Impact of Thermo-Oxidative Wall Conditioning on the Performance of Diagnostic Mirrors for ITER
Bibliographic record
Abstract
Thermo-oxidation is controlled exposure in an oxygen-containing atmosphere at elevated temperature and is being considered as a technique for the de-tritiation of carbon-based codeposits in ITER. In addition, unplanned oxidation may also occur during accidental air ingress. The impact of thermo-oxidation on ITER diagnostic mirrors causes concerns. A dedicated study was performed in DIII-D, where molybdenum and copper mirrors were installed in the main chamber, in the divertor, and at a location remote from the plasma and exposed for ~2 hours to a mixture containing 80% helium and 20% oxygen at a total pressure of 1.27 kPa. Mirrors in the main chamber and in the divertor were exposed at 350°C to 360°C whereas the temperature of mirrors in the remote area was ~160°C.Reflectivity of all mirrors was degraded after thermo-oxidation showing a decrease in the UV range from 60% to 10% for molybdenum mirrors and a 90% drop for copper mirrors at the wavelength 250 nm. The reflectivity of mirrors exposed at lower temperature was less degraded. Surface analyses revealed formation of oxides on all mirrors.In ITER, shutters planned for mirror protection are ineffective against thermo-oxidation. Nevertheless, in-situ cleaning systems planned for ITER mirrors may efficiently remove oxide layers.
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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 | 0.000 |
| Scholarly communication | 0.001 | 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".