Autophilic Effect: Wetting of Hydrophobic Surfaces by Surfactant Solutions
Bibliographic record
Abstract
This paper resolves questions in the literature regarding the autophilic effect (i.e., movement of surfactant past the advancing contact line-leading to an increase in drop radius beyond that due to the advance) and its importance to quasi-static sessile drop wetting. Various systems (SDS, HTAB, and MEGA 10 surfactant solutions at three concentrations each and pure water and ethylene glycol on hydrophobic Teflon and OTS-coated silicon) are probed to determine the existence, time constant, and magnitude of the autophilic effect, using quasi-static advancing and receding sessile drops. From spreading results and advancing contact angle measurements, it is inferred that the autophilic effect does not occur for our systems (in contradiction of some literature) for the following reasons. First, no relation exists between the time constant for spreading and surfactant concentration, meaning the spreading seen is likely inertial in cause and not due to surfactants. Second, advancing contact angle decreases between tests on clean surfaces and those pre-exposed to surfactant, ruling out the possibility that the autophilic effect is faster than the advance. Third, spreading is seen after the end of the advance over both clean and pre-exposed surfaces, ruling out the possibility that the autophilic effect is slower than the advance. Finally, the pure liquids spread in a similar fashion to surfactant solutions on Teflon and similar contact angle measurements are seen for surfactant solutions and pure liquids of similar surface tension.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".