Bibliographic record
Abstract
In this review we discuss clay-reinforced polymeric nano-composites (PNC). The advantages of using clays are availability, cost, and aspect ratio; the main disadvantage is their hygroscopic character. It is a relatively simple task to disperse clay platelets in water-soluble solvents, monomers, or oligomers. However, preparation of PNC in a hydrophobic, high molecular weight, polymer (e.g., polypropylene) is difficult. The way to approach the problem is to consider the process from the perspective of compatibilization of antagonistically immiscible components and diffusion-cont rolled mixing. The preferred cl ay is montmorillonite (MMT) with micron-sized particles formed by stacks of hundreds of layered crystals, each of 0.96 nm thickness and an average diameter of 100–2000 nm. The MMT unit cell offers two types of reactive sites: anions on the silicate fl at surfaces and hydroxyl (–OH) groups on the edges. Historically, “compatibilization” involved forming an ionic bond between the clay surface and organophilic onium cation, e.g., ammonium. More recently, “compatibilizers” containing epoxy or acid anhydride groups were reacted with the side –OH groups. Since the solid–solid interactions between MMT layers are about 100 times stronger than liquid–liquid ones, good bonding between clay particle and the matrix is imperative. To prepare PNC with well-exfoliated clay, the best strategy is to do it in multiple steps. Initial swelling of MMT in water may expand the interlayer spacing from the initially dry state of 0.96 to about 1.3 nm. Intercalation with suitable organophilic molecules, onium or Lewis-base type increases it to about 4 nm. Reactive compatibilization of the organo–clay/matrix polymer system under flow results in exfoliation of the clay platelets (interlayer spacing larger than 8.8 nm).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".