Pixel-based open-space microfluidics for versatile surface processing
Bibliographic record
Abstract
An increasing number of applications in biology, chemistry, and material sciences require fluid manipulation beyond what is possible with current automated pipette handlers, such as gradient generation, interface reactions, reagent streaming, and reconfigurability. In this article, we introduce the pixelated chemical display (PCD), a scalable strategy for highly parallel, reconfigurable liquid handling on open surfaces. Microfluidic "pixels" are created when a fluid stream injected above a surface is confined by neighboring identical fluid streams, forming a repeatable flow unit that can be used to tesselate a surface. PCDs generating up to 144 pixels are fabricated and used to project "chemical moving pictures" made of several reagents over both immersed and dry surfaces, without any physical barrier or wall. This work distinguishes itself from previous work in open-space microfluidics by presenting a device architecture where the number of confinement areas can be scaled to any size. Furthermore, it challenges the open-space tenet that the aspiration rate must be higher than the injection rate for reagents to be confined. Overall, this article sets the foundation for massively parallel surface processing using continuous flow streams and showcases possibilities in both wet and dry surface patterning and roll-to-roll 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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".