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Record W1891753293 · doi:10.1063/1.4934961

Effects of surface wettability on fast liquid transfer

2015· article· en· W1891753293 on OpenAlexafffund
H. Chen, Tian Tang, Alidad Amirfazli

Bibliographic record

VenuePhysics of Fluids · 2015
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsYork UniversityUniversity of Alberta
FundersAlberta Innovates - Technology FuturesXerox Foundation
KeywordsWettingPhysicsInertiaMechanicsContact angleSurface (topology)Wetting transitionViscous liquidBoundary value problemThermodynamicsClassical mechanicsGeometry

Abstract

fetched live from OpenAlex

A systematic experimental study was performed to understand the role of surface contact angles in affecting the process of fast liquid transfer. Surfaces with different wettabilities were used, and the transfer ratio (α, the amount of liquid transferred to the acceptor surface over the total amount of liquid) was measured for each pair of surfaces. A numerical model based on the volume of fluid method was developed to help understand the experimental results. The surface wettability was shown to significantly affect the boundaries between three regimes based on stretching speeds: quasi-static (surface force dominated), transition (surface/viscous/inertia forces all important) and dynamic (viscous/inertia forces dominated). Specifically, the values of the boundary speeds were found to increase with |α0 − 0.5|, where α0 is the transfer ratio in the quasi-static regime, and α0 is governed by the surface receding contact angles. Based on our results, an empirical equation to describe the transfer ratio as function of stretching speed was proposed. This equation can also be used as a prediction tool for the value of α for a fast transfer system.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.028
GPT teacher head0.257
Teacher spread0.229 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations39
Published2015
Admission routes2
Has abstractyes

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