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Record W4244885947 · doi:10.1002/9783527687596.ch10

Bioinspired Icephobicity

2018· other· en· W4244885947 on OpenAlexaff
Ri Li

Bibliographic record

Venuenot available
Typeother
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsIcingWettingContact angleDrop (telecommunication)Materials scienceSessile drop techniqueSubstrate (aquarium)SupercoolingSolid surfaceNucleationComposite materialNanotechnologyChemistryGeologyChemical physicsMeteorologyMechanical engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

Many flora and fauna surfaces in nature exhibit superhydrophobic and self-cleaning properties. The fabricated flora and fauna surfaces need to be characterized to assess the hydrophobicity and icephobicity. The contact angle of water sessile drops is measured to evaluate the surface wetting properties, while icing of individual water drops on surfaces is used to evaluate the icephobicity. This chapter focuses on the relationship between surface wetting and the icing of water drops, rather than on the surface design and fabrication. It then discusses the fundamentals involved in the water-to-ice nucleation inside a water drop located on a substrate. The chapter further considers an ideal case, for which the water drop is pure and the substrate surface is perfectly smooth. The icing characteristics of surfaces have been studied by investigating the freezing of water drops on different surfaces. The icing on surfaces is also studied by observing the impact of water drops onto supercooled surfaces.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.012

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)0.0040.001

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 head0.249
Teacher spread0.229 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations4
Published2018
Admission routes1
Has abstractyes

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