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Record W2889352768 · doi:10.1063/1.5039443

Shedding of multiple sessile droplets by an airflow

2018· article· en· W2889352768 on OpenAlexafffund
Aysan Razzaghi, Sayed Abdolhossein Banitabaei, Alidad Amirfazli

Bibliographic record

VenuePhysics of Fluids · 2018
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of CanadaEuropean Space AgencyNorth Atlantic Treaty Organization
KeywordsAirflowLaminar flowSquare (algebra)WettingDiamondPhysicsMechanicsFlow (mathematics)Substrate (aquarium)Materials scienceGeometryComposite materialThermodynamicsMathematicsGeology

Abstract

fetched live from OpenAlex

Shedding of multiple sessile droplets by an airflow in triangle, square, reversed triangle, and diamond arrangements is examined. The interaction of the flow around the sessile droplets is found to be influenced by the type of the arrangement and the spacing of the sessile droplets in each arrangement. Consequently, the minimum airflow velocity required to shed the droplet (Ucr) also changes. Water droplets of 5 and 10 μl were used on both hydrophilic and hydrophobic surfaces in a laminar airflow. In general, at the minimum spacing, the highest increase in Ucr for the upstream droplet(s) (compared with that for a single droplet) was observed for the triangle arrangement (∼40%), followed by the diamond, reversed triangle, and square arrangements. Increasing the spacing resulted in a reduction of the Ucr for all the arrangements, except for the square arrangement where increasing the spacing does not show a substantial change in Ucr. Neither the size of the droplets nor the wettability of the substrate was found to significantly affect the amount of the change in the Ucr.

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)0.000
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 score0.015
Threshold uncertainty score0.357

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 head0.024
GPT teacher head0.275
Teacher spread0.252 · 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.

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

Citations16
Published2018
Admission routes2
Has abstractyes

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