Growth dynamics of surface nanodroplets during solvent exchange at varying flow rates
Bibliographic record
Abstract
Solvent exchange is a simple solution-based process to produce surface nanodroplets over a large area. The final size of the droplets is determined by both the flow and solution conditions for a given substrate. In this work, we investigate the growth dynamics of surface nanodroplets during solvent exchange by using total internal reflection fluorescence microscopy (TIRF). The results show that during the solvent exchange, the formation of surface nanodroplets advanced on the surface in the direction of the flow. The time for the number density and surface coverage of the droplets to reach their respective plateau values is determined by the flow rate. From the observed evolution of the droplet volume and of the size of individual growing droplets, we are able to determine that the growth time of the droplets scales with the Peclet number Pe with a power law ∝Pe-1/2. This is consistent with Taylor-Aris dispersion, shedding light on the diffusive growth dynamics during the solvent exchange. Further, the spatial rearrangement of the droplets during coalescence demonstrates a preference in position shift based on size inequality, namely, the coalesced droplet resides closer to the larger of the two parent droplets. These findings provide a valuable insight toward controlling droplet size and spatial distribution.
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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".