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Record W2564598342 · doi:10.1002/ppap.201600172

Influence of substrate outgassing on the plasma properties during wood treatment in He dielectric barrier discharges at atmospheric pressure

2016· article· en· W2564598342 on OpenAlexafffund
O. Levasseur, Reetesh Kumar Gangwar, Jacopo Profili, Nicolas Naudé, Nicolas Ghérardi, Luc Stafford

Bibliographic record

VenuePlasma Processes and Polymers · 2016
Typearticle
Languageen
FieldMedicine
TopicPlasma Applications and Diagnostics
Canadian institutionsUniversité de Montréal
FundersFonds de recherche du Québec – Nature et technologiesUniversité de MontréalAgence Nationale de la Recherche
KeywordsOutgassingAtmospheric pressurePlasmaDielectric barrier dischargeDielectricMaterials scienceAnalytical Chemistry (journal)Radiative transferMetastabilityAtomic physicsElectron temperatureSubstrate (aquarium)Atmospheric-pressure plasmaChemistryIonEnvironmental chemistryOpticsMeteorology

Abstract

fetched live from OpenAlex

This work analyzes the effect of wood outgassing on the properties of He dielectric barrier discharges operated in a homogenous regime. Over the 60 min wood treatment investigated, the discharge current increased by almost a factor of two due to the release of air and humidity. Based on optical emission spectroscopy and collisional‐radiative modeling, these effects further yield to an increase of the electron temperature and a significant decrease of the relative number density of He metastable atoms. Experiments were also performed in presence of trace amounts of N2, O2, and dry air to simulate wood outgassing. The amount of air released from the wood sample decreased with plasma treatment time, with a two‐slope first‐order decay behavior.

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 head0.012
GPT teacher head0.220
Teacher spread0.208 · 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

Citations18
Published2016
Admission routes2
Has abstractyes

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