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Record W1966195034 · doi:10.1021/ac048216x

Accelerated Particle Electrophoretic Motion and Separation in Converging−Diverging Microchannels

2005· article· en· W1966195034 on OpenAlexafffund
Xiangchun Xuan, Bo Xu, Dongqing Li

Bibliographic record

VenueAnalytical Chemistry · 2005
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsUniversity of TorontoUniversity of New Brunswick
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsElectrophoresisMicrochannelElectric fieldParticle (ecology)ChemistryMechanicsParticle sizeMagnetosphere particle motionDielectrophoresisAnalytical Chemistry (journal)PhysicsChromatography

Abstract

fetched live from OpenAlex

Accelerated particle electrophoretic motions were visualized in converging-diverging microchannels on poly(dimethylsiloxane) chips. The accelerated particle electrophoretic separation is highly desirable in on-chip flow cytometry and high-speed electrophoresis. The effects of electric field, particle size, particle trajectory, and channel structure on the particle electrophoretic motion are examined. We find that the ratio of the particle velocity in the throat to that in the straight channel is significantly lower than their cross-sectional area ratio. This discrepancy may be attributed to the locally higher electric field around the two poles of a particle, as compared to other regions inside the microchannel. We also find that the particle velocity ratio is increased for smaller particles moving through symmetric converging-diverging channels under lower electric fields. These variations may be attributed to the negative dielectrophoretic force that is generated by the nonuniform electric field in the converging-diverging section. In addition, we find that particle trajectory has insignificant influences on the maximum velocity ratio obtained in the throat.

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 score0.190
Threshold uncertainty score0.494

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 head0.008
GPT teacher head0.230
Teacher spread0.222 · 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

Citations50
Published2005
Admission routes2
Has abstractyes

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