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Oxidative Plasma Treatment of Fluorocarbon Surfaces for Blood-Contacting Applications

2018· article· en· W2906081438 on OpenAlexafffund
Vanessa Montaño‐Machado, Livia Angeloni, Pascale Chevallier, Marie Cécile Klak, Stéphane Turgeon, Marco Rossi, Diego Mantovani

Bibliographic record

VenueMaterials science forum · 2018
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Surface Interaction Studies
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceContact angleRestenosisWettingCoatingX-ray photoelectron spectroscopyFluorocarbonSurface modificationCeramicChemical engineeringAdhesionBiomedical engineeringStentNanotechnologyComposite materialSurgery

Abstract

fetched live from OpenAlex

Surgical implantation of metallic stents is today a common procedure for restoring narrowed arteries. However, main complications as in-stent restenosis, partial or total thrombosis, inflammation and devices degradation are still a serious clinical concern. The coating of stents with fluorocarbon (CF x ) ultrathin films represents a valuable strategy to limit these complications. Moreover, an additional step for the modification of some key surface properties of CF x coatings could further enhance their blood compatibility. Therefore, the objective of this work was to develop an oxidation process specific to ultrathin CF x coatings based on a methanol plasma treatment to modulate their biological response. Oxidized and non-oxidized coatings were investigated by XPS, ToF-SIMS, water contact angle, SEM and AFM. Tunable oxidation of the surface of CF x coatings was obtained by methanol plasma treatment, thus producing an increase of surface wettability, without affecting morphology, roughness and adhesion of the coatings. Blood test results showed an increased hemocompatibility of the oxidized samples, confirming the hypothesis that such treatment can succeed in modulating the blood contact behavior of the CF x oxidized coatings.

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)0.001
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 score0.005
Threshold uncertainty score0.654

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
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 head0.025
GPT teacher head0.310
Teacher spread0.284 · 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

Citations3
Published2018
Admission routes2
Has abstractyes

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