New insights into icephobic material assessment: Introducing the human motion–inspired automated apparatus (HMA)
Bibliographic record
Abstract
The impact of winter on exposed structures and transportation poses significant dangers and costs to various industries, particularly the transportation sector. Icephobic surfaces are currently being developed to reduce winter-related impacts. Creating such surfaces requires considering various factors, including reducing and preventing ice accumulation, significantly decreasing ice adhesion, and/or delaying water solidification. Although established methods such as centrifugal force and push-off tests exist for measuring ice adhesion, the results may not always correlate or offer the needed information for specific applications. To better assess icephobic properties, we have developed a novel device called the human motion–inspired automated apparatus (HMA) that mimics manual de-icing performed by humans in a scraping mode. The primary objective of the HMA is to emulate human removal of ice-covered surfaces, providing a more realistic evaluation of icephobic properties according to the ease of ice removal. This apparatus aims to revolutionize icephobic material assessment by offering improved accuracy, repeatability, and versatility in testing. We developed a unique procedure using low icing conditions, which are challenging to evaluate using conventional methods, and assessed four surfaces: aluminum as a reference, an epoxy-based hydrophilic coating, a hydrophobic silicone elastomer coating, and a hydrophobic epoxy–silicone coating. Our HMA characterizes surfaces according to several crucial parameters, including the normal force required to initiate ice scraping removal, the maximum force achieved, the angle of attack, and the equivalent force, all consistent with validation tests conducted by humans. Among the evaluated surfaces, the silicone coating required the lowest normal force, and the epoxy–silicone coating had the lowest maximum and equivalent forces. Our HMA results align well with validation tests conducted by humans. The HMA enables evaluating various critical icing conditions and promises a broad range of applications in research and development. • Introduction of a novel Human Motion-Inspired Automated Apparatus (HMA) to assess icephobic properties. • HMA mimics real-world human scraping actions to provide accurate and repeatable measurements. • HMA testing shows silicone and epoxy–silicone surfaces require significantly less force for de-icing compared to aluminum. • The study demonstrates improved methodologies for evaluating icephobic coatings under realistic icing conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".