General Methodology for Evaluating the Adhesion Force of Drops and Bubbles on Solid Surfaces
Bibliographic record
Abstract
The shortcomings of the current formulation for calculating the adhesion force for drops and bubbles with noncircular contact lines are discussed. A general formulation to evaluate the adhesion force due to surface forces is presented. Also, a novel methodology, that is, IBAFA, image based adhesion force analysis, was developed to allow implementation of the general formulation. IBAFA is based on the use of multiple profile images of a drop. The images are analyzed (1) to accurately reconstruct the contact line shape, which is analytically represented by a Fourier cosine series, and (2) to measure contact angles at multiple locations along the contact line and determine the contact angle distribution based on a linear piecewise interpolation routine. The contact line shape reconstruction procedure was validated with both actual experiments and simulated experiments. The procedure for the evaluation of the adhesion force was tested using simulated experiments with synthetic drops of known shapes. A comparison with current methods showed that simplifying assumptions (e.g., elliptical contact line or linear contact angle distribution) used in these methods result in errors up to 76% in the estimated adhesion force. However, the drop adhesion force evaluated using IBAFA results in small errors on the order of 1%.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".