Effects of Chemical and Geometric Microstructures on the Crystallization of Surface Droplets during Solvent Exchange
Bibliographic record
Abstract
In this work, we investigate the crystallization of droplets formed on micropatterned surfaces. By solvent exchange in a microchamber, a ternary solution consisting of a model compound β-alanine, water, and isopropanol was displaced by a flow of isopropanol. In the process, oiling-out droplets formed and crystallized. Our results showed that the shape and size of the crystals on surfaces with chemical micropatterns could be simply mediated by the flow conditions of solvent exchange. More uniform crystals formed on hydrophilic microdomains compared to hydrophobic microdomains or homogeneous surfaces. Varying flow rates or channel heights led to the formation of thin films with microholes, connected networks of crystals, or small diamond-shaped crystals. Physical microstructures (represented by microlenses) on the surface allowed the easy detachment of crystals from the surface. Beyond oiling-out crystallization, we demonstrated that the crystal formation of another solute dissolved in the droplets could be triggered by solvent exchange. The length of crystal fibers after the solvent-exchange process was shorter at a faster flow rate. This study may provide further understanding to effectively obtain the crystallization of surface droplets through the solvent-exchange approach.
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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) | 0.000 | 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".