Organic acids catalyzed polymerization of ε‐caprolactone: Synthesis and characterization
Bibliographic record
Abstract
Abstract Environmentally friendly organocatalytic synthesis of aliphatic polyesters was studied. The catalysis investigated is novel, and lends itself well to the potential production of valuable biodegradable products. The reactions were based on an organic acids‐catalyzed ring‐opening polymerization of ε‐caprolactone with fatty acid derivatives as the initiator and were performed in the absence of solvents. The chemical structures of the functionalized polymers were confirmed by1H and13C‐NMR spectra. Polymers with different molecular weights, in the range 10,900–15,200 were obtained in the presence of fumaric acid as catalyst. The thermal properties of the functionalized PCLs were determined by modulated differential scanning calorimetry and thermogravimetric analysis. The MDSC results verified that the crystallinity and the melting point of the lipid‐functionalized polymers were lower than that of the unfunctionalized poly(ε‐caprolactone). The hydrolytic degradation of the functionalized polymer was also investigated. The result shows the degradation rate was affected by the presence of oleic acid derivatives in the polymer molecule. The lipid‐functionalized polymers synthesized by the metal‐free polymerization systems seem to be suitable biodegradable polyesters for use in biomedical and pharmacological applications. © 2010 Wiley Periodicals, Inc. J Appl Polym Sci, 2011
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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".