Developing chemoselective and biodegradable polyester elastomers for bioscaffold application
Bibliographic record
Abstract
Thermal polyesterification has emerged as a successful method for synthesizing polyesters for biomedical applications. However, to date, no general functionalization strategy has been incorporated into materials designed by the thermal polycondensation of polyacids and polyols. Herein, we report the design of several elastomers based on the thermal polycondensation of 4-ketopimelic acid, citric acid, and one of two diols: 1,6-hexanediol or 1,4-cyclohexanedimethanol. By varying the diol and the curing conditions, several elastomers were designed with a range of physical and mechanical properties. Poly(diol 4-ketopimelate-co-diol citrate) achieved Young's modulus, ultimate tensile stress, and rupture strain values of 0.39-1.13 MPa, 0.27-1.04 MPa, and 108-426%, respectively. Additionally, the incorporation of the ketone from 4-ketopimelic acid gave these materials two advantageous characteristics: a site for covalent functionalization through oxime formation and the ability to covalently bond to the surrounding tissue through imine linkages. Biocompatibility was studied both in vitro and in vivo in order to gain a complete understanding as to how biological systems respond to these novel materials. Based on preliminary results, we believe that poly(diol 4-ketopimelate-co-diol citrate) polyketoesters are excellent candidates for biomaterials.
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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 | 0.001 | 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".