Carbon Dioxide–Derived Poly(propylene carbonate) as a Matrix for Composites and Nanocomposites: Performances and Applications
Bibliographic record
Abstract
Abstract The conversion of CO 2 into polymers such as poly(propylene carbonate) (PPC) can contribute to the reduction of dependence on fossil fuel resourced polymers. PPC is a polymer synthesized from the catalyzed copolymerization between CO 2 and propylene oxide. The global demand for renewable and biodegradable polymers coupled with the recent success in catalysis for the copolymerization of CO 2 with epoxides has paved the way for an increased interest and growth in PPC polymers. On the contrary, the extensive utilization of PPC in many applications is still challenging due to its poor thermal stability, mechanical strength, and dimensional stability. Thus, many research efforts currently focus on improving these limitations. On the other hand, polymer processing and application development efforts have continued to utilize the existing PPC. This article presents a comprehensive review of PPC polymer as a matrix component of polymer composites and nanocomposites. Progress in current research on PPC‐based material applications, including industrial packaging, electromagnetic shielding, energy storage, and biomedical applications are included. A critical review of the biodegradability, compostability, and overall sustainability of PPC is also conducted. Finally, challenges that limit the extensive use of such materials, and future research and development directions are highlighted.
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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) | 0.000 | 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 | 0.000 |
| 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".