Contact angle measurement with a smartphone
Bibliographic record
Abstract
In this study, a smartphone-based contact angle measurement instrument was developed. Compared with the traditional measurement instruments, this instrument has the advantage of simplicity, compact size, and portability. An automatic contact point detection algorithm was developed to allow the instrument to correctly detect the drop contact points. Two different contact angle calculation methods, Young-Laplace and polynomial fitting methods, were implemented in this instrument. The performance of this instrument was tested first with ideal synthetic drop profiles. It was shown that the accuracy of the new system with ideal synthetic drop profiles can reach 0.01% with both Young-Laplace and polynomial fitting methods. Conducting experiments to measure both static and dynamic (advancing and receding) contact angles with the developed instrument, we found that the smartphone-based instrument can provide accurate and practical measurement results as the traditional commercial instruments. The successful demonstration of use of a smartphone (mobile phone) to conduct contact angle measurement is a significant advancement in the field as it breaks the dominate mold of use of a computer and a bench bound setup for such systems since their appearance in 1980s.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".