Degradation of poly(ε-caprolactone)-based 'green' plasticizers for poly(vinyl chloride)
Bibliographic record
Abstract
Poly(vinyl chloride) (PVC) requires the addition of large quantities of plasticizers in order to improve the flexibility and processability of the polymer. Since the plasticizer is not bound to the PVC polymer matrix, there is a tendency of the plasticizer to migrate into the surroundings. The most commonly used PVC plasticizer, di-2-ethylhexyl phthalate (DEHP), has become a ubiquitous environmental contaminant. As with other plasticizers, it may either accumulate or lead to the formation of metabolites, which resist further degradation and can themselves be toxic. Accordingly, there is incentive for the development of a 'green' plasticizer as a replacement to DEHP in order to minimize the environmental impact of PVC production and use. In this study, a method was developed to quantify the degradation of plasticizers used in PVC formulation or proposed as alternatives. The method is based on derivatization in order to lower the boiling points of the compounds and allow for analysis by gas chromatography (GC). Using this developed method, the biodegradation of two different families of potential 'green' plasticizers was considered. The biodegradation was assessed using Rhodococcus rhodochrous and evaluated based on the rate and completeness as well as the toxicity and stability of any observed metabolites. It was found that the poly([ε]-caprolactone)-based plasticizers containing octanoate-terminal groups degraded much more rapidly and completely than the benzoate-terminated ones. Furthermore, no metabolites were observed during the degradation of octanoate-terminated plasticizers while the benzoate-terminated plasticizers gave rise to metabolites which contributed substantial toxicity to the culture media. Under ideal conditions, these metabolites were shown to be biodegraded themselves. The methodology and data presented in this thesis can be used as a tool in selecting a 'green' plasticizer based on the degradation criteria, and aid in the selection of alternative plasticizers to DEHP.
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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) | 0.000 | 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".