/combinations of source materials (binary or ternary)/reagents, MgO (0 to 5%), MWCNT (0 to 0.6%), and rGO (0 to 0.6%) were evaluated in terms of the mini-slump flow, setting times, viscosity, yield stress, compressive strength, ultrasonic pulse velocity (UPV), and microstructural properties. The results showed that the addition of finer MgO/nano-fillers produced a higher viscosity and yield stress accompanied by a lower slump flow and setting times. The addition of 5% MgO resulted in the lowest slump flow of 80 mm, 2-2.5 times higher viscosity, and the reduction in the initial and final setting times of about 21% and 16%, respectively. Mixes with MWCNT showed about 5-10% higher viscosity whereas for mixes with rGO, the values were noted to be 8% higher, on average, than the mixes with no MWCNT or rGO. All the developed AAMs exhibited shear-thinning behavior. The 28-day compressive strength of the AAMs ranged from 37 MPa to 49 MPa with 5% MgO and up to a 0.3% MWCNT/rGO addition increased the compressive strength. Correlations among the fresh state, rheological, and mechanical properties such as the viscosity, slump flow, setting time, compressive strength, and UPV are also described.
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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".