A Sustainable Autoclaved Material Made of Glass Sand
Bibliographic record
Abstract
Far-reaching technological progress, manufacturing, and rapidly advancing globalization dictate new conditions for the development and changes in the construction industry. Valorization of by-products and the use of secondary materials in the production of building materials have attracted a lot of attention. Silicate materials were assessed on the basis of their compressive property. An orthogonal compositional plan type 3k (with k = 2), that is, a full two-factor experiment was applied in order to carry out the compressive strength and bulk density tests. Glass sand was added to the silicate mass as a modification. The results show that the compressive strength was higher than that of traditional bricks. Scanning electron microscopy coupled with energy dispersive spectrometry SEM/EDS was used to study the microstructure, whereas the XRD analysis was applied to examine the structures. Laboratory tests were performed on samples with dimensions of 50 × 50 × 50 mm. The results show the bulk density increase to the value of 1.75 kg/dm3, which increases the acoustic performance of the new products. The results of the modifications also indicate changes in the structure of the new bricks. The reference sample contained α-quartz, zeolite, tobermorite 9A, and calcium aluminum silicate (Ca2Al4Si12O32), whereas the samples modified with glass sand, the presence of phases such as α-cristobalite, natrolite, tobermorite 11A, gyrolite, and analcite was recorded.
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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 | 0.001 | 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) | 0.004 | 0.002 |
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".