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Record W2073793249 · doi:10.1063/1.2193650

Liquid transport based on electrostatic deformation of fluid interfaces

2006· article· en· W2073793249 on OpenAlexaff
Januk Swarup Aggarwal, A. Kotlicki, Michele Mossman, Lorne Whitehead

Bibliographic record

VenueJournal of Applied Physics · 2006
Typearticle
Languageen
FieldEngineering
TopicElectrowetting and Microfluidic Technologies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsElectrowettingWettingElectric fieldContact angleHeat transferMaterials scienceElectrostaticsVolume of fluid methodElectrohydrodynamicsSessile drop techniqueMechanicsFluid dynamicsChemical physicsDrop (telecommunication)Oil dropletSubstrate (aquarium)Digital microfluidicsSurface energyFlow (mathematics)Composite materialChemistryOptoelectronicsMechanical engineering

Abstract

fetched live from OpenAlex

We have developed a method for moving liquid along a surface using purely electrostatic effects, without the need for mechanically moving parts. In this approach, liquid drops are confined to specific regions of the substrate by a printed pattern that has the appropriate hydrophilic and hydrophobic wetting characteristics. Using a variation of well-known electrowetting techniques, the shape of the droplet can be changed by applying an electric field that changes the surface energy relationship. Specifically, a bead of oil confined to a hydrophobic region of the surface can be pinched into drops using localized electrostatic fields, and by changing the applied field pattern, these oil drops can be moved to cause net liquid flow. This approach may have useful applications in heat transfer. We have demonstrated that with drops traveling at approximately 15cm∕s, it is possible to transport heat more effectively than by using an equivalent volume of solid copper.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.004
GPT teacher head0.180
Teacher spread0.176 · 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

Citations8
Published2006
Admission routes1
Has abstractyes

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