Durable bauxite-residue geopolymers: Enhancing scaling resistance with fly ash and waste glass powder
Bibliographic record
Abstract
Improving the scaling resistance of geopolymers is crucial for the durability of these materials in cold regions. This study presents experimental research on the optimal ratio of waste glass powder to fly ash for a wet filter-pressed bauxite residue–based geopolymer. Glass waste powder partially replaced fly ash in amounts of 0 %, 25 %, 50 %, 75 %, and 100 %. We used scaling resistance to evaluate the material’s performance in cold and extreme environmental conditions and assess its durability and long-term performance. Tests measured mass loss and the cumulative mass of scaled-off particles. Geopolymer durability improved with 75 % waste glass substitution. Scanning electron microscopy, X-ray diffraction, and Fourier transform infrared spectroscopy characterized geopolymer morphology and chemical bonding. Samples with 75 % replacement of fly ash by waste glass exhibited sharper Si-O-T peaks, indicating enhanced geopolymerization. Nevertheless, in non-structural or low-load applications with minor compressive strength requirements, RF100's improved scaling resistance and higher waste utilization may provide practical benefits. These potential merits additional exploration using specialized mix designs or additions to compensate for alumina deficit.
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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".