Electrokinetic Concentration Gradient Generation Using a Converging-Diverging Microchannel
Bibliographic record
Abstract
Creation of concentration gradients is important in the study of biological and chemical processes that are sensitive to concentration variations. This paper presents a simple method to generate a linear concentration gradient in electroosmotic flow in microchannels with converging and diverging geometries. The method is based on the enhanced diffusive mixing inside the microchannel. By varying the converging-diverging geometries, the degree of diffusive mixing can be controlled. Different concentration gradients can be obtained by varying the applied potential and the geometry. Concentration profiles with minimal axial variations can be achieved with a deviation of 7% and 3% over a channel length of 3mm and 1mm, respectively, for a 400μm wide microchannel. Although the underlying physics and mechanisms for creating concentration profiles in a converging-diverging microchannel are the same as a T-shaped micromixer, the converging-diverging microchannel can produce desired concentration profiles in a much shorter distance (shorter by a factor of 2∼3.5 compared to a T-shape mixer). A serially connected concentration gradient generator is also realized with the ability to generate two concentration gradient ranges in the same microchannel. Numerical simulations and experiments were carried out to investigate the factors contributed to the generation of the concentration gradients.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".