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Record W2936076287 · doi:10.1039/c9cp01567a

Energy conversion efficiency in low- and atmospheric-pressure plasma polymerization processes with hydrocarbons

2019· article· en· W2936076287 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenuePhysical Chemistry Chemical Physics · 2019
Typearticle
Languageen
FieldMedicine
TopicPlasma Applications and Diagnostics
Canadian institutionsPolytechnique Montréal
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of Canada
KeywordsAtmospheric pressurePolymerizationAtmospheric-pressure plasmaPlasmaPlasma polymerizationLow energyEnergy transformationChemistryPhotochemistryMaterials scienceOrganic chemistryThermodynamicsAtomic physicsPolymerMeteorologyPhysicsNuclear physics

Abstract

fetched live from OpenAlex

Since the earliest days of this field there has been an interest in correlating the structure of plasma polymer (PP) coatings with deposition parameters, most particularly with energy input per monomer molecule, Em. Both of our laboratories have developed methods for measuring Em (or somewhat equivalent, the apparent activation energy, Ea) in low- (LP) and atmospheric-pressure (AP) electrical discharge plasmas. We recently proposed a new parameter, energy conversion efficiency (ECE), which for the first time permits direct comparison of LP and AP experiments. Here, we report the case of small hydrocarbons, namely acetylene, ethylene and methane. "Critical" Em (or Ea) values that demarcate ECE regimes separating different reaction mechanisms are found to agree remarkably well, and to correlate with specific reaction mechanisms, including dissociation, recombination, gas-phase oligomerization, and surface processes.

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.

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 score0.061
Threshold uncertainty score0.688

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 head0.003
GPT teacher head0.196
Teacher spread0.193 · 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