Melt compounding of different grades of polystyrene with organoclay. Part 2: Rheological properties
Bibliographic record
Abstract
Abstract Polystyrene‐based nanocomposites (PNC) were prepared using three grades of polystyrene (different molecular weights). The resin was melt‐compounded with 0 to 10 wt% of commercial organoclay in a co‐rotating twin‐screw extruder. Owing to thermo‐oxidative degradation the degree of dispersion was poor. The rheological properties of PNC were determined under dynamic and steady state shear as well as under extensional flow conditions. At the higher clay content, dynamic strain sweep demonstrated that the storage and loss moduli decrease continuously with an increase of strain. To characterize this nonlinear viscoelastic behavior, the Fourier‐transform rheology was applied. The low strain frequency sweep showed that the storage and loss moduli increase with organoclay content. The extracted zero‐shear viscosity data were used to calculate the intrinsic viscosity and then the aspect ratio of dispersions. In spite of nonlinear viscoelastic behavior, the time‐temperature superposition was observed in the full range of concentration. The horizontal and vertical shift factors were found to be almost independent of organoclay content and molecular weight of PS. For comparison, PNC was also prepared by the solution method. A high degree of dispersion was obtained, reflected in the aspect ratio: p = 269, to be compared with p = 16 calculated for the melt‐compounded PNC. Polym. Eng. Sci. 44:1061–1076, 2004. © 2004 Society of Plastics Engineers.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".