Systematic development of degradable polyester biomaterials via ring-opening copolymerization of succinic anhydride and epoxides
Bibliographic record
Abstract
ABSTRACT Degradable polyester materials are widely utilized in medicine as resorbable sutures, implantable devices, and drug delivery. These applications require precise and tunable degradation control; predictable number-average molecular weight ( M̅ n ), narrow polydispersity (PDI), and diverse material properties define polyester utility, which are not easily achieved through well-established synthesis approaches. Ring-opening copolymerization (ROCOP) provides reproducible M̅ n control, narrow PDI, and expands monomer diversity. In this work, poly(cyclohexene succinate) (PCS) and poly(propylene succinate) (PPS) were synthesized through a central composite design of experiments approach, systematically varying anhydride:epoxide ratio, monomer:catalyst ratio, reaction temperature, and reaction time. Reduced synthesis factor-response models explained significant variation for all characterized properties relevant to degradation control. PCS and PPS readily degraded under base-catalyzed hydrolysis conditions with significantly higher mass loss in PPS materials compared to PCS, highlighting the monomer selection influence in degradation behaviour. These findings highlight the potential for ROCOP to generate degradable biomaterials with reproducible material properties in application-specific biomedical use.
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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 | 0.001 | 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".