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Effect of Fin-Shaped Electrodes on Flow Mixing and Pressure Drop in an Electroosmotic Micromixer

2023· article· en· W7110841721 on OpenAlexaff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2023
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsElectrodeMicromixerMixing (physics)Pressure dropDrop (telecommunication)Flow (mathematics)VortexVolumetric flow rate

Abstract

fetched live from OpenAlex

Production of a homogeneous solution is of great interest for Lab-on-a-Chip (LOC) applications. Since the fluid flow in microchannels is laminar, the LOC devices have low mixing efficiency in passive mixers. The present study proposes a novel electroosmotic micromixer in which the electrodes have a fin-shaped structure in the mixing chamber. In other words, the combined effect of obstacle and electro-osmosis is evaluated. The effect of various parameters such as electrode angle, electrode height, inlet velocity, alternating current, and frequency on mixing index and pressure drop is investigated. Vortices are formed around the electrodes due to the applied electric field and their fin-shaped structure. It is revealed that the mixing index is an increasing function of applied voltage. The results demonstrate that there is an optimal value for the parameters, including frequency, electrode height, inlet velocity, and electrode angle. An increase in the mixing efficiency is accompanied by an enhancement in the pressure drop. It is revealed that the maximum efficiency is achieved when the electrode height is 5 μm and the electrode angle is 60°. The coefficient of performance of the proposed micromixer is more than that of the reference mixer when the electrode height is 2.5 μm and the electrode angle is 90°.

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 score0.000
Threshold uncertainty score0.001

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 integrity0.0000.000
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 head0.060
GPT teacher head0.442
Teacher spread0.382 · 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
Published2023
Admission routes1
Has abstractyes

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