Lubricant‐Infused Nanoparticulate Coatings Assembled by Layer‐by‐Layer Deposition
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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 it