Heterogeneous Surface Charge Enhanced Micro-Mixer for Electrokinetic Flows
Bibliographic record
Abstract
Enhancing species mixing in microfluidic applications is key to reducing analysis time and increasing device portability. Mixing in electroosmotic flows is usually diffusion-dominated requiring extended channel lengths and retention times. Recent numerical studies have indicated that the introduction of electrically charged surface heterogeneities may augment mixing efficiencies by creating localized regions of flow circulation. In this study, we experimentally visualize the effects of surface charge patterning and develop an optimized electrokinetic micromixer applicable to the low Reynolds number regime. Using the optimized micro-mixer, mixing efficiencies were improved between 22% and 68% for applied potentials ranging from 70–555 V/cm when compared with the homogeneous case. For 95% mixture, this equates to a potential decrease in required mixing channel length of up to 88% for flows with Pe´clet numbers between 190 and 1500.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".