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Record W2007359284 · doi:10.1021/ac035451r

Heterogeneous Surface Charge Enhanced Micromixing for Electrokinetic Flows

2004· article· en· W2007359284 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAnalytical Chemistry · 2004
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMicromixingMicromixerElectrokinetic phenomenaMixing (physics)ChemistryReynolds numberMicrofluidicsMechanicsSurface chargeDiffusionFlow (mathematics)Electro-osmosisChemical physicsAnalytical Chemistry (journal)NanotechnologyChromatographyElectrophoresisThermodynamicsTurbulenceMaterials sciencePhysical chemistry

Abstract

fetched live from OpenAlex

Enhancing the species mixing in microfluidic applications is key to reducing analysis time and increasing device portability. The mixing in electroosmotic flow is usually diffusion-dominated. Recent numerical studies have indicated that the introduction of electrically charged surface heterogeneities may augment mixing efficiencies by creating localized regions of flow circulation. In this study, we experimentally visualized the effects of surface charge patterning and developed an optimized electrokinetic micromixer applicable to the low Reynolds number regime. Using the optimized micromixer, mixing efficiencies were improved between 22 and 68% for the applied potentials ranging from 70 to 555 V/cm when compared with the negatively charged homogeneous case. For producing a 95% mixture, this equates to a potential decrease in the required mixing channel length of up to 88% for flows with Péclet numbers between 190 and 1500.

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.

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 score0.028
Threshold uncertainty score0.735

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 head0.005
GPT teacher head0.207
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