A Yeast-Fermented Porous Coating for Anti-Icing Enabled by Photothermal Trapping and Crack Promotion
Bibliographic record
Abstract
Photothermal anti-icing surfaces harness solar energy for efficient deicing and hold promise for preventing ice accretion. However, under prolonged severe conditions, ice buildup is inevitable, making low ice adhesion a critical safeguard for effective removal. Here, we report a yeast-fermented porous coating (YFPC) that integrates strong photothermal performance with interfacial crack promotion to overcome these limitations. The coating is fabricated scalably in a single step by spraying a silicone/carbon nanoparticles (CNPs)/yeast emulsion: CNPs assemble at droplet interfaces to generate a hierarchical light-trapping texture, while yeast fermentation forms the porous structure. This design endows the YFPC with exceptional photothermal performance, demonstrating a 55 °C temperature increase under one-sun irradiation. Crucially, the porous structure within the coating amplifies stress heterogeneity at the ice-coating interface, which triggers multiple microcracks along the adhesive interface, enabling rapid ice detachment with minimal force. By combining efficient solar-driven heating with crack-assisted ice release in a scalable fabrication process, this work establishes a new strategy for high-performance anti-icing coatings.
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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) | 0.000 | 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 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".