Physical proprieties of DC glow discharges in a neon–argon gas mixture
Bibliographic record
Abstract
This paper reports a detailed study of 90% Ne – 10% Ar gas mixture DC glow discharge at low pressure, wherein 15 chemical reactions are considered. The second-order fluid model is used. The parameters of particle transport and their rate coefficients strictly depend on mean electron energy. In the framework of the local electric field approximation, we have developed an analytical expression of the drift velocity of positive argon ions in a neon gas [Formula: see text], which is in good agreement with the experimental results, and serves to give best results than the results obtained using [Formula: see text] that exist in the literature. The results show that the argon ion density is more important than the neon ion density despite the presence of more constant background neon gas density in the mixture. The current density reaches 0.1729 mA/cm 2 for 250 V applied potential under 2 Torr pressure in a gas mixture. The spatio-temporal evolution of both electric and energetic characteristics, as well as their spatial distribution in the steady state, are shown and discussed. The maximum value of the neon metastable atom density is 4.54957 × 10 8 cm −3 , and for argon metastable atom density is 5.4689 × 10 8 cm −3 . The model is verified experimentally and theoretically in the particular case.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".