Nanosecond pulsed gliding arc plasma for ammonia synthesis: better insight from discharge mode and vibrational temperature
Bibliographic record
Abstract
Abstract Low-temperature plasma technology is a promising technological route to achieve green and efficient ammonia synthesis at ambient temperature and pressure. In this work, a Laval nozzle type gliding arc plasma reactor was designed for the direct synthesis of ammonia from N2 and H2 discharges ignited by a high voltage nanosecond pulsed power supply to investigate the effect of different electrode gaps, pulse voltages, and V N2:V H2 on ammonia synthesis. The nanosecond pulsed plasma discharges were characterized through oscilloscope and optical emission spectroscopy (OES). The maximum rate of NH3 synthesis was 538.12 μmol·h−1 at 1.5 mm electrode gap, 16 kV peak pulse voltage, 6 kHz pulse repetition frequency, 100 ns pulse width, 100 ns pulse rising edge, 100 ns pulse falling edge, and 200 mL·min−1 total gas flow rate with V N2:V H2 = 1:1. It was demonstrated that the discharge mode of the nanosecond pulsed gliding arc plasma can transit from a unipolar state to a bipolar state determined by the duty cycle accompanied with higher discharge power and vibrational temperature. Bipolar discharge mode is beneficial to improve the efficiency of plasma ammonia synthesis because of it can strengthen the plasma discharge and increase the vibrational temperature. The ammonia synthesis rate and N2 conversion rate increased with the increase of the discharge power and vibrational temperature.
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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) | 0.000 | 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".