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Versatile Snail-Inspired Superamphiphobic Coatings with Repeatable Adhesion and Recyclability

2020· dataset· en· W3125800032 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.

Bibliographic record

VenueAuthorea · 2020
Typedataset
Languageen
FieldEngineering
TopicAdhesion, Friction, and Surface Interactions
Canadian institutionsQueen's University
FundersNational Science and Technology Planning Project
KeywordsMaterials scienceAdhesionNanotechnologyCoatingNanomaterialsAdhesiveRigidity (electromagnetism)Composite materialLayer (electronics)

Abstract

fetched live from OpenAlex

Superamphiphobic surfaces with extreme repellency toward both water and oily liquids have been developed from various nanocomposites with fluorinated compounds. However, the inherent rigidity and low-surface-energy of these composites restrict their adhesion and practical application in adjusting the surface wettabilities of materials. Here we report a strategy to create hybrid superamphiphobic coating with rapid contact adhesion to various kinds of substrates, strong and controllable adhesive strength, unprecedented capability of mechanical deformations, facile removal, repeatable adhesion, and simple recyclization. Our approach, inspired by snail's ideal combination of hard shell and soft epiphragm, is versatile and industrially-viable because we use the hydrogel primer to bond the fluorinated nanoparticle finish and substrates. Considering the unique characteristics of these coatings as well as the wide range of available hydrogels and nanomaterials that can be used via this approach, we envision that this snail-inspired strategy will facilitate the development and large-scale production of superamphiphobic coatings.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.004
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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.012
GPT teacher head0.214
Teacher spread0.202 · 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