Capillary microfluidics for diagnostic applications: fundamentals, mechanisms, and capillarics
Bibliographic record
Abstract
Microfluidic systems, especially those using capillary forces, have recently attracted considerable interest due to their potential to facilitate passive fluid management in portable diagnostic devices and point-of-care settings. These systems utilize capillary forces to autonomously regulate fluid flow, eliminating the requirement for external power and providing a more straightforward and economical option compared to active microfluidic systems. This review examines the fundamental concepts of capillary-driven microfluidics, emphasizing significant progress in the design of capillary pumps and valves, as well as the influence of surface tension, wettability, and the geometrical configurations of microchannels on the enhancement of fluid dynamics. Furthermore, the review explores other configurations, such as porous and solid substrates, to illustrate their potential for healthcare and biochemical applications. Moreover, the challenges related to managing flow rates and enhancing the reproducibility of devices are addressed, alongside recent innovations designed to overcome these challenges. Capillary systems offer an effective and reliable foundation for developing miniaturized diagnostic instruments, which hold significant potential across various domains, including biological research and environmental monitoring.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".