Synthetic, layered nanoparticles for polymeric nanocomposites (PNCs)
Bibliographic record
Abstract
Abstract This review discusses preparation and use of the synthetic layered nanoparticles in polymer matrices, i.e., in the polymeric nanocomposites (PNCs). Several types of synthetic or semi‐synthetic layered materials are considered, namely the phyllosilicates (clays), silicic acid (magadiite), layered double hydroxides (LDHs), zirconium phosphates (ZrPs), and di‐chalcogenides. The main advantage of synthetic clays is their chemical purity (e.g. absence of amorphous and gritty contaminants, as well as arsenic, iron, and other heavy metals), white to transparent color that assures reproducibly of brightly colored products, as well as a wide range of aspect ratios, p = 20 to ≤6000. Several large scale production facilities have been established. The synthetic clay and LDH industries are oriented toward big volume markets: catalysis, foodstuff, cosmetics, pharmaceuticals, toiletry, etc. The use of these materials in PNCs is limited to synthetic clays and LDHs, mainly for reinforcement, permeability control, reduction of flammability, and stabilization, e.g. during dehydrohalogenation of chlorinated macromolecules. The use of lamellar ZrPs and di‐chalcogenides is at the laboratory stage of functional polymeric systems development, e.g. for electrically conductive materials, catalysts or support for catalysts, in photochemistry, molecular and chiral recognition, or in fuel cell technologies, etc. Copyright © 2007 John Wiley & Sons, Ltd.
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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".