Interlaboratory Study of Ice Adhesion Using Different Techniques
Bibliographic record
Abstract
Low ice adhesion surfaces are a promising anti-icing strategy. However, reported ice adhesion strengths cannot be directly compared between research groups. This study compares results obtained from testing the ice adhesion strength on two types of surfaces at two different laboratories, testing two different types of ice with different ice adhesion test methods at temperatures of −10 and −18 °C. One laboratory used the centrifuge adhesion test and tested precipitation ice and bulk water ice, while the other laboratory used a vertical shear test and tested only bulk water ice. The surfaces tested were bare aluminum and a commercial icephobic coating, with all samples prepared in the same manner. The results showed comparability in the general trends, surprisingly, with the greatest differences for bare aluminum surfaces at −10 °C. For bulk water ice, the vertical shear test resulted in systematically higher ice adhesion strength than the centrifugal adhesion test. The standard deviation depends on the surface type and seems to scale with the absolute value of the ice adhesion strength. The experiments capture the overall trends in which the ice adhesion strength surprisingly decreases from −10 to −18 °C for aluminum and is almost independent of temperature for a commercial icephobic coating. In addition, the study captures similar trends in the effect of ice type on ice adhesion strength as previously reported and substantiates that ice formation is a key parameter for ice adhesion mechanisms. Repeatability should be considered a key parameter in determining the ideal ice adhesion test method.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".