Pulsed nanosecond air discharge in contact with water: influence of voltage polarity, amplitude, pulse width, and gap distance
Bibliographic record
Abstract
Abstract Plasma technology is a highly promising and advantageous technology for liquid processing. In air in-contact with water, plasma produces highly reactive species (ions, electrons, radicals, photons, etc) that diffuse into the water volume and initiate physical and chemical phenomena of interest, e.g. organic and inorganic pollutant degradation. In this study, we investigate the influence of basic parameters, such as voltage polarity, voltage amplitude, plasma lifetime, and air-gap distance, on the properties of a discharge in air in-contact with deionized water. Specifically, we analyze the electrical characteristics, the plasma behaviour at the water surface, water acidity and conductivity, and the decoloration rate of a standard organic dye (methylene blue). The concentration of the main reactive oxygen and nitrogen species produced in water is also reported. Compared to positive polarity conditions, the negative polarity voltages enhance the decolaration rate of methylene blue. For instance, under negative polarity voltages and while applying 4 and 6 kV, the decoloration rate is relatively low (<30%) and reaches 100% after 25 min of processing at −10 kV. The decoloration rate of MB is also strongly influenced by air-gap distance. Under positive polarity conditions, the decoloration rate decreases from ∼80 to ∼0% as the air-gap distance increases from 0.5 to 7.5 mm, whereas, under negative polarity conditions, the decoloration rate is ∼100%, irrespective of the air-gap distance (0.5 and 4.5 mm).
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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 | 0.001 |
| 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".