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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".