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Record W2035928321 · doi:10.1063/1.2193194

Electric-field-induced force on a charged spherical colloid embedded in an electrolyte-saturated Brinkman medium

2006· article· en· W2035928321 on OpenAlexaff
Reghan J. Hill

Bibliographic record

VenuePhysics of Fluids · 2006
Typearticle
Languageen
FieldChemistry
TopicElectrostatics and Colloid Interactions
Canadian institutionsMcGill University
Fundersnot available
KeywordsElectrokinetic phenomenaElectrolytePhysicsElectric fieldColloidMechanicsCharged particleParticle (ecology)Permeability (electromagnetism)ElectrophoresisRange (aeronautics)Classical mechanicsChemical physicsNanotechnologyIonMaterials scienceChemistryComposite materialChromatographyPhysical chemistry

Abstract

fetched live from OpenAlex

When an electric field is applied to an electrolyte-saturated polymer gel immobilizing charged colloidal particles, the force that must be exerted by the hydrogel on each particle reflects a delicate balance of electrical and hydrodynamic stresses. This article adopts a simple boundary-layer analysis to derive a convenient formula for the force in terms of the particle, electrolyte and gel characteristics. Comparisons with numerically exact solutions of the full set of electrokinetic transport equations are presented. These reveal that a fortuitous cancellation of errors leads to reasonably accurate predictions of the force over a much wider range of the parameter space than should be expected. It is noteworthy that, in gels with low permeability, an adverse pressure gradient yields a net force that exceeds the bare electrical force. The analytical theory also provides a convenient formula for the incremental pore mobility, which is a convenient measure of the electro-osmotic pumping capacity of dilute random arrays of charged inclusions.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.010
GPT teacher head0.260
Teacher spread0.250 · 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 designSimulation or modeling
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

Citations25
Published2006
Admission routes1
Has abstractyes

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