Experimental Characterization and Multi-Factor Modelling to Achieve Desired Flow, Set and Strength of N-A-S-H Geopolymers
Bibliographic record
Abstract
The interaction between compositional ratios, namely, SiO2/Al2O3, Na2O/Al2O3, H2O/Na2O and the liquid-to-solid ratio, triggers mutual sacrifice between workability, setting time and strength for N-A-S-H geopolymers. The present study characterizes the mechanism underlying the effect of these compositional ratios and, in turn, develops guidelines for mixture design that requires a simultaneous and satisfactory delivery of these engineering properties. The experimental results show that an increase in either the SiO2/Al2O3, Na2O/Al2O3 or H2O/Na2O ratio raises the liquid-to-solid ratio, which in turn improves the workability of fresh mixtures. A continuous increase beyond 2.8 for the SiO2/Al2O3 ratio boosts its strength, but also significantly extends its final set. Lowering the Na2O/Al2O3 ratio from 1.3 to 0.75 raises the compressive strength significantly, while the shortest final set was seen at the median value, 1.0. A H2O/Na2O ratio of 9~10 yields the highest strength and the fastest final set simultaneously, due to the maximized degree of geopolymerization. Moreover, the accompanying sensitivity analysis indicates that the workability depends chiefly upon the H2O/Na2O ratio, the final setting time on the SiO2/Al2O3 ratio and, that the compressive strength relies on both of them. Also, this study proposes an optimal range of 2.8~3.6 for SiO2/Al2O3, 0.75~1.0 for Na2O/Al2O3 and 9~10 for H2O/Na2O to guarantee high strength, together with high flow and within the allowable final setting time. Furthermore, multi-factor predictive models are established with acceptable accuracy for practitioners to regulate oxide compositions in N-A-S-H geopolymers, which will guide future mixture design.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".