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Record W2081514964 · doi:10.1115/fedsm-icnmm2010-31201

Sessile-Water-Droplet Contact Angle: the Effect of Adsorption

2010· article· en· W2081514964 on OpenAlexaff
Hadi Ghasemi, C. A. Ward

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsContact angleSurface tensionCurvatureAdsorptionTension (geology)WettingMaterials scienceCapillary surfaceSolid surfaceMechanicsThermodynamicsWater vaporChemistryComposite materialGeometryChemical physicsPhysics

Abstract

fetched live from OpenAlex

A method has been recently proposed for determining the surface tension of solid-vapor interfaces. The proposed method was used in conjunction with Gibbsian thermodynamics to investigate both analytically and experimentally the possible role of line tension in determining the contact angle of sessile-water-droplets. After forming a sessile-water-droplet in a closed system, its contact angle was determined by measuring the curvature of three-phase contact line and the height of the axisymmetric droplet on its centerline. The total number of the moles in the closed system was determined from the minimum in the Helmholtz function. The total number of moles in the system was then changed to a new value and the system allowed to come to equilibrium again. The contact angle in the new equilibrium condition could be measured and predicted by taking the adsorption at the solid-liquid and solid-vapor interfaces into account but with line tension completely neglected. The predicted values of contact angle are in closed agreement with those measured indicating line tension plays no role in determining the contact angle of mm-sized water droplets on a polished Cu surface. The surface tension of the solid-vapor interface was approximately constant and equal to the surface tension of adsorbing fluid; that is, the Young equation could be simplified.

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.

How this classification was reachedexpand

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 categoriesInsufficient 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 score0.995

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)0.0050.001

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 head0.242
Teacher spread0.233 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2010
Admission routes1
Has abstractyes

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