Influence of the 3D Heterogeneous Roughness on Electroosmotic Flow in Microchannels
Bibliographic record
Abstract
Surface roughness has been considered as a passive means of enhancing the species mixing in electroosmotic flow through microfluidic systems. It is highly desirable to understand the synergetic effect of the 3D roughness and the surface heterogeneity on the electrokinetic flow through microchannels. In this study, we developed a three-dimensional, finite-volume-based numerical model to simulate electroosmotic transport in a slit microchannel (formed between two parallel plates) with numerous heterogeneous prismatic roughness elements arranged symmetrically and asymmetrically on the microchannel walls. The results showed that, the rough channel’s geometry and the electroosmotic mobility ratio of the roughness elements’ surface to that of the substrate, εμ, have dramatic influence on the induced pressure field, the electroosmotic flow patterns and the electroosmotic flow rate in the heterogeneous rough microchannels. The associated sample species transport in the heterogeneous rough microchannels presents tidal-wave-like concentration field at the intersection between four neighboring rough elements when under low εμ values, and presents the concentration field similar to that of the smooth channels when under high εμ values.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".