Equilibrium Deposition Rate of Silicon in Si-I<sub>2</sub>and Si-H<sub>2</sub>-Cl<sub>2</sub>Systems
Bibliographic record
Abstract
The thermodynamic equilibrium deposition rates of silicon in Si-I 2 and Si-H 2 -Cl 2 systems were analyzed. These rates are discussed in terms of their applicability in industrial processes of Si purification. The behavior of silicon deposition for silicon tetraiodide (SiI 4 ), silicon di-iodide and whole spectrum of Silicon-Hydrogen-Chlorine compounds were evaluated within a wide range of pressures and temperatures. A strong agreement between the theoretical model predictions and the experimental data was found for the SiI 4 compound. Alternatives to the Herrick-Krieble's approximation for experimental SiI 4 decomposition rates have been proposed for the actual operating pressures. The di-iodine technology has been analyzed in terms of its deficient SiI 2 formation at 1200°C and the subsequent Si deposition and SiI 4 formation at 800–900°C. An original approach was created to analyze the theoretical results for the Si-H 2 -Cl 2 system using quasi-binary system coordinates: SiH 4 -SiCl 4 . The analysis of the results shows that the silicon deposition rate is independent of the temperature and the pressure. Such independence assumes that one, single mechanism governs the decomposition reaction for the (Si-H 2 -Cl 2 ) system. It was concluded that a homogeneous nucleation is taking place for all Silicon-Hydrogen-Chlorine compounds within a temperature of 800–1400°C and a pressure of 1–100 atm.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".