Investigation of an electro-osmotic micromixer with heterogeneous zeta-potential distribution at the wall
Bibliographic record
Abstract
Abstract In this study, the effects of zeta-potential, Debye-Huckel parameter and Reynolds number on electrokinetic mixing through heterogeneous microchannels are investigated. In previous studies, relatively complicated approaches were applied to examine the electrokinetically produced vortices; on the other hand, a relatively simple innovative micromixer consisted of a non-homogeneous rectangular microchannel with the prescribed arrangements of zeta-potential at the walls is considered in this paper. In other words, the mentioned microchannel has heterogeneous zeta-potential distribution at its wall, while other surface properties are assumed to be uniform and homogenous. Moreover, in order to investigate the mixing efficiency of microfluidic devices based on the electroosmotic flow is proposed. Actually, in the electroosmotic phenomenon, the fluid flow is caused by applying a potential across the microchannel. To achieve the electro-osmotic mixing, the Navier-Stokes, Nernst-Planck, Laplace and convection-diffusion equations are solved numerically for the velocity field, ions distribution, electrical potential, and concentration field, sequentially. Having examined the results, one can easily figure out that the performance of electro-osmotic micromixers intensively depends on the wall zeta-potential value and its distribution. Moreover, it can be inferred that the mixing efficiency is really dependent on Debye-Huckel or Reynolds number so that it will increase as soon as theses mentioned parameters decreases. One of the most important achievements of this paper is that a better mixing performance can be attained by the asymmetric charge pattern. In other words, it is really essential to arrange the charge pattern more asymmetric to achieve the highest mixing efficiency.
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 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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".