Foaming Behaviour of Glass-Based Geopolymers by Adding Magnesium Carbonate and Borax
Bibliographic record
Abstract
This research presents the results of synthesis foaming geopolymer using glass cullet.They were prepared by activating glass waste powder from recycled green glass bottles using potassium hydroxide solution with different molar concentrations (3, 6, and 9) without/with foaming agent additives.Magnesium carbonate and borax were used as pore-forming agents with a 10-weight percentage replacement from glass powder.Additionally, cement paste was prepared as a reference.All specimens were subjected to thermal treatment at different temperatures (450, 550, 650, 750, and 850)℃ for 1 hour.An investigation involves analysis of the chemical and mineralogical composition, microstructure, and physical and mechanical characteristics.The compressive strength values dropped to less than 10.5 MPa after heat treatment at 650, 750, and 850℃.These values are usually indicated as foaming materials.Also, an increase in volume was recorded for all geopolymer formulations due to the occurrence of foaming phenomena at temperatures of 650, 750, and 850℃.Additionally, the weight loss for geopolymer paste (without/with additives) increases as the thermal treatment temperatures increase.The GK9-Mg10 paste exhibited the highest weight loss (17.71 to 20.01%).It is worth mentioning that these foaming geopolymers can be utilized in the building industry for various applications, such as thermal insulation, fire protection, and sound absorption.
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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".