Influence of nitrogen on deuterium retention in tungsten under sequential and simultaneous irradiation
Bibliographic record
Abstract
Nitrogen is a candidate for impurity seeding to reduce the edge plasma temperature for ITER’s tungsten divertor. Radiation characteristics and plasma performance are improved with N compared to other candidates neon and argon, however questions remain in terms of how the introduction of N might impact deuterium retention in W. The current study compares the influence of N on D retention in W with sequential (SEQ) and simultaneous (SIM) irradiation of D-3%N at 300–700 K with energies 500 eV/D+ and 1000 eV/N+. Thermal desorption spectroscopy (TDS) is used to measure the D retained, and X-ray photoelectron spectroscopy (XPS) is used to investigate nitride formation at different implantation temperatures. The XPS results show that for the beam composition of this study, the removal of N by D is the dominant interaction, working against N retention in W. Differences found between the N layer with D/N co-bombardment vs. N pre-irradiation might be worth considering when extrapolating sequential experiments to reactor conditions. The observed effect of the N-containing layer on the temperature dependence of deuterium release during TDS supports the XPS findings, suggesting that the phases of the W:N layer produced are different at different temperatures. It is found that SIM irradiation resulted in an overall increase (up to a factor of ∼4) in total D retention compared to SEQ and D-only experiments above 500 K. At 300–500 K, the D retention was not significantly changed by nitrogen pre-, post- or simultaneous irradiation.
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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 | 0.000 |
| 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".