Rapid Fabrication of Multilayer Microfluidic Devices Using the Liquid Crystal Display-Based Stereolithography 3D Printing System
Bibliographic record
Abstract
We present a generalized process to characterize a 3D printer for fabrication of microfluidic devices. With this process, researchers are able to determine the capability of SLA printers for a specific resin. We employed a liquid crystal display (LCD)-based SLA 3D printer to demonstrate the feasibility of the process and applied optimized parameters for fabricating multilayer 3D microfluidic devices. It has been found that the LCD-based SLA 3D printer can support fabrication of microfluidic devices with the features down to 400 μm for in-plane features and 800 μm for vertical and interconnection features. The optimized curing time of the 100-μm-thick layer is 5.5, 6.5, and 7.5 s for yellow, light green, and dark green resins, respectively. The 3D printed flow-focusing droplet generator worked properly and could generate droplets with sizes between 50 and 185 mm2. Taken together, the presented strategy can be used to quantitatively analyze and understand the capabilities of SLA 3D printing systems, which greatly facilitate optimization of device design and fabrication processes.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".