Study Of Ceramic Masses Obtained Using Raw Materials Of Technogenic Origin
Bibliographic record
Abstract
Waste from industrial coal preparation contains an insufficient amount of clay particles.In studies, coal preparation waste was mixed with mechanically activated loam.From the resulting raw material mixture with a moisture content of 16.7%, samples were molded, fired at 800 and 870 0 C, and then the strength was determined.The conducted research revealed that with an increase in the loam content from 10…30%, the strength and average density of the samples increases, and with a higher content it decreases.Studies of the phase composition and microstructure of materials were carried out using a complex of instrumental methods of physicochemical analysis.The physical and mechanical properties of the resulting materials were studied using standard methods in accordance with current regulatory documents.Studies of the system "loam -coal preparation waste -Na-Fe-containing red mud" were carried out using: red mud from enterprises was used as a clay component of loess-like loam, coal preparation waste from a central processing plant and as a Na-Fe-containing component.Material from a complex activated raw material mixture, including 70% coal processing waste, 20% loam, 10% red mud, as well as material from a mechanically activated raw material mixture, including 80% coal processing waste and 20% loam, is characterized by an almost uniform structure.Lamellar and leaf-shaped microaggregates indicate the presence of dehydrated particles of chlorite and montmorillonite in the structure of the material, and granular microaggregates indicate the presence of dehydrated mica.A comparison of SEM images of the structure of materials (samples) fired at 870 0 C indicates that in samples from complexly activated raw materials there are more granular particles 0.4-1 μm in size, microaggregates of colloidal particles have appeared, as well as more compounds formed from low-melting eutectics.
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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) | 0.002 | 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".