One‐Pot Synthesis of Unsaturated Polyester Bioelastomer with Controllable Material Curing for Microscale Designs
Bibliographic record
Abstract
Synthetic polyester elastomeric constructs have become increasingly important for a range of healthcare applications, due to tunable soft elastic properties that mimic those of human tissues. A number of these constructs require intricate mechanical design to achieve a tunable material with controllable curing. Here, the synthesis and characterization of poly(itaconate-co-citrate-co-octanediol) (PICO) is presented, which exhibits tunable formation of elastomeric networks through radical crosslinking of itaconate in the polymer backbone of viscous polyester gels. Through variation of reaction times and monomer molar composition, materials with modulation of a wide range of elasticity (36-1476 kPa) are generated, indicating the tunability of materials to specific elastomeric constructs. This correlated with measured rapid and controllable gelation times. As a proof of principle, scaffold support for cardiac tissue patches is developed, which presents visible tissue organization and viability with appropriate elastomeric support from PICO materials. These formulations present potential application in a range of healthcare applications with requirement for elastomeric support with controllable, rapid gelation under mild conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
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".