Tuning the mechanical and dielectric properties of clay‐containing thermoplastic elastomer nanocomposites
Bibliographic record
Abstract
In this study, the mechanical strength and the AC short term breakdown strength of polystyrene‐b‐poly(ethylene‐co‐butylene)‐b‐polystyrene (SEBS) thermoplastic elastomer clay‐containing nanocomposites have been investigated as function of their morphologies. The SEBS/clay nanocomposites with tailored morphologies were prepared previously by different processing techniques. They featured different orientations of clay platelets as well as polystyrene (PS) block nanodomains, namely: isotropic, oriented, and partially oriented morphologies. In unfilled SEBS matrices, the mechanical strength was mainly tuned by the orientation of PS block nanodomains. A good correlation between the dielectric breakdown strength and the mechanical stiffness was observed overall: the higher the mechanical strength was, the higher the breakdown strength was. In the nanocomposites, the orientation of clay platelets as well as the degree of order and the characteristic sizes of the block copolymer domains were seen to affect strongly the breakdown strength behavior in addition to the mechanical strength. In particular, the partially oriented morphology achieved by film blowing extrusion exhibited the maximum increase of the breakdown strength by 25% with optimized mechanical stiffness evaluated between that of the oriented morphology as a lower limit and that of the isotropic morphology as an upper limit. POLYM. ENG. SCI., 58:E174–E181, 2018. © 2018 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) | 0.000 | 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 | 0.000 |
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".