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Record W2261405283 · doi:10.1680/jsuin.15.00009

Effect of extractives in plasma modification of wood surfaces

2015· article· en· W2261405283 on OpenAlexaff
Jean-Michel Hardy, Mirela Vlad, Leron Vandsburger, Luc Stafford, Bernard Riedl

Bibliographic record

VenueSurface Innovations · 2015
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsUniversité de MontréalFPInnovationsUniversité Laval
Fundersnot available
KeywordsContact angleWettingSurface modificationNitrogenDielectric barrier dischargeOxygenMaterials sciencePlasmaAtmospheric pressureSolventBlack spruceAtmospheric-pressure plasmaPlasma cleaningChemical engineeringChemistryAnalytical Chemistry (journal)Composite materialDielectricEnvironmental chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

This paper presents data on wettability of freshly sanded black spruce (Picea mariana) wood surfaces after treatment in the flowing afterglow of nitrogen (N2) and nitrogen–oxygen (N2/O2) dielectric barrier discharges at atmospheric pressure. Water contact angle measurements showed that plasma-treated wood samples became more hydrophobic and less hygroscopic, with the more prominent changes observed in nitrogen–oxygen plasma mixtures. Natural ageing experiments over a time period of 14 days indicated a change in plasma-treated wood surfaces to contact angles approaching those of untreated samples. On the other hand, when lower molecular mass molecules were removed from black spruce by various solvent extraction methods, plasma-induced modification seems much less pronounced. In addition, the latter samples were much more stable over time, indicating that wood extractives play a very critical role in such instability phenomenon.

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 score0.003

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 head0.047
GPT teacher head0.317
Teacher spread0.270 · 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

Citations9
Published2015
Admission routes1
Has abstractyes

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