Effect of Si contamination of a reactor on the determination of surface loss coefficients of hydrogen atoms
Bibliographic record
Abstract
Surface loss coefficients of hydrogen atoms (H) are of great importance, in particular, for numerical simulations of industrial plasma processes. In this work, the surface loss coefficient of H atoms was obtained for stainless steel and tungsten as a function of the plasma treatment time. The surface loss coefficients were determined by measuring the density distribution of H atoms, close to the investigated surfaces, using two-photon absorption laser-induced fluorescence, while H atoms were generated in an electron-cyclotron resonance plasma at 5 Pa. In parallel, the surface element composition was monitored using x-ray photo-electron spectroscopy (XPS). An exponential decay of the surface loss coefficient as a function of the plasma treatment time was observed on both surfaces due to surface contamination by silicon (Si) detected thanks to the XPS measurements. Possible sources of Si contamination were explored, among which the employed commercial plasma source turned out to be such, whereupon a new Si-free chamber was built. In the Si-free reactor, values of 0.34±0.02 and 0.33±0.02 for the surface loss coefficients of hydrogen atoms were obtained on stainless steel and tungsten, respectively. Cross-comparison with XPS measurements confirmed the absence of any Si contamination. This work demonstrates the influence of Si contamination of the sample surfaces on the surface loss coefficient, highlighting the necessity for surface and vacuum characterization when reporting on surface loss coefficient measurements. Better characterization and reporting of a surface state and composition would allow for more conclusive data and easier comparison between different setups.
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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 | 0.001 | 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 | 0.000 |
| 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".