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Record W2519238035 · doi:10.1063/1.4962875

Sheathless electrokinetic particle separation in a bifurcating microchannel

2016· article· en· W2519238035 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

VenueBiomicrofluidics · 2016
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsUniversity of Waterloo
FundersDivision of Chemical, Bioengineering, Environmental, and Transport SystemsHigher Education Discipline Innovation ProjectNational Science Foundation
KeywordsElectrokinetic phenomenaMicrochannelDielectrophoresisElectric fieldParticle (ecology)NanotechnologyMicrofluidicsMechanicsCascadeMaterials scienceChemistryPhysicsChromatography

Abstract

fetched live from OpenAlex

Particle separation has found practical applications in many areas from industry to academia. Current electrokinetic particle separation techniques primarily rely on dielectrophoresis, where the electric field gradients are generated by either active microelectrodes or inert micro-insulators. We develop herein a new type of electrokinetic method to continuously separate particles in a bifurcating microchannel. This sheath-free separation makes use of the inherent wall-induced electrical lift to focus particles towards the centerline of the main-branch and then deflect them to size-dependent flow paths in each side-branch. A theoretical model is also developed to understand such a size-based separation, which simulates the experimental observations with a good agreement. This electric field-driven sheathless separation can potentially be operated in a parallel or cascade mode to increase the particle throughput or resolution.

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 score0.021
Threshold uncertainty score0.614

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 head0.010
GPT teacher head0.218
Teacher spread0.208 · 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