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Record W1445321963 · doi:10.1021/acs.langmuir.5b02794

Energetics of Molecular Excitation, Fragmentation, and Polymerization in a Dielectric Barrier Discharge with Argon Carrier Gas

2015· article· en· W1445321963 on OpenAlexafffund
Sean Watson, Bernard Nisol, Sophie Lerouge, M. R. Wertheimer

Bibliographic record

VenueLangmuir · 2015
Typearticle
Languageen
FieldMedicine
TopicPlasma Applications and Diagnostics
Canadian institutionsÉcole de Technologie SupérieureCentre Hospitalier de l’Université de MontréalPolytechnique Montréal
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of Canada
KeywordsDielectric barrier dischargePolymerizationPlasma polymerizationArgonMonomerChemistryAnalytical Chemistry (journal)DielectricPlasmaFormic acidMaterials scienceOrganic chemistryPolymerOptoelectronics

Abstract

fetched live from OpenAlex

We report experiments at atmospheric pressure (AP) using a dielectric barrier discharge (DBD) reactor designed for plasma polymerization (PP) with "monomers" at ‰ concentrations in ca.10 standard liters per minute of argon (Ar) carrier gas. We have perfected a method for measuring Eg, the energy dissipated per cycle of the applied a.c. high voltage, Va(f), but the focus here is on ΔEg, the energy difference with and without a flow, Fd, of monomer in the Ar flow, with the plasma being sustained at Va(f) = 2.8 kVrms, f = 20 kHz. From ΔEg and Fd, we derive a characteristic energy per molecule, Em (in eV), and investigate plots of Em versus Fd and 1/Fd for three model "monomers": formic, acetic, and acrylic acid. These data, along with those for lighter or heavier organic compounds, reveal novel information about energy absorption from the plasma and ensuing polymerization reactions.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.293
Threshold uncertainty score0.219

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 head0.008
GPT teacher head0.240
Teacher spread0.232 · 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 designObservational
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

Citations21
Published2015
Admission routes2
Has abstractyes

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