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Plasma Surface Modification of 316L Stainless Steel for Cardiovascular Stent Coating

2010· article· en· W2058837214 on OpenAlexaff
Enrico Gallino, Michaël Tatoulian, Farzaneh Arefi‐Khonsari, Diego Mantovani

Bibliographic record

VenueAdvanced materials research · 2010
Typearticle
Languageen
FieldEngineering
TopicMetal and Thin Film Mechanics
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsCoatingMaterials scienceAllylamineRestenosisAdhesiveComposite materialStentPlasma polymerizationBare metalSurface modificationPlasmaAtmospheric-pressure plasmaCorrosionMetallurgyLayer (electronics)PolymerizationChemical engineeringPolymerSurgeryMedicine

Abstract

fetched live from OpenAlex

Coronary stents are metallic (316L stainless steel) devices employed during balloon angioplasty to reopen and prevent the re-obstruction of a diseased narrowed area within a coronary artery. To reduce restenosis rate, bare metal stent coating is a promising solution. The coating can act as an anticorrosive barrier against the aggressive properties of biological environment, improving the long-term safety of the device. The goal of this study is to develop a dry process to isolate metallic surface from the biological environment by depositing a thin plasma polymerized allylamine (CH2=CH-CH2-NH2) film on the metallic surface. Plasma polymerized allylamine films were deposited on flat electropolished 316L stainless steel samples in a low pressure plasma reactor (70 kHz). Chemical composition of the coatings has been analysed as a function of the discharge power and treatment time. Moreover, special attention has been paid on the stability of the coating after immersion during 24 hours in D.I. water. Finally, to mimic stent expansion conditions, a “small punch test” has been used to investigate the adhesive properties of the coating. Our results demonstrate that is possible to deposit a stable, cohesive and adhesive plasma polymerized allylamine thin film which can be used as a coating for cardiovascular stents

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.445

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.065
GPT teacher head0.323
Teacher spread0.258 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2010
Admission routes1
Has abstractyes

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