Sample Manipulation in Microfluidic Devices With Electrical Conductivity Gradients
Bibliographic record
Abstract
A common application in microfluidic devices is on-chip capillary electrophoresis (CE). In this process, sample species are transported by electroosmotic flow and separated based on their electrophoretic mobilities. Separated analytes are typically detected using laser-induced fluorescence. It has been found that the sample shape and size, which is critical to the later detection processes or the quality of other analytical techniques, depends on many parameters, such as the sample diffusion coefficient, the applied voltages, and the electrical conductivity difference between sample and buffer. The conductivity difference can alter the electric field strength, which is the driving force behind both the electroosmotic bulk flow and the electrophoretic velocity of individual species. Therefore, the manipulation technique is required to consider the transport processes with conductivity differences. A numerical model presented in this paper is used to simulate the sample transport process with the consideration of conductivity gradient in order to develop the sample manipulation techniques. There are two situations studied here, which are sample pumping (where bulk transport is increased and analyte separation is delayed using a relatively high conductivity sample), and sample stacking (where bulk transport is decreased and analyte separation is expedited using a relatively low conductivity sample). The effects of applied electrical potential, sample diffusion coefficient and the extent of conductivity difference on the sample control are investigated through the developed model. The simulation results show that the sample transport with the consideration of conductivity gradient differs significantly from that of uniform conductivity case.
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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) | 0.000 | 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 | 0.000 | 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".