Exploring the tensile ice adhesion strength of surfaces using a newly designed and verified measurement apparatus
Bibliographic record
Abstract
In cold regions, icing is a serious challenge that people face daily. Ice accretion on bridge cables, wind turbines and ship hulls are only a few examples of where icing can have fatal effect. Much research has been done to overcome this problem using various solutions such as producing surfaces that delay ice formation or preventing ice accumulation using coatings exhibiting low adhesion to ice. Among these, elastomeric materials have repeatedly been reported as successful coatings with ultra-low adhesion values to ice, arising from an interfacial instability which was recently proposed as the underlying phenomena. This instability, interfacial cavitation, occurs when tensile forces are indirectly generated at the ice/substrate interface during shear. Most research has focused on studying, measuring, and manipulating ice adhesion of surfaces by shear forces. In this work, a high throughput, low-cost apparatus was designed and benchmarked to measure the tensile ice adhesion strength of various surfaces. The performance and precision of the setup was verified using experimental trials and the effect of various parameters such as temperature, pull-off speed, substrate thickness, and ice/substrate interfacial area were characterized. We then delved deeper in the ice adhesion behavior of polydimethylsiloxane (PDMS), a commonly used elastomeric ice phobic coating, comparing its pure tensile adhesive fracture to its typical shear adhesion behavior. It was found that tensile ice adhesion strength of PDMS is usually higher than the shear ice adhesion strength. Also, all parameters (such as thickness, elastic modulus, and probe speed) affected the tensile ice adhesion strength in the same manner they did with the shear ice adhesion strength, except roughness. When roughness of the PDMS surface was increased to up to an Sq = 32 μm, the shear ice adhesion strength remained almost constant. We also developed a superhydrophobic PDMS that maintained the low tensile and shear ice adhesion value of σice= 11.2 kPa and τice= 8.6 after harsh abrasion periods. This work elucidates tensile ice/elastomer adhesion mechanisms and behavior, which is crucial for designing future elastomeric coatings that facilitate ice removal through interfacial instabilities.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".