The relationships of polymer type specificity to the production of polymer–clay nanocomposites
Bibliographic record
Abstract
Complexity of polyolefin–montmorillonite nanocomposites Preparing polyolefin–montmorillonite nanocomposites presents another challenge in relation to the preparation of block copolymer–montmorillonite nanocomposites found in Chapter 6. An excellent example of the complexity of exfoliating organomontmorillonite into a pure hydrocarbon polymer is found in the work by Hotta and Paul [1]. Linear low-density polyethylene (LLDPE; Dowlex 2032 manufactured by Dow Chemical) was melt blended with two different organomontmorillonites (Cloisite 20A and montmorillonite exchanged with trimethyl hydrogenated tallow quaternary ammonium ion). The importance of blending maleic anhydride grafted LLDPE (LLDPE–g–MA; 0.9 wt. % MA content; Fusabond MB266D produced by DuPont, Canada) with LLDPE as regards achieving exfoliation was determined in this study. The procedures and equipment that were employed in this work were identical to those utilized by Fornes and Paul in the preparation of melt-blended nylon 6–montmorillonite nanocomposites described in Chapter 5. As one may anticipate from the studies in Chapters 5 and 6, the more hydrophobic Cloisite 20A was more efficient in producing exfoliated composites. The presence of the LLDPE–g–MA in the polymer blend further encouraged the exfoliation of Cloisite 20A. When the weight ratio of LLDPE–g–MA to Cloisite 20A is increased to 4 and subsequently to 11, the WAXS indicated good exfoliation with a loading of 4.6 and 4.9 wt.%, respectively, of montmorillonite (determined by incineration of the polymer composite in an oven). The TEM for the composite with a ratio of 11 at 4.9% montmorillonite indicated good exfoliation.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".