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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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 (N 2 ) and nitrogen–oxygen (N 2 /O 2 ) 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.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.034
Threshold uncertainty score0.495

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.047
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