Evaporation and Fluid Dynamics of a Multicomponent Sessile Droplet on Nanostructured Doubly Reentrant Surfaces
Bibliographic record
Abstract
The fluid dynamics and evaporation of a sessile multicomponent droplet on nanostructured doubly reentrant surfaces are complex and du to superhydrophobic wettability of the surfaces.This kind surface is very promising in controlling droplet dynamics, heat transfer and evaporation.In this paper, we investigate the interfacial effects of nanostructured doubly reentrant surfaces on the flow behaviors and local concentration evolution during the evaporation of an ethanol/water multicomponent droplet.Using micro-resolution particle image velocimetry (µPIV) and novel aggregate-induced emission-based (AIE) techniques, the flow patterns and local concentration distributions on both hydrophobic and nanostructured doubly reentrant surfaces are probed and compared.It is found that in addition to the established Marangoni flow dominated stage, transition stage, and buoyancy-induced flow dominated stage, a new transition stage and rolling stage for the nanostructured doubly reentrant surface are detected in the late evaporation period.Differences in the local concentration distribution evolution occur, depending on the hydrophobicity of the surface on which the droplet is placed.The theoretical analysis combining PIV and AIE visualization results reveals that the variations in droplet concentration distributions on surfaces with different hydrophobicity exert a significant impact on evaporative behaviors.These behaviors in turn affect the evolution of local concentration 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 | 0.000 |
| 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) | 0.000 | 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".