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Record W2083093300 · doi:10.1063/1.4821356

A dynamic model of the electroosmotic droplet switch

2013· article· en· W2083093300 on OpenAlexaff
Dominik P. J. Barz, Paul H. Steen

Bibliographic record

VenuePhysics of Fluids · 2013
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsQueen's University
Fundersnot available
KeywordsBistabilityPhysicsMechanicsCapillary actionFlow (mathematics)Volume (thermodynamics)MicrofluidicsThermodynamicsOptoelectronics

Abstract

fetched live from OpenAlex

A capillary switch is a bi-stable system of liquid/gas interfaces with a trigger to toggle back and forth between the two stable equilibrium states. We use an electro-osmotic pump as trigger. The pump, consisting of two electrodes and a porous substrate arranged between the droplets, moves volume between the droplets. This bistable system is called an electro-osmotic droplet switch. With the pump off, for low total volumes, the stable states are a pair of identical sub-hemispherical droplets or, for large enough total volumes, a large-small droplet configuration (two mirror-symmetric states). With the pump on, these stationary states are shifted and, if the pump strength is too great, there are no stationary states at all. In this article, we report the pump-on behavior as a modification of the pump-off behavior. To build the dynamic model of the system, we first develop a characterization of the electro-osmotic pump in the spirit of the Blake-Kozeny correlation for viscous flow through a packed bed. The control-volume model is based on center-of-mass motion. Model predictions compare favorably to observation.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.187
Teacher spread0.181 · 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 designSimulation or modeling
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

Citations7
Published2013
Admission routes1
Has abstractyes

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