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Record W2111439042 · doi:10.1109/icmens.2005.92

Numerically Modeled Dynamic Response of Perfect and Leaky Dielectric Droplets in an Electric Field

2006· article· en· W2111439042 on OpenAlexaff
Graeme Supeene, Charles Robert Koch, S. Bhattacharjee

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectrowetting and Microfluidic Technologies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsElectrowettingDielectricElectric fieldFinite element methodMechanicsDeformation (meteorology)MicrofluidicsTransient (computer programming)Materials scienceNonlinear systemField (mathematics)PhysicsComputer scienceNanotechnologyOptoelectronicsComposite material

Abstract

fetched live from OpenAlex

Summary form only given. A dielectric liquid droplet in an immiscible dielectric medium will deform when subjected to an external electric field. This effect is a component of the motion seen in electrically actuated droplets in a lab-on-a-chip setting, and is being studied in order to provide a basis for modeling electrowetting actuation behaviour. It is, however, a widely studied phenomenon in its own right, and can be employed in various emulsion technologies. The present work is a finite element analysis of both the perfect and leaky dielectric models, taking into account the time dependence of the deformation and the nonlinearity encountered when the deformation becomes large. In order to assess the accuracy of the model, comparisons are made with the classical small-deformation analytic results and with recent finite element results. The effects of the fluid properties and the applied field on the transient response and the resulting deformation are observed, and the transient response in particular is analyzed in order to provide a basis for dynamic control of the system. This could lead to a strategy for reliably controlling the behaviour of droplets under electrowetting actuation on a microfluidic chip.

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 score0.120
Threshold uncertainty score0.521

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.003
GPT teacher head0.197
Teacher spread0.194 · 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

Citations0
Published2006
Admission routes1
Has abstractyes

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