A numerical simulation study for the effect of applied magnetic field in growth of CdTe single crystals by the traveling heater method
Bibliographic record
Abstract
A numerical simulation study was carried out for the growth of CdTe crystals by the Travelling Heater Method to examine the effects of applied stationary and rotating magnetic fields, and also small non-uniformities in the stationary magnetic field. A strong stationary field is applied to suppress the natural convection in the solution zone, and a small but rotating magnetic field is considered for better mixing in the horizontal plane. The spatial distribution of the stationary field is almost uniform before the furnace and the growth system are lowered into the magnet opening. However, the field uniformity may be altered by the presence of the growth system. In order to determine the effect of such field non-uniformities, small magnetic body force components are considered in the model. Results show that higher stationary field levels are better in suppressing convection in the solution, but enhance the compositional non-uniformity in the solution. Small but unintentional non-uniformities in the stationary magnetic field also enhance compositional non-uniformity. A rotating magnetic field, on the other hand, is beneficial for mixing in the horizontal plane, and reduces the compositional non-uniformity in the solution. Mixing is enhanced at higher rotating magnetic field frequencies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".