X-ray Photoelectron Emission Microscopy and Time-of-Flight Secondary Ion Mass Spectrometry Analysis of Ultrathin Fluoropolymer Coatings for Stent Applications
Bibliographic record
Abstract
Fluoropolymer plasma coatings have been investigated for application as stent coatings due to their chemical stability, conformability, and hydrophobic properties. The challenge resides in the capacity for these coatings to remain adherent, stable, and cohesive after the in vivo stent expansion, which can generate local plastic deformation of up to 25%. Plasma-coated samples have been prepared by a multistep process on 316L stainless steel substrates, and some coated samples were plastically deformed to mimic a stent expansion. Analyses were then performed by X-ray photoelectron spectroscopy (XPS), X-ray photoelectron emission microscopy (X-PEEM), and time-of-flight secondary ion mass spectrometry (TOF-SIMS) to determine the chemical and physical effects of such a deformation on both the coating and the interfacial region. While XPS analyses always showed a continuous coating with no significant effect of the deformation, TOF-SIMS and near-edge X-ray absorption fine structure (derived from X-PEEM) data indicated the presence of a certain density of porosity and pinholes in all coatings as well as sparse fissures and molecular fragmentation in the deformed ones. The smallness of the area fraction affected by the defects and the subtlety of the chemical changes could only be evidenced through the higher chemical sensitivity of these latter techniques.
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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".