Liquid film thinning-thickening anomaly in electrolyte solutions
Bibliographic record
Abstract
HYPOTHESIS: The distribution of dissolved ions depends largely on the position and shape of air-water interfaces. Following the interfacial interaction, both the surface deformation and reduced separation distance may induce different ion concentrations in liquid film and bulk solutions. The consequent Marangoni effect should change the flow dynamics inside the liquid films and thus influence the film evolution process. EXPERIMENTS: In this study, a home-made interferometer is employed to obtain the spatiotemporal evolution of film profiles during the bubble-solid surface interaction. The quantification of film thickness enables the determination of micro/nanoscale fluid flow from liquid films. FINDINGS: Unlike the common cases of continuous film drainage behaviors, the film thickness at the rim exhibits a counterintuitive increase, causing the previously approaching bubble to retract from solid surfaces. It is revealed how the inward flow can surpass the drainage outflow and reverse the normal film evolution. Theoretical developments involving the electrolyte type, ion concentration and film deformation are provided to describe this competition and address the film thinning-thickening anomaly. Our finding enriches the understanding of the microscopic fluid flow in highly confined and deformed regions, and the research outcome creates the possibility of modulating near-wall bubble dynamics as required.
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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 | 0.001 |
| 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".