Capillary microfluidics in microchannels: from microfluidic networks to capillaric circuits
Bibliographic record
Abstract
Microfluidics offer economy of reagents, rapid liquid delivery, and potential for automation of many reactions, but often require peripheral equipment for flow control. Capillary microfluidics can deliver liquids in a pre-programmed manner without peripheral equipment by exploiting surface tension effects encoded by the geometry and surface chemistry of a microchannel. Here, we review the history and progress of microchannel-based capillary microfluidics spanning over three decades. To both reflect recent experimental and conceptual progress, and distinguish from paper-based capillary microfluidics, we adopt the more recent terminology of capillaric circuits (CCs). We identify three distinct waves of development driven by microfabrication technologies starting with early implementations in industry using machining and lamination, followed by development in the context of micro total analysis systems (μTAS) and lab-on-a-chip devices using cleanroom microfabrication, and finally a third wave that arose with advances in rapid prototyping technologies. We discuss the basic physical laws governing capillary flow, deconstruct CCs into basic circuit elements including capillary pumps, stop valves, trigger valves, retention valves, and so on, and describe their operating principle and limitations. We discuss applications of CCs starting with the most common usage in automating liquid delivery steps for immunoassays, and highlight emerging applications such as DNA analysis. Finally, we highlight recent developments in rapid prototyping of CCs and the benefits offered including speed, low cost, and greater degrees of freedom in CC design. The combination of better analytical models and lower entry barriers (thanks to advances in rapid manufacturing) make CCs both a fertile research area and an increasingly capable technology for user-friendly and high-performance laboratory and diagnostic tests.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".