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Record W2941681768 · doi:10.1126/science.aav1266

Low–interfacial toughness materials for effective large-scale deicing

2019· article· en· W2941681768 on OpenAlexfundno aff
Kevin Golovin, Abhishek Dhyani, M.D. Thouless, Anish Tuteja

Bibliographic record

VenueScience · 2019
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsnot available
FundersAir Force Office of Scientific ResearchOffice of Naval ResearchNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsToughnessMaterials scienceAirplaneAdhesionComposite material

Abstract

fetched live from OpenAlex

Ice accretion has adverse effects on a range of commercial and residential activities. The force required to remove ice from a surface is typically considered to scale with the iced area. This imparts a scalability limit to the use of icephobic coatings for structures with large surface areas, such as power lines or ship hulls. We describe a class of materials that exhibit a low interfacial toughness with ice, resulting in systems for which the forces required to remove large areas of ice (a few square centimeters or greater) are both low and independent of the iced area. We further demonstrate that coatings made of such materials allow ice to be shed readily from large areas (~1 square meter) merely by self-weight.

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 score0.003
Threshold uncertainty score0.009

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.010
GPT teacher head0.269
Teacher spread0.260 · 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

Citations571
Published2019
Admission routes1
Has abstractyes

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