High-resolution low-cost LCD 3D printing of microfluidics
Bibliographic record
Abstract
Abstract The fabrication of microfluidic devices has progressed from cleanroom manufacturing to replica molding in polymers, and more recently to direct manufacturing by subtractive (e.g., laser machining) and additive (e.g., 3D printing) techniques, notably digital light processing (DLP) photopolymerization. However, many methods require technical expertise and while DLP 3D printers remain expensive at a cost ∼15-30K USD with ∼8M pixels that are 25-40 µm in size. Here, we introduce (i) the use of low-cost (∼150-600 USD) liquid crystal display (LCD) photopolymerization 3D printing with ∼8M-58M pixels that are 18-35 µm in size for direct microfluidic device fabrication and (ii) a poly(ethylene glycol) diacrylate-based ink developed for LCD 3D printing (PLInk). We optimized PLInk for high resolution, fast 3D printing and biocompatibility while considering the illumination inhomogeneity and low power density of LCD 3D printers. We made lateral features as small as 75 µm, 22-µm-thick embedded membranes, and circular channels with a 110 µm radius. We 3D printed microfluidic devices previously manufactured by other methods, including an embedded 3D micromixer, a membrane microvalve, and an autonomous capillaric circuit (CC) deployed for interferon-γ detection with excellent performance (limit of detection: 12 pg mL -1 , CV: 6.8%), and we demonstrated compatibility with cell culture. Finally, large area manufacturing was illustrated by printing 42 CCs with embedded microchannels in <45 min. LCD 3D printing together with tailored inks pave the way for democratizing access to high-resolution manufacturing of ready-to-use microfluidic devices by anyone, anywhere.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".