Marine Antifouling Behavior of Lubricant-Infused Nanowrinkled Polymeric Surfaces
Bibliographic record
Abstract
A new family of polymeric, lubricant-infused, nanostructured wrinkled surfaces was designed that effectively retains inert nontoxic silicone oil, after draining by spin-coating and vigorous shear for 2 weeks. The wrinkled surfaces were fabricated using three different polymers (Teflon AF, polystyrene, and poly(4-vinylpyridine)) and two shrinkable substrates (Polyshrink and shrinkwrap), and Teflon on Polyshrink was found to be the most effective system. The volume of trapped lubricant was quantified by adding Nile red to the silicone oil before infusion and then extracting the oil and Nile red from the surfaces in heptane and measuring by fluorimetry. Higher volumes of lubricant induced lower roll-off angles for water droplets, and in turn induced better antifouling performance. The infused surfaces displayed stability in seawater and inhibited growth of Pseudoalteromonas spp . bacteria up to 99%, with as little as 0.9 μL cm –2 of the silicone oil infused. Field tests in the waters of Sydney Harbor over 7 weeks showed that silicone oil infusion inhibited the attachment of algae, but the algal attachment increased as the silicone oil was slowly depleted over time. The infused wrinkled surfaces have high transparency and are moldable, making them suited to protect the windows of underwater sensors and cameras.
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) | 0.000 | 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".