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Record W2142654205 · doi:10.1109/tdei.2009.4815182

Electrohydrodynamic micropumps with asymmetric electrode geometries for microscale electronics cooling

2009· article· en· W2142654205 on OpenAlexaff
P. Zangeneh Kazemi, P.R. Selvaganapathy, C.Y. Ching

Bibliographic record

VenueIEEE Transactions on Dielectrics and Electrical Insulation · 2009
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsMcMaster University
Fundersnot available
KeywordsElectrohydrodynamicsMicroscale chemistryMicropumpMaterials scienceMicrofabricationHeat transferElectronics coolingLiquid dielectricComputer coolingHeat fluxMechanical engineeringMechanicsElectrodeDielectricOptoelectronicsNanotechnologyEngineeringChemistryPhysicsThermal management of electronic devices and systems

Abstract

fetched live from OpenAlex

The development of effective microscale thermal management solutions is important to further advance microscale electronics because of increasing heat flux densities in these devices. There is an increasing trend towards liquid based cooling solutions because of their much higher heat removal capacities compared to air cooling methods. Electrohydrodynamic (EHD) micropumps are ideally suited for these applications due to their small form factor, low power consumption and ability to work with dielectric heat transfer fluids. In addition, EHD micropumps have no moving parts and are amenable to conventional microfabrication techniques. Current EHD micropump designs, however, generate very low flowrates and pressure head to be practically useful. Here, we demonstrate for the first time, that an asymmetry in the electrode geometry (both 2D and 3D) will result in significantly higher pressure generation with lower power consumption than conventional symmetric electrode designs.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.209
Teacher spread0.202 · 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 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

Citations30
Published2009
Admission routes1
Has abstractyes

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