Development of Eco-Efficient Fly Ash–Based Alkali-Activated and Geopolymer Composites with Reduced Alkaline Activator Dosage
Bibliographic record
Abstract
The eco-efficiency and economy of geopolymer composites largely depend on their alkaline activator dosage. In this study, the effect of applying a pre-setting pressure and variation of the aggregate-to-fly ash ratio on the alkaline solution dosage and mechanical strength of fly ash–based geopolymer composites was explored. It is shown that through control of the aggregate-to-ash ratio and application of pre-setting pressure, compressive strength could be increased by 102% and 86% for Class F fly ash–based geopolymer and alkali-activated Class C fly ash–based mixture, respectively. The total alkaline solution consumption could be reduced from 718 to 188 kg/m3 and from 769 to 262 kg/m3 for Class F fly ash–based geopolymer and alkali-activated Class C fly ash–based mixture, respectively. The proposed method reduced the alkaline solution consumption per compressive strength (6.2 kg·m−3·MPa−1) by up to 85% compared to that of the reference manually consolidated control geopolymer. The findings demonstrate that fly ash–based geopolymers could be made more sustainable and eco-efficient through tailored production techniques.
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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".