Oleophobicity of Biomimetic Micropatterned Surface and Its Effect on the Adhesion of Frozen Oil
Bibliographic record
Abstract
The relationship between the oleophobicity of micropatterned surfaces and the reduction of oil adhesion at low temperatures was explored by using siloxane elastomer surfaces as a model system. Polydimethylsiloxane (PDMS) surfaces were fabricated with varying oleophobicity from oleophilic to superoleophobic by combing the blending of trichloro(1H,1H,2H,2H-perfluorooctyl)silane (FDTS) into PDMS with the construction of bioinspired micropillars. The oil contact angles of micropillars were >130°, with the largest contact angle measured to be 146°. The micropillared surface showed remarkable self-cleaning properties; the contact angle hysteresis was <15°. The transparent oil droplets on PDMS surfaces of varied oleophobicity were frozen into a white-colored solid at -25 °C with the aid of a cooling system. Adhesion forces of the frozen oil droplets were obtained from the knock-off tests, showing that the adhesion forces dropped with the increased oleophobicity. The largest adhesion force was observed on the oleophilic flat surface, while the lowest adhesion force was on the highest oleophobic micropillared surface. The relative effectiveness of chemical and physical modifications on adhesion strength reduction was studied in terms of FDTS and micropillars, respectively. The results showed that a reduction of adhesion strength by 4% was reached by blending FDTS into flat PDMS, while a much more pronounced reduction of frozen oil adhesion strength by 60% was achieved by blending FDTS into PDMS micropillars; these results suggested a possible synergic effect of the FDTS chemistry and micropillar on the reduction of adhesion strength of frozen oil droplets.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".