Electrokinetic Flow Mixing of Viscoplastic Fluids through a Microconduit-Connected Microchamber with Obstacle
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".