Dynamic control of capillary flow in porous media by electroosmotic pumping
Bibliographic record
Abstract
Microfluidic paper-based analytical devices (μPADs) rely on capillary flow to achieve filling, mixing and delivery of liquids. We investigate the use of electroosmotic (EO) pumping as a mechanism for dynamic control of capillary flow in paper-based devices. The applied voltage can accelerate or decelerate the baseline capillary-driven velocity, as well as be used to create a tunable valve that reversibly switches the flow on and off in an electrically controlled manner. The method relies on simple fabrication and allows repeated actuation, providing a high degree of flexibility for automation of liquid delivery. We adapt the Lucas-Washburn model to account for EO pumping and provide an experimentally validated analytical model for the distance penetrated by the liquid as a function of time and the applied voltage. We show that the EO-pump can reduce filling time by 6.5-fold for channels spanning several cm in length, relative to capillary filling alone. We demonstrate the utilization of the EO-pump for a tunable and dynamic flow control that accelerates, decelerates and stops the flow on demand. Finally, we present the use of the EO-pump for fluid flow sequencing on a paper-based device.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".