Lubricant‐Infused Nanoparticulate Coatings Assembled by Layer‐by‐Layer Deposition
Bibliographic record
Abstract
Omniphobic coatings are designed to repel a wide range of liquids without leaving stains on the surface. A practical coating should exhibit stable repellency, show no interference with color or transparency of the underlying substrate and, ideally, be deposited in a simple process on arbitrarily shaped surfaces. We use layer‐by‐layer (LbL) deposition of negatively charged silica nanoparticles and positively charged polyelectrolytes to create nanoscale surface structures that are further surface‐functionalized with fluorinated silanes and infiltrated with fluorinated oil, forming a smooth, highly repellent coating on surfaces of different materials and shapes. We show that four or more LbL cycles introduce sufficient surface roughness to effectively immobilize the lubricant into the nanoporous coating and provide a stable liquid interface that repels water, low‐surface‐tension liquids and complex fluids. The absence of hierarchical structures and the small size of the silica nanoparticles enables complete transparency of the coating, with light transmittance exceeding that of normal glass. The coating is mechanically robust, maintains its repellency after exposure to continuous flow for several days and prevents adsorption of streptavidin as a model protein. The LbL process is conceptually simple, of low cost, environmentally benign, scalable, automatable and therefore may present an efficient synthetic route to non‐fouling materials.
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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 | 0.001 |
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".