Range of Validity of Drop Shape Techniques for Surface Tension Measurement
Bibliographic record
Abstract
Drop shape techniques are widely used for surface tension measurement. As the shape becomes close to spherical, the performance of drop shape techniques deteriorates. A quantitative criterion called shape parameter was previously introduced to quantify the meaning of "well-deformed" drops and "close to spherical" drops. In this paper, a modified definition of the shape parameter that does not depend on the radius of curvature at the drop apex is proposed. Such definition is applicable to different constellations of pendant and sessile drops/bubbles. Dimensional analysis is used to describe similarity in constrained sessile drop shapes and to express the problem using appropriate dimensionless groups. The proposed shape parameter is found to depend only on two dimensionless groups: the dimensionless volume (drop volume normalized by the cube of the holder radius) and the Bond number (using the drop holder's radius as the length scale). A critical shape parameter (minimum value of the shape parameter that guarantees a specified accuracy) is shown to depend only on Bond number. A set of experiments were performed with pure liquids to illustrate the change of the critical shape parameter with the Bond number.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".