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Record W1965306339 · doi:10.1063/1.4813484

Study on an alternating current electrothermal micropump for microneedle-based fluid delivery systems

2013· article· en· W1965306339 on OpenAlexafffund
Rumi Zhang, G.A. Jullien, Colin Dalton

Bibliographic record

VenueJournal of Applied Physics · 2013
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaCMC Microsystems
KeywordsMicropumpElectrokinetic phenomenaVolumetric flow rateFluid dynamicsVoltageFlow control (data)Materials scienceCurrent (fluid)Flow (mathematics)Delivery systemBiomedical engineeringElectrical engineeringMechanicsComputer scienceNanotechnologyEngineeringPhysics

Abstract

fetched live from OpenAlex

In this paper, we report on a modeling study of an AC electrothermal (ACET) micropump with high operating pressures as well as fast flow rates. One specific application area is for fluid delivery using microneedle arrays which require higher pressures and faster flow rates than have been previously reported with ACET devices. ACET is very suitable for accurate actuation and control of fluid flow, since the technique has been shown to be very effective in high conductivity fluids and has the ability to create a pulsation free flow. However, AC electrokinetic pumps usually can only generate low operating pressures of 1 to 100 Pa, where flow reversal is likely to occur with an external load. In order to realize a high performance ACET micropump for continuous fluid delivery, applying relatively high AC operating voltages (20 to 36 Vrms) to silicon substrate ACET actuators and using long serpentine channel allows the boosting of operating pressure as well as increasing the flow rates. Fast pumping flow rates (102–103 nl/s) and high operating pressures (1–12 kPa) can be achieved by applying both methods, making them of significant importance for continuous fluid delivery applications using microneedle arrays and other such biomedical devices.

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 score0.020
Threshold uncertainty score0.686

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 head0.021
GPT teacher head0.236
Teacher spread0.216 · 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

Citations28
Published2013
Admission routes2
Has abstractyes

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