Influence of voltage and gap distance on the dynamics of the ionization front, plasma dots, produced by nanosecond pulsed discharges at water surface
Bibliographic record
Abstract
Abstract A streamer discharge is a highly reactive and dynamic non-thermal plasma. It has been used in many applications, including environmental remediation, medicine, and material processing. Although the physics of streamer discharges in gaseous media is well understood, its interaction with a solid and liquid dielectric surfaces remains under investigation, in particular when quantitative data are searched for. In this study, we investigate the influence of voltage amplitude (V a) and interelectrode air gap distance (d) on the pulsed discharge behavior at the surface of distilled water. Time resolved images show the formation and propagation of plasma dots (ionization front of streamers) at water surface. Because of its stochastical nature, a large number of discharge was performed to address the influence of V a and d on the number of plasma dots (N Dots) as well on the charge per dot (Q Dot). As expected, for a given V a, the breakdown voltage is found to increase with d. Moreover, N Dots decreases linearly with d at the rate of ∼1 dot by 200 μm of increase, while the total injected charge decays linearly with a rate of ∼8–9 nC by 200 μm of increase. Based on the measurement of the propagation velocity of the plasma dots and on the estimation of the electric field in the medium, an average mobility of plasma dots of ∼1.5 cm2 Vs−1 is evaluated. From both this value and the instantaneous measured propagation velocity, the temporal evolution of Q Dot and charge number are determined. The observations reported here are of interest for fundamental studies as well as for applications where well-controlled charge transfer to surfaces is crucial.
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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".