Influence of cathode material type on the electrical breakdown behaviors of DC discharge
Bibliographic record
Abstract
Paschen curves were studied using different cathode materials such as magnesium, zinc, and carbon graphite by discharge in argon gas of a pressure range between 0.08 and 3 Torr using a parallel plates configuration. The first and second Townsend coefficients (α and γ, respectively) and the ionization efficiency (η) of different cathode materials were deduced from Paschen curves as a function of the reduced field (E/P). The minimum breakdown voltage was found to be about 242 V for Mg material, which has the lowest work function, while carbon graphite has a higher breakdown voltage of 283 V due to its higher work function. The second coefficient γ was increased as a function of E/P and has higher values for materials of lower work functions, and a similar trend of γ is obtained as a function of the ion mean energy. On the other hand, the first coefficient α has a reverse behavior with both E/P and the work function of the cathode materials compared with the second coefficient. The ionization efficiency of the three cathode materials is identical, as η depends only on the gas properties and not the cathode material. η has a maximum value of about 0.025 V−1 for an E/P of about 185 Vcm−1Torr−1, corresponding to the maximum ionizing ability of electrons. The validation of the breakdown results has been confirmed by conferring with other published experimental measurements.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".