Separation and Recovery of Refined Si from Al–Si Melt by Modified Czochralski Method
Bibliographic record
Abstract
Separation of refined silicon from Al-Si melt is still a puzzle for the solvent refining process, resulting in considerable waste of acid and silicon powder. A novel modified Czochralski method within the Al-Si alloy is proposed. After the modified Czochralski process, a large amount of refined Si particles was enriched around the seed crystalline Si and separated from the Al-Si melt. As for the Al-28%Si with the pulling rate of 0.001 mm/min, the recovery of refined Si in the pulled-up alloy (PUA) sample is 21.5%, an improvement of 22% compared with the theoretical value, which is much larger 1.99 times than that in the remained alloy (RA) sample. The content of impurities in the PUA is much less than that in the RA sample, which indicates that the modified Czochralski method is effective to improve the removal fraction of impurities. The apparent segregation coefficients of boron (B) and phosphorus (P) in the PUA and RA samples were evaluated. These results demonstrate that the modified Czochralski method for the alloy system is an effective way to enrich and separate refined silicon from the Al-Si melt, which provide a potential and clean production of solar grade silicon (SoG-Si) for the future industrial application.
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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 | 0.001 |
| 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".