Porcelain Stoneware Slabs: An Investigation into Pyroplastic Deformation in New Body Formulations Containing Waste Glasses
Bibliographic record
Abstract
The ceramics industry has consistently applied circular economy principles by efficiently reusing and recovering raw materials in the production process. Incorporating secondary raw materials, such as production scraps and recycled materials, reduces costs and improves sustainability. In the ceramics sector, raw materials like feldspars can be substituted with secondary raw materials from other production processes, particularly glass-based ones. This study selected waste glass, such as bottle glass or glass fiber processing scraps, to investigate its performance in a porcelain stoneware slab mixture. The behavior of this mixture was compared to a traditional porcelain stoneware mixture and a mixture containing a glass-ceramic frit, which exhibits significantly different behavior from waste glass. The study involved a comprehensive characterization of the fired samples, with a specific focus on addressing pyroplastic deformation—an issue that occurs in large slabs during the firing process. Although pyroplastic deformation has been extensively studied in the past using various waste glasses, this research work uniquely employed waste glass fiber and glass-ceramic material to mitigate pyroplastic deformation compared to the more commonly studied waste soda–lime glass. The pyroplastic deformation tests were conducted using an instrument from Expert Lab Service-MDF.
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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".