Succinate‐based plasticizers: Effect of plasticizer structure on the mechanical and thermal performance of poly(vinyl chloride) plastic formulations
Bibliographic record
Abstract
Abstract A new family of succinate‐based plasticizers, consisting of molecules with a linear alkyl chain capped with n ‐alkyl succinates on both ends, was evaluated as potential bio‐based plasticizers for stiff polymers. The influence of the central and side alkyl chain lengths on the mechanical and thermal properties as well as the migration behavior of poly(vinyl chloride) (PVC)/plasticizer blends was evaluated. The central chain length had the greatest influence on plasticizer performance, with shorter chains leading to blends with higher stress at break and surface hardness, whereas long chains produced softer blends. An optimum chain central length of five carbon atoms was observed, with longer chains leading to reduced compatibility and exudation of the plasticizer at higher plasticizer concentrations. The entire family of plasticizers performed comparably or better than the commercial plasticizer di(2‐ethylhexyl) phthalate (DEHP) when incorporated into the blend at concentrations of 20–60 parts per hundred resin (phr). Overall, the succinate‐based plasticizers/PVC blends all exhibited equal or improved tensile properties (by up to 77%), surface hardness (reduced by up to 43%), glass transition temperature (reduced by up to 11°C), and migration into organic media (reduced by up to 38%) when compared with blends with DEHP at 40 phr.
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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) | 0.001 | 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".