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Record W3178967349 · doi:10.1002/ppap.201800186

Energetics of reactions in a dielectric barrier discharge with argon carrier gas: VII anhydrides

2019· article· en· W3178967349 on OpenAlexafffund
J. Mertens, Sean Watson, Bernard Nisol, M. R. Wertheimer, François Reniers

Bibliographic record

VenuePlasma Processes and Polymers · 2019
Typearticle
Languageen
FieldMedicine
TopicPlasma Applications and Diagnostics
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of CanadaFonds de recherche du Québec – Nature et technologiesFédération Wallonie-Bruxelles
KeywordsDielectric barrier dischargeMonomerArgonMoleculePolymerAnalytical Chemistry (journal)Degree of unsaturationDielectricChemistryOxygenX-ray photoelectron spectroscopyActivation energyDeposition (geology)Materials sciencePhysical chemistryPolymer chemistryChemical engineeringOrganic chemistry

Abstract

fetched live from OpenAlex

The method developed for fundamental understanding of energetic exchanges between monomer molecules and argon carrier gas in a dielectric barrier discharge (DBD) has previously proven merit. In this seventh article related to this methodology, research has been extended to a new family of precursors: anhydrides. Monomers (typically ‰) were mixed with 10 slm of Ar in a 20 kHz, 8 kV (peak‐to‐peak) DBD corresponding to an energy per cycle of 1600 μJ for pure Ar. For each of the investigated monomers Em, the energy absorbed per molecule was plotted as a function of precursor flow rate. The influence of chemical structure (C/O ratio, unsaturation) has been investigated and compared with previous data for other types of precursors. Thin plasma polymer coatings were deposited; in addition to measuring deposition rates, we also present relationships between Em values, spectro‐ellipsometric and FTIR measurements.

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 score0.002
Threshold uncertainty score0.005

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)0.0020.000

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 head0.006
GPT teacher head0.224
Teacher spread0.218 · 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

Citations4
Published2019
Admission routes2
Has abstractyes

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