Interface of Nanoparticle-Coated Electropolished Stents
Bibliographic record
Abstract
Nanostructures entail a high potential for improving implant surfaces, for instance, in stent applications. The electrophoretic deposition of laser-generated colloidal nanoparticles is an appropriate tool for creating large-area nanostructures on surfaces. Until now, the bonding and characteristics of the interface between deposited nanoparticles and the substrate surface has not been known. It is investigated using X-ray photoelectron spectroscopy, Auger electron spectroscopy, and transmission electron microscopy to characterize an electropolished NiTi stent surface coated by laser-generated Au and Ti nanoparticles. The deposition of elemental Au and Ti nanoparticles is observed on the total 3D surface. Ti-coated samples are composed of Ti oxide and Ti carbide because of nanoparticle fabrication and the coating process carried out in 2-propanol. The interface between nanoparticles and the electropolished surface consists of a smooth, monotone elemental depth profile. The interface depth is higher for the Ti nanoparticle coating than for the Au nanoparticle coating. This smooth depth gradient of Ti across the coating-substrate intersection and the thicker interface layer indicate the hard bonding of Ti-based nanoparticles on the surface. Accordingly, electron microscopy reveals nanoparticles adsorbed on the surface without any sorption-blocking intermediate layer. The physicomechanical stability of the bond may benefit from such smooth depth gradients and direct, ligand-free contact. This would potentially increase the coating stability during stent application.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".