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Record W1942470626 · doi:10.1002/ppap.201400210

Nitrogen‐Rich Plasma Polymer Coatings for Biomedical Applications: Stability, Mechanical Properties and Adhesion Under Dry and Wet Conditions

2015· article· en· W1942470626 on OpenAlexafffund
Sophie Lerouge, Jérémie Barrette, Juan‐Carlos Ruiz, M. Sbai, Houman Savoji, Bachir Saoudi, Matthieu Gauthier, M. R. Wertheimer

Bibliographic record

VenuePlasma Processes and Polymers · 2015
Typearticle
Languageen
FieldEngineering
TopicMetal and Thin Film Mechanics
Canadian institutionsPolytechnique MontréalÉcole de Technologie SupérieureCentre Hospitalier de l’Université de Montréal
FundersNatural Sciences and Engineering Research Council of CanadaFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsQuartz crystal microbalanceMaterials scienceDissolutionAdhesionTetrafluoroethyleneAqueous solutionPolymerChemical engineeringSwellingComposite materialSolubilityChemistryCopolymerOrganic chemistryAdsorption

Abstract

fetched live from OpenAlex

Plasma polymerized coatings (PP) are increasingly used for biomedical applications in contact with body fluids. We optimized and studied the stability, mechanical properties, and adhesion in air and aqueous media of amine (NH2)‐rich PP in low‐pressure r.f. plasma from [NH3/C2H4] mixtures (L‐PPE:N). Quartz crystal microbalance with dissipation monitoring (QCM‐D) revealed partial dissolution or water‐uptake (swelling) in real‐time in various solutions, which helped identify the best gas mixture ratio for compromise between stability and high concentration of (NH2) groups. Nanoindention revealed strong decrease of Young's modulus and hardness in water. Finally, a Cross‐Hatch peel test was used to optimize L‐PPE:N adhesion on poly(tetrafluoroethylene) (PTFE): plasma pre‐treatment prevented both dry and wet delamination.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.000
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.043
GPT teacher head0.238
Teacher spread0.195 · 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 source (direct Gemma or distilled Codex), 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

Citations27
Published2015
Admission routes2
Has abstractyes

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