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Record W2989631351 · doi:10.1021/acs.iecr.9b04535

Performance Evaluation of Liquid Mixing in a T-Junction Passive Micromixer with a Twisted Tape Insert

2019· article· en· W2989631351 on OpenAlexaff
Jundika C. Kurnia, Agus P. Sasmito

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2019
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsMcGill University
Fundersnot available
KeywordsMicromixerPressure dropReynolds numberLaminar flowMaterials scienceMixing (physics)Mass transferPorosityMechanicsComposite materialNanotechnologyTurbulencePhysicsMicrofluidics

Abstract

fetched live from OpenAlex

An innovative passive mixing enhancement method by using a T-junction micromixer with twisted tape is proposed and evaluated. A three-dimensional model is developed and validated by considering the single-phase mixing of the laminar Newtonian miscible fluid. Several key parameters are evaluated, namely, the inlet Reynolds number, twisted tape width, nature of the twisted tape (solid and porous), and permeability of the porous tape. Mixing index, pressure drop, and performance index, defined as the ratio of mixing enhancement over the ratio of friction factor, are compared. The results suggest that adding twisted tape can enhance the mixing index by almost twice at the cost of a higher pressure drop. Porous tape with the permeability of 10–10 m2 is found to have the best performance at low to medium Reynolds numbers, while solid twisted tape improves the performance at a higher Reynolds number. The proposed method can be used to enhance mass transfer for micromixers in chemical processes and pharmaceutical applications.

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)0.001
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 score0.028
Threshold uncertainty score0.672

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.038
GPT teacher head0.271
Teacher spread0.233 · 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

Citations30
Published2019
Admission routes1
Has abstractyes

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