The Effect of Geometry on Sample Leakage in Multi-Channel Microfluidic Devices
Bibliographic record
Abstract
Electrokinetic sheath-flow is one of the techniques used to manipulate sample migration and prevent cross-contamination in multi-channel microfluidic devices. To achieve a successful design, it is important to predict the sample behaviour in advance. We use finite element method to investigate the effect of channel geometry on sample leakage in the presence of electrokinetic sheath-flow. A typical multi-channel device consisting of a main fractionation channel connected to a few collection channels is considered. It has been observed experimentally that the depth of different components of the microfluidic device can change the sample leakage. In-detail investigations are made here in order to find the fundamental cause of the observed behaviour. Simulation results confirmed that by increasing the depth ratio of the collection channels to the main channel the sample leakage would decrease. Simulations are also performed to find criteria for choosing an optimum depth ratio for the channels in terms of both high functionality and ease of fabrication for any specific application.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".