Recent developments in Liquid Phase Electroepitaxial growth of bulk crystals under magnetic field
Bibliographic record
Abstract
This review article presents recent developments in Liquid Phase Electroepitaxial (LPEE) growth of bulk single crystals of alloy semiconductors under an applied static magnetic field. The growth rate in LPEE is proportional to the applied electric current. However, at higher electric current levels the growth becomes unstable due to the strong convection occurring in the liquid zone. In order to address this problem, a significant body of research has been performed in recent years to suppress and control the natural convection for the purpose of prolonging the growth process to grow larger crystals. LPEE growth experiments show that the growth rate under an applied static magnetic field is also proportional and increases with the field intensity level. The modeling of LPEE growth under magnetic field was also the subject of interest. Two-dimensional mathematical models developed for the LPEE growth process predicted that the natural convection in the liquid zone would be suppressed almost completely with increasing the magnetic field level. However, experiments and also three-dimensional models have shown that there is an optimum magnetic field level below which the growth process is stable and the convection in the liquid zone is suppressed, but above such a field level the convective flow becomes very strong and leads to unstable growth with unstable interfaces.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".