Effect of Phase Composition on Leaching Behavior and Mechanical Properties of Ceramics from Ferrochrome Slag and Tundish Slag
Bibliographic record
Abstract
(3.88 wt.%) and MnO (18.69 wt.%), respectively. In this study, batches of ceramics were prepared from FS and TS, and their Cr/Mn leaching behaviors, mechanical properties and microstructures were investigated. Results showed that ceramics with 80 wt.% FS or 85 wt.% TS had acceptable properties. By controlling its composition and sintering temperature, pyroxene or spinel phases could become the main crystalline phases of the fired ceramics containing either of the two slags. For both slag series, pyroxene phases contributed to higher bending strengths, whereas spinel phases led to lower Cr/Mn leaching rates. Both ceramic containing 20 wt.% FS and ceramic containing 85 wt.% TS had the main crystals of pyroxene phases and possessed the highest bending strengths (FS20: 114.52 MPa and TS85: 124.61 MPa). However, both ceramic containing 80 wt.% FS and ceramic containing 25 wt.% TS with main crystals from the spinel phases had the lowest Cr/Mn leaching rates (FS80: Cr 0.05% and TS25: Mn 0.43%). Therefore, optimum designs for the compositions of ceramics from different slags were achieved by changing the proportions of pyroxene and spinel phases to obtain a balance between the high strengths of materials and the stable retention of heavy metal ions. This study provides an important basis for long-term research on the large-scale reuse of heavy metal-containing slags in the ceramic industry.
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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".