Optimization of RF-ICP Tungsten Deposits for Plasma Facing Components
Bibliographic record
Abstract
Tungsten (W) and its alloys are promising candidate materials for plasma facing components (PFC) in future fusion reactors. Despite having many advantages (the highest melting point of all metals, high density, low vapor pressure), tungsten also possesses several drawbacks, such as poor machinability and weldability. One of the nowadays considered PFC production routes is plasma spraying. For this, the conventional gas-stabilized atmospheric plasma torches have inherent critical limitations (insufficient plasma enthalpy to efficiently melt the tungsten particles, susceptibility to oxidation at elevated temperatures). The novel radio frequency inductively coupled plasma (RF-ICP) is a unique system capable of overcoming these limitations. Using RF-ICP, more efficient melting of tungsten particles and controlled atmosphere provide a unique possibility to produce dense, oxides-free deposits.\nIn our study, TekSpray-15 RF-ICP (Tekna, Canada) was used for the preparation of tungsten coatings from two different feedstock powders under varying system parameters (torch power, chamber pressure). Their influence was studied via SEM observations of the deposits microstructure as well as XRD analyses.
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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) | 0.001 | 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".