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Record W2223334598 · doi:10.1063/1.4939245

Magnetohydrodynamic actuation of droplets for millimetric planar fluidic systems

2016· article· en· W2223334598 on OpenAlexafffund
Ali Ahmadi, C. M. McDermid, Loïc Markley

Bibliographic record

VenueApplied Physics Letters · 2016
Typearticle
Languageen
FieldEngineering
TopicElectrowetting and Microfluidic Technologies
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFluidicsElectrowettingDigital microfluidicsMicrofluidicsMagnetohydrodynamic driveMaterials scienceMicroscale chemistryMagnetElectrohydrodynamicsMechanicsElectrodeHysteresisPlanarMagnetic fieldVoltageNanotechnologyDielectricOptoelectronicsMagnetohydrodynamicsMechanical engineeringPhysicsElectrical engineeringCondensed matter physicsComputer science

Abstract

fetched live from OpenAlex

In this work, a magnetohydrodynamic method is proposed for the actuation of droplets in small-scale planar fluidic systems, providing an alternative to commonly used methods such as electrowetting-on-dielectric. Elementary droplet-based operations, including transport, merging, and mixing, are demonstrated. The forces acting on millimetric droplets are carefully investigated, with a primary focus on the magnetic actuation force and on the unbalanced capillary forces that arise due to hysteresis. A super-hydrophobic channel is 3D printed to guide the droplets, with thin wires installed as contact electrodes and permanent magnets providing a static magnetic field. It is shown that droplet motion is enhanced by increasing the droplet size and minimizing the electrode contact surface. The effects of channel geometry on threshold voltage and minimum moveable droplet volume are characterized. Finally, the presence of electrolysis is investigated and mitigating strategies are discussed.

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 score0.250
Threshold uncertainty score0.468

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 head0.006
GPT teacher head0.180
Teacher spread0.174 · 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

Citations4
Published2016
Admission routes2
Has abstractyes

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