Pozzolanic properties of ultrafine sugar cane bagasse ash produced by controlled burning
Bibliographic record
Abstract
This paper evaluated the feasibility of using residual sugar cane bagasse ash with a high carbon content (as-received SCBA) as raw material to produce a pozzolan after controlled recalcination and grinding. Initially, the as-received SCBA was re-burned using rotary (continuous) and muffle (batch) kilns, both at 600 °C for 1 h. Next, the resulting ash was ground in a closed-circuit ball mill with an air classifier system to obtain a product with 50% passing particle size of approximately 10 μm (SCBA600). SCBA600 was then characterized in terms of oxide composition, loss on ignition, density, specific surface area, and pozzolanic activity. A hydration study was carried out using isothermal calorimetry, thermogravimetric analysis and mercury intrusion porosimetry. Additionally, the performance of SCBA600 in mortars was evaluated by axial compression tests. The combination of recalcination at 600 °C, low-energy ultrafine grinding of the material and classification resulted in pozzolanic SCBA. The results also showed that including SCBA600 in cement mortars reduced total accumulated heat and portlandite content in cement-based pastes, in addition to refining pore structure and significantly increasing compressive strength after 3 days of curing.
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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".