Automated Device for Multi-Stage Paper-Based Assays Enabled by an Electroosmotic Pumping Valve
Bibliographic record
Abstract
This work presents the use of electroosmotic flow generation in porous media in combination with a hydrophobic air gap to create a controllable valve capable of operating in either finite dosing or continuous flow mode, enabling the implementation of multi-step biochemical assays on paper-based devices. A hierarchical superhydrophobic surface placed between two paper pads creates an air gap, keeping the valve nominally closed. To open the valve, a pair of electrodes are activated to generate electroosmotic pressure that overcomes the barrier. The study provides an experimentally validated model describing the governing parameters, and a detailed investigation of the closed valve stability. From these, a straightforward design for a compact and fully automated device is derived. The design is based on paper pads placed on printed circuit boards (PCB), equipped with heating and actuation electrodes and additional power and logic capabilities. The device is applied to the detection of SARS-CoV-2 sequences directly from raw saliva samples, using loop-mediated isothermal amplification (LAMP) requiring sample lysis followed by enzymatic deactivation and sample distribution to multiple amplification pads. Since PCB costs scale favorably with mass production, we believe that this approach could lead to low-cost diagnostic devices with the sensitivity of amplification methods.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".