How pinning and contact angle hysteresis govern quasi-static liquid drop transfer
Bibliographic record
Abstract
This paper presents both experimental and numerical simulations of liquid transfer between two solid surfaces with contact angle hysteresis (CAH). Systematic studies on the role of the advancing contact angle (θa), receding contact angle (θr) and CAH in determining the transfer ratio (volume of the liquid transferred onto the acceptor surface over the total liquid volume) and the maximum adhesion force (Fmax) were performed. The transfer ratio was found to be governed by contact line pinning at the end of the transfer process caused by CAH of surfaces. A map based on θr of the two surfaces was generated to identify the three regimes for liquid transfer: (I) contact line pinning occurs only on the donor surface, (II) contact line pinning occurs on both surfaces, and (III) contact line pinning occurs only on the acceptor surface. With this map, an empirical equation is provided which is able to estimate the transfer ratio by only knowing θr of the two surfaces. The value of Fmax is found to be strongly influenced by the contact line pinning in the early stretching stage. For symmetric liquid bridges between two identical surfaces, Fmax may be determined only by θa, only by θr, or by both θa and θr, depending on the magnitude of the contact angles. For asymmetric bridges, Fmax is found to be affected by the period when contact lines are pinned on both surfaces.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".