3D-Printed Autonomous Capillaric Circuits <sup>†</sup>
Bibliographic record
Abstract
Abstract Capillaric circuits (CCs) are advanced capillary microfluidic devices that move liquids in complex pre-programmed sequences without external pumps and valves-relying instead on microfluidic control elements powered by capillary forces. CCs were thought to require high-precision micro-scale features manufactured by photolithography in a cleanroom, which is slow and expensive. Here we present rapidly and inexpensively 3D-printed autonomous CCs. Molds for CCs were fabricated with a benchtop 3D-printer, Poly(dimethylsiloxane) replicas were made, and fluidic functionality was verified with aqueous solutions. We established design rules for 3D-printed CCs by a combination of modelling and experimentation. The functionality and reliability of 3D-printed trigger valves-an essential fluidic element that stops one liquid until flow is triggered by a second liquid-was tested for different geometries and different solutions. Trigger valves with geometries up to 80-fold larger than cleanroom-fabricated ones were found to function reliably. We designed 3D-printed retention burst valves that encode sequential liquid drainage and delivery using capillary pressure differences encoded by varying valve height and width. Using an electrical circuit analogue of the CC, we established circuit design rules for ensuring strictly sequential liquid delivery. We realized a 3D-printed CC with reservoir volumes 60 times larger than cleanroom-fabricated circuits and autonomously delivered eight liquids in a pre-determined sequence in < 7 min, exceeding the number of sequentially-encoded, self-regulated fluidic delivery events apreviously reported. Taken together, our results demonstrate that 3D-printing enables rapid prototyping of reliable CCs with improved functionality and potential applications in diagnostics, research and education.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".