Pneumatic pumping in centrifugal microfluidic platforms
Bibliographic record
Abstract
Centrifugal microfluidics has emerged as a unique approach to the development of integrated total analysis systems for medical diagnostics. However, despite its many advantages, the platform has a size limitation due to the centripetal pumping mechanism in which fluids can only be moved from the center of the disc to the rim. This limits the footprint of the microfluidic network to one radius of the disc, and this in turn limits the amount of space available to embed complex assays. In order to overcome this space limitation problem, we are developing new techniques to pump fluids back toward the center of the disc as to allow greater path lengths for the fluidic network. This study presents a novel pumping mechanism for centrifugal microfluidics utilizing a combination of centrifugation and pneumatic compression. Pneumatic energy is stored during high-speed centrifugation with sample fluids trapping then compressing air in specially designed chambers. The accumulated pneumatic energy is released by spinning down, which expands the trapped air and thus pumps liquids back toward the center of the CD. This newly developed method overcomes current limitations of centripetal pumping avoiding external manipulation or surface treatments. In this article, we explore the design of appropriate chambers to induce pneumatic pumping and analytically describe the mechanics behind the pumping action. For proof of principle, we have applied pneumatic pumping to siphon priming.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".