Methodology for High Accuracy Contact Angle Measurement
Bibliographic record
Abstract
A new version of axisymmetric drop shape analysis (ADSA) called ADSA-NA (ADSA-no apex) was developed for measuring interfacial properties for drop configurations without an apex. ADSA-NA facilitates contact angle measurements on drops with a capillary protruding into the drop. Thus a much simpler experimental setup, not involving formation of a complete drop from below through a hole in the test surface, may be used. The contact angles of long-chained alkanes on a commercial fluoropolymer, Teflon AF 1600, were measured using the new method. A new numerical scheme was incorporated into the image processing to improve the location of the contact points of the liquid meniscus with the solid substrate to subpixel resolution. The images acquired in the experiments were also analyzed by a different drop shape technique called theoretical image fitting analysis-axisymmetric interfaces (TIFA-AI). The results were compared with literature values obtained by means of the standard ADSA for sessile drops with the apex. Comparison of the results from ADSA-NA with those from TIFA-AI and ADSA reveals that, with different numerical strategies and experimental setups, contact angles can be measured with an accuracy of less than 0.2 degrees. Contact angles and surface tensions measured from drops with no apex, i.e., by means of ADSA-NA and TIFA-AI, were considerably less scattered than those from complete drops with apex. ADSA-NA was also used to explore sources of improvement in contact angle resolution. It was found that using an accurate value of surface tension as an input enhances the accuracy of contact angle measurements.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.008 |
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".