Numerical Simulation of On-Chip Injection Process With Spatial Gradients of Electrical Conductivity
Bibliographic record
Abstract
The systematic design and precise control of the microfluidic dispenser (crossing microchannels etched into a plastic of glass plate) is key to the performance of many lab-on-a-chip devices. The fundamental understanding of the complicated electrokinetic phenomena in microfluidic dispensers therefore is necessary. In the literature, a few theoretical models studying the transport phenomena in similar crossing microchannels didn’t consider the spatial gradients of conductivity due to its complexity. A new theoretical model was developed in this paper to simulate the transport phenomena in a microfluidic dispenser with the consideration of a large range of spatial gradient of electric conductivity. This developed model was used to simulate the potential field, flow field, and concentration field of the injection processes where the conductivity of the sample-carrying buffer differs significantly from that of the driving buffer. The transport phenomena are found to be very sensitive to the conductivity difference between the sample-carrying buffer and the driving buffer. The developed model can be employed to find the optimal voltages for controlling the dispensed sample size and to provide guidance for designing such a microfluidic dispenser in lab-on-a-chip devices.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".