Particle-in-cell/Monte Carlo collision simulation of the ionization process of surface-wave plasma discharges resonantly excited by surface plasmon polaritons
Bibliographic record
Abstract
Although surface-wave plasma (SWP) sources have many industrial applications, the ionization process for SWP discharges is not yet well understood. The resonant excitation of surface plasmon polaritons (SPPs) has recently been proposed to produce SWP efficiently, and this work presents a numerical study of the mechanism to produce SWP sources. Specifically, SWP resonantly excited by SPPs at low pressure (0.25 Torr) are modeled using a two-dimensional in the working space and three-dimensional in the velocity space particle-in-cell with the Monte Carlo collision method. Simulation results are sampled at different time steps, in which the detailed information about the distribution of electrons and electromagnetic fields is obtained. Results show that the mode conversion between surface waves of SPPs and electron plasma waves (EPWs) occurs efficiently at the location where the plasma density is higher than 3.57 × 1017 m−3. Due to the effect of the locally enhanced electric field of SPPs, the mode conversion between the surface waves of SPPs and EPWs is very strong, which plays a significant role in efficiently heating SWP to the overdense state.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".