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Record W1987922845 · doi:10.3390/mi1020036

Analysis of Electrokinetic Mixing Techniques Using Comparative Mixing Index

2010· article· en· W1987922845 on OpenAlexaff
Mranal Jain, Anthony Yeung, K. Nandakumar

Bibliographic record

VenueMicromachines · 2010
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMixing (physics)Electrokinetic phenomenaMechanicsVolumetric flow rateChannel (broadcasting)Residence time (fluid dynamics)Flow (mathematics)Materials scienceOpticsSimulationComputer sciencePhysicsEngineeringNanotechnologyTelecommunications

Abstract

fetched live from OpenAlex

The performance of micro-mixers is evaluated in terms of deviations from perfectly mixed state and mixing length (i.e., device length required to achieve perfect mixing). Different variations of T-mixer are reported for improved mixing performance, including geometric constrictions/obstacles embedded in the channel wall, heterogeneously charged walls, grooves on channel base, etc. Most of the reported designs provide improved mixing at the expense of reduced flow rate; there exists therefore a tradeoff between mixing and transport. The reduced flow rate, which affects species residence time, is unfortunately not taken into account in most micro-mixing performance analyses. This issue is addressed by the comparative mixing index (CMI), which evaluates mixing performance more appropriately by normalizing the effect of residence time among different designs. In this study, the performance of several mixing strategies are evaluated based on the CMI; these are mixer designs that incorporate (a) physical constrictions, (b) induced charge electro-osmotic (ICEO) effects, and (c) heterogeneously charged walls. The present analysis clearly identifies conditions under which a given mixer design is superior to a T-mixer.

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 score0.035
Threshold uncertainty score0.717

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.009
GPT teacher head0.251
Teacher spread0.242 · 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

Citations8
Published2010
Admission routes1
Has abstractyes

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