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Record W4413103119 · doi:10.1021/acs.iecr.5c01416

Electrokinetic Flow Mixing of Viscoplastic Fluids through a Microconduit-Connected Microchamber with Obstacle

2025· article· en· W4413103119 on OpenAlexaff
Subhajyoti Sahoo, Ameeya Kumar Nayak

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2025
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsToronto Metropolitan University
FundersScience and Engineering Research BoardMinistry of Education, IndiaUniversity Grants Commission
KeywordsElectrokinetic phenomenaViscoplasticityObstacleMixing (physics)MicrofluidicsFlow (mathematics)Materials scienceNanotechnologyChemical engineeringMechanicsChemistryThermodynamicsPhysicsConstitutive equationEngineering

Abstract

fetched live from OpenAlex

This study investigates the enhancement of the micromixing of a non-Newtonian fluid within a nozzle-diffuser shaped microchannel under the influence of the combined pressure and electroosmotic-driven flow. The microchannel is connected to a microchamber (of diamond and trapezoidal shape) along with a rectangular obstacle positioned at its core to optimize the mixing efficiency. The Herschel–Bulkley fluid model is employed to characterize the viscoplastic behavior of the aqueous solution within the narrow fluidic confinements. A wide range of flow behavior indices (0.3 ≤ n ≤ 1.7) are considered to investigate the impact of electric double layers associated with the interfacial phenomena. A numerical simulation is conducted using the staggered grid finite volume method to solve the coupled Poisson–Nernst–Planck–Navier–Stokes equations involving the induced and external electric field. The results are obtained to analyze the electroosmotic flow phenomena of the viscoplastic fluid involving the charge distribution and species concentration. The study aims to enhance mixing efficiency and assess the performance metrics by minimizing pressure drop while optimizing rheological parameters and geometric configurations. Mixing efficiency improves with increasing the yield stress, flow behavior index, and various geometric parameters. However, achieving optimal mixing efficiency comes at the cost of a substantial pressure drop, which severely complicates the practical operation of micromixers. To minimize these complications, mixing efficiency is scaled with pressure drop to define a mixing performance factor that peaks at intermediate yield stress (τ 0 = 0.1) and moderate flow behavior indices ( n = 1.4), offering a balanced optimization framework. The results accurately capture species distribution patterns, providing valuable insights to address technical challenges in designing electrically actuated microfluidic devices.

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)0.000
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.265
Teacher spread0.243 · 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

Citations4
Published2025
Admission routes1
Has abstractyes

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