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The Relationship between Water Wetting and Ice Adhesion

2009· article· en· W2028748742 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

VenueJournal of Adhesion Science and Technology · 2009
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsIcingContact angleWettingMaterials sciencePolycarbonateAdhesionComposite materialSurface roughnessSurface finishIce formationMeteorologyGeologyAtmospheric sciences

Abstract

fetched live from OpenAlex

Ice accretion on aircraft leads to difficulties in aircraft flight control due to weight increase and change in weight distribution. Conventionally these difficulties are overcome using anti-icing or de-icing products, such as freezing depressants and heating devices. A more cost effective way to solve these problems would be to use ice repellent surfaces (ice-phobic). As a first step in this direction the relationship between water wettability and ice adhesion was investigated. Using the appropriate chemistry and tailoring the surface roughness a variety of polycarbonate-coated surfaces were created: these included ultra-hydrophilic and ultra-hydrophobic surfaces and surfaces with surface properties in between the extreme ultra-surfaces. Ice adhesion tests and contact angle measurements indicated that the higher the contact angle the lower is the adhesion of ice. The best results were obtained in the case of ultra-hydrophobic surface treatment that led to an 18 fold decrease in ice adhesion compared to the untreated aluminum surface.

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)0.003
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 score0.081
Threshold uncertainty score0.681

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.039
GPT teacher head0.298
Teacher spread0.259 · 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