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Record W2002325542 · doi:10.1063/1.3457121

Novel index for micromixing characterization and comparative analysis

2010· article· en· W2002325542 on OpenAlexaff
Mranal Jain, K. Nandakumar

Bibliographic record

VenueBiomicrofluidics · 2010
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMicromixingMicromixerStatic mixerMixing (physics)Residence time (fluid dynamics)Harmonic mixerBenchmark (surveying)DiffusionComputer scienceMaterials scienceMechanicsMicrofluidicsNanotechnologyEngineeringPhysicsThermodynamicsTelecommunicationsTurbulenceLocal oscillator

Abstract

fetched live from OpenAlex

The most basic micromixer is a T- or Y-mixer, where two confluent streams mix due to transverse diffusion. To enhance micromixing, various modifications of T-mixers are reported such as heterogeneously charged walls, grooves on the channel base, geometric variations by introducing physical constrictions, etc. The performance of these reported designs is evaluated against the T-mixer in terms of the deviation from perfectly mixed state and mixing length (device length required to achieve perfect mixing). Although many studies have noticed the reduced flow rates for improved mixer designs, the residence time is not taken into consideration for micromixing performance evaluation. In this work, we propose a novel index, based on residence time, for micromixing characterization and comparative analysis. For any given mixer, the proposed index identifies the nondiffusive mixing enhancement with respect to the T-mixer. Various micromixers are evaluated using the proposed index to demonstrate the usefulness of the index. It is also shown that physical constriction mixer types are equivalent to T-mixers. The proposed index is found to be insightful and could be used as a benchmark for comparing different mixing strategies.

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 score0.269
Threshold uncertainty score0.676

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 head0.015
GPT teacher head0.231
Teacher spread0.216 · 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

Citations28
Published2010
Admission routes1
Has abstractyes

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