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Record W2073137936 · doi:10.1115/icmm2005-75174

Electrokinetic Generation Efficiency With Finite External Loads

2005· article· en· W2073137936 on OpenAlexafffund
Fuzhi Lu, Ali Mansouri, Tuck Y. How, Larry W. Kostiuk, Daniel Y. Kwok

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsElectrokinetic phenomenaReciprocity (cultural anthropology)MechanicsMicrofluidicsMaterials scienceCurrent (fluid)Power (physics)Electricity generationControl theory (sociology)OptoelectronicsPhysicsThermodynamicsComputer scienceNanotechnology

Abstract

fetched live from OpenAlex

Electrokinetic (EK) pumping efficiency has recently been a hot topic due largely to its applications in microfluidics for liquid transport. As the Onsager reciprocity relation suggests that the exact opposite of EK pumping is EK generation, experimental study on the efficiency of the latter is seldom reported. This paper examines electrokinetic generation efficiency in the presence of finite external loads through the use of different ceramic filters. The electrical output power was measured experimentally as a function of different external loadings. When the experiments were performed continuously on subsequent days, the EK generation efficiency for all the tested filters can be made to increase and approach a maximum limit. The experimental data also indicated that, when pore size of the filter decreases, the curve for potential versus current becomes more non-linear and the maximum efficiency does not occur when the current is half the zero-potential current.

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)0.001
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
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.005
GPT teacher head0.168
Teacher spread0.164 · 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

Citations0
Published2005
Admission routes2
Has abstractyes

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