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Record W2157897703 · doi:10.1002/mame.201300112

Biomimetic Micro‐<scp>P</scp>atterning of Epoxy Coatings for Enhanced Surface Hydrophobicity and Low Friction

2013· article· en· W2157897703 on OpenAlex
Brendan McDonald, Hamed Shahsavan, Boxin Zhao

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

VenueMacromolecular Materials and Engineering · 2013
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsEpoxyMaterials scienceAdhesionRigidity (electromagnetism)Composite materialPolymerNanotechnologyFlexibility (engineering)Surface modificationChemical engineering

Abstract

fetched live from OpenAlex

Biomimetic micro‐structures are fabricated with a two‐stage pattern transfer process on conventional epoxy coatings. The pattern transfer process uses a soft‐polymer negative stamp, where the flexibility of the stamp allows for easy conformation to both flat and curved surfaces. The hydrophobicity and friction behavior of the micro‐patterned epoxy coatings are systematically investigated, showing that surface patterning can be used as an effective way to improve hydrophobicity while reducing the surface adhesion and friction without a loss of the structural integrity or rigidity typical of epoxy coatings. This work demonstrates a feasible process for the utilization of biomimetic micro‐patterns within large‐scale industrial applications to improve the performance of conventional epoxy 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 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 score0.017
Threshold uncertainty score0.859

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 integrity0.0000.000
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 head0.006
GPT teacher head0.193
Teacher spread0.187 · 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