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Record W3217511283 · doi:10.1039/d1sm01399h

On the icephobicity of damage-tolerant superhydrophobic bulk nanocomposites

2021· article· en· W3217511283 on OpenAlexafffund
E. Vazirinasab, K. Maghsoudi, Gelareh Momen, Reza Jafari

Bibliographic record

VenueSoft Matter · 2021
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsUniversité du Québec à Chicoutimi
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSandpaperMaterials scienceNanocompositeComposite materialAbrasion (mechanical)Silicone rubberFumed silicaVulcanizationNatural rubber

Abstract

fetched live from OpenAlex

, high freezing delay time and low ice adhesion strength. We verified the extended duration of superhydrophobicity within the bulk nanocomposite using sandpaper abrasion, severe cutter scratching, tape peeling, and water-jet impacts. This study represents the first evaluation, to the best of our knowledge, of the icephobic properties of both the surface and bulk of the produced nanocomposite subjected to several cycles of sandpaper abrasion. Interestingly, even after multiple abrasion cycles, the samples demonstrated considerably low ice adhesion strength confirming their bulk icephobicity. In a nutshell, our findings are very promising for the fabrication of mechanically robust icephobic materials.

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 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 score0.017
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)0.0210.004

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 head0.015
GPT teacher head0.226
Teacher spread0.211 · 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; both teacher heads agree on what is shown here.

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

Citations8
Published2021
Admission routes2
Has abstractyes

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