Combined ns pulsed-RF excitation and impedance matching considerations for the production of moderate E/n atmospheric pressure discharges for gas conversion
Bibliographic record
Abstract
Abstract Sub-breakdown radiofrequency (RF) discharges enabled by a nanosecond (ns) pulse ignition source are studied at atmospheric pressure in a range of gas mixtures from completely inert (in Ar) to completely reactive (in CO2). An electrical characterisation of the continuous wave (CW) RF discharge (13.56 MHz) is performed to determine plasma impedance and plasma power dissipation. Two different measurement methods to electrically characterize the system are described and compared. One method uses in-situ measurements of discharge parameters (voltage, current and the phase angle), and the other method performs ex-situ measurements of the load circuit using a vector network analyser. It was found that RF plasma power deposition depended on the applied RF power as well as the gas mixture composition. Using the in-situ voltage, current and phase angle measurements, plasma power deposition was calculated to be as much as 85% and 76% of the applied RF power for the pure Ar and pure CO2 cases, respectively. A preliminary qualitative assessment of the plasma composition was performed by optical emission spectroscopy, and CO2 conversion by mass spectrometry. CO2 to CO conversions of 11.2% and 5.5% in a 20:80 (CO2:Ar) mixture and in 100% CO2, respectively, were observed. This study demonstrates a RF plasma source for gas conversion applications at atmospheric pressure in a completely reactive gas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".