Rheology of Green Plasticizer/Poly(vinyl chloride) Blends via Time–Temperature Superposition
Why this work is in the frame
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Bibliographic record
Abstract
Plasticizers are commonly added to poly(vinyl chloride) (PVC) and other brittle polymers to improve their flexibility and processing properties. Phthalate plasticizers such as di(2-ethylhexyl phthalate) (DEHP) are the most common PVC plasticizers and have recently been linked to a wide range of developmental and reproductive toxicities in mammals. Our group has developed several replacement compounds that have good biodegradation kinetics, low toxicity profiles, and comparable plasticizer properties to DEHP. Knowledge of the rheology of PVC–plasticizer blends at elevated temperatures is crucial for understanding and predicting the behavior of the compounds during processing. In this work, the time–temperature profiles of PVC blended with our replacement green plasticizers—succinates, maleates, and dibenzoates, of varying alkyl chain length—are compared to blends prepared with DEHP and a commercially available non-phthalate plasticizer, di(isononyl cyclohexane-1,2-dicarboxylate) (Hexamoll® DINCH®). The relationship between the plasticizer molecular structure and viscoelastic response was examined by applying time–temperature superposition. All compounds except the diethyl esters showed a comparable viscoelastic response to DEHP and Hexamoll® DINCH®, and dihexyl succinate exhibited the most effective reduction of the storage modulus G′. All of the dibenzoate blends exhibited a lower stiffness than the DEHP blends. These experiments help to show that the green plasticizers described herein are viable replacements for DEHP, providing a less toxic alternative with comparable processing and rheological performance.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Open science | 0.001 | 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 it