Contact angle measurement with a smartphone
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it