MétaCan
Menu
Back to cohort
Record W2083461929 · doi:10.1063/1.2180430

Effects of dc-dielectrophoretic force on particle trajectories in microchannels

2006· article· en· W2083461929 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

VenueJournal of Applied Physics · 2006
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMicrochannelDielectrophoresisElectric fieldMechanicsParticle (ecology)ElectrophoresisFinite element methodMaterials scienceElectrodeClassical mechanicsMicrofluidicsPhysicsNanotechnologyChemistryThermodynamics

Abstract

fetched live from OpenAlex

A method of controlling the particle trajectory in a microchannel is demonstrated. The method utilizes the dc-dielectrophoretic (dc-DEP) force created around an insulating hurdle in a microchannel under an applied dc electric field. This method does not require a complicated electrode array which is commonly used in the conventional ac-DEP system. The “proof-of-principle” experiments were carried out using a straight microchannel with a rectangle-shaped hurdle in the middle. The experiments showed that the trajectories of the micron-sized particles can be controlled by the DEP force under electric-field strengths of 5–20kV∕m. To compare with the experimental results, the particle motion was simulated using the Lagrangian tracking method, taking into consideration of the electrophoretic force, the dielectrophoretic force, and the dielectric interaction between the particle and the channel wall. The numerical simulation based on the finite-element method showed a reasonable agreement with the experimental data.

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 score0.011
Threshold uncertainty score0.363

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 head0.004
GPT teacher head0.177
Teacher spread0.173 · 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