Helium plasma immersion ion implantation studies of tungsten and tungsten heavy alloys for fusion plasma facing components
Bibliographic record
Abstract
Plasma fusion devices will require plasma-facing components (PFCs) which can withstand the extreme environment at the edge of a hot fusion plasma. Despite the excellent properties of tungsten(W) as a hard refractory metal, adverse effects such as embrittlement, melting, and morphological evolution have been observed in W when it is bombarded by a high-fluence of low-energy ions including helium. This study investigates the effect of helium ion bombardment on pure tungsten and a tungsten heavy alloy (W-HA)(NAECOMET 1000). Pure tungsten and NAECOMET 1000 samples were implanted with 3 keV helium ions with fluences ranging from 1.15×1021m−2 to 2.21×1022m−2, using Plasma Immersion Ion Implantation (PIII). After PIII treatment, samples were analysed using scanning electron microscopy (SEM) and atomic force microscopy (AFM), which revealed differences in surface morphology and topography. Although the melting and cracking of the Ni/Cu binder phase in the NAECOMET 1000 samples was seen under all implantation conditions, the Ni/Cu presence did somewhat slow the formation of W fuzz in comparison to pure tungsten. X-ray diffraction (XRD) studies showed peak shifts increasing with helium ion fluence for both the pure W and NAECOMET 1000 samples, as well as increase in mean crystallite size, confirming the distortion of the lattice. XPS compositional analysis showed a strong oxidation (>97%) near the metal surface, after helium PIII treatment, for both pure W and NAECOMET 1000 W-HA samples. Some conclusions about the potential suitability of W-HA materials for fusion plasma PFCs are drawn.
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".