Metallic Plate Buckling As a Low Adhesion Mechanism for Durable and Scalable Icephobic Surface Design
Bibliographic record
Abstract
Abstract Ice accretion is significantly detrimental to a range of different industries worldwide. Current methods for reducing ice adhesion include the use of lubricants, hydrophobic coatings, or soft elastomers, all of which exhibit limited durability. As an alternative, here sparsely confined metallic sheets are suspended and the surface buckling instability is tailored, resulting in ice adhesion strengths on par with these prior strategies but without the use of any coatings. These Buckling Elastomer‐like Anti‐icing Metallic Surfaces, or BEAMS, exhibit ultra‐low ice adhesion (<1 kPa) and the mechanical resilience of metals. Results from an icing wind tunnel confirmed the efficacy of BEAMS toward impact ice accreted in realistic conditions via the high‐speed impingement of ≈20 µm droplets at −20 °C. The BEAMS sheet confinement, boundary conditions, and physical dimensions of both the ice and the metallic plates can be altered to minimize ice adhesion via the mechanics of plate buckling. Additionally, BEAMS that detach ice without directly contacting it are designed, the scalability of BEAMS is demonstrated, and their durability is verified using rain erosion, sandblasting, thermal extremes, and repeated icing/de‐icing, both in an icing wind tunnel and on a benchtop system.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".