Capillarics: pre-programmed, self-powered microfluidic circuits built from capillary elements
Bibliographic record
Abstract
Microfluidic capillary systems employ surface tension effects to manipulate liquids, and are thus self-powered and self-regulated as liquid handling is structurally and chemically encoded in microscale conduits. However, capillary systems have been limited to perform simple fluidic operations. Here, we introduce complex capillary flow circuits that encode sequential flow of multiple liquids with distinct flow rates and flow reversal. We first introduce two novel microfluidic capillary elements including (i) retention burst valves and (ii) robust low aspect ratio trigger valves. These elements are combined with flow resistors, capillary retention valves, capillary pumps, and open and closed reservoirs to build a capillary circuit that, following sample addition, autonomously delivers a defined sequence of multiple chemicals according to a preprogrammed and predetermined flow rate and time. Such a circuit was used to measure the concentration of C-reactive protein. This work illustrates that as in electronics, complex capillary circuits may be built by combining simple capillary elements. We define such circuits as "capillarics", and introduce symbolic representations. We believe that more complex circuits will become possible by expanding the library of building elements and formulating abstract design rules.
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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 | 0.000 | 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 | 0.001 |
| 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".