Boron and Phosphorus Removal from Si-Cu Alloy Using CaO-SiO2-Na2O-Al2O3 Slag
Bibliographic record
Abstract
A combination of solvent refining and slag treatment was employed to investigate the possibility of removing boron and phosphorus from crude silicon to acceptable levels for solar applications. Metallurgical grade silicon (MG-Si) was alloyed with pure copper and the alloy was subjected to refining by liquid CaO-SiO2-Na2O-Al2O3 slags at 1773K (1500째C). The distribution of B and P between the slags and the alloy was measured under a range of slag compositions, varying in CaO:SiO2 and SiO2:Al2O3 ratios and the amount of Na2O. The results showed that both basicity and oxygen potential have a strong influence on the distribution of B and P. With silica affecting both parameters in these slags, a critical P_(O_2 ) could be identified that yields the highest impurity pick-up. The addition of Na2O to the slag was found to increase the distributions of boron and phosphorus into the slag. A thermodynamic evaluation of the system showed that alloying copper with MG-Si leads to substantial increase of boron distribution coefficient. The highest boron and phosphorus distributions were 47 and 1.1, respectively. Using these optimum slags to reduce boron and phosphorus in MG-Si to solar grade level, a slag mass about 0.3 times and 17 times mass of alloy would be required, respectively. The loss of Na2O and B2O3 from CaO-SiO2-Al2O3 slags containing a high content of Al2O3 and Na2O was studied in the temperature range of 1573-1773K (1300-1500째C) by thermogravimetric analysis (TGA) under isothermal conditions. Higher basicity and larger B2O3 and Na2O concentrations were also found to increase the evaporation rate. The rate appears to be controlled by chemical reaction at the surface in the beginning, followed by a mixed chemical reaction-mass transfer regime, and finally a liquid-phase mass transport step. The apparent activation energies for the evaporation reaction were calculated for different slag compositions.
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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) | 0.000 | 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".