Digital Manufacturing of Functional Ready-to-Use Microfluidic Systems
Bibliographic record
Abstract
Abstract Digital manufacturing (DM) strives for the seamless manufacture of a functional device from a digital file. DM holds great potential for microfluidics, but requirements for embedded conduits and high resolution beyond the capability of common manufacturing equipment, and microfluidic systems’ dependence on peripherals (e.g. connections, power supply, computer), have limited its adoption. Microfluidic capillaric circuits (CCs) are structurally-encoded, self-contained microfluidic systems that operate and self-fill thanks to precisely tailored hydrophilicity. CCs were heretofore hydrophilized in a plasma chamber, but which only produces transient hydrophilicity, lacks reproducibility, and limits CC design to open surface channels sealed with a tape. Here we introduce the additive DM of monolithic, fully functional and intrinsically hydrophilic CCs. CCs were 3D printed with commonly available light engine-based 3D printers using polyethylene(glycol)diacrylate-based ink co-polymerized with hydrophilic acrylic acid crosslinkers and optimized for hydrophilicity and printability. A new, robust capillary valve design and embedded conduits with circular cross-sections that prevent bubble trapping are presented, and complex interwoven circuit architectures created, and their use illustrated with an immunoassay. Finally, the need for external paper capillary pumps is eliminated by directly embedding the capillary pump in the chip as a porous gyroid structure, realizing fully functional, monolithic CCs. Thence, a computer-aided design file can be made into a CC by commonly available 3D printers in less than 30 minutes enabling low-cost, distributed, DM of fully functional ready-to-use microfluidic systems.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".