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Record W2145556720 · doi:10.1002/ppap.200700077

Nitrogen‐Rich Plasma‐Polymer Films for Biomedical Applications

2007· article· en· W2145556720 on OpenAlexafffund
Florina Truica‐Marasescu, M. R. Wertheimer

Bibliographic record

VenuePlasma Processes and Polymers · 2007
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsX-ray photoelectron spectroscopyPolymerMaterials scienceEllipsometryNitrogenPlasmaEthylenePlasma polymerizationAnalytical Chemistry (journal)Deposition (geology)PolymerizationChemical engineeringThin filmChemistryNanotechnologyOrganic chemistryComposite materialCatalysis

Abstract

fetched live from OpenAlex

Abstract Low‐pressure plasma co‐polymerisation of binary gas mixtures of ethylene and ammonia was investigated in order to deposit N‐rich plasma polymer coatings for biomedical applications. Deposition kinetics and relevant surface characteristics were investigated as a function of r.f. power, gas pressure, and NH3/C2H4 ratio. Physico‐chemical properties of the coatings were determined by surface‐sensitive techniques: for example, total nitrogen concentrations up to 40 at.‐% were measured by XPS, while those of primary amines were determined by chemical derivatisation followed by XPS analysis. L‐PPE:N films were further characterised by UV‐vis and IR spectroscopic ellipsometry and by CAG. magnified image

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)0.000
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.018
GPT teacher head0.265
Teacher spread0.247 · 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

Citations238
Published2007
Admission routes2
Has abstractyes

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