Nanoporous Membranes Enable Concentration and Transport in Fully Wet Paper-Based Assays
Bibliographic record
Abstract
Low-cost paper-based assays are emerging as the platform for diagnostics worldwide. Paper does not, however, readily enable advanced functionality required for complex diagnostics, such as analyte concentration and controlled analyte transport. That is, after the initial wetting, no further analyte manipulation is possible. Here, we demonstrate active concentration and transport of analytes in fully wet paper-based assays by leveraging nanoporous material (mean pore diameter ≈ 4 nm) and ion concentration polarization. Two classes of devices are developed, an external stamp-like device with the nanoporous material separate from the paper-based assay, and an in-paper device patterned with the nanoporous material. Experimental results demonstrate up to 40-fold concentration of a fluorescent tracer in fully wet paper, and directional transport of the tracer over centimeters with efficiencies up to 96%. In-paper devices are applied to concentrate protein and colored dye, extending their limits of detection from ∼10 to ∼2 pmol/mL and from ∼40 to ∼10 μM, respectively. This approach is demonstrated in nitrocellulose membrane as well as paper, and the added cost of the nanoporous material is very low at ∼0.015 USD per device. The result is a major advance in analyte concentration and manipulation for the growing field of low-cost paper-based assays.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".