Modeling Liquid Bridge between Surfaces with Contact Angle Hysteresis
Bibliographic record
Abstract
This paper presents the behaviors of a liquid bridge when being compressed and stretched in a quasi-static fashion between two solid surfaces that have contact angle hysteresis (CAH). A theoretical model is developed to obtain the profiles of the liquid bridge given a specific separation between the surfaces. Different from previous models, both contact lines in the upper and lower surfaces were allowed to move when the contact angles reach their advancing or receding values. When the contact angles are between their advancing and receding values, the contact lines are pinned while the contact angles adjust to accommodate the changes in separation. Effects of CAH on both asymmetric and symmetric liquid bridges were analyzed. The model was shown to be able to correctly predict the behavior of the liquid bridge during a quasi-static compression/stretching loading cycle in experiments. Because of CAH, the liquid bridge can have two different profiles at the same separation during one loading and unloading cycle, and more profiles can be obtained during multiple cycles. The maximum adhesion force generated by the liquid bridge is found to be influenced by the CAH of surfaces. CAH also leads to energy cost during a loading cycle of the liquid bridge. In addition, the minimum separation between the two solid surfaces is shown to affect how the contact radii and angles change on the two surfaces as the liquid bridge is stretched.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".