Numerical and Experimental Studies on Electrical Potential Distribution of Pressure Driven Flow in Parallel Plate Microchannels
Bibliographic record
Abstract
A number of papers have been published on the computational approaches to electrokinetic flows. Nearly all of these decoupled approaches rely on the assumption of the Poisson-Boltzmann equation and do not consider the effect of velocity field on the electric double layers. By means of a charge continuity equation, we present here a numerical model for the simulation of pressure driven flow with electrokinetic effects in parallel-plate microchannels. Our approach is similar to that given by van Theemsche et al. [Anal. Chem., 74, 4919 (2002)] except that we assumed liquid conductivity to be constant and allows simulation to be performed in experimental dimension. The numerical simulation requires the solution of the Poisson equation, charge continuity equation and the incompressible Navier-Stokes equations. The simulation is implemented in a finite-volume based Matlab code. To validate the model, we measured the electrical potential downstream along the channel surface. The simulated results were also compared with known analytical solutions and experimental data. Results indicate that the linear potential distribution assumption in the streaming direction is in general not valid, especially when the flow rate is large for the specific channel geometry. The good agreement between numerical simulation and experimental data suggests that the present model can be employed to predict pressure-driven flow in microchannels.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".