Effects of liquid conductivity differences on multi-component sample injection, pumping and stacking in microfluidic chips
Bibliographic record
Abstract
As an increasing number of processes are being integrated into Lab-on-a-chip devices, there is an increasing need for flexible and accurate sample manipulation techniques for effective transport and separation. Conductivity differences between running buffer and analyte samples can arise as a product of on-chip processing, or by design. The two situations studied here are sample pumping (where bulk transport is increased and separation of charged analytes is delayed using a relatively high conductivity sample), and sample stacking (where bulk transport is decreased and separation of charged analytes is expedited using a relatively low conductivity sample). A recently developed dynamic loading method for on-chip sample injection in a straight-cross channel configuration is applied here to both pumping and stacking cases. A key characteristic of the dynamic loading method is the ability to inject samples of high concentration density and uniformity of any length. By employing the conductivity differences alone, the effectiveness of either sample transport or sample separation are shown to improve over the uniform conductivity case. Then it is demonstrated that increasing the sample length, through dynamic loading, greatly increases the effectiveness of sample pumping, evidenced in an eight-fold increase in peak height as well as a decrease in total sample length at a downstream detector. Dynamic loading in the sample stacking case was shown to also increase peak intensity height (three-fold) in rapid separations. These results demonstrate that the dynamic loading technique, used in conjunction with strategic conductivity differences, significantly extends the capabilities of microfluidic chips.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".