Strength and chloride resistance of mortars blended with SCBA: the effect of calcination and particle sizing on its pozzolanic activity
Bibliographic record
Abstract
Sugarcane bagasse was variously treated by three different processing protocols to improve the pozzolanic activity of the resulting sugarcane bagasse ash (SCBA). Three parameters were examined namely, the particle size, the calcination temperature and the duration of calcination. The resulting SCBA was blended with Portland cement to examine potential benefits upon the strength and chloride resistance of mortar specimens. As expected, the particle sizing and re-calcination, together imparted greater pozzolanic activity to SCBA. The results demonstrate that the optimal SCBA, possessing an acidic oxide (SiO2+Fe2O3+Al2O3) content over 70% and LOI can be reduced to 4.3%, resulted from grinding the boiler residue to 35 μm, followed by calcination for 90 min at 600 °C (P3-T90). In addition, the XRD test reveals that increasing the calcination temperature up to 600 °C could remove the residual carbon and other volatile compounds effectively. However, any further increase was noted to convert amorphous silica to cristobalite in SCBA itself and, also to enlarge the microcrack at ITZ in the hardened mortar. Furthermore, the optimal SCBA was found as P3-T90 in this study to produce the best blended mortar, as evident from an 18% increase in compressive strength, a 15% increase in flexural strength and a 43% decrease in chloride diffusion coefficient. This is firstly attributed to the improved pozzolanic activity and in turn, to the increasing C–S–H phase. Besides, the associated porosity and mean pore size were minimized to 15.78% and 36 nm, respectively.
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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 | 0.001 |
| 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".