Autonomous microfluidic capillaric circuits replicated from 3D-printed molds
Bibliographic record
Abstract
We recently developed capillaric circuits (CCs) - advanced capillary microfluidic devices assembled from capillary fluidic elements in a modular manner similar to the design of electric circuits (Safavieh & Juncker, Lab Chip, 2013, 13, 4180-4189). CCs choreograph liquid delivery operations according to pre-programmed capillary pressure differences with minimal user intervention. CCs were thought to require high-precision micron-scale features manufactured by conventional photolithography, which is slow and expensive. Here we present CCs manufactured rapidly and inexpensively using 3D-printed molds. 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 CCs by a combination of modelling and experimentation. The functionality and reliability of 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 retention burst valves that encode sequential liquid delivery using capillary pressure differences encoded by systematically varied heights and widths. Using an electrical circuit analogue of the CC, we established design rules to ensure strictly sequential liquid delivery. CCs autonomously delivered eight liquids in a pre-determined sequence in <7 min. Taken together, our results demonstrate that 3D-printing lowers the bar for other researchers to access capillary microfluidic valves and CCs for autonomous liquid delivery with 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 | 0.001 |
| 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".