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Record W2088120975 · doi:10.1088/0169-5983/41/4/045506

Flow within a water droplet subjected to an air stream in a hydrophobic microchannel

2009· article· en· W2088120975 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.

Bibliographic record

VenueFluid Dynamics Research · 2009
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsUniversity of Victoria
FundersNational Science Foundation
KeywordsMicrochannelMaterials sciencePolydimethylsiloxaneParticle image velocimetryMechanicsFlow (mathematics)Two-phase flowAir waterAirflowOpen-channel flowChannel (broadcasting)Composite materialThermodynamicsNanotechnologyPhysicsElectrical engineering

Abstract

fetched live from OpenAlex

Two-phase air–water flow in an experimental model of a polymer electrolyte membrane fuel cell (PEMFC) gas distribution channel is investigated using quantitative flow imaging of the liquid phase. A rectangular gas channel model was fabricated from polydimethylsiloxane (PDMS), glass and carbon paper. A micro-digital-particle-image-velocimetry (micro-DPIV) technique was used to provide qualitative and quantitative visualizations of flow inside a water droplet adhering to the bottom wall of a gas channel and exposed to an air flow within the channel. Velocity measurements in a central cross-sectional plane inside a droplet placed in the channel are reported for a range of air flow rates. The relationships between air velocity in the channel, secondary rotational flow inside a droplet, droplet deformation and contact angle hysteresis are examined. The resulting flow fields provide insight into the interactions between the air and water flows that occur at the gas–liquid interface.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.490
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.002

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 head0.040
GPT teacher head0.334
Teacher spread0.294 · 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