Effects of molecular design parameters on plasticizer performance in poly(vinyl chloride): a comprehensive molecular simulation study
Bibliographic record
Abstract
Using all-atom molecular simulation, a wide range of plasticizers for poly(vinyl chlorid) (PVC), including ortho- and tere-phthalates, trimellitates, citrates, and various aliphatic dicarboxylates, are systematically studied. We focus on the effects of plasticizer molecular structure on its performance, as measured by performance metrics including its thermodynamic compatibility with PVC, effectiveness of reducing the material's Young's modulus, and migration rate in the PVC matrix. The wide variety of plasticizer types covered in the study allows us to investigate the effects of seven molecular design parameters. Experimental findings about the effects of plasticizer molecular design are also compiled from various literature sources and reviewed. Comparison with experiments establishes the reliability of our simulation predictions. The study aims to provide a comprehensive set of guidelines for the selection and design of high-performance plasticizers at the molecular level. Molecular mechanisms for how each design parameter influences plasticizer performance metrics are also discussed. Moreover, we report a nontrivial dependence of plasticizer migration rate on temperature, which reconciles seemingly conflicting experimental reports on the migration tendency of different plasticizers.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".