Analysis of the high-energy electron population in surface-wave plasma columns in presence of collisionless resonant absorption
Bibliographic record
Abstract
Abstract In surface-wave plasmas, the energy can be transferred to the plasma electrons through both ohmic (collisional) and collisionless heating mechanisms. At very low pressure, when the electron–neutral collision frequency is much lower than the wave frequency (collisionless regime), a resonance is excited close to the tube walls where the electron plasma frequency in the radially-inhomogeneous plasma column reaches the wave frequency. In such conditions, the sharp rise of the component of the surface-wave electric field perpendicular to the tube axis can induce transit-time heating. At the resonant point, the long-wavelength electromagnetic surface wave can also be converted into short-wavelength electrostatic Langmuir waves that propagate down the density gradient. In this work, spatially-resolved trace-rare-gases optical emission spectroscopy combined with collisional-radiative modeling is used to analyze the electron energy distribution function (EEDF) and wave–particle interactions in low-pressure argon plasma columns sustained by an electromagnetic surface wave at 600 MHz (over-dense plasma). The EEDF is found to depart from a Maxwellian with the presence of a high-energy tail. The relative population of high-energy electrons increases with the axial distance towards the end of the plasma column where the electron density decreases and the resonance point becomes closer to the discharge axis. Over the range of experimental conditions examined, the high-energy tail increases with the characteristic length of the plasma density gradient at the resonance point; a feature that can be linked to collisionless electron heating by Landau damping of Langmuir waves.
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".