A Critical Review of Evaluation Methods of Ice Adhesion Strength on the Surface of Materials
Bibliographic record
Abstract
Several techniques have already been proposed to determine ice adhesion strength. This study made a critical review of the existing methods and proposed three other techniques for ice/substrate systems, where the latter techniques have already been used to other adhesive/substrate systems. In this study, these methods are compared for their performance and limitations, with a selection of the most promising ones. The main conclusions are: in most techniques, test procedures required a long time while showing a low degree of reproducibility. Cohesive and adhesive failures, as well as a combination of both, were observed in the measurements. Macroscopic or microscopic/nanoscopic scale tests were used to evaluate ice adhesion strength Micro/nano scale tests using Atomic Force Microscopy (AFM) or nano indentation instrument can be used to estimate the nature and surface energy of water-repellent materials, and identify materials with low-ice adhesion. Among various methods the combination of AFM /nano-indentation technique with either lap-shear or combined lap-shear and tensile modes should be the most powerful.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".