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Record W2035663324 · doi:10.1021/la702059v

Ion Size and Image Effect on Electrokinetic Flows

2008· article· en· W2035663324 on OpenAlexafffund
Y. Liu, Meilin Liu, W. M. Lau, Jingsi Yang

Bibliographic record

VenueLangmuir · 2008
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsElectrokinetic phenomenaNanofluidicsStreaming currentMicrofluidicsNanotechnologyChemistryIonElectric fieldChemical physicsMechanicsMaterials sciencePhysics

Abstract

fetched live from OpenAlex

Electrokinetic phenomena play a major role in microfluidic systems, and such a role becomes even more significant in nanofluidic systems due to the increase of the surface-to-volume ratio. Description of the electric double layer (EDL) at a solid-liquid interface is the key to understand and utilize electrokinetic phenomena. However, the traditional Gouy-Chapman (GC) theory for the EDL, which has been successfully used in many microfluidic applications, does not include some important characteristics such as ion size and image effect. These characteristics are indeed important in nanofluidics. This paper explores the impacts of ion size and the image effect on micro- and nanoscale electrokinetic flows. An advanced theory, the modified Poisson-Boltzmann (MPB) theory proposed by Outhwaite and his co-workers,1,26 is adopted to describe the EDL. Electrokinetic flows in micro- and nanochannels are reinvestigated. The results show that ion size has significant effects on electrokinetic flows in nanosystems in terms of both the flow field and the streaming potential, while the image effect only significantly affects the streaming potential.

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 score0.126
Threshold uncertainty score0.335

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 head0.003
GPT teacher head0.177
Teacher spread0.174 · 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

Citations21
Published2008
Admission routes2
Has abstractyes

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