Clay-containing polymeric nanocomposites, characterization, high pressure dilatometry
Bibliographic record
Abstract
In structural clay-containing polymeric nanocomposites (CPNC) ca. 2 – 5 wt% clay is dispersed in polymer matrix: thermoplastic, thermoset or elastomeric. Since most clay/polymer systems are antagonistically immiscible, in analogy to immiscible polymer blends, a two-step compatibilization is required: (1) intercalation of clay into organoclay and (2) addition of functional compatibilizer(s). The volume of these compatibilizing species usually is larger than that of clay itself. These additives affect the thermodynamic, rheology and other performance characteristics of CPNC. Since the system is immiscible, i.e., sensitive to stresses during the compounding and forming stages, the reproducibility of behavior may be a problem. Furthermore, from the chemical and physical points of view the natural and synthetic clays are complex. The purified natural clay may contain 2-5 wt% contaminants (humic derivatives, quartz, gypsum, dolomite, ...), whereas the synthetic ones may be a mixture of different crystallographic forms (e.g., lamellar and needle-like). Both types of clay have polydispersed platelets shape and size. Accordingly, characterization of CPNC should start with that of clay, its platelet size, their inherent dispersibility (absence of interlamellar crystalline welding) and presence of contaminants. CPNC are being characterized by the rheological methods in the solid and molten state. Their mechanical, barrier, dielectric and other properties are determined following the standard methods. However, the use of the high pressure dilatometry (HPD) is relatively rare, even when this is the simplest way for determining the free volume and the thermodynamic interaction parameters as well as the key engineering quantities: the thermal expansion and compressibility coefficients. The HPD measurements are important, especially in view of the kinetic nature of transitions (vitrification, crystallization) that stretches into the non-equilibrium melt. An overview of the method and results obtained for CPNC with amorphous or semi-crystalline polymeric matrices are described.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".