Experimental investigation and 2D fluid simulation of a positive nanosecond discharge in air in contact with liquid at various dielectric permittivity and electrical conductivity values
Bibliographic record
Abstract
Abstract Streamer discharges exhibit high reactivity and are pivotal in several plasma-based applications, especially those involving plasma–liquid interactions. This study investigates the effects of liquid dielectric permittivity (ϵr = 32, 56, 80) and electrical conductivity (σ = 2, 500, 1000 μS cm−1 ) on positive nanosecond discharges in ambient air in a pin-to-liquid setup. Increased ϵr and σ values lead to higher discharge currents. ICCD imaging reveals that elevated ϵr decreases the extension of the discharge radially over the liquid surface and lowers the number of filaments at the liquid surface. Similarly, higher σ values result in a shorter propagation of the discharge. A previously developed fluid model was adapted to include solution conductivity and is utilized to elucidate the discharge dynamics. The results demonstrate that increased ϵr or σ decrease the radial component of the electric field produced by the surface ionization wave while increasing the density of electrons in the gap. The simulations and ICCD images are used to determine the charge number (Ns ) at the filament front. Ns is in the order of magnitude of Meek’s criterion (∼108) during propagation and reaches ∼107 when propagation stops for all ϵr - and σ-conditions. We find that Ns is higher for low ϵr and decreases more rapidly at higher σ. The findings reported in this paper enhance our understanding of streamer-surface interactions, which are crucial for advancing plasma 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".