Criteria for Quick and Consistent Synthesis of Poly(glycerol sebacate) for Tailored Mechanical Properties
Bibliographic record
Abstract
Poly(glycerol sebacate) (PGS) and its derivatives make up an attractive class of biomaterial owing to their tunable mechanical properties with programmable biodegradability. In practice, however, the application of PGS is often hampered by frequent inconsistency in reproducing process conditions. The inconsistency stems from the volatile nature of glycerol during the esterification process. In this study, we suggest that the degree of esterification (DE) can be used to predict precisely the physical status, the mechanical properties, and the degradation of the PGS materials. Young's modulus is shown to linearly increase with DE, which is in agreement with an entropic spring theory of rubbers. To provide a processing guideline for researchers, we also provide a physical status map as a function of curing temperature and time. The amount of glycerol loss, obtainable by monitoring the evolution of the total mass loss and the DE during synthesis, is shown to make the predictions even more precise. We expect that these strategies can be applicable to different categories of polymers that involve condensation polymerization with the volatility of the reactants. In addition, we demonstrate that microwave-assisted prepolymerization is a time- and energy-efficient pathway to obtain PGS. For example, 15 min of microwave time is shown to be as efficient as prepolymerization in nitrogen atmosphere for 6 h at 130 °C. The quick synthesis method, however, causes a severe evaporation of glycerol, resulting in a large distortion in the monomer ratio between glycerol and sebacic acid. Consequently, more rigid PGS is produced under a similar curing condition compared to the conventional prepolymerization method. Finally, we demonstrate that the addition of molecularly rigid cross-linking agents and network-structured inorganic nanoparticles are also effective in enhancing the mechanical properties of the PGS-derived materials.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".