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Record W3019843929 · doi:10.1002/admi.201902110

Sprayable, Superhydrophobic, Electrically, and Thermally Conductive Coating

2020· article· en· W3019843929 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAdvanced Materials Interfaces · 2020
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsYork UniversityUniversity of AlbertaUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceCoatingWettingComposite materialContact angleElectrical conductorHysteresisSuperhydrophobic coatingElectrical resistivity and conductivityCarbon nanofiberCarbon nanotube

Abstract

fetched live from OpenAlex

Abstract A simple to make, multifunctional, heatable, and sprayable superhydrophobic and electrically conductive coating is developed by dispersing carbon nanofibers (CNFs) into a water repelling polymer matrix. The developed coating exhibits an average static and hysteresis contact angles of 160° and 5°, respectively. An electrical conductivity of 1100 S m −1 and a thermal conductivity of 0.001 W m −1 K −1 are obtained with a sample of dimensions: 3 cm × 1 cm × 20 µm. A 12 µm thick coating under an average electric current of 75 mA reaches to a surface temperature of more than 140 °C for a dry coating. The coating when in contact with ice or water (water at 25 °C for 300 h, and at 85 °C for less than an hour) does not show a deterioration of wetting performance. Furthermore, it is shown how this coating can be used to mitigate the ice formation on cold surfaces. The ability of application of the developed coating to various substrates is also shown.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.005
Threshold uncertainty score1.000

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.023
GPT teacher head0.247
Teacher spread0.224 · 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