Capillary microfluidics in microchannels: from microfluidic networks to capillaric circuits
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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 it