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Record W2333315212 · doi:10.1021/la104277q

Superhydrophobic Surfaces: Are They Really Ice-Repellent?

2010· article· en· W2333315212 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

VenueLangmuir · 2010
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsUniversité du QuébecUniversity of British Columbia
Fundersnot available
KeywordsIcingGlazeMaterials scienceCondensationAdhesionIce nucleusComposite materialWork (physics)MeteorologyChemistryEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

This work investigates the anti-ice performance of various superhydrophobic surfaces under different conditions. The adhesion strength of glaze ice (similar to that deposited during "freezing rain") is used as a measure of ice-releasing properties. The results show that the ice-repellent properties of the materials deteriorate during icing/deicing cycles, as surface asperities appear to be gradually damaged. It is also shown that the anti-icing efficiency of superhydrophobic surfaces is significantly lower in a humid atmosphere, as water condensation both on top of and between surface asperities takes place, leading to significantly larger values of ice adhesion strength. This work thus shows that superhydrophobic surfaces are not always ice-repellent and their use as anti-ice materials may therefore be limited.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.304
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

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)0.0040.004

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 head0.016
GPT teacher head0.242
Teacher spread0.225 · 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