Nonequilibrated situations of pulse modulated Ar–H2 and Ar–N2 thermal plasmas at atmospheric pressure
Bibliographic record
Abstract
Atmospheric pressure inductively coupled thermal plasmas with pulsing mode are generated for 104 slpm standard liters per minute total flow rate of Ar−H2 and Ar−N2 mixtures (Ar 94% molar concentration) inside a torch of 55 mm inner diameter. The inverter-type power source of 22 kW maximum output supplied coil current of 1 MHz fundamental frequency at 17.3 kW level, and the coil current is modulated by an external pulsed signal to produce the desired pulse modulated plasmas. Spectroscopic measurements are carried out for the temporal radiation intensity and coil current intensity. In addition, a time-dependent, two-dimensional model, which works with coil current input, is solved by using the same coil current profile measured in the experiment. Experimental and theoretical results for 67% duty factor with 10 ms on-time are compared. The theoretical predictions are found comparable with the experimental findings although the theoretical time-dependent response is seen slower, especially over the time just after on-pulsation. An estimation of electron temperature helps to describe this discrepancy fairly adequately. Experimentally the shimmer current level is found to vary with gas composition as well as with on- and off-time of pulsing signal. Both the experimental and theoretical findings suggest that plasma responds slowly at any axial positions below midcoil compared to that at midcoil level.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".