Stretch-Induced Ice-Shedding Protective Sheet by an Auxetic Skeletal Structure Embedded in an Organogel Matrix
Bibliographic record
Abstract
Dealing with slush and ice buildup is common in northern climates. These contaminants can easily block important sensors for cars, such as cameras and LiDAR. Current anti-icing surfaces and deicing technologies are often not effective during the height of winter. This work describes the fabrication and testing of a protective composite sheet consisting of a slippery organogel matrix and an auxetic structure that both passively and actively sheds ice from the surface to protect these sensors. The organogel matrix excretes a slippery lubricating layer that prevents ice from building on the surface, and a uniaxially stretched auxetic structure creates an area mismatch between the surface of the sheet and the ice. The organogel matrix consists of a silicone elastomer precursor with silicone oil, providing optical transparency. The auxetic structure is 3d printed with a combination of Poly(dimethylsiloxane) (PDMS) with fumed silica added as a rheological modifier to provide additional strength to the structure. Image analysis reveals that the composite sheet has a staggering negative Poisson’s ratio of −0.58. In order to quantify the ice adhesion on the shield, home-built testing equipment is devised to assess the effectiveness of the protective sheets in removing ice built on the surface. A small critical shear stress to remove ice cubes from the sheet (<10 kpa) is achieved in various elastomer and organogel surfaces in static ice adhesion tests. However, active deice testing involving repetitive uniaxial stretching reveals that the auxetic property of the sheet is strictly necessary to remove ice buildup effectively. In conclusion, our composite sheets with a negative Poisson’s ratio with slippery, anti-icing surfaces are effective in removing ice buildup, suggesting their potential usage as a shield for optical sensors in autonomous vehicles and more.
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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 | 0.000 |
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".