Influence of stereoerrors on the formation of helices during early stage crystallization of isotactic polyproyplene
Bibliographic record
Abstract
Abstract Coarse‐grained, on‐lattice Monte Carlo simulations are performed to investigate the role of stereotacticity defects along an isotactic polypropylene chain on the formation of 31‐helices, which form the basic crystalline order within the chain. For this reason, systems with various stereoerror configurations are studied and are compared to neat isotactic polypropylene. All systems are equilibrated above the melting temperature and are cooled to lower temperatures in a stepwise manner, making sure each system is equilibrated at every temperature. Results indicate that chain ends have the lowest probability of being found in helices. Addition of a single stereoerror (mrm) decreases the probability of five repeat units' participation in helices (the repeat unit that contains the stereoerror and two nearest repeat units on both sides). The probability profile becomes more complicated when the number of stereoerrors increases, however, the results indicate that the effects of many stereoerrors can be explained by a simple addition of the effect of each stereoerror considered individually. The results also indicate that the presence of even a single stereoerror eliminates (within the temperature range studied) the transition to longer, more stable helices observed in neat isotactic polypropylene. © 2007 Wiley Periodicals, Inc. J Polym Sci Part B: Polym Phys 45: 3349–3360, 2007
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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 | 0.001 |
| 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".