Influence of the microstructure on the diffusion barrier performance of Nb-based coatings for cyclotron targets
Bibliographic record
Abstract
The number of medical procedures involving the use of cyclotron-produced radionuclides is constantly growing year by year. The design and construction of the cyclotron targets appropriate for the production of the radionuclides of interest are the most challenging issues. The cyclotron targets for the medical radionuclide production suffer from two main corrosion problems: the corrosion due to proton-irradiated water and liquid metal embrittlement. The design of the target for radionuclide production limits the ability to select an ideal material that meets all of the following requirements: machinability or ease of construction, high melting temperature, high thermal exchange performance, excellent chemical inertness, etc. The use of thermally and mechanically suitable substrate materials protected by chemically resistant coatings can be a good compromise. These two corrosion problems can be attributed to the mechanism of diffusion by the aggressive particles through the protective coating. In this research, niobium has been chosen as the principal material for the design of thin film protective coatings. The coating microstructure was correlated to specific deposition parameters to provide chemical resistance to both proton-irradiated water corrosion and liquid metal embrittlement. Film densification and amorphization were pursued to achieve niobium-based thin films efficient as diffusion barriers to proton-irradiated water and liquid metal. The most important conclusions were that the performance of thin films as diffusion barriers varied dramatically based on various deposition parameters and deposition technologies. Among the configurations studied, only three are acceptable as anticorrosion coatings: niobium deposited on axis with unbalanced magnetron sputtering, niobium coated at a high sputtering rate and on a water-cooled sample holder, and the niobium-titanium alloy sputtered at a low argon pressure.
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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) | 0.000 | 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) | 0.000 | 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".