Creating fast flow channels in paper fluidic devices to control timing of sequential reactions
Bibliographic record
Abstract
This paper reports the development of a method to control the flow rate of fluids within paper-based microfluidic analytical devices. We demonstrate that by simply sandwiching paper channels between two flexible films, it is possible to accelerate the flow of water through paper by over 10-fold. The dynamics of this process are such that the height of the liquid is dependent on time to the power of 1/3. This dependence was validated using three different flexible films (with markedly different contact angles) and three different fluids (water and two silicon oils with different viscosities). These covered channels provide a low-cost method for controlling the flow rate of fluid in paper channels, and can be added following printing of reagents to control fluid flow in selected fluidic channels. Using this method, we redesigned a previously published bidirectional lateral flow pesticide sensor to allow more rapid detection of pesticides while eliminating the need to run the assay in two stages. The sensor is fabricated with sol-gel entrapped reagents (indoxyl acetate in a substrate zone and acetylcholinesterase, AChE, in a sensing zone) present in an uncovered "slow" flow channel, with a second, covered "fast" channel used to transport pesticide samples to the sensing region through a simple paper-flap valve. In this manner, pesticides reach the sensing region first to allow preincubation, followed by delivery of the substrate to generate a colorimetric signal. This format results in a uni-directional device that detects the presence of pesticides two times faster than the original bidirectional sensors.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".