Improvements on Pozzolanic Reactivity of Coal Refuse by Thermal Activation
Bibliographic record
Abstract
Today, coal refuse as industrial solid waste stockpiled on the ground is one of the greatest threats to the environment. One of the practical solutions to utilize this huge amount of solid waste is to activate the coal refuse and utilize it as substitution for portion of ordinary Portland cement. The key purpose of activation is to enhance the pozzolanic property of the coal refuse.Many scientists and engineers found that thermal activation is a practical approach on increasing pozzolanic property. For thermal activation, temperature and time are two important parameters which significantly determine the activation effect. In this paper, a systematic research has been conducted to seek for anoptimal solution for enhancing pozzolanic reactivity of the relatively inert solid waste-coal refuse in order to improve the utilization efficiency and economy benefit forconstruction and building materials.The mechanical property analysis shows that coal refusethat activated at 700°C to 800°C with 1 hour to 1.5 hours has much higher reactivity when compared with coal refuse activated at 500°C to 600 °C with 1 hour to 1.5 hours. And 28-dayscompressive strength value of prepared blended cementitious material containing 25% of the 700°C 1h activated coal refuse based pozzolanareaches 43.4MPa, which is higher than 28-days strength of OPC group as control.
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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".