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Record W4391823295 · doi:10.3390/ma17040875

Investigating the Behavior of Thin-Film Formation over Time as a Function of Precursor Concentration and Gas Residence Time in Nitrogen Dielectric Barrier Discharge

2024· article· en· W4391823295 on OpenAlexafffund
Faegheh Fotouhiardakani, Alex Destrieux, Jacopo Profili, Morgane Laurent, Sethumadhavan Ravichandran, Gowri Dorairaju, Gaétan Laroche

Bibliographic record

VenueMaterials · 2024
Typearticle
Languageen
FieldMedicine
TopicPlasma Applications and Diagnostics
Canadian institutionsUniversité LavalHôpital Saint-François d'Assise
FundersNatural Sciences and Engineering Research Council of CanadaSaint-GobainCentre québécois sur les matériaux fonctionnels
KeywordsX-ray photoelectron spectroscopyFourier transform infrared spectroscopyDielectric barrier dischargeMaterials scienceThin filmCoatingAnalytical Chemistry (journal)NitrogenDielectricFluoropolymerChemical engineeringChemistryComposite materialNanotechnologyOrganic chemistryPolymer

Abstract

fetched live from OpenAlex

This study aims to establish a correlation between the fragmentation process and the growth mechanisms of a coating deposited on a fluoropolymer. Deposition was carried out using dielectric barrier discharge at atmospheric pressure, employing an oxygen-containing organic precursor in a nitrogen environment. The findings reveal that the impact of precursor concentration on the formation of specific functionalities is more significant than the influence of treatment time. The X-ray photoelectron spectroscopy (XPS) results obtained indicate a reduction in the N/O ratio on the coating’s surface as the precursor concentration in the discharge increases. Fourier transform infrared spectroscopy (FTIR) analysis, conducted in the spectral range of 1500 cm−1 to 1800 cm−1, confirmed the connection between the chemical properties of plasma-deposited thin films and the concentration of organic precursors in the discharge. Furthermore, the emergence of nitrile moieties (C≡N) in the FTIR spectrum at 2160 cm−1 was noted under specific experimental conditions.

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)0.000
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 score0.027
Threshold uncertainty score0.275

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.010
GPT teacher head0.253
Teacher spread0.243 · 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

Citations5
Published2024
Admission routes2
Has abstractyes

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