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Record W3048915444 · doi:10.1002/ppap.202000091

Analysis of transport phenomena during plasma deposition of hydrophobic coatings on porous cellulosic substrates in plane‐to‐plane dielectric barrier discharges at atmospheric pressure

2020· article· en· W3048915444 on OpenAlexafffund
Sara Babaei, Jacopo Profili, Siavash Asadollahi, Andranik Sarkassian, Annie Dorris, Stephanie Beck, Luc Stafford

Bibliographic record

VenuePlasma Processes and Polymers · 2020
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsFPInnovationsPlasmionique (Canada)Université de Montréal
FundersNatural Sciences and Engineering Research Council of CanadaFPInnovationsPublic Risk Management Association
KeywordsHexamethyldisiloxaneMaterials scienceDielectric barrier dischargeAtmospheric pressureCoatingContact angleComposite materialAtmospheric-pressure plasmaChemical engineeringDielectricAnalytical Chemistry (journal)PlasmaChemistryOrganic chemistryOptoelectronics

Abstract

fetched live from OpenAlex

Abstract In this study, four‐layer stacks of bleached, unrefined Kraft papers are exposed to a plane‐to‐plane dielectric barrier discharge at atmospheric pressure operated in a glow‐like discharge regime and sustained in a mixture of hexamethyldisiloxane and helium gases. Scanning electron microscopy confirms that the plasma‐deposited coating follows the roughness of the porous cellulosic substrate. Whereas fairly spatially homogeneous coatings are obtained on silicon, significant variations of the coatings' thickness, organic content, and water contact angle are observed along the gas flow lines on the Kraft paper. These effects are even more important in the presence of substrate outgassing. In addition, plasma‐generated species sequentially penetrate down to the fourth layer of the cellulosic substrate, rendering surfaces hydrophobic with very low water absorption kinetics.

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 head0.009
GPT teacher head0.203
Teacher spread0.194 · 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

Citations19
Published2020
Admission routes2
Has abstractyes

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