Experimental Study on the Evolution of Contact Angles with Temperature Near the Freezing Point
Bibliographic record
Abstract
Measurements of the water contact angle as a function of temperature down to freezing gives valuable information for the development of anti-icing coatings. Advancing (ACA) and receding (RCA) contact angles were measured by depositing drops of water on different material surfaces for temperatures ranging from room temperature to 0 °C. No changes in the contact angles as a function of temperature have been observed for polished silicon, polished aluminum, roughened silicon, gold, high density polyethylene, PTFE (polytetrafluoroethylene), and PMMA (poly(methyl methacrylate)) for the entire temperature range. However both the ACA and RCA decrease and the hysteresis increases at temperatures below 5 °C for all nanostructured materials used in this study, such as nanopatterned PMMA, PTFE nanoparticles film, and HIREC-100 (a super water-repellent coating blended with TiO 2; developed by NTT Advanced Technology Corporation ( http://www.ntt-at.com )). This behavior was attributed mainly to the condensation from the vapor phase of the water drop for temperatures below 5 °C. The resulting thin water film decreases the contact angles, especially for the receding contact, enhancing the hysteresis and water drop adherence. These experimental results could explain the adherence of ice on superhydrophobic nanostructured surfaces.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".