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Record W2020060018 · doi:10.1080/00218464.2014.934361

Adhesion of Wax Droplets to Porous Polymer Surfaces

2014· article· en· W2020060018 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

VenueThe Journal of Adhesion · 2014
Typearticle
Languageen
FieldEngineering
TopicAdhesion, Friction, and Surface Interactions
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceWaxComposite materialPolyethyleneContact areaAdhesionAdhesivePorosityContact angleUltimate tensile strengthPolymerSubstrate (aquarium)Contact mechanicsLayer (electronics)

Abstract

fetched live from OpenAlex

An experimental study was done to measure the force of adhesion of molten wax droplets, 3.1 mm in diameter, dropped from heights ranging from 20 to 50 mm onto porous polyethylene and Teflon surfaces. The Teflon surface had 0.25-mm holes drilled in it and the three polyethylene surfaces had random pores with mean diameters of 35, 70, and 125 μm, respectively. The force required to remove the solidified ink from the surface was measured using a pull test. Wax splats were attached to the substrate by both adhesive and cohesive forces. The cohesive force was calculated by multiplying the ultimate tensile strength of the wax (2.2 MPa) by the cross-sectional area of the wax penetrating into surface pores. The adhesive force was obtained by multiplying the contact area between the wax and substrate by the adhesion strength per unit area, estimated to be 0.2 MPa for polyethylene and 0.1 MPa for Teflon surfaces. The contact area between splats and the substrate was typically about 60–70% of the splat area. The edges of splats lifted up, preventing complete contact.

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)0.001
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 score0.030
Threshold uncertainty score0.419

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.009
GPT teacher head0.223
Teacher spread0.214 · 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