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Record W4213266137 · doi:10.1039/d1an02196f

Conductivity-difference-enhanced DC dielectrophoretic particle separation in a microfluidic chip

2022· article· en· W4213266137 on OpenAlexafffund
Deyu Li, Weicheng Yu, Teng Zhou, Mengqi Li, Yongxin Song, Dongqing Li

Bibliographic record

VenueThe Analyst · 2022
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsUniversity of Waterloo
FundersFundamental Research Funds for the Central UniversitiesNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsConductivityBody orificeMicrofluidicsElectric fieldMaterials scienceVoltageElectrolyteParticle (ecology)Analytical Chemistry (journal)DielectrophoresisDirect currentElectrical resistivity and conductivityElectrophoresisMechanicsChemistryNanotechnologyElectrodeChromatographyElectrical engineeringPhysics

Abstract

fetched live from OpenAlex

A conductivity-difference-based method for increasing dielectrophoretic (DEP) force for particle separation in a microfluidic chip is presented in this paper. By applying a direct-current (DC) voltage across two immiscible electrolyte solutions with a conductivity difference, an enhanced electric field gradient is generated at the liquid-liquid interface. Theoretical analysis based on equivalent circuit theory found that the gradient of the electric field squared increases with the decrease in the conductivity ratio of the two liquids (main channel to the side channel). As a result, the particle separation distance (an indicator of DEP force) increases with the decrease in the conductivity ratio, which is both numerically predicted and experimentally verified. Numerical simulations also show that the separation distance increases with the increase in the magnitude of the electric field and the decrease in the width of the orifice. The method presented in this paper is simple and advantageous for increasing DEP force without applying higher DC voltages or fabricating smaller orifices.

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 score0.008
Threshold uncertainty score0.345

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.015
GPT teacher head0.230
Teacher spread0.216 · 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

Citations11
Published2022
Admission routes2
Has abstractyes

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