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Record W2049036224 · doi:10.1088/0960-1317/14/2/018

Analysis of electrokinetic flow in microfluidic networks

2003· article· en· W2049036224 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 Micromechanics and Microengineering · 2003
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsElectrokinetic phenomenaMicrofluidicsFlow (mathematics)NanotechnologyMaterials scienceMechanicsPhysics

Abstract

fetched live from OpenAlex

A general model for electrokinetic flow in a one-to-multi-branch microchannel system is developed. This model can be extended to more complex microfluidic networks. The liquid flow may be generated by applying pressure gradients (pressure-driven flow) or electric fields (electro-osmotic flow) to the microchannels. Phenomenological coefficients in non-equilibrium thermodynamics are employed to describe the effects of channel size and surface electrokinetic properties on microfluidic characteristics. Analytical solutions of the flow rate and the streaming potential (for pressure-driven flow) or the electric current (for electro-osmotic flow) are obtained for each branch-channel, in addition to the distributions of pressure and electric potential. The flow behaviors in such a microchannel network can thus be predicted by these analytical solutions. As examples, a two-section heterogeneous microchannel and a one-to-two-branch microchannel system are analyzed using the derived model.

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 score0.150
Threshold uncertainty score0.724

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.003
GPT teacher head0.171
Teacher spread0.168 · 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