Features in Microfluidic Paper-Based Devices Made by Laser Cutting: How Small Can They Be?
Bibliographic record
Abstract
In this paper, we determine the smallest feature size that enables fluid flow in microfluidic paper-based analytical devices (µPADs) fabricated by laser cutting. The smallest feature sizes fabricated from five commercially available paper types: Whatman filter paper grade 50 (FP-50), Whatman 3MM Chr chromatography paper (3MM Chr), Whatman 1 Chr chromatography paper (1 Chr), Whatman regenerated cellulose membrane 55 (RC-55) and Amershan Protran 0.45 nitrocellulose membrane (NC), were 139 ± 8 µm, 130 ± 11 µm, 103 ± 12 µm, 45 ± 6 µm, and 24 ± 3 µm, respectively, as determined experimentally by successful fluid flow. We found that the fiber width of the paper correlates with the smallest feature size that has the capacity for fluid flow. We also investigated the flow speed of Allura red dye solution through small-scale channels fabricated from different paper types. We found that the flow speed is significantly slower through microscale features and confirmed the similar trends that were reported previously for millimeter-scale channels, namely that wider channels enable quicker flow speed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| 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".