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Record W1990118206 · doi:10.1002/ppap.200800054

Chemical Characterisation of Nitrogen‐Rich Plasma‐Polymer Films Deposited in Dielectric Barrier Discharges at Atmospheric Pressure

2008· article· en· W1990118206 on OpenAlexaff
Pierre‐Luc Girard‐Lauriault, P. Desjardins, Wolfgang E. S. Unger, Andreas Lippitz, M. R. Wertheimer

Bibliographic record

VenuePlasma Processes and Polymers · 2008
Typearticle
Languageen
FieldMedicine
TopicPlasma Applications and Diagnostics
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsDielectric barrier dischargeX-ray photoelectron spectroscopyPolymerNitrogenAtmospheric pressureAtmospheric-pressure plasmaXANESMaterials scienceAnalytical Chemistry (journal)Chemical engineeringPlasmaDielectricSpectroscopyChemistryOrganic chemistryComposite materialOptoelectronics

Abstract

fetched live from OpenAlex

Abstract We have used an atmospheric pressure DBD apparatus to deposit novel families of N‐rich plasma polymers (PP:N), using mixtures of three different hydrocarbon precursors in nitrogen at varying respective gas flow ratios. This research focuses on the overall chemical characterisation of those materials, with specific attention to (semi)‐quantitative analysis of functional groups. Well‐established and some lesser‐known analytical techniques have been combined to provide the best possible chemical and structural characterisations of these three families of PP:N thin films, namely XPS, NEXAFS and FT‐IR spectroscopy. 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 score0.003

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 head0.009
GPT teacher head0.219
Teacher spread0.211 · 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

Citations78
Published2008
Admission routes1
Has abstractyes

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