Energy transfer in interaction of a cold atmospheric pressure plasma jet with substrates
Bibliographic record
Abstract
Abstract The energy flux of a nanosecond pulsed cold atmospheric pressure (CAP) plasma jet in contact with a substrate surface was measured to improve the understanding of the correlation between energy flux, flow dynamics and applied electrical power. The flow pattern properties of the CAP jet were imaged using Rayleigh scattering showing a transition from laminar to turbulent flow at Reynolds number of 700, significantly smaller than the conventional critical Reynolds number of 2040. The energy flux to the surface was determined using a passive thermal probe as a substrate dummy. As expected, the energy flux decreases with increasing distance to the nozzle. Measurements of the floating potential of the probe revealed a strong positive charging (up to 165 V) attributed to ion flux originating mainly from Penning ionization by helium metastables. Negative biasing of the probe doubled the energy flux and showed a significantly increased ion contribution up to a nozzle distance of 6 mm to the surface. For positive biasing an increased contribution of electrons and negative ions was only found at 3 mm distance. The relevance of particle transport to the surface is shown by switching from laminar to turbulent flow resulting in a decreased energy flux. Furthermore, a linear correlation of energy flux and input power was found.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".