Investigation of Helium Ion Induced Damage in Nano-W Using UNU/ICPT Dense Plasma Focus Device
Bibliographic record
Abstract
Tungsten is the first choice for Plasma Facing Components because of their favorable properties such as high melting point, high threshold energy, low threshold shock resistance, resistance to form co-deposits with tritium. However, He irradiation defects on W may lead to Deuterium-Tritium reaction failure in the magnetic fusion reactors. In this work, we investigate the effects of helium ions flux, created by a UNU/ICPT dense plasma focus (DPF) device, on a PLANSEE double forged W and nanostructurized tungsten (nano-W). For the poly-W samples, SEM images showed the extent of surface and subsurface of W damage due to helium flux irradiation. The increase in numbers of hydrogen shots results in micro-cracks and blisters on the sample surface followed by re-solidification of the sputtered and melted surface. Nanostructurization of W has been achieved by exposing the W samples to Argon plasma in the UNU/ICPT DPF device to improve the performance. This results in well-distributed highly dense nanoparticles of ∼ 20–50 nm size. He ions exposed nano-W samples showed micro-cracks and nanopores, instead of blisters and holes. Back scattered imaging of the nano-W provides an indication of He bubbles trapping in the grain boundaries. The nano-W with improved surface and structural properties can be good candidate for plasma-facing materials.
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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".