Numerical studies of electrokinetic control of DNA concentration in a closed‐end microchannel
Bibliographic record
Abstract
A major challenge in lab-on-a-chip devices is how to concentrate sample molecules from a dilute solution, which is critical to the effectiveness and the detection limit of on-chip bio-chemical reactions. A numerical study of sample concentration control by electrokinetic microfluidic means in a closed-end microchannel is presented in this paper. The present method provides a simple and efficient way of concentration control by using electrokinetic trapping of a charged species of interest, controlling liquid flow and separating different sample molecules in the microchannel. The electrokinetic-concentration process and the controlled transport of the sample molecules are numerically studied. In this system, in addition to the electroosmotic flow and the electrophoresis, the closed-end of the chamber causes velocity variation at both ends of the channel and induces a pressure gradient and the associated fluid movement in the channel. The combined effects determine the final concentration field of the sample molecules. The influences of a number of parameters such as the channel dimensions, electrode size and the applied electric field are investigated.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".