Observation of the Time-Invariant Nature of Evaporating Thin Films: Insights from SPR Imaging
Bibliographic record
Abstract
The evaporating thin film (ETF) plays a critical role in heat dissipation due to the substantially enhanced evaporation flux. Despite its fundamental importance in interfacial evaporation dynamics, direct experimental characterization of the ETF [1-3] has remained a significant challenge, resulting in a limited understanding of its temporal evolution. This presentation provides the first direct experimental evidence demonstrating the time-invariant nature of ETF profiles during the pinning stage of droplet evaporation, offering novel insights into interfacial evaporation mechanisms using the surface plasmon resonance (SPR) imaging [4-5]. The ultra-thin liquid film profiles are measured under varying surface wettability conditions through a self-assembled monolayer (SAM) on a gold substrate. A theoretical model incorporating kinetic theory and the augmented Young-Laplace equation is also employed to quantitatively examine the finite evaporation flux in the ETF region. The findings reveal that the ETF profile remains time-invariant during the pinned stage, exhibiting local equilibrium characteristics, and theoretical predictions show good agreement with experimental observations. Notably, it is found that surface wettability has a minor influence on variations in ETF profiles, and the disjoining pressure is found to be higher on hydrophilic substrates, contributing to further thinning of the ETF. Furthermore, these findings indicate that higher surface wettability enhances ETF thinning, thereby improving heat dissipation via maximum evaporation flux.
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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 | 0.001 | 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".