Improving the radiopacity of Fe–Mn biodegradable metals by magnetron-sputtered W–Fe–Mn–C coatings: Application for thinner stents
Bibliographic record
Abstract
In this exploratory work, micrometric radiopaque W–Fe–Mn–C coatings were produced by magnetron sputtering plasma deposition, for the first time, with the aim to make very thin Fe–Mn stents trackable by fluoroscopy. The power of Fe–13Mn-1.2C target was kept constant at 400 W while that of W target varied from 100 to 400 W producing three different coatings referred to as P 100, P 200, P 400. The effect of the increased W power on coatings thickness, roughness, structure, corrosion behavior and radiopacity was investigated. The coatings showed a power-dependent thickness and W concentration, different roughness values while a similar and uniform columnar structure. An amorphous phase was detected for both P 100 and P 200 coatings while γ-Fe, bcc-W and W 3 C phases found for P 400. Moreover, P 200 and P 400 showed a significantly higher corrosion rate (CR) compared to P 100. The presence of W, W 3 C as well as the Fe amount variation determined two different micro-galvanic corrosion mechanisms significantly changing the CR of coatings, 0.26 ± 0.02, 59.68 ± 1.21 and 59.06 ± 1.16 μm/year for P 100, P 200 and P 400, respectively. Sample P 200 with its most uniform morphology, lowest roughness (RMS = 3.9 ± 0.4 nm) and good radiopacity (∼6%) appeared the most suitable radiopaque biodegradable coating investigated in this study.
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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) | 0.000 | 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".