Studying the Effect of High Temperature on the Content of Glass and Brick Waste Binders after Alkaline Activation
Bibliographic record
Abstract
The high-temperature mechanical behaviour of a glass and brick waste alkaline to synthesize geopolymer mortar was studied. The mortar in question contained 100% glass powder GWP and 90% of a blend of brick waste GBWP, brick waste BWP and a solid activator (10 mol concentration of NaOH mixed with glass water (Na2SiO3). The material was tested during exposure to high temperatures to establish its density, weight loss, compressive strength, accessible porosity in water, expansion of pastes, XRD and TG-DSC analysis using an innovative methodology to notch the hydrated geopolymers paste specimens after exposed to five maximum temperatures, 100℃, 200℃, 400℃, 600℃ and 800℃ without any imposed load during the heating. The results were found that GBWP gave better values in compressive strength, reaching 38.3 MPa at 100℃ and other proportional values at 400℃, 600℃ respectively compared with GWP. As for the density, the high temperature contributed to its decrease, which caused the presence of high porosity at 800℃. On the other hand, the high temperature helped to improve the mechanical and physical behavior of BWP, where the resistance reached 24.91 MPa at 200℃. In addition, for the microstructure and different particles related to the interactions were identified through the XRD and TG-DSC analysis procedure, in order to know the highest temperatures that allow changing the structure and properties of this type of alternative binders.
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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) | 0.002 | 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".