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Record W3161685166 · doi:10.1002/ppap.202000248

Fourier‐transform infrared spectroscopy of ethyl lactate decomposition and thin‐film coating in a filamentary and a glow dielectric barrier discharge

2021· article· en· W3161685166 on OpenAlexafffund
Natalia Milaniak, Gaétan Laroche, F. Massines

Bibliographic record

VenuePlasma Processes and Polymers · 2021
Typearticle
Languageen
FieldMedicine
TopicPlasma Applications and Diagnostics
Canadian institutionsUniversité LavalHôpital Saint-François d'Assise
FundersNatural Sciences and Engineering Research Council of CanadaAgence Nationale de la Recherche
KeywordsGlow dischargeDielectric barrier dischargeAnalytical Chemistry (journal)Fourier transform infrared spectroscopyDielectricMaterials scienceInfrared spectroscopyThin filmChemical vapor depositionPlasmaPlasma-enhanced chemical vapor depositionDecompositionChemistryOrganic chemistryChemical engineeringNanotechnologyOptoelectronics

Abstract

fetched live from OpenAlex

Abstract Glow and filamentary regimes of atmospheric pressure plasma‐enhanced chemical vapor deposition in a planar dielectric barrier discharge configuration were compared for thin‐film deposition from ethyl lactate (EL). EL decomposition in the plasma phase and thin‐film composition were both characterized by Fourier‐transform infrared spectroscopy. EL chemical bonds' concentration along the gas flow decreases progressively in the glow dielectric barrier discharge (GDBD), whereas it drastically oscillates in the filamentary dielectric barrier discharge (FDBD), with values higher than that of the initial mixture. EL decomposition route depends on the discharge regime, as the decrease of the concentration of the different investigated bonds is different for an identical amount of energy provided to EL molecules. CO2 is systematically formed reaching concentrations of 25 and 40 ppm, respectively, in FDBD and GDBD.

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 score0.002

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 head0.007
GPT teacher head0.260
Teacher spread0.253 · 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

Citations10
Published2021
Admission routes2
Has abstractyes

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