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Record W3093281335 · doi:10.1002/ppap.202000158

Modification of microfibrillated cellulosic foams in a dielectric barrier discharge at atmospheric pressure

2020· article· en· W3093281335 on OpenAlexafffund
Louis‐Félix Meunier, Jacopo Profili, Sara Babaei, Siavash Asadollahi, A. Sarkissian, Annie Dorris, Stephanie Beck, Nicolas Naudé, Luc Stafford

Bibliographic record

VenuePlasma Processes and Polymers · 2020
Typearticle
Languageen
FieldEngineering
TopicElectrohydrodynamics and Fluid Dynamics
Canadian institutionsFPInnovationsPlasmionique (Canada)Université de Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceDielectric barrier dischargeAtmospheric pressureComposite materialCelluloseDielectricCellulosic ethanolHexamethyldisiloxaneHeliumChemical engineeringPlasmaChemistryOrganic chemistryOptoelectronics

Abstract

fetched live from OpenAlex

Abstract This study explores the plasma‐induced modification of microfibrillated cellulose (MFC) foams in a plane‐to‐plane atmospheric‐pressure dielectric barrier discharge with helium and hexamethyldisiloxane as carrier and precursor gases, with and without a gas gap. When the foam took up all of the gas gap, filamentary discharges were generated and burn‐like damage was produced. This resulted in highly inhomogeneous deposits having both hydrophilic and hydrophobic domains. MFC foams taking up only a portion of the gas gap volume generated a homogeneous discharge and induced cellulose defibrillation. They generated effective hydrophobic surfaces on both the top and bottom of the foams. Oleophilicity measurements were also carried out, which support the possibility of an effective separation of oily wastewater using a green and renewable material.

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 score0.000
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)0.0000.000

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 head0.006
GPT teacher head0.184
Teacher spread0.178 · 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

Citations11
Published2020
Admission routes2
Has abstractyes

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