Characterization of flashover plasma across a large-scale ceramic vacuum interface initiated by explosive electron emission
Bibliographic record
Abstract
Flashover plasma characteristics across a large-scale ceramic vacuum interface initiated by explosive electron emission (EEE) are investigated with the experimental and simulation methods. Driven by a negative high voltage pulse (-300 kV, 150 ns), flashover plasma luminescence processes were observed by a high-speed framing camera and the accompanied spectrum characteristics were measured by a spectrograph. Compared with flashover initiated by field electron emission (FEE), a faster light expansion velocity and higher electron temperature of flashover initiated by EEE were obtained which were 200 cm/μs and 4.57 eV, respectively. Radial and angular components characteristics of expansion velocity were analyzed as well and their maximum values were 200 cm/μs and 50 cm/μs. Furthermore, comparisons of flashover initiated by EEE and FEE were conducted by particle-in-cell methods and the results agreed with the experimental observations. From these results it can be concluded that due to higher primary electron energy and stronger secondary electron avalanche and gas ionization, flashover along the ceramic interface initiated by EEE has an easier and faster develop process compared with that initiated by FEE. This work can give a reference for evaluation and design of ceramic vacuum interfaces for high-current applications.
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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".