Термодинамический анализ условий образования карбида кремния в сложных металлических расплавах
Bibliographic record
Abstract
The Me–Si–C systems which contain metal melt are the basis of a promising technique for growing single crystals of silicon carbide. Such techniques necessitate the search for metal compositions with relatively low melting points, which would preserve the ability to dissolve silicon and carbon in sufficient amounts. The increasing complexity of metal melt compositions based on the elements of the iron subgroup enables to achieve the desired effect. To search new compositions of fusible metal melts, i.e. catalysts of silicon carbide crystals growth, it is advisable to use the liquidus surface of the Fe–(...)–Si–C systems. The aim of this work was to make thermodynamic modeling of the Fe–(...)–Si–C systems to identify opportunities for reducing the metal melt temperature in equilibrium with silicon carbide. The “Phase Diagram” block of the software package “FactSage” (version 6.4) developed by “Thermfact” (Canada) and “GTT Technologies” (Germany) were used for thermodynamic modeling. During this investigation liquidus surfaces of the Fe–Ni–Si–C, Fe–Co–Si–C, Fe–Mn–Si–C and Fe–Ni–Co–Mn–Si–C systems were calculated. Phase diagrams presented in the form of surface liquidus enablesilicon carbide; synthesis in a metal melt; iron; nickel; cobalt; manganese not only to determine metal composition with the lowest melting point, but also to visualize the range of concentrations and temperatures for which SiC is the reaction product of metal components. This is especially useful in the mode selection process of growing silicon carbide crystals.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".