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Retarding spreading of surfactant drops on solid surfaces: Interplay between the Marangoni effect and capillary flows

2020· article· en· W3082415335 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

VenuePhysical Review Fluids · 2020
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsMarangoni effectCapillary actionPulmonary surfactantDrop (telecommunication)Marangoni numberSolid surfaceMechanicsMaterials scienceContact angleSurface tensionChemistryChemical physicsComposite materialThermodynamicsConvectionPhysics

Abstract

fetched live from OpenAlex

Early time spreading of a water drop on a hydrophilic surface is characterized by the wetted radius which grows linearly in time. We report the unexpected result that the initial spread of surfactant-laden drops is impeded by Marangoni stresses, leading to a large increase in total spreading time. The nonuniform distribution of surfactants at the interface generates Marangoni stresses before the drop-solid contact suppresses film drainage and droplet expansion. Our experiments show that, remarkably, surfactants delay the initial fast motion of the three-phase contact lines.

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 score0.018
Threshold uncertainty score0.563

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.025
GPT teacher head0.318
Teacher spread0.293 · 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