Formation, growth and applications of femtoliter droplets on a microlens
Bibliographic record
Abstract
Formation and growth of femtoliter droplets on surface microstructures are important in many fundamental and practical interfacial processes, such as water collection, vapour condensation in cooling devices, drop self-removal on anti-icing surfaces and fabrication of droplet-templated functional microstructures. In this work, we experimentally and theoretically investigate the growth of femtoliter oil-like liquid on the microlens surrounded by a hydrophilic planar area. The droplets were produced by solvent exchange, a process where the droplets nucleate and grow from an oversaturation created by displacing a good solvent by a poor solvent of the droplet liquid. Our results showed that the droplet fully coats the lens surface and the contact angle of the droplet relative to the flat surface is finely tuned over a large range by the droplet volume. The growth of the droplet on a microlens is largely described by the constant contact radius model. To demonstrate the new opportunities provided by the controlled formation of the droplet situated on a microlens, we will show a simple and effective approach for production of arrays of composite microlenses consisting of two types of polymers with different refractive indices. A high curvature of the composite microlens results in desirable diffraction patterns with potential application for enhanced light harvesting. Moreover, we demonstrate that extraction of traces of a hydrophobic solute from the flow is much faster as the droplet is lifted up from the channel wall by the microlens, promising a time effective in situ detection process in narrow channels.
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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) | 0.001 | 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".