Comparative study on the influence of surface characteristics on de-icing evaluation
Bibliographic record
Abstract
Abstract A comparative study of de-icing evaluation methods was conducted in this work, and their variations in response to surface characteristics were investigated. The mechanical de-icing measurements include centrifugal, push, and tensile methods. The centrifugal and the horizontal push (shear) methods suggested a linear relationship of ice adhesion strength with surface roughness, whereas the tensile (normal) method indicated an inverse curvilinear relationship with contact angle hysteresis. A partial correlation of contact angle hysteresis on the shear-based methods was also indicated over a specified range of surface roughness. Further attempts were also made on 1H,1H,2H,2H-perfluorooctyltriethoxysilane-coated surfaces, and the ice adhesion indicated a clear reduction in the normal de-icing method, whereas the shear-based methods did not show a considerable change in ice adhesion, highlighting their mechanical forces-centric response. Lastly, a further evaluation using a hybrid de-icing method was conducted, to verify the influence of surface characteristics on ice removal involving heating, which demonstrated a partial correlation of energy consumption with the ice adhesion strength over a specified range of surface roughness. The results obtained in this study provide crucial information on the influence of surface characteristics on ice adhesion and offer material-dependent correlations of the popular de-icing evaluation methods. The conclusions could be applied to define an appropriate testing method for the evaluation of icephobic surfaces and coatings. Graphical abstract
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".