Behavior of the seed electrons and analysis of the memory effect in diffuse dielectric barrier discharge in air
Bibliographic record
Abstract
Abstract Seed electron production is a prerequisite for the generation of diffuse dielectric barrier discharge. This is driven by many processes occurring on the dielectric surface and/or in the gas bulk. In nitrogen, both surface and gas bulk phenomena leading to seed electron production are linked to N 2 (A 3 Σ u + ) metastable molecules. In air, N 2 (A 3 Σ u + ) state is rapidly quenched by oxygen species and cannot be responsible for the generation of diffuse discharge. Nevertheless, electrical and optical imaging experiments confirm that it is possible to achieve a diffuse regime in air, as in other gases, albeit after one or more filamentary discharges. To determine the mechanisms underlying this memory effect in air, the behaviors of discharges ignited with alumina dielectrics from two manufacturers were analyzed. While both surfaces revealed surface roughness values in the 150–200 μ m range, only the smoother alumina dielectrics yielded a diffuse discharge. However, electrostatic simulations indicate that the local enhancement of the electric field on the rough surfaces is not enough to induce surface mechanisms through which seed electrons can effectively be produced. Space-resolved analyses of current–voltage characteristics further reveal that the gas breakdown voltage increases with gas residence time, thereby impeding the memory effect. This may be attributed to the gas phase formation of electronegative species, principally ozone, consuming seed electrons by attachment.
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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".