Highly porous nanocoatings tailored for inverse nanoparticle‐polymer composites
Bibliographic record
Abstract
Abstract A novel nanoparticle‐polymer composite is proposed, named inverse nanocomposites in this work. First, a rigid percolating scaffold of nanoparticles is formed, which is filled with a matrix and then polymerized. Targeted for use in thin‐film applications, these mesoporous nanoparticle scaffolds are prepared by combining the sol–gel chemistry of functionalized silanes with nanoparticles in dispersions. The nanoparticle coatings have high porosity, low density, good adhesion to the substrate, and interesting non‐classical properties, such as absorbency of highly viscous fluids. The porosity, which can be adjusted by changing the composition and preparation parameters, reaches 75%. The porous scaffold can be infiltrated with various fluids, including acrylic and epoxy monomers and even highly viscous pressure‐sensitive adhesives. If the monomers are polymerized after imbibition, the inverse nanocomposite is formed, consisting of a percolating particle network surrounded by a polymeric binder. Hence, the morphology comprises an interpenetrating system of two co‐continuous phases and not merely particles dispersed in a polymeric phase, as is typical for conventionally prepared nanocomposites.
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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 | 0.000 |
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".