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Record W2105986783 · doi:10.1039/c1lc20229d

Micro-valve using induced-charge electrokinetic motion of Janus particle

2011· article· en· W2105986783 on OpenAlexafffund
Yasaman Daghighi, Dongqing Li

Bibliographic record

VenueLab on a Chip · 2011
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsJanusElectrokinetic phenomenaJanus particlesParticle (ecology)MicrofluidicsMagnetosphere particle motionMicrochannelMechanicsNanotechnologyElectric fieldMaterials scienceVortexTracking (education)Mechanical engineeringEngineeringPhysicsMagnetic field

Abstract

fetched live from OpenAlex

A new micro-valve using the electrokinetic motion of a Janus particle is introduced in this paper. A Janus particle with a conducting hemisphere and a non-conducting hemisphere is placed in a junction of several microchannels. Under an applied electric field, the induced-charge electrokinetic flow around the conducting side of the Janus particle forms vortices. The vortices push the particle moving forwards to block the entrance of a microchannel. By switching the direction of the applied electric field, the motion of the Janus particle can be changed to block different microchannels. This paper develops a theoretical model and conducts numerical simulations of the three-dimensional transient motion of the Janus particle. The results show that this Janus particle-based micro-valve is feasible for switching and controlling the flow rate in a microfluidic chip. This method is simple in comparison with other types of micro-valve methods. It is easy for fabrication, for operation control, and has a fast response time. To better understand the micro-valve functions, comparisons with a non-conducting particle and a fully conducting particle were made. Results proved that only a Janus particle can fulfill the requirements of such a micro-valve.

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 score0.005
Threshold uncertainty score0.449

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 head0.024
GPT teacher head0.215
Teacher spread0.191 · 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

Citations55
Published2011
Admission routes2
Has abstractyes

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