Durable Metallic Surfaces Capable of Passive and Active De‐Icing
Bibliographic record
Abstract
Ice accretion has adverse effects on several industrial sectors worldwide. Sparsely suspended, thin metallic sheets (buckling elastomer‐like anti‐icing metallic surfaces, or BEAMS) recently demonstrated extremely low ice adhesion strengths while maintaining durability. Here, BEAMS are designed using elastomeric suspension points shaped as channels, enabling active de‐icing by flowing air underneath the suspended sheet. The channel geometry is optimized using computational fluid dynamics by iterating through various channel dimensions and flow conditions. An experimental setup is constructed and utilized to assess BEAMS comprised of 1–4 channels. Active de‐icing is achieved by pressurizing the air within the channels to bulge the sheet outward and delaminate accreted ice from the interface. Active de‐icing is achieved by flowing room temperature air through the channels to heat the surface and melt the interface. Passive de‐icing is also observed under atmospheric icing conditions. Rime is accreted within an icing wind tunnel on multichannel BEAMS. Ice adhesion strengths <8 kPa are maintained after ten consecutive icing/de‐icing runs, demonstrating substantial durability. The active and passive de‐icing capability of channeled BEAMS makes it a promising candidate for improving the operational efficiency of infrastructure in cold environments.
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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 | 0.001 | 0.000 |
| 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".