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Record W1995981644 · doi:10.1038/srep07454

Under-water superoleophobicity of fish scales

2014· article· en· W1995981644 on OpenAlexafffund
Prashant R. Waghmare, Naga Siva Kumar Gunda, Sushanta K. Mitra

Bibliographic record

VenueScientific Reports · 2014
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsYork UniversityTransport CanadaUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Alberta
KeywordsFish <Actinopterygii>Contact angleMucusLayer (electronics)Materials scienceNanotechnologyScale (ratio)Chemical engineeringFisheryBiologyEcologyComposite materialPhysics

Abstract

fetched live from OpenAlex

Recent surge in the development of superhydrophobic/superoleophobic surfaces has been motivated by surfaces like fish scales that have hierarchical structures, which are believed to promote water or oil repellency. In this work, we show that the under-water oil repellency of fish scales is entirely due to the mucus layer formation as part of its defense mechanism, which produces unprecedented contact angle close to 180°. We have identified the distinct chemical signatures that are responsible for such large contact angle, thereby making fish scale behave highly superoleophobic inside the water medium. In absence of the mucus layer, it is found that the contact angle decreases quite dramatically to around 150°, making it less oleophobic, the degree of such oleophobicity can then be contributed to its inherent hierarchical structures. Hence, through this systematic study, for the first time we have conclusively shown the role of the fish's mucus layer to generate superoleophobicity and negate the common notion that hierarchical structure is the only reason for such intrinsic behavior of the fish scales.

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 head0.019
GPT teacher head0.237
Teacher spread0.218 · 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

Citations94
Published2014
Admission routes2
Has abstractyes

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