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Record W2076819870 · doi:10.1021/jp0359485

Oscillating Streaming Potential and Electro-osmosis of Multilayer Membranes

2004· article· en· W2076819870 on OpenAlexaff
Jun Yang, Karina Grundke, Cornelia Bellmann, Stefan Michel, Larry W. Kostiuk, Daniel Y. Kwok

Bibliographic record

VenueThe Journal of Physical Chemistry B · 2004
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsElectrokinetic phenomenaStreaming currentElectro-osmosisMembraneZeta potentialSuperposition principleElectric fieldMechanicsFlow (mathematics)Electric potentialMaterials scienceAnalytical Chemistry (journal)ChemistryBiological systemElectrophoresisChromatographyNanotechnologyPhysicsVoltageEngineeringMathematicsElectrical engineeringMathematical analysis

Abstract

fetched live from OpenAlex

Artificial membranes consist of multilayers that have different physical or chemical properties. They are often considered as an equivalent single-layer membrane without taking into account the detail inside. Based on the Debye−Hückel approximation, we have provided analytical solutions of oscillating electrokinetic flow in multilayer membranes. Both pressure-driven flow (streaming potential) and electric-field-driven flow (electro-osmosis) were studied. The pressure and electric-field distributions in each layer can be obtained from our analytical solutions. This allows a better understanding of electrokinetic flow in multilayer membranes and benefits the design and selection of artificial membranes. The derived analytical solutions are useful for more-general time-dependent problems through a superposition of time-harmonic solutions weighted by appropriate Fourier coefficients. The properties of each membrane layer are reflected as complex quantities; therefore, a method is proposed to determine the electrokinetic properties (such as the zeta potential) of each layer by applying a high-frequency alternating electric field or pressure.

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 score0.002
Threshold uncertainty score0.228

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 head0.004
GPT teacher head0.190
Teacher spread0.186 · 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

Citations3
Published2004
Admission routes1
Has abstractyes

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