Numerical Simulation of Liquid Phase Diffusion Growthof SiGe Single Crystals under Zero Gravity
Bibliographic record
Abstract
Liquid Phase Diffusion (LPD) growth of SixGe1 x single crystals has been numerically simulated under zero gravity. The objective was to examine growth rate and silicon concentration distribution in the LPD grown crystals under diffusion dominated mass transport prior to the planned LPD space experiments on the International Space Station (ISS). Since we are interested in predicting growth rate and crystal composition, the gravitational fluctuation of the ISS (g-jitter) was neglected and the gravity level was taken as zero for simplicity. A fixed grid approach has been utilized for the simulation. An integrated top-level solver was developed in OpenFOAM to carry out numerical simulations for the melting and solidification periods of the LPD growth process. The solver employs the well-known enthalpy method for modeling the initial melting process and uses the virtual front-tracking method, originally developed to model dendritic growth. This simulates the solidification as driven by saturation and precipitation as is the case for this solution growth technique. The melting simulation determines the initial conditions for growth interface, temperature, and concentration. The solver then calculates the onset of solidification, the evolution of the growth interface. In addition, the concentration and temperature fields are calculated in the melt and grown crystal. The present simulation results agree qualitatively with the radial and axial silicon distributions in the grown crystals of the Earth-bound experiments, and also with those previously predicted numerically. The computed total growth rate also agrees quantitatively with that of the experiment. However, the simulation shows slight differences in the interface shapes and predicts faster initial growth rate. Such a small discrepancy is expected since the contribution of natural convection in the melt was not included in the present simulation. A well-design LPD space experi- ment may shed light on this prediction.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".