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Record W4361275166 · doi:10.3390/mi14040758

Multiphase Actuation of AC Electrothermal Micropump

2023· article· en· W4361275166 on OpenAlexafffund
Stirling Cenaiko, Thomas Lijnse, Colin Dalton

Bibliographic record

VenueMicromachines · 2023
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaAlberta InnovatesCMC Microsystems
KeywordsMicropumpMultiphysicsVolumetric flow rateMaterials sciencePhase (matter)Two-phase flowElectrokinetic phenomenaMechanicsDielectrophoresisMicrofluidicsFlow (mathematics)Fluid dynamicsRange (aeronautics)ElectrohydrodynamicsElectric fieldFinite element methodPhysicsNanotechnologyThermodynamicsComposite material

Abstract

fetched live from OpenAlex

Electrothermal micropumps apply an AC electric field to a conductive fluid within the range of 10 kHz-1 MHz to generate fluid flow. In this frequency range, coulombic forces dominate fluid interactions over opposing dielectric forces, resulting in high flow rates (~50-100 μm/s). To date, the electrothermal effect-using asymmetrical electrodes-has been tested only with single-phase and 2-phase actuation, while dielectrophoretic micropumps have shown improved flow rates with 3- and 4-phase actuation. Simulating muti-phase signals in COMSOL Multiphysics requires additional modules and a more involved implementation to accurately represent the electrothermal effect in a micropump. Here, we report detailed simulations of the electrothermal effect under multi-phase conditions, including single-phase, 2-phase, 3-phase and 4-phase actuation patterns. These computational models indicate that 2-phase actuation leads to the highest flow rate, with 3-phase resulting in a 5% reduced flow rate and 4-phase resulting in an 11% reduced flow rate compared to 2-phase. With these modifications to the simulation, various actuation patterns can later be tested in COMSOL for a range of electrokinetic techniques.

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 score0.023
Threshold uncertainty score0.446

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 head0.198
Teacher spread0.191 · 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

Citations6
Published2023
Admission routes2
Has abstractyes

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