Bio‐based glycerol plasticizers for flexible poly(vinyl chloride) blends
Bibliographic record
Abstract
Abstract A series of glycerol‐based compounds were investigated for their application as plasticizers for flexible poly(vinyl chloride) (PVC) blends. The effect of plasticizer chemical structure on the performance, migration behavior, and blend morphology were evaluated and compared to blends produced using the commercial plasticizer dioctyl terephthalate (DOTP). Blends containing 40 phr (parts per hundred rubber) of glycerol‐based plasticizer showed a considerable reduction (between 54 to 86°C) in glass transition temperature (Tg) relative to neat PVC (Tg ~ 80°C). Tensile testing of samples prepared with the glycerol analogs demonstrated higher ductility (elongation at break values of up to 97%) than DOTP (elongation at break value of 75%) at identical plasticizer loadings. The surface morphologies showed excellent incorporation of the glycerol plasticizers functionalized with alkyl chains longer than four carbons in length into the PVC matrix, whereas droplet formation was observed in blends with shorter chain glycerol derivatives. Leaching behavior of the plasticized samples were evaluated into different media and showed that plasticizers comprised of branched, or longer alkyl chains produced 2‐ to 4‐fold lower migration rates compared to those with shorter alkyl chains into polar solutions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".