Influence of mixing protocols on flow retention of one-part alkali activated slag systems
Bibliographic record
Abstract
Poor workability and rapid flowability loss are among several challenges hindering the wider acceptance of alkali-activated slag (AAS) (i.e. shrinkage, efflorescence). Several attempts were made to solve this issue, focusing more on selected ingredients, proportions, type and dosage of admixtures, and addition sequence. This study shifted the focus to mixing protocol, highlighting its potential and effects on AAS flow retention considering mixing duration, sequence, and relaxation time. Various one-part AAS mixtures activated by 6 %, 8 %, and 10 % sodium metasilicate were tested. Mixing protocols were applied for 10–90 min, mixing with and without relaxation time. Mini-slump, flowability-loss rate, setting time, the heat of hydration, rheological properties, and compressive strength were evaluated. Changes in hydration products were also examined by XRD, FTIR and TGA. Pearson correlation coefficient and ANOVA were applied to identify the dominant factors. Results reveal the dependency of the optimum mixing protocol on the used activator dosage. A long mixing period with r intervals was more effective for high activator dosages due to the continuous breakdown of formed hydration products' links. Modifying the mixing protocol did not alter the amount of formed hydration products, regardless of the activator dosage. These findings pave the way to understanding the role played by the mixing protocol in mineral hydration for wider in-situ implementation of AAS and the flourishing potential of the ready-mix AAS market. • Extending the mixing period was more effective for low-activator mixtures. • Extending the mixing period did not alternate strength at later ages. • Continuous mixing protocols accelerated the activator dissolution and formation of hydration products. • Remixing breaks formed hydration products and linkages facilitating mixture flow. • Discrete mixing up to 60 min for AAS while maintaining adequate workability is feasible.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".