Deuterium retention in recrystallized tungsten irradiated with simultaneous deuterium–neon ion beams
Bibliographic record
Abstract
Although neon has been considered for impurity seeding in the ITER tungsten divertor, there have been few studies on its effects on deuterium retention in tungsten. We investigate the effects of simultaneous (SIM) D-2.5% Ne ion beam irradiation on D retention in recrystallized W at 300–700 K, with 500 eV/D+ and 1 keV/Ne+ ion energies, and compare to the effects of SIM D-3% He irradiation with 500 eV/He+. Thermal desorption spectroscopy (TDS) up to 1473 K, nuclear reaction analysis (NRA), and elastic recoil detection analysis (ERDA) are used to measure D, He, and Ne in the specimens. Ne is more effective than He at reducing D retention for higher exposure temperatures, even though less Ne is retained than He. He appears to modify the D TDS spectra peak shapes more than Ne, while He addition leads to increased D trapping within a few µm depth according to NRA. D retention may be reduced due to Ne sputtering, as well as a near surface interaction with Ne which blocks D diffusion past the implantation range and leads to higher surface re-emission.
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".