Design, microfabrication, and characterization of a moulded PDMS/SU-8 inkjet dispenser for a Lab-on-a-Printer platform technology with disposable microfluidic chip
Bibliographic record
Abstract
In this paper, we present a disposable inkjet dispenser platform technology and demonstrate the Lab-on-a-Printer concept, an extension of the ubiquitous Lab-on-a-Chip concept, whereby microfluidic modules are directly integrated into the printhead. The concept is demonstrated here through the integration of an inkjet dispenser and a microfluidic mixer enabling control over droplet composition from a single nozzle in real-time during printing. The inkjet dispenser is based on a modular design platform that enables the low-cost microfluidic component and the more expensive actuation unit to be easily separated, allowing for the optional disposal of the former and reuse of the latter. To limit satellite droplet formation, a hydrophobic-coated and tapered micronozzle was microfabricated and integrated with the fluidics to realize the dispenser. The microfabricated devices generated droplets with diameters ranging from 150-220 μm, depending mainly on the orifice diameter, with printing rates up to 8000 droplets per second. The inkjet dispenser is capable of dispensing materials with a viscosity up to ∼19 mPa s. As a demonstration of the inkjet dispenser function and application, we have printed type I collagen seeded with human liver carcinoma cells (cell line HepG2), to form patterned biological structures.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".