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Record W2187774604

Characterization of two apertures microfluidic probe

2010· article· en· W2187774604 on OpenAlexaff
Mohammadali Safavieh, Mohammad A. Qasaimeh, Roozbeh Safavieh, David Juncker

Bibliographic record

Venue14th International Conference on Miniaturized Systems for Chemistry and Life Sciences 2010, MicroTAS 2010 · 2010
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsMcGill University
Fundersnot available
KeywordsDimensionless quantityMicrofluidicsMechanicsMaterials scienceScalingVolumetric flow rateAperture (computer memory)Shear stressFlow (mathematics)Finite element methodOpticsGeometryPhysicsThermodynamicsAcousticsMathematicsNanotechnology
DOInot available

Abstract

fetched live from OpenAlex

The Microfluidic probe (MFP) is a mobile channel-less microfluidic system where a liquid is injected from one aperture into one open space and re-aspirated from a second aperture at a higher aspiration flow rate forming a Hydodynamically Confined Stream (HCS). In this work, we characterize both analytically and numerically the geometry of the HCS and the shear stress at the bottom substrate with respect to the ratio of aspiration to injection flow rates, gap size, and the diffusion coefficients of the injected liquid in the solute. The finite element method is used to simulate numerically the flow confinement, and dimensionless analysis is employed to characterize the concentration and shear stress profiles. We found that width and length of the HCS increases linearly with increasing gap size, and it shrinks linearly with respect to the ratio of aspiration to injection flow rates. Thanks to the establishment of the scaling laws and the numerical model we developed here, the parameters of the MFP can be predicted easily through simulation instead of having to determine them experimentally by trial and error.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.253
Teacher spread0.235 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2010
Admission routes1
Has abstractyes

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