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Record W2033652979 · doi:10.1021/jp048277z

Flow Field Effect on Electric Double Layer during Streaming Potential Measurements

2004· article· en· W2033652979 on OpenAlexafffund
Fuzhi Lu, Jun Yang, Daniel Y. Kwok

Bibliographic record

VenueThe Journal of Physical Chemistry B · 2004
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsStreaming currentElectrokinetic phenomenaMechanicsElectric fieldElectrolyteMicrochannelFlow (mathematics)Electric potentialCurrent (fluid)Electro-osmosisDouble layer (biology)Open-channel flowPotential flowMaterials scienceChemistryLayer (electronics)PhysicsVoltageThermodynamicsNanotechnologyElectrodeElectrophoresis

Abstract

fetched live from OpenAlex

It is typically assumed that flow field has very little or no effect on the electric double layer distribution. Here, we study this effect by means of a dilute electrolyte (deionized water) in a streaming potential mode for a 40-μm parallel-plate microchannel. By measuring the electrical potential downstream along the channel surface, we have shown that the potential distribution along a streamwise direction can be nonlinear. By means of a current continuity equation, we also presented numerical simulation results of potential distribution for pressure-driven flow with electrokinetic effects. The simulated results agree well with those from experiment. Results indicate that the conduction current in the streamwise direction is only a fraction of the streaming current. As a result, the ζ-potential values determined from streaming potential measurements are generally smaller than those from streaming current measurements. It is expected that this discrepancy would be smaller for a more concentrated electrolyte.

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 score0.013
Threshold uncertainty score0.338

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 head0.208
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 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

Citations23
Published2004
Admission routes2
Has abstractyes

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