Switchable capillary and drainage containers for programmable three-dimensional liquid manipulation
Bibliographic record
Abstract
Abstract Capillarity-guided liquid manipulations are ubiquitous in nature. Multifarious bioinspired capillary microfluidic devices have been developed to control different liquid behaviors. However, current capillary systems still suffer substantial limitations in flexible three-dimensional (3D) liquid manipulation, especially in reversible liquid capture and release, programmable 3D liquid patterning, and large-scale multi-liquid manipulation. Here, we propose “switchable capillary and drainage containers” composed of connected frame units for versatile programmable 3D liquid manipulation. A small difference in the frame connections induces vastly distinct liquid behaviors, namely, liquid capture in capillary containers and liquid release in drainage containers. Liquid capture or release can be reversibly switched by establishing or breaking the liquid continuity between containers. Using predefined frame connections allows programmable 3D patterning of unary and binary liquids, enabling parallel multi-variable studies. The containers are proved to be powerful fluidic platforms with applications including reversible capillary sampling and release, high-flow evaporative humidifier, and efficient CO2 capture. We envision that the containers will open broad applications in materials science, interfacial chemistry, and biomedical research. Main text
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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 | 0.000 |
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".