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Record W2031806574 · doi:10.1002/aic.10612

Measurement of flow in microfluidic networks with micrometer‐sized flow restrictors

2005· article· en· W2031806574 on OpenAlexafffund
Jian‐Bin Bao, D. Jed Harrison

Bibliographic record

VenueAIChE Journal · 2005
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Alberta
KeywordsHagen–Poiseuille equationReynolds numberVolumetric flow rateMechanicsFlow (mathematics)Open-channel flowMicrofluidicsFlow coefficientMicrometerFlow measurementRange (aeronautics)Hele-Shaw flowFluid dynamicsMaterials scienceMathematicsSimulationEngineeringThermodynamicsMechanical engineeringPhysicsTurbulence

Abstract

fetched live from OpenAlex

Abstract The fluid dynamics in micrometer‐sized flow restrictors was evaluated, to test the predictions of the Hagen–Poiseuille equation in small microchip devices. A photobleaching, time‐of‐flight linear flow rate measurement method was developed for the evaluation of flow in slot‐shaped channels with at least one dimension in the size range of 1–3 μm. For a linear flow rate range in the narrowest dimension of 0.3 to 16 mm/s (volumetric flow rates of 0.6 to 32 nL/s, maximum Reynolds number of 0.3), observed flow rates agreed with theoretical predictions within experimental error (2–3%). An empirical equation for the geometric form factor of a D‐shaped channel was developed, providing a useful working curve for use of the Hagen–Poiseuille equation in microfluidics design. The Hagen–Poiseuille solution can be used to design multiport devices with intersecting flow channel networks, achieving the intended flow rate ratios in channels with differing flow resistance with an accuracy of 1–2%. Despite the differences between achieved etched dimensions and the nominal design values a deviation of only 1–4% in the flow ratios was also achieved. This observation provides a useful guideline for predicting tolerance sensitivity in device manufacture. © 2005 American Institute of Chemical Engineers AIChE J, 2006

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 score0.174
Threshold uncertainty score0.600

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 head0.007
GPT teacher head0.187
Teacher spread0.180 · 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

Citations18
Published2005
Admission routes2
Has abstractyes

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