Ice-phobic Coatings Based on Silicon-Oil-Infused Polydimethylsiloxane
Bibliographic record
Abstract
A simple and low-cost technique for the preparation of silicon-oil-infused polydimethylsiloxane (PDMS) coatings with different silicon oil contents have been developed and studied. This material is designed for ice-phobic applications, and thus a high hydrophobic property of PDMS is maintained by avoiding any polar groups such as C═O and OH in the structure. Therefore, the polymer main chain was attached with vinyl and Si-H groups to obtain a cross-linking capability, meanwhile to ensure a nonpolar chemical structure. Its ice-phobic property has been investigated in terms of ice adhesion strength (tensile and shear), water contact angle, icing dynamics using high-speed photography and morphology using TEM, SEM and AFM. The prepared coating surface shows a low surface energy and very low ice adhesion strength of 50 kPa, only about 3% of the value on a bare aluminum (Al) surface. In the silicon oil infused PDMS coatings, the low surface energy of the silicon oil and PDMS, and the high mobility of silicon oil played an important role on the ice-phobic property. Both of these factors offer the surface a large water contact angle and hence a small contact area, leading to the formation of a loose ice layer. In addition, the oil infused polymer structure significantly reduces the contact area of the ice with solid substrate since the ice mostly contacts with the mobile oil. This leads to a very weak interaction between the substrate and ice, consequently significantly reduces the ice adhesion strength on the surface. Therefore, such material could be a good candidate for ice-phobic coatings on which the accumulated ice may be easily removed by a nature force, such as wind, gravity, and vibration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".