Melt compounding of different grades polystyrene with organoclay. Part 3: Mechanical properties
Bibliographic record
Abstract
Abstract We discuss the effects of the melt compounding variables, matrix molecular weight and organoclay content on the X‐ray diffraction (XRD) and mechanical properties of polystyrene (PS)/organoclay nanocomposites (PNC) prepared in a twin‐screw extruder. An increase of residence time reduced the height of the first XRD peak and increased that of the second peak. Barrel temperature and screw configuration had a little influence on the tensile properties and impact strength. Young's modulus increased with organoclay content and it was almost independent of matrix PS molecular weight. The stress‐at‐break and impact strength decreased with organoclay content and increased with PS molecular weight. Young's modulus and impact strength decreased with residence time. Since the slopes of these dependencies for PNC were similar to that of the neat PS, the matrix degradation seems to play the major role. The relationship between impact strength and elongation at break of PNC showed high dependency on matrix grade. However, better empirical correlation was observed between the impact and tensile strengths. According to theoretical model of Ji et al. [], the Young's modulus vs. clay concentration dependence indicated the presence of low interphase thickness. POLYM. ENG. SCI. 45:827–837, 2005. © 2005 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 | 0.000 | 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) | 0.002 | 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".